pax_global_header00006660000000000000000000000064121414611400014504gustar00rootroot0000000000000052 comment=ba322c344b6516ec60ead36c7988cecdede3fa6c rlichtenwalter-LPmade-ba322c3/000077500000000000000000000000001214146114000162745ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/changes.txt000066400000000000000000000026001214146114000204430ustar00rootroot00000000000000v1.2.2 - Fixed issue with auroc on 32-bit architectures that caused integer wraparounds that produced incorrect results. v1.2.1 - Fixed issue related to Ubuntu g++ header inclusion. - Fixed issue related to the specific command "predict -n 0 -d I" that would cause existing edges to receive predictions. v1.2.0 - Changed Makefile.paths to Makefile.options. - Preliminary support for vertex collocation profile (VCP) make target (make vcp). - Generalization of WEKA classification support from the classify target to any target requiring classification (such as vcp). - Changed BAGS user variable in Makefile.common to NUM_BAGS. - Changed default EVALUATION_METRICS value to "roc auroc aupr". - Enforced use of bash by GNU make by setting shell variable. - Added SHELLOPTS export in Makefile.options and added errexit option. v1.1.2 - Added this change log. - Changed link predictor score output from 6 to 15 points of precision. Need to modify label_predictions binary to do the same. - Link predictor score output precision is specified as a maximum precision rather than a required precision. - Increased the speed of the Katz predictor. - Preliminarily added vertex collocation profile (VCP) predictors to LPmade. Currently, these predictors output their vectors to standard error and -1 to standard output. Need to better incorporate VCPs, include them in build system, and include documentation. rlichtenwalter-LPmade-ba322c3/clean_all.sh000077500000000000000000000017471214146114000205560ustar00rootroot00000000000000#! /bin/sh # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. 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L*ש+܉[Y%N`?1HRY~sabH鈕„\OϖٍA\g1"+_*dk.AY*'`N-Ry]N@lS(~ t;m|3/~i7HэF%D)r=b+2 7J_5pLڽ[ЭoW@fQJdO-7,x0&{au_(d, X(@%[Y/Iw|*LrLWg"6'Q_C"R{Wӏ1@&NHLWuL#v)oC-D/4(v =dq,HFu3Z)tc酝l ]Qj Jkjuc׻ ':^:yGCz`]Z< Nz{[ sGXN`0 -m|/%.)$ɶp7}GFIȐ/$W'LDŽH"QkF!jgw|>cZ@~Q=9٨oeKxϋ weA'_\_X߄'?#c1<ڈ #OUvzaZINM|9c߶'Nu"-Mwyv,j3jƪ3㛢1 KrNU,ET܄c'0' x{͗'l&lzq#O˙Em*f+oEL`UfioR{V?Ϩuvzt{qt|wtx^5_T޼}|WsId|^oެc۩\*To]}oݟO?.vNNn!2 0Km묧PY{ҭwoǸs~d/`Kb2č6FMEx-/dUgt+>Pu7;0 Gٻw{?aMH ,z9/74ln"xx<'g?¶aL`AE{I4=&TЋI=P5^gO6"'tgs޵VóQ 1a1#":nniw_?ˉ:>xT;:p}xM'IfVhT> 3(%?㈢6P`myBڐn+H8Q]۟NT?p "Ru.MpM7uמ-e/o2X( T+nqShhv`FQ% &2{ŊN}ѿ<><><>7_rlichtenwalter-LPmade-ba322c3/gpl-3.0.txt000066400000000000000000001045131214146114000201210ustar00rootroot00000000000000 GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. 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Developers that use the GNU GPL protect your rights with two steps: (1) assert copyright on the software, and (2) offer you this License giving you legal permission to copy, distribute and/or modify it. For the developers' and authors' protection, the GPL clearly explains that there is no warranty for this free software. For both users' and authors' sake, the GPL requires that modified versions be marked as changed, so that their problems will not be attributed erroneously to authors of previous versions. Some devices are designed to deny users access to install or run modified versions of the software inside them, although the manufacturer can do so. This is fundamentally incompatible with the aim of protecting users' freedom to change the software. The systematic pattern of such abuse occurs in the area of products for individuals to use, which is precisely where it is most unacceptable. Therefore, we have designed this version of the GPL to prohibit the practice for those products. If such problems arise substantially in other domains, we stand ready to extend this provision to those domains in future versions of the GPL, as needed to protect the freedom of users. Finally, every program is threatened constantly by software patents. States should not allow patents to restrict development and use of software on general-purpose computers, but in those that do, we wish to avoid the special danger that patents applied to a free program could make it effectively proprietary. To prevent this, the GPL assures that patents cannot be used to render the program non-free. The precise terms and conditions for copying, distribution and modification follow. TERMS AND CONDITIONS 0. Definitions. "This License" refers to version 3 of the GNU General Public License. "Copyright" also means copyright-like laws that apply to other kinds of works, such as semiconductor masks. "The Program" refers to any copyrightable work licensed under this License. Each licensee is addressed as "you". "Licensees" and "recipients" may be individuals or organizations. To "modify" a work means to copy from or adapt all or part of the work in a fashion requiring copyright permission, other than the making of an exact copy. The resulting work is called a "modified version" of the earlier work or a work "based on" the earlier work. A "covered work" means either the unmodified Program or a work based on the Program. To "propagate" a work means to do anything with it that, without permission, would make you directly or secondarily liable for infringement under applicable copyright law, except executing it on a computer or modifying a private copy. Propagation includes copying, distribution (with or without modification), making available to the public, and in some countries other activities as well. To "convey" a work means any kind of propagation that enables other parties to make or receive copies. Mere interaction with a user through a computer network, with no transfer of a copy, is not conveying. An interactive user interface displays "Appropriate Legal Notices" to the extent that it includes a convenient and prominently visible feature that (1) displays an appropriate copyright notice, and (2) tells the user that there is no warranty for the work (except to the extent that warranties are provided), that licensees may convey the work under this License, and how to view a copy of this License. If the interface presents a list of user commands or options, such as a menu, a prominent item in the list meets this criterion. 1. Source Code. The "source code" for a work means the preferred form of the work for making modifications to it. "Object code" means any non-source form of a work. 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The "Corresponding Source" for a work in object code form means all the source code needed to generate, install, and (for an executable work) run the object code and to modify the work, including scripts to control those activities. However, it does not include the work's System Libraries, or general-purpose tools or generally available free programs which are used unmodified in performing those activities but which are not part of the work. For example, Corresponding Source includes interface definition files associated with source files for the work, and the source code for shared libraries and dynamically linked subprograms that the work is specifically designed to require, such as by intimate data communication or control flow between those subprograms and other parts of the work. The Corresponding Source need not include anything that users can regenerate automatically from other parts of the Corresponding Source. The Corresponding Source for a work in source code form is that same work. 2. Basic Permissions. All rights granted under this License are granted for the term of copyright on the Program, and are irrevocable provided the stated conditions are met. This License explicitly affirms your unlimited permission to run the unmodified Program. The output from running a covered work is covered by this License only if the output, given its content, constitutes a covered work. This License acknowledges your rights of fair use or other equivalent, as provided by copyright law. You may make, run and propagate covered works that you do not convey, without conditions so long as your license otherwise remains in force. You may convey covered works to others for the sole purpose of having them make modifications exclusively for you, or provide you with facilities for running those works, provided that you comply with the terms of this License in conveying all material for which you do not control copyright. Those thus making or running the covered works for you must do so exclusively on your behalf, under your direction and control, on terms that prohibit them from making any copies of your copyrighted material outside their relationship with you. Conveying under any other circumstances is permitted solely under the conditions stated below. Sublicensing is not allowed; section 10 makes it unnecessary. 3. Protecting Users' Legal Rights From Anti-Circumvention Law. No covered work shall be deemed part of an effective technological measure under any applicable law fulfilling obligations under article 11 of the WIPO copyright treaty adopted on 20 December 1996, or similar laws prohibiting or restricting circumvention of such measures. When you convey a covered work, you waive any legal power to forbid circumvention of technological measures to the extent such circumvention is effected by exercising rights under this License with respect to the covered work, and you disclaim any intention to limit operation or modification of the work as a means of enforcing, against the work's users, your or third parties' legal rights to forbid circumvention of technological measures. 4. Conveying Verbatim Copies. You may convey verbatim copies of the Program's source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice; keep intact all notices stating that this License and any non-permissive terms added in accord with section 7 apply to the code; keep intact all notices of the absence of any warranty; and give all recipients a copy of this License along with the Program. You may charge any price or no price for each copy that you convey, and you may offer support or warranty protection for a fee. 5. Conveying Modified Source Versions. You may convey a work based on the Program, or the modifications to produce it from the Program, in the form of source code under the terms of section 4, provided that you also meet all of these conditions: a) The work must carry prominent notices stating that you modified it, and giving a relevant date. b) The work must carry prominent notices stating that it is released under this License and any conditions added under section 7. This requirement modifies the requirement in section 4 to "keep intact all notices". c) You must license the entire work, as a whole, under this License to anyone who comes into possession of a copy. This License will therefore apply, along with any applicable section 7 additional terms, to the whole of the work, and all its parts, regardless of how they are packaged. This License gives no permission to license the work in any other way, but it does not invalidate such permission if you have separately received it. d) If the work has interactive user interfaces, each must display Appropriate Legal Notices; however, if the Program has interactive interfaces that do not display Appropriate Legal Notices, your work need not make them do so. A compilation of a covered work with other separate and independent works, which are not by their nature extensions of the covered work, and which are not combined with it such as to form a larger program, in or on a volume of a storage or distribution medium, is called an "aggregate" if the compilation and its resulting copyright are not used to limit the access or legal rights of the compilation's users beyond what the individual works permit. Inclusion of a covered work in an aggregate does not cause this License to apply to the other parts of the aggregate. 6. Conveying Non-Source Forms. You may convey a covered work in object code form under the terms of sections 4 and 5, provided that you also convey the machine-readable Corresponding Source under the terms of this License, in one of these ways: a) Convey the object code in, or embodied in, a physical product (including a physical distribution medium), accompanied by the Corresponding Source fixed on a durable physical medium customarily used for software interchange. b) Convey the object code in, or embodied in, a physical product (including a physical distribution medium), accompanied by a written offer, valid for at least three years and valid for as long as you offer spare parts or customer support for that product model, to give anyone who possesses the object code either (1) a copy of the Corresponding Source for all the software in the product that is covered by this License, on a durable physical medium customarily used for software interchange, for a price no more than your reasonable cost of physically performing this conveying of source, or (2) access to copy the Corresponding Source from a network server at no charge. c) Convey individual copies of the object code with a copy of the written offer to provide the Corresponding Source. This alternative is allowed only occasionally and noncommercially, and only if you received the object code with such an offer, in accord with subsection 6b. d) Convey the object code by offering access from a designated place (gratis or for a charge), and offer equivalent access to the Corresponding Source in the same way through the same place at no further charge. You need not require recipients to copy the Corresponding Source along with the object code. If the place to copy the object code is a network server, the Corresponding Source may be on a different server (operated by you or a third party) that supports equivalent copying facilities, provided you maintain clear directions next to the object code saying where to find the Corresponding Source. Regardless of what server hosts the Corresponding Source, you remain obligated to ensure that it is available for as long as needed to satisfy these requirements. e) Convey the object code using peer-to-peer transmission, provided you inform other peers where the object code and Corresponding Source of the work are being offered to the general public at no charge under subsection 6d. A separable portion of the object code, whose source code is excluded from the Corresponding Source as a System Library, need not be included in conveying the object code work. A "User Product" is either (1) a "consumer product", which means any tangible personal property which is normally used for personal, family, or household purposes, or (2) anything designed or sold for incorporation into a dwelling. In determining whether a product is a consumer product, doubtful cases shall be resolved in favor of coverage. For a particular product received by a particular user, "normally used" refers to a typical or common use of that class of product, regardless of the status of the particular user or of the way in which the particular user actually uses, or expects or is expected to use, the product. A product is a consumer product regardless of whether the product has substantial commercial, industrial or non-consumer uses, unless such uses represent the only significant mode of use of the product. "Installation Information" for a User Product means any methods, procedures, authorization keys, or other information required to install and execute modified versions of a covered work in that User Product from a modified version of its Corresponding Source. The information must suffice to ensure that the continued functioning of the modified object code is in no case prevented or interfered with solely because modification has been made. If you convey an object code work under this section in, or with, or specifically for use in, a User Product, and the conveying occurs as part of a transaction in which the right of possession and use of the User Product is transferred to the recipient in perpetuity or for a fixed term (regardless of how the transaction is characterized), the Corresponding Source conveyed under this section must be accompanied by the Installation Information. But this requirement does not apply if neither you nor any third party retains the ability to install modified object code on the User Product (for example, the work has been installed in ROM). The requirement to provide Installation Information does not include a requirement to continue to provide support service, warranty, or updates for a work that has been modified or installed by the recipient, or for the User Product in which it has been modified or installed. Access to a network may be denied when the modification itself materially and adversely affects the operation of the network or violates the rules and protocols for communication across the network. Corresponding Source conveyed, and Installation Information provided, in accord with this section must be in a format that is publicly documented (and with an implementation available to the public in source code form), and must require no special password or key for unpacking, reading or copying. 7. Additional Terms. "Additional permissions" are terms that supplement the terms of this License by making exceptions from one or more of its conditions. Additional permissions that are applicable to the entire Program shall be treated as though they were included in this License, to the extent that they are valid under applicable law. If additional permissions apply only to part of the Program, that part may be used separately under those permissions, but the entire Program remains governed by this License without regard to the additional permissions. When you convey a copy of a covered work, you may at your option remove any additional permissions from that copy, or from any part of it. (Additional permissions may be written to require their own removal in certain cases when you modify the work.) You may place additional permissions on material, added by you to a covered work, for which you have or can give appropriate copyright permission. Notwithstanding any other provision of this License, for material you add to a covered work, you may (if authorized by the copyright holders of that material) supplement the terms of this License with terms: a) Disclaiming warranty or limiting liability differently from the terms of sections 15 and 16 of this License; or b) Requiring preservation of specified reasonable legal notices or author attributions in that material or in the Appropriate Legal Notices displayed by works containing it; or c) Prohibiting misrepresentation of the origin of that material, or requiring that modified versions of such material be marked in reasonable ways as different from the original version; or d) Limiting the use for publicity purposes of names of licensors or authors of the material; or e) Declining to grant rights under trademark law for use of some trade names, trademarks, or service marks; or f) Requiring indemnification of licensors and authors of that material by anyone who conveys the material (or modified versions of it) with contractual assumptions of liability to the recipient, for any liability that these contractual assumptions directly impose on those licensors and authors. All other non-permissive additional terms are considered "further restrictions" within the meaning of section 10. If the Program as you received it, or any part of it, contains a notice stating that it is governed by this License along with a term that is a further restriction, you may remove that term. If a license document contains a further restriction but permits relicensing or conveying under this License, you may add to a covered work material governed by the terms of that license document, provided that the further restriction does not survive such relicensing or conveying. If you add terms to a covered work in accord with this section, you must place, in the relevant source files, a statement of the additional terms that apply to those files, or a notice indicating where to find the applicable terms. Additional terms, permissive or non-permissive, may be stated in the form of a separately written license, or stated as exceptions; the above requirements apply either way. 8. Termination. You may not propagate or modify a covered work except as expressly provided under this License. Any attempt otherwise to propagate or modify it is void, and will automatically terminate your rights under this License (including any patent licenses granted under the third paragraph of section 11). However, if you cease all violation of this License, then your license from a particular copyright holder is reinstated (a) provisionally, unless and until the copyright holder explicitly and finally terminates your license, and (b) permanently, if the copyright holder fails to notify you of the violation by some reasonable means prior to 60 days after the cessation. Moreover, your license from a particular copyright holder is reinstated permanently if the copyright holder notifies you of the violation by some reasonable means, this is the first time you have received notice of violation of this License (for any work) from that copyright holder, and you cure the violation prior to 30 days after your receipt of the notice. Termination of your rights under this section does not terminate the licenses of parties who have received copies or rights from you under this License. If your rights have been terminated and not permanently reinstated, you do not qualify to receive new licenses for the same material under section 10. 9. Acceptance Not Required for Having Copies. You are not required to accept this License in order to receive or run a copy of the Program. Ancillary propagation of a covered work occurring solely as a consequence of using peer-to-peer transmission to receive a copy likewise does not require acceptance. However, nothing other than this License grants you permission to propagate or modify any covered work. These actions infringe copyright if you do not accept this License. Therefore, by modifying or propagating a covered work, you indicate your acceptance of this License to do so. 10. Automatic Licensing of Downstream Recipients. Each time you convey a covered work, the recipient automatically receives a license from the original licensors, to run, modify and propagate that work, subject to this License. You are not responsible for enforcing compliance by third parties with this License. An "entity transaction" is a transaction transferring control of an organization, or substantially all assets of one, or subdividing an organization, or merging organizations. If propagation of a covered work results from an entity transaction, each party to that transaction who receives a copy of the work also receives whatever licenses to the work the party's predecessor in interest had or could give under the previous paragraph, plus a right to possession of the Corresponding Source of the work from the predecessor in interest, if the predecessor has it or can get it with reasonable efforts. You may not impose any further restrictions on the exercise of the rights granted or affirmed under this License. For example, you may not impose a license fee, royalty, or other charge for exercise of rights granted under this License, and you may not initiate litigation (including a cross-claim or counterclaim in a lawsuit) alleging that any patent claim is infringed by making, using, selling, offering for sale, or importing the Program or any portion of it. 11. Patents. A "contributor" is a copyright holder who authorizes use under this License of the Program or a work on which the Program is based. The work thus licensed is called the contributor's "contributor version". A contributor's "essential patent claims" are all patent claims owned or controlled by the contributor, whether already acquired or hereafter acquired, that would be infringed by some manner, permitted by this License, of making, using, or selling its contributor version, but do not include claims that would be infringed only as a consequence of further modification of the contributor version. For purposes of this definition, "control" includes the right to grant patent sublicenses in a manner consistent with the requirements of this License. Each contributor grants you a non-exclusive, worldwide, royalty-free patent license under the contributor's essential patent claims, to make, use, sell, offer for sale, import and otherwise run, modify and propagate the contents of its contributor version. In the following three paragraphs, a "patent license" is any express agreement or commitment, however denominated, not to enforce a patent (such as an express permission to practice a patent or covenant not to sue for patent infringement). To "grant" such a patent license to a party means to make such an agreement or commitment not to enforce a patent against the party. If you convey a covered work, knowingly relying on a patent license, and the Corresponding Source of the work is not available for anyone to copy, free of charge and under the terms of this License, through a publicly available network server or other readily accessible means, then you must either (1) cause the Corresponding Source to be so available, or (2) arrange to deprive yourself of the benefit of the patent license for this particular work, or (3) arrange, in a manner consistent with the requirements of this License, to extend the patent license to downstream recipients. "Knowingly relying" means you have actual knowledge that, but for the patent license, your conveying the covered work in a country, or your recipient's use of the covered work in a country, would infringe one or more identifiable patents in that country that you have reason to believe are valid. If, pursuant to or in connection with a single transaction or arrangement, you convey, or propagate by procuring conveyance of, a covered work, and grant a patent license to some of the parties receiving the covered work authorizing them to use, propagate, modify or convey a specific copy of the covered work, then the patent license you grant is automatically extended to all recipients of the covered work and works based on it. A patent license is "discriminatory" if it does not include within the scope of its coverage, prohibits the exercise of, or is conditioned on the non-exercise of one or more of the rights that are specifically granted under this License. You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license (a) in connection with copies of the covered work conveyed by you (or copies made from those copies), or (b) primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007. Nothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law. 12. No Surrender of Others' Freedom. If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program. 13. Use with the GNU Affero General Public License. Notwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such. 14. Revised Versions of this License. The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. Each version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License "or any later version" applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation. If the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy's public statement of acceptance of a version permanently authorizes you to choose that version for the Program. Later license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version. 15. Disclaimer of Warranty. THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 16. Limitation of Liability. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. 17. Interpretation of Sections 15 and 16. If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee. END OF TERMS AND CONDITIONS How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. Copyright (C) This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see . The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read . rlichtenwalter-LPmade-ba322c3/license.txt000066400000000000000000000500211214146114000204550ustar00rootroot00000000000000==================== LPmade ==================== This file contains license information for LPmade and its components. The LPmade software package as an aggregate is released under the GNU GENERAL PUBLIC LICENSE Version 3. That license is available in the file './gpl-3.0.txt'. LPmade includes as part of its distribution two other works: - gnuplot - WEKA ==================== gnuplot ==================== The LPmade software requires a more recent version of gnuplot than is included in many Linux distributions. For user convenience, gnuplot v4.4.3 is bundled. gnuplot v4.2.5 is also confirmed to work. The bundled gnuplot is entirely unmodified. It is built and prepared automatically by './make_all.sh'. License terms for gnuplot are included within the './gnuplot/gnuplot-4.4.3.tar.gz' tarball. License for gnuplot: [ Copyright 1986 - 1993, 1998, 2004 Thomas Williams, Colin Kelley Permission to use, copy, and distribute this software and its documentation for any purpose with or without fee is hereby granted, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation. Permission to modify the software is granted, but not the right to distribute the complete modified source code. Modifications are to be distributed as patches to the released version. Permission to distribute binaries produced by compiling modified sources is granted, provided you 1. distribute the corresponding source modifications from the released version in the form of a patch file along with the binaries, 2. add special version identification to distinguish your version in addition to the base release version number, 3. provide your name and address as the primary contact for the support of your modified version, and 4. retain our contact information in regard to use of the base software. Permission to distribute the released version of the source code along with corresponding source modifications in the form of a patch file is granted with same provisions 2 through 4 for binary distributions. This software is provided "as is" without express or implied warranty to the extent permitted by applicable law. ] ==================== WEKA ==================== A modified version of WEKA v3.6 is included as a necessity for working with extremely large data sets. The distributed version of WEKA is licensed under GNU GENERAL PUBLIC LICENSE Version 2. The text of that license is included below. [ GNU GENERAL PUBLIC LICENSE Version 2, June 1991 Copyright (C) 1989, 1991 Free Software Foundation, Inc. 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The licenses for most software are designed to take away your freedom to share and change it. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change free software--to make sure the software is free for all its users. This General Public License applies to most of the Free Software Foundation's software and to any other program whose authors commit to using it. (Some other Free Software Foundation software is covered by the GNU Library General Public License instead.) You can apply it to your programs, too. When we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for this service if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs; and that you know you can do these things. To protect your rights, we need to make restrictions that forbid anyone to deny you these rights or to ask you to surrender the rights. These restrictions translate to certain responsibilities for you if you distribute copies of the software, or if you modify it. For example, if you distribute copies of such a program, whether gratis or for a fee, you must give the recipients all the rights that you have. You must make sure that they, too, receive or can get the source code. And you must show them these terms so they know their rights. We protect your rights with two steps: (1) copyright the software, and (2) offer you this license which gives you legal permission to copy, distribute and/or modify the software. Also, for each author's protection and ours, we want to make certain that everyone understands that there is no warranty for this free software. If the software is modified by someone else and passed on, we want its recipients to know that what they have is not the original, so that any problems introduced by others will not reflect on the original authors' reputations. Finally, any free program is threatened constantly by software patents. We wish to avoid the danger that redistributors of a free program will individually obtain patent licenses, in effect making the program proprietary. To prevent this, we have made it clear that any patent must be licensed for everyone's free use or not licensed at all. The precise terms and conditions for copying, distribution and modification follow. GNU GENERAL PUBLIC LICENSE TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION 0. This License applies to any program or other work which contains a notice placed by the copyright holder saying it may be distributed under the terms of this General Public License. The "Program", below, refers to any such program or work, and a "work based on the Program" means either the Program or any derivative work under copyright law: that is to say, a work containing the Program or a portion of it, either verbatim or with modifications and/or translated into another language. (Hereinafter, translation is included without limitation in the term "modification".) Each licensee is addressed as "you". Activities other than copying, distribution and modification are not covered by this License; they are outside its scope. The act of running the Program is not restricted, and the output from the Program is covered only if its contents constitute a work based on the Program (independent of having been made by running the Program). Whether that is true depends on what the Program does. 1. You may copy and distribute verbatim copies of the Program's source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice and disclaimer of warranty; keep intact all the notices that refer to this License and to the absence of any warranty; and give any other recipients of the Program a copy of this License along with the Program. You may charge a fee for the physical act of transferring a copy, and you may at your option offer warranty protection in exchange for a fee. 2. You may modify your copy or copies of the Program or any portion of it, thus forming a work based on the Program, and copy and distribute such modifications or work under the terms of Section 1 above, provided that you also meet all of these conditions: a) You must cause the modified files to carry prominent notices stating that you changed the files and the date of any change. b) You must cause any work that you distribute or publish, that in whole or in part contains or is derived from the Program or any part thereof, to be licensed as a whole at no charge to all third parties under the terms of this License. c) If the modified program normally reads commands interactively when run, you must cause it, when started running for such interactive use in the most ordinary way, to print or display an announcement including an appropriate copyright notice and a notice that there is no warranty (or else, saying that you provide a warranty) and that users may redistribute the program under these conditions, and telling the user how to view a copy of this License. (Exception: if the Program itself is interactive but does not normally print such an announcement, your work based on the Program is not required to print an announcement.) These requirements apply to the modified work as a whole. If identifiable sections of that work are not derived from the Program, and can be reasonably considered independent and separate works in themselves, then this License, and its terms, do not apply to those sections when you distribute them as separate works. But when you distribute the same sections as part of a whole which is a work based on the Program, the distribution of the whole must be on the terms of this License, whose permissions for other licensees extend to the entire whole, and thus to each and every part regardless of who wrote it. Thus, it is not the intent of this section to claim rights or contest your rights to work written entirely by you; rather, the intent is to exercise the right to control the distribution of derivative or collective works based on the Program. In addition, mere aggregation of another work not based on the Program with the Program (or with a work based on the Program) on a volume of a storage or distribution medium does not bring the other work under the scope of this License. 3. You may copy and distribute the Program (or a work based on it, under Section 2) in object code or executable form under the terms of Sections 1 and 2 above provided that you also do one of the following: a) Accompany it with the complete corresponding machine-readable source code, which must be distributed under the terms of Sections 1 and 2 above on a medium customarily used for software interchange; or, b) Accompany it with a written offer, valid for at least three years, to give any third party, for a charge no more than your cost of physically performing source distribution, a complete machine-readable copy of the corresponding source code, to be distributed under the terms of Sections 1 and 2 above on a medium customarily used for software interchange; or, c) Accompany it with the information you received as to the offer to distribute corresponding source code. (This alternative is allowed only for noncommercial distribution and only if you received the program in object code or executable form with such an offer, in accord with Subsection b above.) The source code for a work means the preferred form of the work for making modifications to it. For an executable work, complete source code means all the source code for all modules it contains, plus any associated interface definition files, plus the scripts used to control compilation and installation of the executable. However, as a special exception, the source code distributed need not include anything that is normally distributed (in either source or binary form) with the major components (compiler, kernel, and so on) of the operating system on which the executable runs, unless that component itself accompanies the executable. If distribution of executable or object code is made by offering access to copy from a designated place, then offering equivalent access to copy the source code from the same place counts as distribution of the source code, even though third parties are not compelled to copy the source along with the object code. 4. You may not copy, modify, sublicense, or distribute the Program except as expressly provided under this License. Any attempt otherwise to copy, modify, sublicense or distribute the Program is void, and will automatically terminate your rights under this License. However, parties who have received copies, or rights, from you under this License will not have their licenses terminated so long as such parties remain in full compliance. 5. You are not required to accept this License, since you have not signed it. However, nothing else grants you permission to modify or distribute the Program or its derivative works. These actions are prohibited by law if you do not accept this License. Therefore, by modifying or distributing the Program (or any work based on the Program), you indicate your acceptance of this License to do so, and all its terms and conditions for copying, distributing or modifying the Program or works based on it. 6. Each time you redistribute the Program (or any work based on the Program), the recipient automatically receives a license from the original licensor to copy, distribute or modify the Program subject to these terms and conditions. You may not impose any further restrictions on the recipients' exercise of the rights granted herein. You are not responsible for enforcing compliance by third parties to this License. 7. If, as a consequence of a court judgment or allegation of patent infringement or for any other reason (not limited to patent issues), conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot distribute so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not distribute the Program at all. For example, if a patent license would not permit royalty-free redistribution of the Program by all those who receive copies directly or indirectly through you, then the only way you could satisfy both it and this License would be to refrain entirely from distribution of the Program. If any portion of this section is held invalid or unenforceable under any particular circumstance, the balance of the section is intended to apply and the section as a whole is intended to apply in other circumstances. It is not the purpose of this section to induce you to infringe any patents or other property right claims or to contest validity of any such claims; this section has the sole purpose of protecting the integrity of the free software distribution system, which is implemented by public license practices. Many people have made generous contributions to the wide range of software distributed through that system in reliance on consistent application of that system; it is up to the author/donor to decide if he or she is willing to distribute software through any other system and a licensee cannot impose that choice. This section is intended to make thoroughly clear what is believed to be a consequence of the rest of this License. 8. If the distribution and/or use of the Program is restricted in certain countries either by patents or by copyrighted interfaces, the original copyright holder who places the Program under this License may add an explicit geographical distribution limitation excluding those countries, so that distribution is permitted only in or among countries not thus excluded. In such case, this License incorporates the limitation as if written in the body of this License. 9. The Free Software Foundation may publish revised and/or new versions of the General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. Each version is given a distinguishing version number. If the Program specifies a version number of this License which applies to it and "any later version", you have the option of following the terms and conditions either of that version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of this License, you may choose any version ever published by the Free Software Foundation. 10. If you wish to incorporate parts of the Program into other free programs whose distribution conditions are different, write to the author to ask for permission. For software which is copyrighted by the Free Software Foundation, write to the Free Software Foundation; we sometimes make exceptions for this. Our decision will be guided by the two goals of preserving the free status of all derivatives of our free software and of promoting the sharing and reuse of software generally. NO WARRANTY 11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. END OF TERMS AND CONDITIONS Appendix: How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively convey the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. Copyright (C) 19yy This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. Also add information on how to contact you by electronic and paper mail. If the program is interactive, make it output a short notice like this when it starts in an interactive mode: Gnomovision version 69, Copyright (C) 19yy name of author Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, the commands you use may be called something other than `show w' and `show c'; they could even be mouse-clicks or menu items--whatever suits your program. You should also get your employer (if you work as a programmer) or your school, if any, to sign a "copyright disclaimer" for the program, if necessary. Here is a sample; alter the names: Yoyodyne, Inc., hereby disclaims all copyright interest in the program `Gnomovision' (which makes passes at compilers) written by James Hacker. , 1 April 1989 Ty Coon, President of Vice This General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Library General Public License instead of this License. ] rlichtenwalter-LPmade-ba322c3/make_all.sh000077500000000000000000000021221214146114000203750ustar00rootroot00000000000000#! /bin/sh # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ cd netlib && make && cd ..; cd metrics && make all && cd ..; cd gnuplot && tar -xzf gnuplot-4.4.3.tar.gz && cd gnuplot-4.4.3 && ./configure --prefix=$(cd .. && pwd) \ --disable-wxwidget --without-x --without-lua \ && make && make install && cd .. && rm -rf gnuplot-4.4.3 && cd ..; rlichtenwalter-LPmade-ba322c3/man/000077500000000000000000000000001214146114000170475ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/man/adjacency_list.1000066400000000000000000000031201214146114000221010ustar00rootroot00000000000000.TH adjacency_list 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME adjacency_list \- construct an adjacency list from LPmade format .SH SYNOPSIS .B adjacency_list [OPTIONS] ... .SH DESCRIPTION Reads the Pajek-like LPmade network file format and produces an adjacency list with the external name of the vertex followed by a space-separated list of comma-separated neighbor-weight tuples. Each vertex occupies a separate line. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE adjacency_list returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net adjacency_list > mynet.adj .PP Input: .RS .nf *Vertices 9 0 0 1 2 2 3 3 4 4 5 5 7 6 18 7 47 8 285 *Edges 8 0 1 1 0 4 2 0 5 1 2 6 1 5 3 1 6 2 1 7 1 1 8 7 1 .RE .PP Output: .RS .nf 0 2,1 5,2 7,1 2 3 18,1 4 5 7 4,1 18 3,1 47 2,1 285 47,1 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/all_pairs.1000066400000000000000000000026671214146114000211120ustar00rootroot00000000000000.TH all_pairs 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME all_pairs \- print all pairs of vertices .SH SYNOPSIS .B all_pairs [OPTIONS] ... .SH DESCRIPTION Produces the complete list of |V| choose 2 pairs in the network ordered first by source identifier and then by target identifier. In undirected networks, pairs are printed in both directions. The network is expected at standard input. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net all_pairs > mynet.pairs .PP Input: .RS *Vertices 3 0 1 1 2 2 3 *Edges 2 0 1 1 1 2 3 .RE .PP Output: .RS 1 2 1 3 2 1 2 3 3 1 3 2 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/assortativity_coefficient.1000066400000000000000000000030301214146114000244100ustar00rootroot00000000000000.TH assortativity_coefficient 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME assortativity_coefficient \- print the assortativity coefficient of the network .SH SYNOPSIS .B assortativity_coefficient [OPTIONS] ... .SH DESCRIPTION Computes the assortativity coefficient of the network, which is the tendency of vertices with a given degree to be connected to other vertices with a similar degree. The network is expected at standard input. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net assortativity_coefficient .PP Input: .RS *Vertices 9 0 0 1 2 2 3 3 4 4 5 5 7 6 18 7 47 8 285 *Edges 8 0 1 1 0 4 2 0 5 1 2 6 1 5 3 1 6 2 1 7 1 1 8 7 1 .RE .PP Output: .RS -0.258199 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/aupr.1000066400000000000000000000032571214146114000201070ustar00rootroot00000000000000.TH aupr 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME aupr \- compute area under the precision-recall curve for a sorted list of score-label pairs .SH SYNOPSIS .B aupr [OPTIONS] ... precision num_positive ... .SH DESCRIPTION A highly optimized calculator for area under the precision-recall curve. This program assumes that its input will come in terms of space-separated score-label pairs sorted so that the highest scores are at the top. It will properly handle all C-style double-precision input. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < predictions.sprob aupr 0.001 $(predictions.sprob grep -c '1$') .PP < predictions.sprob.gz gunzip | aupr 1 $(predictions.sprob.gz gunzip | grep -c '1$') .PP Input: .RS 0.9 1 0.8 0 0.7 1 0.6 1 0.5 0 0.4 0 .RE .PP Output: .RS 0.763888889 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/auroc.1000066400000000000000000000030311214146114000202370ustar00rootroot00000000000000.TH auroc 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME auroc \- compute area under the ROC curve for a sorted list of score-label pairs .SH SYNOPSIS .B auroc [OPTIONS] ... .SH DESCRIPTION A highly optimized calculator for area under the ROC curve. This program assumes that its input will come in terms of space-separated score-label pairs sorted so that the highest scores are at the top. It will properly handle all C-style double-precision input. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.sprob auroc .PP < mynet.sprob.gz gunzip | auroc .PP Input: .RS 0.9 1 0.8 0 0.7 1 0.6 1 0.5 0 0.4 0 .RE .PP Output: .RS 0.777777778 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/average_clustering_coefficient.1000066400000000000000000000026731214146114000253500ustar00rootroot00000000000000.TH average_clustering_coefficient 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME average_clustering_coefficient \- print the average clustering coefficient of the network .SH SYNOPSIS .B average_clustering_coefficient [OPTIONS] ... .SH DESCRIPTION Computes the arithmetic mean of clustering coefficients of all vertices in the network. The network is expected at standard input. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net average_clustering_coefficient .PP Input: .RS *Vertices 4 0 0 1 1 2 2 3 3 *Edges 6 0 1 1 0 2 1 0 3 1 1 2 1 1 3 1 2 1 1 .RE .PP Output: .RS 0.125 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/betweenness.1000066400000000000000000000032651214146114000214610ustar00rootroot00000000000000.TH betweenness 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME betweenness \- print betweenness centralities for all nodes in the network .SH SYNOPSIS .B betweenness [OPTIONS] ... .SH DESCRIPTION Computes betweenness centralities in O(|V||E|) time for all vertices in the network using Brandes' algorithm. This implementation ignores edge weights. The network is expected at standard input. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net betweenness .PP Input: .RS *Vertices 10 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 *Edges 24 0 1 1 1 0 1 1 2 1 1 3 1 2 1 1 2 4 1 3 1 1 3 5 1 4 2 1 4 6 1 4 7 1 4 8 1 5 3 1 5 8 1 6 4 1 6 9 1 7 4 1 7 9 1 8 4 1 8 5 1 8 9 1 9 6 1 9 7 1 9 8 1 .RE .PP Output: .RS 0.000000e+00 2.133333e+01 1.933333e+01 8.000000e+00 2.966667e+01 1.066667e+01 2.333333e+00 2.333333e+00 1.800000e+01 6.333333e+00 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/clustering_coefficient_distribution.1000066400000000000000000000030261214146114000264460ustar00rootroot00000000000000.TH clustering_coefficient_distribution 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME clustering_coefficient_distribution \- print the clustering coefficient of each vertex .SH SYNOPSIS .B clustering_coefficient_distribution [OPTIONS] ... .SH DESCRIPTION Computes the clustering coefficient of each vertex, which is the tendency of vertices with a given degree to be connected to other vertices with a similar degree. The network is expected at standard input. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net clustering_coefficient_distribution .PP Input: .RS *Vertices 4 0 0 1 1 2 2 3 3 *Edges 6 0 1 1 0 2 1 0 3 1 1 2 1 1 3 1 2 1 1 .RE .PP Output: .RS 0.5 0 0 0 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/clustering_spectrum.1000066400000000000000000000030241214146114000232310ustar00rootroot00000000000000.TH clustering_spectrum 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME clustering_spectrum \- print the average clustering coefficient aggregated by vertex degree .SH SYNOPSIS .B clustering_spectrum [OPTIONS] ... .SH DESCRIPTION Computes the clustering spectrum of the network, the arithmetic mean of clustering coefficients by degree. The network is expected at standard input. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net clustering_spectrum .PP Input: .RS *Vertices 9 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 *Edges 16 0 1 1 0 2 1 1 2 1 1 3 1 1 4 1 2 5 3 3 6 1 4 5 1 5 2 1 6 3 1 6 7 1 7 1 1 8 0 1 8 1 7 8 2 1 8 3 1 .RE .PP Output: .RS 1 0 2 0.25 3 0 4 0.333333 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/combine_networks.1000066400000000000000000000030261214146114000225020ustar00rootroot00000000000000.TH combine_networks 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME combine_networks \- combine one or more networks into a single network .SH SYNOPSIS .B combine_networks [OPTIONS] ... ... .SH DESCRIPTION Accepts as parameters two or more network files and outputs a network that contains all vertices and edges in either network. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP combine_networks mynet1.net mynet2.net .PP Input: .RS *Vertices 4 0 1 1 2 2 3 3 4 *Edges 3 0 1 1 1 2 1 2 3 1 -------------------- *Vertices 4 0 3 1 4 2 5 3 6 *Edges 3 0 1 2 1 2 1 2 3 3 .RE .PP Output: .RS *Vertices 6 0 1 1 2 2 3 3 4 4 5 5 6 *Edges 5 0 1 1 1 2 1 2 3 3 3 4 1 4 5 3 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/count_edges.1000066400000000000000000000026371214146114000214400ustar00rootroot00000000000000.TH count_edges 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME count_edges \- print the number of edges in the network .SH SYNOPSIS .B count_edges [OPTIONS] ... .SH DESCRIPTION Outputs the number of edges in the network. This program reads the network into memory to compute its result. The result is most efficiently achieved by using grep to search for 'Edges' in the LPmade network format. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net count_edges .PP Input: .RS *Vertices 4 0 1 1 2 2 3 3 4 *Edges 3 0 1 1 1 2 1 2 3 1 .RE .PP Output: .RS 3 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/count_mutuals.1000066400000000000000000000024231214146114000220340ustar00rootroot00000000000000.TH count_mutuals 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME count_mutuals \- print the number of bidirectional edges in the network .SH SYNOPSIS .B count_mutuals [OPTIONS] ... .SH DESCRIPTION Outputs the number of bidirectional edges in the network. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net count_mutuals .PP Input: .RS *Vertices 3 0 1 1 2 2 3 *Edges 3 0 1 1 1 2 1 2 1 1 .RE .PP Output: .RS 1 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/count_vertices.1000066400000000000000000000027051214146114000221710ustar00rootroot00000000000000.TH count_vertices 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME count_vertices \- print the number of vertices in the network .SH SYNOPSIS .B count_vertices [OPTIONS] ... .SH DESCRIPTION Outputs the number of vertices in the network. This program reads the network into memory to compute its result. The result is most efficiently achieved by using the command head on the network file or using grep to search for 'Vertices'. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net count_vertices .PP Input: .RS *Vertices 4 0 1 1 2 2 3 3 4 *Edges 3 0 1 1 1 2 1 2 3 1 .RE .PP Output: .RS 4 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/duplicate.1000066400000000000000000000031431214146114000211040ustar00rootroot00000000000000.TH duplicate 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME duplicate \- output an identical copy of the network .SH SYNOPSIS .B duplicate [OPTIONS] ... .SH DESCRIPTION Creates an identical copy of the input network and writes it to output. This program reads the network into memory to output its result. The functionality is most efficiently achieved with cp, cat, or redirection depending on the desired goal. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net duplicate > mynetcopy.net .PP Input: .RS *Vertices 9 0 0 1 2 2 3 3 4 4 5 5 7 6 18 7 47 8 285 *Edges 8 0 1 1 0 4 2 0 5 1 2 6 1 5 3 1 6 2 1 7 1 1 8 7 1 .RE .PP Output: .RS *Vertices 9 0 0 1 2 2 3 3 4 4 5 5 7 6 18 7 47 8 285 *Edges 8 0 1 1 0 4 2 0 5 1 2 6 1 5 3 1 6 2 1 7 1 1 8 7 1 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/eccentricity_distribution.1000066400000000000000000000030461214146114000244200ustar00rootroot00000000000000.TH eccentricity_distribution 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME eccentricity_distribution \- output the eccentricity of each vertex in the network .SH SYNOPSIS .B eccentricity_distribution [OPTIONS] ... .SH DESCRIPTION Computes the eccentricity of each vertex, the longest shortest path from it to any other vertex in the network. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net eccentricity_distribution .PP Input: .RS *Vertices 10 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 *Edges 24 0 1 1 1 0 1 1 2 1 1 3 1 2 1 1 2 4 1 3 1 1 3 5 1 4 2 1 4 6 1 4 7 1 4 8 1 5 3 1 5 8 1 6 4 1 6 9 1 7 4 1 7 9 1 8 4 1 8 5 1 8 9 1 9 6 1 9 7 1 9 8 1 .RE .PP Output: .RS 5 4 3 4 3 3 4 4 4 5 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/free_choice_mutuality.1000066400000000000000000000027211214146114000235030ustar00rootroot00000000000000.TH free_choice_mutuality 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME free_choice_mutuality \- output the free choice mutuality score of the network .SH SYNOPSIS .B free_choice_mutuality [OPTIONS] ... .SH DESCRIPTION Computes the mutuality score of the network based on the free choice model. For more information about the free choice model of mutality, see 'Social Network Analysis' by Stanley Wasserman and Katherine Faust. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net free_choice_mutuality .PP Input: .RS *Vertices 3 0 1 1 2 2 3 *Edges 3 0 1 1 1 2 1 2 1 1 .RE .PP Output: .RS 0.333333 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/label_predictions.1000066400000000000000000000033671214146114000226240ustar00rootroot00000000000000.TH label_predictions 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME label_predictions \- label prediction output from the predict binary .SH SYNOPSIS .B label_predictions [OPTIONS] ... label_network ... .SH DESCRIPTION Accepts prediction scores from the predict program and attaches the label information by stripping vertex identifiers and appending labels from another network. Labels are positive (1) if the scored edge exists in the label network and negative (0) otherwise. See also predict(1) for information about generating predictions. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < predictions.pred label_predictions label.net .PP predict -n 2 -f prediction.net -d O AdamicAdar | label_predictions label.net .PP Input: .RS 8 10 0.6 3 5 0.2 4 7 0.7 -------------------- *Vertices 5 0 3 1 4 2 5 3 7 4 9 *Edges 2 0 2 1 0 3 1 .RE .PP Output: .RS 6.000000e-01 0 2.000000e-01 1 7.000000e-01 0 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/largest_scc.1000066400000000000000000000031641214146114000214260ustar00rootroot00000000000000.TH largest_scc 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME largest_scc \- extract and print the largest strongly connected component in the network in LPmade format .SH SYNOPSIS .B largest_scc [OPTIONS] ... .SH DESCRIPTION Creates a new network consisting of the largest strongly connected component in the input network. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net largest_scc > largest_scc.net .PP Input: .RS *Vertices 22 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 *Edges 18 0 1 1 2 3 1 3 2 1 4 5 1 6 5 1 8 7 1 8 9 1 10 11 1 11 12 1 13 14 1 14 15 1 15 13 1 16 17 1 17 18 1 18 16 1 18 19 1 19 20 1 20 19 1 .RE .PP Output: .RS *Vertices 3 0 16 1 17 2 18 *Edges 3 0 1 1 1 2 1 2 0 1 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/largest_wcc.1000066400000000000000000000032141214146114000214260ustar00rootroot00000000000000.TH largest_wcc 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME largest_wcc \- extract and print the largest weakly connected component in the network in LPmade format .SH SYNOPSIS .B largest_wcc [OPTIONS] ... .SH DESCRIPTION Creates a new network consisting of the largest weakly connected component in the input network. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net largest_wcc > largest_wcc.net .PP Input: .RS *Vertices 22 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 *Edges 18 0 1 1 2 3 1 3 2 1 4 5 1 6 5 1 8 7 1 8 9 1 10 11 1 11 12 1 13 14 1 14 15 1 15 13 1 16 17 1 17 18 1 18 16 1 18 19 1 19 20 1 20 19 1 .RE .PP Output: .RS *Vertices 5 0 16 1 17 2 18 3 19 4 20 *Edges 6 0 1 1 1 2 1 2 0 1 2 3 1 3 4 1 4 3 1 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/out_degree_distribution.1000066400000000000000000000026151214146114000240560ustar00rootroot00000000000000.TH out_degree_distribution 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME out_degree_distribution \- print the out degree distribution of the network .SH SYNOPSIS .B out_degree_distribution [OPTIONS] ... .SH DESCRIPTION Produces the out-degree distribution of the network. Output degrees are ordered by vertex identifier order. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net out_degree_distribution > odd.txt .PP Input: .RS *Vertices 4 0 0 1 1 2 2 3 3 *Edges 6 0 1 1 0 2 1 0 3 1 1 2 1 1 3 1 2 1 1 .RE .PP Output: .RS 3 2 1 0 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/pagerank.1000066400000000000000000000033471214146114000207300ustar00rootroot00000000000000.TH pagerank 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME pagerank \- the PageRank scores of vertices in the network .SH SYNOPSIS .B pagerank [OPTIONS] ... d ... .SH DESCRIPTION Computes PageRank scores for every vertex in the network with the PageRank random walk restart parameter d. Scores are output after a convergence of 0.999 across iterations. The implementation is based on the description in the original paper 'The Anatomy of a Large-Scale Hypertextual Web Search Engine' by Brin and Page and does not include subsequent optimizations in efficiency. Nonetheless, this computes very quickly in practice in networks with millions of nodes. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net pagerank 0.85 .PP Input: .RS *Vertices 4 0 0 1 1 2 2 3 3 *Edges 6 0 1 1 0 2 1 0 3 1 1 2 1 1 3 1 2 1 1 .RE .PP Output: .RS 4.924846e-02 3.809763e-01 2.848876e-01 2.848876e-01 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/path_pairs.1000066400000000000000000000036171214146114000212720ustar00rootroot00000000000000.TH path_pairs 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME path_pairs \- find all vertex pairs with reachability dictated by a sequence of directional steps .SH SYNOPSIS .B path_pairs [OPTIONS] ... path_steps ... .SH DESCRIPTION Accepts as a parameter a sequence of directions defined by the regular expression '(O|I|o|i)+' excluding the quotes. Lowercase 'o' and uppercase 'O' both stand for out-edges and lowercase 'i' and uppercase 'I' both stand for in-edges. Given a network at standard input, the program will produce all the pairs of vertices such that a target vertex is reachable from the source vertex by following the specified sequence of in-edges or out-edges. The sequence should not be specified with spaces, but should instead be specified as a single string argument at the command line. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net path_pairs OIO .PP Input: .RS *Vertices 12 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 *Edges 9 0 1 1 1 2 1 2 3 1 4 5 1 6 5 1 6 7 1 9 8 1 10 9 1 10 11 1 .RE .PP Output: .RS 4 7 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/predict.1000066400000000000000000000164711214146114000205740ustar00rootroot00000000000000.TH predict 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME predict \- predict links using unsupervised link prediction algorithms .SH SYNOPSIS .B predict [OPTIONS] ... predictor ... [PREDICTOR OPTIONS] .SH DESCRIPTION The predict binary provides access to all unsupervised link prediction methods implemented within LPmade. When input is provided at standard input it should be specified as space-separated vertex identifier pairs. Output is provided as a space-separated triple of vertex pairs with an associated score. Also see label_predictions(1), which describes the program to attach labels to the output of this program given a second network. Currently, the following unsupervised score generators are available: .TP AdamicAdar The Adamic/Adar link prediction method. This predictor is an inverse frequency measure of common neighbors. The contribution of a common neighbor to the score is weighted in proportion to the rarity of the neighbor. This predictor accepts no additional arguments. .TP CommonNeighbor The common neighbors link prediction method. This predictor is the simple count of common neighbors. In directed networks, a common neighbor exists if there is a neighbor relationship between the source and target and the neighbor in terms of the direction specified by the -d option to predict. This means common neighbors may exist even if the source and target are more than two hops away with respect to paths along the directed edges. This predictor accepts no additional arguments. .TP ClusteringCoefficient The clustering coefficient of the source. For purposes of consistency with the rest of the library, this should be called IClusteringCoefficient, and this change may occur in future releases. This predictor accepts no additional arguments. .TP Distance The geodesic distance between the source and the target. This predictor is only useful in scenarios when the -c flag is provided to predict or when -n 0 is used. This predictor accepts no additional arguments. .TP IDegree The degree of the source. The -d option to predict determines whether to use in-degree or out-degree. This predictor accepts no additional arguments. .TP JDegree The degree of the target. The -d option to predict determines whether to use in-degree or out-degree. This predictor accepts no additional arguments. .TP IPageRank [PREDICTOR OPTIONS] The PageRank score of the source. The -d option to predict determines whether to use in-degree or out-degree. This predictor requires one argument: the PageRank d walk restart probability. .TP JPageRank [PREDICTOR OPTIONS] The PageRank score of the target. The -d option to predict determines whether to use in-degree or out-degree. This predictor requires one argument: the PageRank d walk restart probability. .TP IVolume The total sum of the weights in or out of the source. Equivalent to IDegree in unweighted networks. The -d option to predict determines whether to use in-degree or out-degree. This predictor accepts no additional arguments. .TP JVolume The total sum of the weights in or out of the target. Equivalent to JDegree in unweighted networks. The -d option to predict determines whether to use in-degree or out-degree. This predictor accepts no additional arguments. .TP JaccardCoefficient The Jaccard coefficient link prediction method. This predictor produces a score corresponding to the quotient of the intersection of the two neighbor sets and the union of the two neighbors sets. This predictor accepts no additional arguments. .TP Katz [PREDICTOR OPTIONS] The Katz link prediction method. Because this score is computed by performing traversals on the graph rather than matrix operations, and because the networks of interest are typically large, this predictor accepts as its first parameter a maximum distance away from the source. Its second parameter is the damping parameter beta. .TP Mutuality This predictor outputs 1 if the edge exists in the other direction or 0 otherwise. It is meaningless in undirected networks. This predictor accepts no additional arguments. .TP One This predictor always outputs 1. This predictor is not useful for actually generating predictions, but it may be used as a null model or as a way to quickly generate the set of pairs for a given value of the parameter -n. This predictor accepts no additional arguments. .TP PreferentialAttachment Preferential attachment link prediction method. As implemented in this library, it is the product of the out-degree of the source and the in-degree of the target. Setting -d I will invert this. This predictor accepts no additional arguments. .TP PropFlow [PREDICTOR OPTIONS] The PropFlow link prediction method. This should serve as the reference implementation of PropFow. This predictor accepts a maximum distance to explore the network in the underlying breadth first search before terminating. The predictor accepts a single parameter: the maximum depth of the breadth-first search (0 for full search). .TP RootedPageRank [PREDICTOR OPTIONS] Rooted PageRank link prediction method. This predictor accepts the random walk restart parameter alpha (0,1). .TP ShortestPathCount [PREDICTOR OPTIONS] This predictor outputs the number of shortest paths from the source to the target. It executes a breadth first search terminating at the level at which the target is found and counts the number of times the target is encountered at that level. This predictor accepts a maximum distance to explore the network in the underlying breadth first search before terminating. .TP SimRank [PREDICTOR OPTIONS] The SimRank link prediction method. Because the method requires O(|V|^2) space, it is feasible only for small networks. It accepts the C parameter from the original publication. .TP WeightedRootedPageRank [PREDICTOR OPTIONS] The same as rooted PageRank except that it uses edge weights if they exist to inform the transition probability from one vertex to another. This predictor accepts the random walk restart parameter alpha (0,1). .SH OPTIONS .TP .B \-h display usage information .TP .BR \-d " direction" The direction of edges to use; must be in {I,O} (default is O). .TP .BI \-f " network" File containing the network in which to predict. .TP .BI \-n " distance" Distance of neighborhood in which to predict (default is 2). Should not be used with -c. .TP .B \-c Specifies that node pairs for prediction are available at standard input. Should not be used with -n. .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mypairs.txt predict -c -f mynet.net -d O AdamicAdar > myout.pred .PP predict -f mynet.net -d I -n 2 PropFlow 5 > myout.pred .PP Input (with -c option): 2 3 .PP Output: 2 3 4.57 .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/pseudo_diameter.1000066400000000000000000000035251214146114000223070ustar00rootroot00000000000000.TH pseudo_diameter 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME pseudo_diameter \- a generally accurate approximation of the diameter of the network .SH SYNOPSIS .B pseudo_diameter [OPTIONS] ... .SH DESCRIPTION A fast approximation algorithm that executes repeated breadth-first searches from the most distant vertex found in the previous step in an attempt to approximate the diameter of the network. The algorithm terminates when the distance does not increase from one iteration to the next. In rare circumstances, the algorithm may overestimate, but it cannot underestimate. This program will return a result within a matter of seconds on networks of virtually any size so long as the network fits in memory. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net pseudo_diameter .PP Input: .RS *Vertices 9 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 *Edges 18 0 1 1 0 2 1 0 7 1 1 0 1 1 8 1 2 0 1 2 8 1 3 4 1 3 5 1 3 6 1 3 8 1 4 3 1 5 3 1 6 3 1 7 0 1 8 1 1 8 2 1 8 3 1 .RE .PP Output: .RS 5 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/remove_edges.1000066400000000000000000000030251214146114000215750ustar00rootroot00000000000000.TH remove_edges 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME remove_edges \- output a network with the edges in the parameter removed .SH SYNOPSIS .B remove_edges [OPTIONS] ... removal_network ... .SH DESCRIPTION Outputs a LPmade format network that contains all the edges from the network at standard input that are not present in the network file specified as a parameter. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net remove_edges removal.net > newnet.net .PP Input: .RS *Vertices 4 0 1 1 2 2 3 3 4 *Edges 3 0 1 1 1 2 1 2 3 1 -------------------- *Vertices 4 0 1 1 2 2 3 3 4 *Edges 1 0 1 1 .PP Output: .RS *Vertices 4 0 1 1 2 2 3 3 4 *Edges 2 1 2 1 2 3 1 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/remove_isolates.1000066400000000000000000000030011214146114000223230ustar00rootroot00000000000000.TH remove_isolates 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME remove_isolates \- output a network with the isolates removed .SH SYNOPSIS .B remove_isolates [OPTIONS] ... .SH DESCRIPTION Produces a copy of the network with all isolated vertices removed. Any vertex with degree one or higher will remain along with all edges in the original network. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net remove_isolates > no_isolates.net .PP Input: .RS *Vertices 9 0 0 1 2 2 3 3 4 4 5 5 7 6 18 7 47 8 285 *Edges 6 0 1 1 0 4 2 0 5 1 5 3 1 7 1 1 8 7 1 .RE .PP Output: .RS *Vertices 7 0 0 1 2 2 4 3 5 4 7 5 47 6 285 *Edges 4 0 1 1 0 3 2 0 4 1 5 2 1 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/reverse_edges.1000066400000000000000000000031701214146114000217540ustar00rootroot00000000000000.TH reverse_edges 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME reverse_edges \- output a network with the edge directions reversed .SH SYNOPSIS .B reverse_edges [OPTIONS] ... .SH DESCRIPTION Produces a copy of the network with all edge directions reversed. Has no effect in undirected networks or directed networks with complete mutuality. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net reverse_edges > reversed.net .PP Input: .RS *Vertices 9 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 *Edges 16 0 1 1 0 2 1 1 2 1 1 3 1 1 4 1 2 5 3 3 6 1 4 5 1 5 2 1 6 3 1 6 7 1 7 1 1 8 0 1 8 1 7 8 2 1 8 3 1 .RE .PP Output: .RS *Vertices 9 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 *Edges 16 0 8 1 1 0 1 1 7 1 1 8 7 2 0 1 2 1 1 2 5 1 2 8 1 3 1 1 3 6 1 3 8 1 4 1 1 5 2 3 5 4 1 6 3 1 7 6 1 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/roc.1000066400000000000000000000034561214146114000177240ustar00rootroot00000000000000.TH roc 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME roc \- compute area under the precision-recall curve for a sorted list of score-label pairs .SH SYNOPSIS .B roc [OPTIONS] ... num_negative num_positive ... .SH DESCRIPTION A highly optimized calculator for area under the precision-recall curve. This program assumes that its input will come in terms of space-separated score-label pairs sorted so that the highest scores are at the top. It will properly handle all C-style double-precision input. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .RS all=$(predictions.sprob.gz gunzip | wc -l) pos=$(predictions.sprob.gz gunzip | grep -c '1$') let neg=all-pos < predictions.sprob.gz gunzip | roc $neg $pos .RE .PP Input: .RS 0.9 1 0.8 0 0.7 1 0.6 1 0.5 0 0.4 0 .RE .PP Output: .RS 0.000000 0.000000 0.000000 0.333333 0.333333 0.333333 0.333333 0.666667 0.333333 1.000000 0.666667 1.000000 1.000000 1.000000 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/scc_distribution.1000066400000000000000000000040441214146114000225020ustar00rootroot00000000000000.TH scc_distribution 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME scc_distribution \- output information about all strongly connected components in the network .SH SYNOPSIS .B scc_distribution [OPTIONS] ... .SH DESCRIPTION Produces a distribution of the size of strongly connected components in the network. The result is space-separated output in which the first column is the identifier of a representative vertex of the SCC, the second column is the number of vertices in the SCC, the third column is the number of edges in the SCC, and the fourth column is the approximate diameter of the SCC. To see how diameter is calculated within the SCCs, see pseudo_diameter(1). .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net scc_distribution > scc_dist.txt .PP Input: .RS *Vertices 22 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 *Edges 18 0 1 1 2 3 1 3 2 1 4 5 1 6 5 1 8 7 1 8 9 1 10 11 1 11 12 1 13 14 1 14 15 1 15 13 1 16 17 1 17 18 1 18 16 1 18 19 1 19 20 1 20 19 1 .RE .PP Output: .RS 1 1 0 0 0 1 0 0 2 2 2 1 5 1 0 0 4 1 0 0 6 1 0 0 7 1 0 0 9 1 0 0 8 1 0 0 12 1 0 0 11 1 0 0 10 1 0 0 13 3 3 2 19 2 2 1 16 3 3 2 21 1 0 0 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/shortest_path_distribution.1000066400000000000000000000030751214146114000246240ustar00rootroot00000000000000.TH shortest_path_distribution 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME shortest_path_distribution \- output the shortest paths between all pairs of vertices .SH SYNOPSIS .B shortest_path_distribution [OPTIONS] ... .SH DESCRIPTION Produces the distribution of |V| choose 2 shortest path lengths in the network excluding those with non-reachability. The output places each path length on its own line ordered first by source and then by target. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net shortest_path_distribution > sp_dist.txt .PP Input: .RS *Vertices 9 0 0 1 2 2 3 3 4 4 5 5 7 6 18 7 47 8 285 *Edges 8 0 1 1 0 4 2 0 5 1 2 6 1 5 3 1 6 2 1 7 1 1 8 7 1 .RE .PP Output: .RS 1 2 1 1 1 1 1 1 2 1 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/simrank.1000066400000000000000000000036741214146114000206070ustar00rootroot00000000000000.TH simrank 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME simrank \- output SimRank scores for all pairs of vertices .SH SYNOPSIS .B simrank [OPTIONS] ... C ... .SH DESCRIPTION Computes the SimRank scores for all pairs of vertices in the network. SimRank computes a similar score between two objects such that two objects are similar if they are related to other objects that are similar. This is only feasible for small networks in this implementation because of O(|V|^2) space requirements. This is the vanilla SimRank implementation as proposed in the original paper by Jeh and Widom. C is a decay factor parameter typically set to 0.8. For more information, see the paper 'SimRank: a measure of structural-context similarity'. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net simrank 0.8 .PP Input: .RS *Vertices 5 0 0 1 1 2 2 3 3 4 4 *Edges 6 0 1 1 0 3 1 1 2 1 2 0 1 3 4 1 4 3 1 .RE .PP Output: .RS 1 0 0 1.280000e-01 0 0 1 0 4.000000e-01 1.024000e-01 0 0 1 4.096000e-02 3.200000e-01 1.280000e-01 4.000000e-01 4.096000e-02 1 7.168000e-02 0 1.024000e-01 3.200000e-01 7.168000e-02 1 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/snowball_sample.1000066400000000000000000000035531214146114000223210ustar00rootroot00000000000000.TH snowball_sample 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME snowball_sample \- output the network that results from performing snowball sampling .SH SYNOPSIS .B snowball_sample [OPTIONS] ... source distance .SH DESCRIPTION Creates a snowball sample of the network to the given distance from the given vertex using out-edges. Outputs the network that results from the sampling procedure including all vertices reached and any edges that connect two vertices in the sample within the distance limit. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. See also snowball_sample_in(1), which differs from this program only in its use of in-edges. .SH EXAMPLE .PP < mynet.net snowball_sample 0 3 > smallnet.net .PP Input: .RS *Vertices 9 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 *Edges 18 0 1 1 0 2 1 1 2 1 1 3 1 1 4 1 2 5 1 3 6 1 4 5 1 5 2 1 6 3 1 6 7 1 7 1 1 8 0 1 8 1 1 8 2 1 8 3 1 8 4 1 8 5 1 8 6 1 8 7 1 .RE .PP Output: .RS *Vertices 9 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 *Edges 9 0 1 1 0 2 1 1 2 1 1 3 1 1 4 1 2 5 1 3 6 1 4 5 1 5 2 1 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/snowball_sample_in.1000066400000000000000000000033311214146114000230010ustar00rootroot00000000000000.TH snowball_sample_in 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME snowball_sample_in \- output the network that results from performing snowball sampling .SH SYNOPSIS .B snowball_sample [OPTIONS] ... source distance .SH DESCRIPTION Creates a snowball sample of the network to the given distance from the given vertex using in-edges. Outputs the network that results from the sampling procedure including all vertices reached and any edges that connect two vertices in the sample within the distance limit. See also snowball_sample(1), which differs from this program only in its use of out-edges. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net snowball_sample_in 0 3 > smallnet.txt .PP Input: .RS *Vertices 5 0 0 1 1 2 2 3 3 4 4 *Edges 5 0 1 1 0 4 1 2 3 1 3 0 1 4 0 1 .RE .PP Output: .RS *Vertices 5 0 0 1 1 2 2 3 3 4 4 *Edges 4 0 4 1 2 3 1 3 0 1 4 0 1 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/snowball_sample_size_dist.1000066400000000000000000000032111214146114000243650ustar00rootroot00000000000000.TH snowball_sample_size_dist 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME snowball_sample_size_dist \- output the distribution of sizes of networks that result from snowball samples .SH SYNOPSIS .B snowball_sample_size_dist [OPTIONS] ... distance .SH DESCRIPTION Outputs the size of each network in terms of number of vertices and number of edges that results from performing snowball sampling from a particular vertex out to the specified distance. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net snowball_sample_size_dist 3 .PP Input: .RS *Vertices 9 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 *Edges 18 0 1 1 0 2 1 1 2 1 1 3 1 1 4 1 2 5 1 3 6 1 4 5 1 5 2 1 6 3 1 6 7 1 7 1 1 8 0 1 8 1 1 8 2 1 8 3 1 8 4 1 8 5 1 8 6 1 8 7 1 .RE .PP Output: .RS 7 9 7 9 2 0 4 1 3 1 2 0 6 4 7 6 9 17 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/stream_to_network.1000066400000000000000000000041531214146114000227020ustar00rootroot00000000000000.TH stream_to_network 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME stream_to_network \- convert a stream of data event records into a network in LPmade format .SH SYNOPSIS .B stream_to_network [OPTIONS] ... separator v1_index v2_index .SH DESCRIPTION Given a stream of edges or events and the separator and index of the source and target, generate a properly formatted network file. There are no restrictions on the order in which events in terms of vertex identifiers. Longitudinal data should be correctly ordered from oldest to newest events. Currently, only numeric vertex identifiers are supported, but these identifiers need not be consecutive. This program is very flexible and can also be used to convert various edge list formats to the LPmade network file format. If edge weights must consider weighting information associated with each event, see weighted_stream_to_network(1). .SH OPTIONS .TP .B \-h display usage information .TP .B \-i ignore self-edges .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mydata.txt stream_to_network -i ' ' 4 6 > mynet.net .PP < mydata.txt.gz gunzip | stream_to_network , 2 3 > mynet.net .PP Input: .RS 18 3 7 4 0 7 3 18 0 5 0 5 0 2 285 47 47 2 .RE .PP Output: .RS *Vertices 9 0 0 1 2 2 3 3 4 4 5 5 7 6 18 7 47 8 285 *Edges 8 0 1 1 0 4 2 0 5 1 2 6 1 5 3 1 6 2 1 7 1 1 8 7 1 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/threshold_edges.1000066400000000000000000000026261214146114000223020ustar00rootroot00000000000000.TH threshold_edges 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME threshold_edges \- remove edges with weights below threshold .SH SYNOPSIS .B threshold_edges [OPTIONS] ... lower_threshold .SH DESCRIPTION Produce a copy of the network with all edges having weights below the threshold removed. .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net threshold_edges > thresholded.net .PP Input: .RS *Vertices 5 0 0 1 1 2 2 3 3 4 4 *Edges 4 0 1 1 0 2 2.2 0 3 2.7 0 4 4 .RE .PP Output: .RS *Vertices 5 0 0 1 1 2 2 3 3 4 4 *Edges 2 0 3 2.7 0 4 4 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/wcc_distribution.1000066400000000000000000000034011214146114000225020ustar00rootroot00000000000000.TH wcc_distribution 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME wcc_distribution \- output information about all weakly connected components in the network .SH SYNOPSIS .B wcc_distribution [OPTIONS] ... .SH DESCRIPTION Produces a distribution of the size of weakly connected components in the network. The result is space-separated output in which the first column is the identifier of a representative vertex of the WCC, the second column is the number of vertices in the WCC, the third column is the number of edges in the WCC, and the fourth column is the approximate diameter of the WCC. To see how diameter is calculated within the WCCs, see pseudo_diameter(1). .SH OPTIONS .TP .B \-h display usage information .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mynet.net wcc_distribution > wcc_dist.txt .PP Input: .PP Output: A space-separated list of WCCs in terms of representative vertex, number of vertices, number of edges, and approximate diameter. .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/man/weighted_stream_to_network.1000066400000000000000000000042771214146114000245710ustar00rootroot00000000000000.TH weighted_stream_to_network 1 "June 20, 2011" "version 1.0" "LPmade User Commands" .SH NAME weighted_stream_to_network \- convert a stream of weighted data event records into a network in LPmade format .SH SYNOPSIS .B weighted_stream_to_network [OPTIONS] ... separator v1_index v2_index w_index .SH DESCRIPTION Given a stream of edges or events and the separator and index of the source and target, generate a properly formatted network file. There are no restrictions on the order in which events in terms of vertex identifiers. Longitudinal data should be correctly ordered from oldest to newest events. Currently, only numeric vertex identifiers are supported, but these identifiers need not be consecutive. This program is very flexible and can also be used to convert various edge list formats to the LPmade network file format. If edge weights need not consider weighting information associated with each event, see stream_to_network(1). .SH OPTIONS .TP .B \-h display usage information .TP .B \-i ignore self-edges .SH RETURN VALUE Returns 0 on successful execution (not necessarily successful completion) or 1 if argument specification is incorrect or the requested predictor is not included in predict. .SH EXAMPLE .PP < mydata.txt weighted_stream_to_network -i ' ' 4 6 > mynet.net .PP < mydata.txt.gz gunzip | stream_to_network , 2 3 > mynet.net .PP Input: .RS 18 3 2 7 4 0.3 0 7 11.4 3 18 9 0 5 1.3 0 5 2 0 2 2.7 285 47 1 47 2 5 .RE .PP Output: .RS *Vertices 9 0 0 1 2 2 3 3 4 4 5 5 7 6 18 7 47 8 285 *Edges 8 0 1 2.7 0 4 3.3 0 5 11.4 2 6 9 5 3 0.3 6 2 2 7 1 5 8 7 1 .RE .SH COPYRIGHT .PP Copyright (C) 2011 Ryan N. Lichtenwalter .PP LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . rlichtenwalter-LPmade-ba322c3/manual.pdf000066400000000000000000011066041214146114000202540ustar00rootroot00000000000000%PDF-1.4 % 1 0 obj << /S /GoTo /D (section.1) >> endobj 4 0 obj (Introduction) endobj 5 0 obj << /S /GoTo /D (section.2) >> endobj 8 0 obj (License) endobj 9 0 obj << /S /GoTo /D (section.3) >> endobj 12 0 obj (Software Contents) endobj 13 0 obj << /S /GoTo /D (section.4) >> endobj 16 0 obj (Installation) endobj 17 0 obj << /S /GoTo /D (subsection.4.1) >> endobj 20 0 obj (Downloading) endobj 21 0 obj << /S /GoTo /D (subsection.4.2) >> endobj 24 0 obj (Building) endobj 25 0 obj << /S /GoTo /D (section.5) >> endobj 28 0 obj (The Network Libary) endobj 29 0 obj << /S /GoTo /D (subsection.5.1) >> endobj 32 0 obj (The WeightedNetwork Class) endobj 33 0 obj << /S /GoTo /D (subsubsection.5.1.1) >> endobj 36 0 obj (Existing Binaries) endobj 37 0 obj << /S /GoTo /D (subsubsection.5.1.2) >> endobj 40 0 obj (Testing) endobj 41 0 obj << /S /GoTo /D (subsection.5.2) 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0000287683 00000 n 0000287803 00000 n 0000287907 00000 n 0000288014 00000 n 0000288052 00000 n 0000288495 00000 n trailer << /Size 472 /Root 470 0 R /Info 471 0 R /ID [ ] >> startxref 288770 %%EOF rlichtenwalter-LPmade-ba322c3/metrics/000077500000000000000000000000001214146114000177425ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/metrics/Makefile000066400000000000000000000024611214146114000214050ustar00rootroot00000000000000# ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ CFLAGS = -Wall -lm -O3 all: hmeasure aupr auroc roc pr ef topnprec cost_curve hmeasure: hmeasure.c point_vector.c gcc -o $@ $^ $(CFLAGS) cost_curve: curve.c point_vector.c gcc -o $@ $^ $(CFLAGS) aupr: aupr.c gcc -o $@ $^ $(CFLAGS) auroc: auroc.c gcc -o $@ $^ $(CFLAGS) roc: roc.c gcc -o $@ $^ $(CFLAGS) pr: pr.c gcc -o $@ $^ $(CFLAGS) ef: ef.c gcc -o $@ $^ $(CFLAGS) topnprec: topnprec.c gcc -o $@ $^ $(CFLAGS) .phony: all clean clean: -rm -f main.o hmeasure aupr auroc roc pr ef topnprec cost_curve rlichtenwalter-LPmade-ba322c3/metrics/aupr.c000066400000000000000000000121211214146114000210520ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include double auc(double step_size, unsigned int p_count) { double prob_val; unsigned int class_val; double area = 0.0; unsigned long last_tp_count = 0; unsigned long last_fp_count = 0; unsigned long tp_count = 0; unsigned long fp_count = 0; double last_prob_val = 1.0; int result = 0; while( (result = fscanf( stdin, "%lg %u\n", &prob_val, &class_val )) != EOF ) { if( class_val != 1 ) { fp_count++; } else { tp_count++; } if( prob_val < last_prob_val ) { unsigned long interp_count = (unsigned long)(((tp_count - last_tp_count) / step_size) ); if( interp_count > 0 ) { // fprintf( stderr, "prob_val: %.3lf - tp_count: %lu - fp_count: %lu - interp_count: %lu\n", prob_val, tp_count, fp_count, interp_count ); double tp_delta = (tp_count - last_tp_count) / (double)interp_count; double fp_delta = (fp_count - last_fp_count) / (double)interp_count; double lower_tp = last_tp_count; double lower_fp = last_fp_count; if( lower_tp == 0 ) { lower_tp = 0.0000000000000001; } while( lower_tp < tp_count ) { double upper_tp = lower_tp + tp_delta; double upper_fp = lower_fp + fp_delta; double lower_recall = lower_tp / p_count; double lower_precision = lower_tp / (lower_tp+lower_fp); // fprintf( stderr, "prob_val: %.3lf - class_val: %u - tp: %.3lf - fp: %.3lf - r: %.3lf - p: %.3lf\n", prob_val, class_val, lower_tp, lower_fp, lower_recall, lower_precision ); double upper_recall = upper_tp / p_count; double upper_precision = upper_tp / (upper_tp+upper_fp); double rectangle_area = (upper_recall-lower_recall)*lower_precision; double triangle_area = 0.5*(upper_recall-lower_recall)*(upper_precision-lower_precision); // fprintf( stderr, "Rectangle Area: %.3lf - Triangle Area: %.3lf\n", rectangle_area, triangle_area ); area += rectangle_area + triangle_area; lower_tp = upper_tp; lower_fp = upper_fp; } } last_tp_count = tp_count; last_fp_count = fp_count; } last_prob_val = prob_val; } // fprintf( stdout, "False Positives: %lu - True Positives: %lu\n", fp_count, tp_count ); unsigned long interp_count = (unsigned long)(((tp_count - last_tp_count) / step_size) ); if( interp_count > 0 ) { // fprintf( stderr, "prob_val: %.3lf - tp_count: %lu - fp_count: %lu - interp_count: %lu\n", prob_val, tp_count, fp_count, interp_count ); double tp_delta = (tp_count - last_tp_count) / (double)interp_count; double fp_delta = (fp_count - last_fp_count) / (double)interp_count; double lower_tp = last_tp_count; double lower_fp = last_fp_count; if( lower_tp == 0 ) { lower_tp = 0.0000000000000001; } while( lower_tp < tp_count ) { double upper_tp = lower_tp + tp_delta; double upper_fp = lower_fp + fp_delta; double lower_recall = lower_tp / p_count; double lower_precision = lower_tp / (lower_tp+lower_fp); // fprintf( stderr, "prob_val: %.3lf - class_val: %u - tp: %.3lf - fp: %.3lf - r: %.3lf - p: %.3lf\n", prob_val, class_val, lower_tp, lower_fp, lower_recall, lower_precision ); double upper_recall = upper_tp / p_count; double upper_precision = upper_tp / (upper_tp+upper_fp); double rectangle_area = (upper_recall-lower_recall)*lower_precision; double triangle_area = 0.5*(upper_recall-lower_recall)*(upper_precision-lower_precision); // fprintf( stderr, "Rectangle Area: %.3lf - Triangle Area: %.3lf\n", rectangle_area, triangle_area ); area += rectangle_area + triangle_area; lower_tp = upper_tp; lower_fp = upper_fp; } } // fprintf( stdout, "FP: %lu - TP: %lu - lFP: %lu - lTP: %lu - cip: %lf - cin: %lf - cum_neg: %lf\n", fp_count, tp_count, last_fp_count, last_tp_count, cip, cin, cum_neg ); return area; } void usage( const char* binary ) { fprintf( stderr, "Usage: %s [options] precision num_positive\n", binary ); fprintf( stderr, "Options: -h Print this usage message and exit\n" ); } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind + 2 ) { usage( argv[0] ); return 1; } double step_size = atof( argv[optind++] ); unsigned long p_count = atoi( argv[optind++] ); double area = auc( step_size, p_count ); fprintf( stdout, "%.9lf\n", area ); return 0; } rlichtenwalter-LPmade-ba322c3/metrics/auroc.c000066400000000000000000000053251214146114000212240ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include double auc() { double prob_val; unsigned int class_val; double area = 0.0; double cum_neg = 0.0; unsigned long last_tp_count = 0; unsigned long last_fp_count = 0; unsigned long tp_count = 0; unsigned long fp_count = 0; double last_prob_val = 1.1; double cip = 0.0; double cin = 0.0; int result = 0; while( (result = fscanf( stdin, "%lg %u\n", &prob_val, &class_val )) != EOF ) { if( prob_val < last_prob_val ) { cip = tp_count - last_tp_count; cin = fp_count - last_fp_count; // fprintf( stdout, "FP: %lu - TP: %lu - lFP: %lu - lTP: %lu - cip: %lf - cin: %lf - cum_neg: %lf\n", fp_count, tp_count, last_fp_count, last_tp_count, cip, cin, cum_neg ); area += cip * (cum_neg + (0.5*cin)); cum_neg += cin; last_tp_count = tp_count; last_fp_count = fp_count; // fprintf( stdout, "Area: %lf\n", area ); } if( class_val != 1 ) { fp_count++; } else { tp_count++; } last_prob_val = prob_val; } // fprintf( stdout, "False Positives: %lu - True Positives: %lu\n", fp_count, tp_count ); cip = tp_count - last_tp_count; cin = fp_count - last_fp_count; // fprintf( stdout, "FP: %lu - TP: %lu - lFP: %lu - lTP: %lu - cip: %lf - cin: %lf - cum_neg: %lf\n", fp_count, tp_count, last_fp_count, last_tp_count, cip, cin, cum_neg ); area += cip * (cum_neg + (0.5*cin)); cum_neg += cin; last_tp_count = tp_count; last_fp_count = fp_count; area /= ((unsigned long long)fp_count*tp_count); return 1 - area; } void usage( const char* binary ) { fprintf( stderr, "Usage: %s [options]\n", binary ); fprintf( stderr, "Options: -h Print this usage message and exit\n" ); } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } double area = auc(); fprintf( stdout, "%.9lf\n", area ); return 0; } rlichtenwalter-LPmade-ba322c3/metrics/curve.c000066400000000000000000000132371214146114000212400ustar00rootroot00000000000000#include #include #include #include "curve.h" #include "point_vector.h" const double TOL = 1e-18; unsigned long STACK_CAPACITY = 65536; int points_sort (const void* e1, const void* e2) { // array elements are point* point* p1 = *((point**)e1); point* p2 = *((point**)e2); double answerx = (p1->x - p2->x); if (answerx > TOL) return 1; else if (answerx < -TOL) return -1; else return 0; } int right_turn(point*, point*, point*); void hull_add_point(point_vector* vp, point* p) { if (vp->size > 1) { while (!right_turn(&(vp->array[vp->size - 2]), &(vp->array[vp->size - 1]), p)) { vp->size--; if (vp->size == 1) break; } } point_vector_push_back(vp, p); } int right_turn(point* p1, point* p2, point* p3) { //printf("rt: %le, %le, %le\n", p1->x, p2->x, p3->x); // p1, p2, p3 specified in increasing order of X. double a1 = p1->x - p2->x; double a2 = p1->y - p2->y; double b1 = p3->x - p2->x; double b2 = p3->y - p2->y; return (a1 * b2 > b1 * a2); } point_vector* read_file(double* prop_positive, long num_negatives, long num_positives) { FILE* fp; long true_positives; long false_positives; double p1, old_p1; int true_class; point_vector* vp; point p; old_p1 = -1; vp = point_vector_create(); p.x = 0; p.y = 0; point_vector_push_back(vp, &p); //fp = fopen(filename, "r"); fp = stdin; // if (!fp) { printf("could not open %s!\n", filename); exit(0); } if (num_positives == -1 && num_negatives == -1) fscanf(fp, "%ld %ld\n", &num_negatives, &num_positives); //true_positives = num_positives; //false_positives = num_negatives; true_positives = 0; false_positives = 0; while (!feof(fp)) { fscanf(fp, "%lf %d\n", &p1, &true_class); if (!feof(fp)) { if (fabs(p1 - old_p1) > TOL && old_p1 > -1) { p.y = (double)true_positives / num_positives; p.x = (double)false_positives / num_negatives; //printf("ROC: (%lf, %lf)\n", p.x, p.y); hull_add_point(vp, &p); } if(true_class == 0) false_positives++; else true_positives++; old_p1 = p1; } } p.y = (double)true_positives / num_positives; p.x = (double)false_positives / num_negatives; hull_add_point(vp, &p); fclose(fp); *prop_positive = (double) num_positives / (num_positives + num_negatives); p.x = 1; p.y = 1; hull_add_point(vp, &p); return vp; } line translate_point(point* p) { line ret; ret.b = p->x; // false positive rate: ROC x-coordinate. ret.m = (1 - p->y) - p->x; // fnr - fpr = (1 - tpr) - fpr return ret; } int intersect(line line1, line line2, point* ret) { //printf("intersecting (%f, %f), (%f, %f)\n", line1->m, line1->b, line2->m, line2->b); if (fabs(line1.m - line2.m) < TOL) { return 0; } ret->x = (line2.b - line1.b) / (line1.m - line2.m); ret->y = line1.m * ret->x + line1.b; //printf ("intersection (%f, %f)\n", ret->x, ret->y); return -1; } point** compute_envelope(point_vector* roc_hull, line** lines) { point** ret = (point**)malloc((roc_hull->size + 1) * sizeof(point*)); unsigned long x; for (x = 0; x < roc_hull->size + 1; x++) ret[x] = (point*)malloc(sizeof(point)); for (x = 0; x < roc_hull->size; x++) (*lines)[x] = translate_point(&roc_hull->array[x]); for (x = 0; x < roc_hull->size - 1; x++) intersect((*lines)[x], (*lines)[x + 1], ret[x + 1]); ret[0]->x = ret[1]->x; ret[0]->y = 0; ret[roc_hull->size]->x = ret[roc_hull->size - 1]->x; ret[roc_hull->size]->y = 0; return ret; } void points_free(point** points, int num_points) { int x; for (x = 0; x < num_points; x++) free(points[x]); free(points); } double euclidean(point* p1, point* p2) { return sqrt((p1->y - p2->y) * (p1->y - p2->y) + (p1->x - p2->x) * (p1->x - p2->x)); } double aacc(point** points, unsigned long numpoints) { double a, b, c, s; double envelope_area = 0; double box_area, min_x, max_x, min_int, max_int; unsigned long x; min_x = points[1]->x; max_x = points[numpoints - 2]->x; min_int = 0.5 * (min_x) - ((min_x - 0.5) * fabs(min_x - 0.5) / 2); max_int = 0.5 * (max_x) - ((max_x - 0.5) * fabs(max_x - 0.5) / 2); box_area = max_int - min_int; for (x = 1; x < numpoints - 1; x++) { a = euclidean(points[0], points[x]); b = euclidean(points[x], points[x + 1]); c = euclidean(points[x + 1], points[0]); s = (a + b + c) / 2; envelope_area += sqrt(s * (s - a) * (s - b) * (s - c)); } return 4 * (box_area - envelope_area); } void plot_curve(line* cost_curve, int num_lines) { FILE* fp; int x; fp = fopen("curves.p", "w"); if (!fp) { fprintf(stderr, "plot file open failed!\n"); exit(0); } fprintf(fp, "set term postscript color\n"); fprintf(fp, "set output 'curves.ps'\n"); fprintf(fp, "set xrange [0:1]\n"); fprintf(fp, "set yrange [0:0.5]\n"); fprintf(fp, "set nokey\n"); fprintf(fp, "plot x"); for(x = 0; x < num_lines; x++) fprintf(fp, ", %lf * x + %lf", cost_curve[x].m, cost_curve[x].b); fprintf(fp, "\n"); fclose(fp); } int main (int argc, char** argv) { line* lines; point_vector* roc_hull; unsigned long num_points; unsigned long num_positives = 0; unsigned long num_negatives = 0; point** points; double prop_positive; double operating_range; unsigned long x; if (argc == 3) { num_negatives = atol(argv[1]); num_positives = atol(argv[2]); } roc_hull = read_file(&prop_positive, num_negatives, num_positives); lines = (line*)malloc((roc_hull->size) * sizeof(line)); points = compute_envelope(roc_hull, &lines); num_points = roc_hull->size + 1; operating_range = points[roc_hull->size]->x - points[0]->x; for (x = 0; x < num_points; x++) fprintf(stderr, "%0.9f %0.3f\n", points[x]->x, points[x]->y); printf("%lE,%lE\n", aacc(points, num_points), operating_range); plot_curve(lines, roc_hull->size); return 0; } rlichtenwalter-LPmade-ba322c3/metrics/curve.h000066400000000000000000000002131214146114000212330ustar00rootroot00000000000000#ifndef _CURVE_H #define _CURVE_H typedef struct { double x; double y; } point; typedef struct { double m; double b; } line; #endif rlichtenwalter-LPmade-ba322c3/metrics/ef.c000066400000000000000000000100751214146114000205030ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include #include void ef( unsigned long bins, unsigned long total_instance_count ) { double double_threshold = (double) total_instance_count / bins; unsigned long threshold = (unsigned long)double_threshold; if( double_threshold > threshold ) { threshold++; } unsigned long length = 16384; double* values = (double*)malloc( length*sizeof(double) ); unsigned long* running_instance_counts = (unsigned long*)malloc( length*sizeof(unsigned long) ); int bin_found = 0; long max_index = -1; unsigned long current_instance = 0; unsigned long median_instance = 0; unsigned long positives = 0; unsigned long negatives = 0; double prob_val; double last_prob_val = 0.0/0.0; unsigned long class_val; int result = 0; while( (result = fscanf( stdin, "%lf %lu\n", &prob_val, &class_val )) != EOF ) { if( prob_val < last_prob_val || isnan(last_prob_val) ) { if( bin_found == 1 ) { double median = 0; unsigned long i; for( i = 0; i <= max_index && median == 0; i++ ) { if( running_instance_counts[i] >= median_instance ) { median = values[i]; } } fprintf( stdout, "%lg %lg\n", median, (double)positives / (positives+negatives) ); bin_found = 0; max_index = -1; current_instance = 0; median_instance = 0; positives = 0; negatives = 0; } if( max_index >= length ) { length *= 2; values = (double*)realloc( values, length*sizeof(double) ); running_instance_counts = (unsigned long*)realloc( running_instance_counts, length*sizeof(unsigned long) ); } max_index++; values[max_index] = prob_val; if( max_index > 0 ) { running_instance_counts[max_index] = running_instance_counts[max_index-1]; } else { running_instance_counts[max_index] = 0; } } // fprintf( stderr, "last_prob_val: %lf - max_index: %ld\n", last_prob_val, max_index ); if( class_val == 0 ) { negatives++; } else { positives++; } current_instance++; median_instance = current_instance / 2; running_instance_counts[max_index]++; // fprintf( stderr, "Threshold: %lu - Current Instance: %lu - Max Index: %ld\n", threshold, current_instance, max_index ); if( current_instance >= threshold ) { bin_found = 1; } last_prob_val = prob_val; } // fprintf( stderr, "median_instance: %lu\n", median_instance ); double median = 0; unsigned long i; for( i = 0; i <= max_index && median == 0; i++ ) { // fprintf( stderr, "\trunning_instance_counts[i]: %lu\n", running_instance_counts[i] ); if( running_instance_counts[i] >= median_instance ) { median = values[i]; } } fprintf( stdout, "%lg %lg\n", median, (double)positives / (positives+negatives) ); free( values ); free( running_instance_counts ); return; } void usage( const char* binary ) { fprintf( stderr, "Usage: %s [options] bins instances\n", binary ); fprintf( stderr, "Options: -h Print this usage message and exit\n" ); } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind + 2 ) { usage( argv[0] ); return 1; } unsigned long bins = atoi( argv[optind++] ); unsigned long total_instance_count = atoi( argv[optind++] ); ef( bins, total_instance_count ); return 0; } rlichtenwalter-LPmade-ba322c3/metrics/hmeasure.c000066400000000000000000000126511214146114000217240ustar00rootroot00000000000000#include #include #include #include #include "curve.h" #include "point_vector.h" const int INTEGRAL_STEPS = 100000; const double TOL = 1e-18; int right_turn(point*, point*, point*); void hull_add_point(point_vector* vp, point* p) { if (vp->size > 1) { while (!right_turn(&(vp->array[vp->size - 2]), &(vp->array[vp->size - 1]), p)) { vp->size--; if (vp->size == 1) break; } } point_vector_push_back(vp, p); } void point_swap(point* p1, point* p2) { point temp = *p1; *p1 = *p2; *p2 = temp; } int right_turn(point* p1, point* p2, point* p3) { //printf("rt: %le, %le, %le\n", p1->x, p2->x, p3->x); // p1, p2, p3 specified in increasing order of X. double a1 = p1->x - p2->x; double a2 = p1->y - p2->y; double b1 = p3->x - p2->x; double b2 = p3->y - p2->y; return (a1 * b2 > b1 * a2); } double betapdf(double x, double a, double b) { return powl(x, (a - 1)) * powl(1 - x, b - 1); } double ibeta(double c, double a, double b) { double ret = 0; int x; for (x = 1; x <= INTEGRAL_STEPS; x++) { double xval = c / INTEGRAL_STEPS * (x - 0.5); ret += betapdf(xval, a, b) * (c / INTEGRAL_STEPS); } return ret; } double betafunc(double c, double a, double b, double beta_constant) { return ibeta(c, a, b) / beta_constant; } // returns ROC covex hull point_vector* read_file(double* prop_positive, long num_negatives, long num_positives) { FILE* fp; long true_positives; long false_positives; double p1, old_p1; int true_class; point_vector* vp; point p; old_p1 = -1; vp = point_vector_create(); p.x = 0; p.y = 0; point_vector_push_back(vp, &p); //fp = fopen(filename, "r"); fp = stdin; // if (!fp) { printf("could not open %s!\n", filename); exit(0); } if (num_positives == -1 && num_negatives == -1) fscanf(fp, "%ld %ld\n", &num_negatives, &num_positives); //true_positives = num_positives; //false_positives = num_negatives; true_positives = 0; false_positives = 0; while (!feof(fp)) { fscanf(fp, "%lf %d\n", &p1, &true_class); if (!feof(fp)) { if (fabs(p1 - old_p1) > TOL && old_p1 > -1) { p.y = (double)true_positives / num_positives; p.x = (double)false_positives / num_negatives; //printf("ROC: (%lf, %lf)\n", p.x, p.y); hull_add_point(vp, &p); } if(true_class == 0) false_positives++; else true_positives++; old_p1 = p1; } } p.y = (double)true_positives / num_positives; p.x = (double)false_positives / num_negatives; hull_add_point(vp, &p); fclose(fp); *prop_positive = (double) num_positives / (num_positives + num_negatives); p.x = 1; p.y = 1; hull_add_point(vp, &p); return vp; } double h_measure(point_vector* convex_hull, double prop_positive, double alpha, double beta) { long size = convex_hull->size; double prop_negative = 1 - prop_positive; double beta_constant = ibeta(1.0, alpha, beta); double beta0 = betafunc(0, alpha + 1, beta, beta_constant); // reversed (see bottom) double beta1 = betafunc(0, alpha, beta + 1, beta_constant); // reversed (see bottom) double hull_dx = convex_hull->array[1].x - convex_hull->array[0].x; // reversed double hull_dy = convex_hull->array[1].y - convex_hull->array[0].y; // reversed double cost = prop_positive * hull_dy / (prop_positive * hull_dy + prop_negative * hull_dx); double beta0_1 = betafunc(cost, alpha + 1, beta, beta_constant); double beta1_1 = betafunc(cost, alpha, beta + 1, beta_constant); double lhat = 0.0; long x; lhat += prop_negative * (1 - convex_hull->array[0].x) * (beta0_1 - beta0); //reversed lhat += prop_positive * convex_hull->array[0].y * (beta1_1 - beta1); //reversed /*printf("LHat: %0.20lf\n", lhat); printf("lhs: %0.20lf\n", (beta0_1 - beta0)); printf("rhs: %0.20lf\n", (beta1_1 - beta1));*/ beta0 = beta0_1; beta1 = beta1_1; for (x = 1; x < size - 1; x++) { hull_dx = convex_hull->array[x + 1].x - convex_hull->array[x].x; hull_dy = convex_hull->array[x + 1].y - convex_hull->array[x].y; // reversed cost = prop_positive * hull_dy / (prop_positive * hull_dy + prop_negative * hull_dx); beta0_1 = betafunc(cost, alpha + 1, beta, beta_constant); beta1_1 = betafunc(cost, alpha, beta + 1, beta_constant); lhat += prop_negative * (1 - convex_hull->array[x].x) * (beta0_1 - beta0); //reversed lhat += prop_positive * convex_hull->array[x].y * (beta1_1 - beta1); //reversed beta0 = beta0_1; beta1 = beta1_1; } beta0_1 = betafunc(1, alpha + 1, beta, beta_constant); // reversed (see top) beta1_1 = betafunc(1, alpha, beta + 1, beta_constant); // reversed (see top) lhat += prop_negative * (1 - convex_hull->array[x].x) * (beta0_1 - beta0); // reversed lhat += prop_positive * convex_hull->array[x].y * (beta1_1 - beta1); // reversed beta0 = betafunc(prop_positive, 1 + alpha, beta, beta_constant); beta1 = betafunc(1, alpha, 1 + beta, beta_constant); beta1 -= betafunc(prop_positive, alpha, 1 + beta, beta_constant); return 1 - lhat / (prop_negative * beta0 + prop_positive * beta1); } int main (int argc, char** argv) { double prop_positive; long num_negatives = -1; long num_positives = -1; point_vector* convex_hull; double hmeasure; if (argc == 3) { num_negatives = atol(argv[1]); num_positives = atol(argv[2]); } else { fprintf( stderr, "required arguments: neg_count pos_count\n" ); return 1; } convex_hull = read_file(&prop_positive, num_negatives, num_positives); hmeasure = h_measure(convex_hull, prop_positive, 2.0, 2.0); printf("%lE\n", hmeasure); return 0; } rlichtenwalter-LPmade-ba322c3/metrics/mse.c000066400000000000000000000031211214146114000206670ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include double auc() { double prob_val; unsigned int class_val; double sum = 0.0; unsigned long count = 0; int result = 0; while( (result = fscanf( stdin, "%lg %u\n", &prob_val, &class_val )) != EOF ) { double delta = class_val - prob_val; double se = delta * delta; sum += se; count++; } return sum / count; } void usage( const char* binary ) { fprintf( stderr, "Usage: %s [options]\n", binary ); fprintf( stderr, "Options: -h Print this usage message and exit\n" ); } int main() { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } double mse = auc(); fprintf( stdout, "%.9lf\n", mse ); return 0; } rlichtenwalter-LPmade-ba322c3/metrics/point_vector.c000066400000000000000000000030201214146114000226140ustar00rootroot00000000000000#include #include #include "curve.h" #include "point_vector.h" #define VECTOR_CAPACITY 16384 point_vector* point_vector_create() { point_vector* vp = (point_vector*)malloc(sizeof(point_vector)); vp->array = (point*)malloc(VECTOR_CAPACITY * sizeof(point)); vp->capacity = VECTOR_CAPACITY; vp->size = 0; return vp; } point_vector* point_vector_create_sized(int size) { point_vector* vp = (point_vector*)malloc(sizeof(point_vector)); vp->array = (point*)malloc(size * sizeof(point)); vp->capacity = size; vp->size = size; return vp; } point point_vector_pop_back(point_vector* vp) { vp->size--; return vp->array[vp->size - 1]; } void point_vector_push_back(point_vector* vp, point* p) { if (vp->size == vp->capacity) { vp->capacity *= 2; vp->array = (point*)realloc(vp->array, vp->capacity * sizeof(point)); } vp->array[vp->size].x = p->x; vp->array[vp->size].y = p->y; vp->size++; } void point_vector_free(point_vector* vp) { free(vp->array); free(vp); } void point_vector_print(point_vector* vp) { int x; double slope = 1.0 / 0.0; printf("["); for (x = 0; x < vp->size; x++) { if (x > 0) slope = (vp->array[x].y - vp->array[x - 1].y) / (vp->array[x].x - vp->array[x - 1].x); printf("(%0.7lf, %0.7lf, %0.7lf); ", vp->array[x].x, vp->array[x].y, slope); } printf("]\n"); } point point_vector_get(point_vector* vp, long index) { #ifdef RANGE_CHECK if (index < 0 || index >= vp->size) fprintf(stderr, "evil index: %ld of %ld\n", index, vp->size); #endif return vp->array[index]; } rlichtenwalter-LPmade-ba322c3/metrics/point_vector.h000066400000000000000000000007461214146114000226350ustar00rootroot00000000000000#ifndef _POINT_VECTOR_H #define _POINT_VECTOR_H #include "curve.h" typedef struct { long size; long capacity; point* array; } point_vector; point_vector* point_vector_create(); point_vector* point_vector_create_sized(int size); point point_vector_pop_back(point_vector* vp); void point_vector_push_back(point_vector* vp, point* p); void point_vector_free(point_vector* vp); void point_vector_print(point_vector* vp); point point_vector_get(point_vector* vp, long index); #endif rlichtenwalter-LPmade-ba322c3/metrics/pr.c000066400000000000000000000066651214146114000205440ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include void auc(double step_size, unsigned int p_count) { double prob_val; unsigned int class_val; unsigned long last_tp_count = 0; unsigned long last_fp_count = 0; unsigned long tp_count = 0; unsigned long fp_count = 0; double last_prob_val = 1.0; int result = 0; while( (result = fscanf( stdin, "%lf %u\n", &prob_val, &class_val )) != EOF ) { if( class_val != 1 ) { fp_count++; } else { tp_count++; } if( prob_val < last_prob_val ) { unsigned long interp_count = (unsigned long)(((tp_count - last_tp_count) / step_size) ); if( interp_count > 0 ) { double tp_delta = (tp_count - last_tp_count) / (double)interp_count; double fp_delta = (fp_count - last_fp_count) / (double)interp_count; double lower_tp = last_tp_count; double lower_fp = last_fp_count; if( lower_tp == 0 ) { lower_tp = 0.0000000000000001; } while( lower_tp < tp_count ) { double upper_tp = lower_tp + tp_delta; double upper_fp = lower_fp + fp_delta; double lower_recall = lower_tp / p_count; double lower_precision = lower_tp / (lower_tp+lower_fp); fprintf( stdout, "%.9lf %.9lf\n", lower_recall, lower_precision ); lower_tp = upper_tp; lower_fp = upper_fp; } } last_tp_count = tp_count; last_fp_count = fp_count; } last_prob_val = prob_val; } unsigned long interp_count = (unsigned long)(((tp_count - last_tp_count) / step_size) ); if( interp_count > 0 ) { double tp_delta = (tp_count - last_tp_count) / (double)interp_count; double fp_delta = (fp_count - last_fp_count) / (double)interp_count; double lower_tp = last_tp_count; double lower_fp = last_fp_count; if( lower_tp == 0 ) { lower_tp = 0.0000000000000001; } while( lower_tp < tp_count ) { double upper_tp = lower_tp + tp_delta; double upper_fp = lower_fp + fp_delta; double lower_recall = lower_tp / p_count; double lower_precision = lower_tp / (lower_tp+lower_fp); fprintf( stdout, "%.9lf %.9lf\n", lower_recall, lower_precision ); lower_tp = upper_tp; lower_fp = upper_fp; } } return; } void usage( const char* binary ) { fprintf( stderr, "Usage: %s [options] precision num_positive\n", binary ); fprintf( stderr, "Options: -h Print this usage message and exit\n" ); } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind + 2 ) { usage( argv[0] ); return 1; } double step_size = atof( argv[optind++] ); unsigned long p_count = atoi( argv[optind++] ); auc( step_size, p_count ); return 0; } rlichtenwalter-LPmade-ba322c3/metrics/roc.c000066400000000000000000000040451214146114000206740ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include void auc( unsigned long negatives, unsigned long positives ) { double prob_val; unsigned int class_val; unsigned long tp_count = 0; unsigned long fp_count = 0; double last_prob_val = 1.1; int result = 0; while( (result = fscanf( stdin, "%lf %u\n", &prob_val, &class_val )) != EOF ) { if( prob_val < last_prob_val ) { fprintf( stdout, "%.6lf %.6lf\n", (double)fp_count/(double)negatives, (double)tp_count/(double)positives ); } if( class_val != 1 ) { fp_count++; } else { tp_count++; } last_prob_val = prob_val; } fprintf( stdout, "%.6lf %.6lf\n", (double)fp_count/(double)negatives, (double)tp_count/(double)positives ); return; } void usage( const char* binary ) { fprintf( stderr, "Usage: %s [options] num_negative num_positive\n", binary ); fprintf( stderr, "Options: -h Print this usage message and exit\n" ); } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind + 2 ) { usage( argv[0] ); return 1; } unsigned long negative = atol( argv[optind++] ); unsigned long positive = atol( argv[optind++] ); auc( negative, positive ); return 0; } rlichtenwalter-LPmade-ba322c3/metrics/topnprec.c000066400000000000000000000040111214146114000217340ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include #include void topnprec( unsigned long total_instance_count ) { unsigned long instances = 0; unsigned long positives = 0; double prob_val; double last_prob_val = 0.0/0.0; unsigned long class_val; int result = 0; while( (result = fscanf( stdin, "%lf %lu\n", &prob_val, &class_val )) != EOF ) { if( prob_val < last_prob_val || isnan(last_prob_val) ) { if( instances > 0 ) { fprintf( stdout, "%lg %lg\n", (double)instances/total_instance_count, (double)positives/instances ); } instances++; if( class_val == 1 ) { positives++; } } } fprintf( stdout, "%lg %lg\n", (double)instances/total_instance_count, (double)positives/instances ); } void usage( const char* binary ) { fprintf( stderr, "Usage: %s [options] num_instances\n", binary ); fprintf( stderr, "Options: -h Print this usage message and exit\n" ); } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind + 1 ) { usage( argv[0] ); return 1; } unsigned long total_instance_count = atoi( argv[optind++] ); topnprec( total_instance_count ); return 0; } rlichtenwalter-LPmade-ba322c3/netlib/000077500000000000000000000000001214146114000175515ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/000077500000000000000000000000001214146114000223225ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/AdamicAdarLinkPredictor.cpp000066400000000000000000000027231214146114000274720ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include "AdamicAdarLinkPredictor.h" AdamicAdarLinkPredictor::AdamicAdarLinkPredictor( const WeightedNetwork& network ) : LinkPredictor(network) { } AdamicAdarLinkPredictor::~AdamicAdarLinkPredictor() { } double AdamicAdarLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { double result = 0.0; neighbor_set_t commonNeighbors = this->network.commonOutNeighbors( vertex, neighbor ); for( neighbor_set_t::const_iterator cnIterator = commonNeighbors.begin(); cnIterator != commonNeighbors.end(); cnIterator++ ) { const vertex_t commonNeighbor = cnIterator->first; result += 1 / log( this->network.inDegree( commonNeighbor ) ); } return result; } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/AdamicAdarLinkPredictor.h000066400000000000000000000022141214146114000271320ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef ADAMICADARLINKPREDICTOR_H #define ADAMICADARLINKPREDICTOR_H #include "../WeightedNetwork.h" #include "LinkPredictor.h" class AdamicAdarLinkPredictor : public LinkPredictor { private: protected: public: AdamicAdarLinkPredictor( const WeightedNetwork& ); ~AdamicAdarLinkPredictor(); virtual double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/ClusteringCoefficientLinkPredictor.cpp000066400000000000000000000023121214146114000317740ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include "ClusteringCoefficientLinkPredictor.h" ClusteringCoefficientLinkPredictor::ClusteringCoefficientLinkPredictor( const WeightedNetwork& network ) : LinkPredictor(network) { } ClusteringCoefficientLinkPredictor::~ClusteringCoefficientLinkPredictor() { } double ClusteringCoefficientLinkPredictor::generateScore( unsigned int vertex, unsigned int neighbor ) { return this->network.clusteringCoefficient( vertex ); } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/ClusteringCoefficientLinkPredictor.h000066400000000000000000000022731214146114000314470ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef CLUSTERINGCOEFFICIENTLINKPREDICTOR_H #define CLUSTERINGCOEFFICIENTLINKPREDICTOR_H #include "../WeightedNetwork.h" #include "LinkPredictor.h" class ClusteringCoefficientLinkPredictor : public LinkPredictor { private: protected: public: ClusteringCoefficientLinkPredictor( const WeightedNetwork& ); ~ClusteringCoefficientLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/CommonNeighborLinkPredictor.cpp000066400000000000000000000022401214146114000304240ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include "CommonNeighborLinkPredictor.h" CommonNeighborLinkPredictor::CommonNeighborLinkPredictor( const WeightedNetwork& network ) : LinkPredictor(network) { } CommonNeighborLinkPredictor::~CommonNeighborLinkPredictor() { } double CommonNeighborLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { return network.commonOutNeighbors( vertex, neighbor ).size(); } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/CommonNeighborLinkPredictor.h000066400000000000000000000022301214146114000300700ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef COMMONNEIGHBORLINKPREDICTOR_H #define COMMONNEIGHBORLINKPREDICTOR_H #include "../WeightedNetwork.h" #include "LinkPredictor.h" class CommonNeighborLinkPredictor : public LinkPredictor { private: protected: public: CommonNeighborLinkPredictor( const WeightedNetwork& ); ~CommonNeighborLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/DistanceLinkPredictor.cpp000066400000000000000000000042451214146114000272570ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include "DistanceLinkPredictor.h" #include using std::cout; using std::cerr; DistanceLinkPredictor::DistanceLinkPredictor( const WeightedNetwork& network, unsigned int l ) : LinkPredictor(network), l(l) { } DistanceLinkPredictor::~DistanceLinkPredictor() { } double DistanceLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { if( this->vertex != vertex ) { this->vertex = vertex; this->scores = vector( this->network.vertexCount() ); vector toSearch; this->scores.at( vertex ) = 0; toSearch.push_back( vertex ); for( unsigned int distance = 1; distance <= this->l; distance++ ) { vector found; for( vector::const_iterator vertexIterator = toSearch.begin(); vertexIterator != toSearch.end(); ++vertexIterator ) { const vertex_t searchVertex = *vertexIterator; const neighbor_set_t& neighbors = this->network.outNeighbors( searchVertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); ++neighborIterator ) { const vertex_t currentNeighbor = neighborIterator->first; if( vertex != currentNeighbor && this->scores.at( currentNeighbor ) == 0 ) { this->scores.at( currentNeighbor ) = 1.0 / distance; found.push_back( currentNeighbor ); } } } toSearch.swap( found ); } } return this->scores.at( neighbor ); } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/DistanceLinkPredictor.h000066400000000000000000000023321214146114000267170ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef DISTANCELINKPREDICTOR_H #define DISTANCELINKPREDICTOR_H #include #include "../WeightedNetwork.h" #include "LinkPredictor.h" using std::vector; class DistanceLinkPredictor : public LinkPredictor { private: unsigned int l; vector scores; protected: public: DistanceLinkPredictor( const WeightedNetwork&, unsigned int ); ~DistanceLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/IDegreeLinkPredictor.cpp000066400000000000000000000021571214146114000270310ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include "IDegreeLinkPredictor.h" IDegreeLinkPredictor::IDegreeLinkPredictor( const WeightedNetwork& network ) : LinkPredictor( network ) { } IDegreeLinkPredictor::~IDegreeLinkPredictor() { } double IDegreeLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { return (double)(this->network.outDegree( vertex )); } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/IDegreeLinkPredictor.h000066400000000000000000000021651214146114000264750ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef IDEGREELINKPREDICTOR_H #define IDEGREELINKPREDICTOR_H #include "../WeightedNetwork.h" #include "LinkPredictor.h" class IDegreeLinkPredictor : public LinkPredictor { private: protected: public: IDegreeLinkPredictor( const WeightedNetwork& ); ~IDegreeLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/IPageRankLinkPredictor.cpp000066400000000000000000000022541214146114000273240ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include "IPageRankLinkPredictor.h" IPageRankLinkPredictor::IPageRankLinkPredictor( const WeightedNetwork& network, double d ) : LinkPredictor( network ), d(d) { this->scores = this->network.pageRank( this->d ); } IPageRankLinkPredictor::~IPageRankLinkPredictor() { } double IPageRankLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { return this->scores.at( vertex ); } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/IPageRankLinkPredictor.h000066400000000000000000000024061214146114000267700ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef IPAGERANKLINKPREDICTOR_H #define IPAGERANKLINKPREDICTOR_H #include #include "../WeightedNetwork.h" #include "LinkPredictor.h" using std::vector; class IPageRankLinkPredictor : public LinkPredictor { private: double d; vector scores; IPageRankLinkPredictor( const WeightedNetwork& ); protected: public: IPageRankLinkPredictor( const WeightedNetwork&, double ); ~IPageRankLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/IVolumeLinkPredictor.cpp000066400000000000000000000021571214146114000271050ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include "IVolumeLinkPredictor.h" IVolumeLinkPredictor::IVolumeLinkPredictor( const WeightedNetwork& network ) : LinkPredictor( network ) { } IVolumeLinkPredictor::~IVolumeLinkPredictor() { } double IVolumeLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { return (double)(this->network.outVolume( vertex )); } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/IVolumeLinkPredictor.h000066400000000000000000000021651214146114000265510ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef IVOLUMELINKPREDICTOR_H #define IVOLUMELINKPREDICTOR_H #include "../WeightedNetwork.h" #include "LinkPredictor.h" class IVolumeLinkPredictor : public LinkPredictor { private: protected: public: IVolumeLinkPredictor( const WeightedNetwork& ); ~IVolumeLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/JDegreeLinkPredictor.cpp000066400000000000000000000021601214146114000270240ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include "JDegreeLinkPredictor.h" JDegreeLinkPredictor::JDegreeLinkPredictor( const WeightedNetwork& network ) : LinkPredictor( network ) { } JDegreeLinkPredictor::~JDegreeLinkPredictor() { } double JDegreeLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { return (double)(this->network.outDegree( neighbor )); } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/JDegreeLinkPredictor.h000066400000000000000000000021651214146114000264760ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef JDEGREELINKPREDICTOR_H #define JDEGREELINKPREDICTOR_H #include "../WeightedNetwork.h" #include "LinkPredictor.h" class JDegreeLinkPredictor : public LinkPredictor { private: protected: public: JDegreeLinkPredictor( const WeightedNetwork& ); ~JDegreeLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/JPageRankLinkPredictor.cpp000066400000000000000000000022561214146114000273270ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include "JPageRankLinkPredictor.h" JPageRankLinkPredictor::JPageRankLinkPredictor( const WeightedNetwork& network, double d ) : LinkPredictor( network ), d(d) { this->scores = this->network.pageRank( this->d ); } JPageRankLinkPredictor::~JPageRankLinkPredictor() { } double JPageRankLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { return this->scores.at( neighbor ); } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/JPageRankLinkPredictor.h000066400000000000000000000024061214146114000267710ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef JPAGERANKLINKPREDICTOR_H #define JPAGERANKLINKPREDICTOR_H #include #include "../WeightedNetwork.h" #include "LinkPredictor.h" using std::vector; class JPageRankLinkPredictor : public LinkPredictor { private: double d; vector scores; JPageRankLinkPredictor( const WeightedNetwork& ); protected: public: JPageRankLinkPredictor( const WeightedNetwork&, double ); ~JPageRankLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/JVolumeLinkPredictor.cpp000066400000000000000000000021611214146114000271010ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include "JVolumeLinkPredictor.h" JVolumeLinkPredictor::JVolumeLinkPredictor( const WeightedNetwork& network ) : LinkPredictor( network ) { } JVolumeLinkPredictor::~JVolumeLinkPredictor() { } double JVolumeLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { return (double)(this->network.outVolume( neighbor )); } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/JVolumeLinkPredictor.h000066400000000000000000000021651214146114000265520ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef JVOLUMELINKPREDICTOR_H #define JVOLUMELINKPREDICTOR_H #include "../WeightedNetwork.h" #include "LinkPredictor.h" class JVolumeLinkPredictor : public LinkPredictor { private: protected: public: JVolumeLinkPredictor( const WeightedNetwork& ); ~JVolumeLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/JaccardCoefficientLinkPredictor.cpp000066400000000000000000000043051214146114000312100ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include "JaccardCoefficientLinkPredictor.h" JaccardCoefficientLinkPredictor::JaccardCoefficientLinkPredictor( const WeightedNetwork& network ) : LinkPredictor(network) { } JaccardCoefficientLinkPredictor::~JaccardCoefficientLinkPredictor() { } double JaccardCoefficientLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { unsigned int intersectionSize = 0; unsigned int unionSize = 0; const neighbor_set_t& vertexNeighbors = this->network.outNeighbors( vertex ); const neighbor_set_t& neighborNeighbors = this->network.outNeighbors( neighbor ); neighbor_set_t::const_iterator first1 = vertexNeighbors.begin(); neighbor_set_t::const_iterator first2 = neighborNeighbors.begin(); neighbor_set_t::const_iterator last1 = vertexNeighbors.end(); neighbor_set_t::const_iterator last2 = neighborNeighbors.end(); // if one or both individuals have no neighbors, define the score as 0 rather than potentially 0/0 == nan if( first1 == last1 || first2 == last2 ) { return 0; } while (true) { if( first1 == last1 ) { unionSize += last2 - first2; return (double)intersectionSize / unionSize; } if( first2 == last2 ) { unionSize += last1 - first1; return (double)intersectionSize / unionSize; } if( first1->first < first2->first ) { first1++; } else if( first2->first < first1->first ) { first2++; } else { first1++; first2++; intersectionSize++; } unionSize++; } } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/JaccardCoefficientLinkPredictor.h000066400000000000000000000022541214146114000306560ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef JACCARDCOEFFICIENTLINKPREDICTOR_H #define JACCARDCOEFFICIENTLINKPREDICTOR_H #include "../WeightedNetwork.h" #include "LinkPredictor.h" class JaccardCoefficientLinkPredictor : public LinkPredictor { private: protected: public: JaccardCoefficientLinkPredictor( const WeightedNetwork& ); ~JaccardCoefficientLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/KatzLinkPredictor.cpp000066400000000000000000000054031214146114000264330ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include #include "KatzLinkPredictor.h" using std::stack; using std::make_pair; using std::cout; using std::cerr; KatzLinkPredictor::KatzLinkPredictor( const WeightedNetwork& network, unsigned int l, double beta ) : LinkPredictor(network), l(l), beta(beta), lookup(vector(l+1)) { for( unsigned int degree = 0; degree <= l; ++degree ) { this->lookup.at( degree ) = pow( beta, degree ); } } KatzLinkPredictor::~KatzLinkPredictor() { } double KatzLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { if( this->vertex != vertex ) { this->vertex = vertex; this->scores = vector( this->network.vertexCount() ); vector oldSearch; vector oldHits( this->network.vertexCount() ); oldSearch.push_back( vertex ); oldHits.at( vertex ) = 1; for( unsigned int degree = 1; degree <= this->l; ++degree ) { vector newHits( this->network.vertexCount() ); vector found( this->network.vertexCount() ); vector newSearch; for( vector::const_iterator vertexIterator = oldSearch.begin(); vertexIterator != oldSearch.end(); ++vertexIterator ) { const vertex_t searchVertex = *vertexIterator; const neighbor_set_t& neighbors = this->network.outNeighbors( searchVertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); ++neighborIterator ) { const vertex_t neighborVertex = neighborIterator->first; newHits.at( neighborVertex ) += oldHits.at( searchVertex ); if( !found.at( neighborVertex ) ) { found.at( neighborVertex ) = true; newSearch.push_back( neighborVertex ); } } } double factor = this->lookup.at( degree ); for( size_t i = 0; i < this->scores.size(); ++i ) { this->scores.at( i ) += factor * newHits.at( i ); } oldSearch.swap( newSearch ); oldHits.swap( newHits ); } } return this->scores.at( neighbor ); } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/KatzLinkPredictor.h000066400000000000000000000024221214146114000260760ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef KATZLINKPREDICTOR_H #define KATZLINKPREDICTOR_H #include "../WeightedNetwork.h" #include "LinkPredictor.h" using std::vector; class KatzLinkPredictor : public LinkPredictor { private: unsigned int l; double beta; vector lookup; vector scores; KatzLinkPredictor( const WeightedNetwork& ); protected: public: KatzLinkPredictor( const WeightedNetwork&, unsigned int, double ); ~KatzLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/LinkPredictor.cpp000066400000000000000000000100321214146114000255730ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "LinkPredictor.h" using std::numeric_limits; using std::cout; using std::cerr; using std::scientific; using std::ios_base; LinkPredictor::LinkPredictor( const WeightedNetwork& network ) : network(network), vertex(INVALID_VERTEX), neighbor(INVALID_VERTEX) { } LinkPredictor::~LinkPredictor() { } void LinkPredictor::printOutNeighborScores( unsigned int degree ) { // if the degree is 0, do all-pairs predictions if( degree == 0 ) { for( vertex_t vertex = 0; vertex < network.vertexCount(); ++vertex ) { for( vertex_t neighbor = 0; neighbor < network.vertexCount(); ++neighbor ) { if( vertex != neighbor && !network.hasEdge( vertex, neighbor ) ) { // DO WE PREDICT LINKS THAT ALREADY EXIST? - // DO PROVIDED NETWORKS INCLUDE LINKS IN BOTH DIRECTIONS double predictedValue = this->generateScore( vertex, neighbor ); unsigned int vertexName = this->network.translateIntToExt( vertex ); unsigned int neighborName = this->network.translateIntToExt( neighbor ); cout << vertexName << " " << neighborName << " " << predictedValue << "\n";; } } } return; } for( vertex_t vertex = 0; vertex < network.vertexCount(); vertex++ ) { vector verticesToPredict; verticesToPredict = this->network.findOutNeighbors( vertex, degree ); for( vector::const_iterator neighborIterator = verticesToPredict.begin(); neighborIterator != verticesToPredict.end(); neighborIterator++ ) { vertex_t neighbor = *neighborIterator; double predictedValue = this->generateScore( vertex, neighbor ); unsigned int vertexName = this->network.translateIntToExt( vertex ); unsigned int neighborName = this->network.translateIntToExt( neighbor ); cout << vertexName << " " << neighborName << " " << predictedValue << "\n";; } } } void LinkPredictor::printInNeighborScores( unsigned int degree ) { // if the degree is 0, do all-pairs predictions if( degree == 0 ) { for( vertex_t vertex = 0; vertex < network.vertexCount(); ++vertex ) { for( vertex_t neighbor = 0; neighbor < network.vertexCount(); ++neighbor ) { if( vertex != neighbor && !network.hasEdge( neighbor, vertex ) ) { // DO WE PREDICT LINKS THAT ALREADY EXIST? - // DO PROVIDED NETWORKS INCLUDE LINKS IN BOTH DIRECTIONS double predictedValue = this->generateScore( vertex, neighbor ); unsigned int vertexName = this->network.translateIntToExt( vertex ); unsigned int neighborName = this->network.translateIntToExt( neighbor ); cout << vertexName << " " << neighborName << " " << predictedValue << "\n";; } } } return; } for( vertex_t vertex = 0; vertex < network.vertexCount(); vertex++ ) { vector verticesToPredict; verticesToPredict = this->network.findInNeighbors( vertex, degree ); for( vector::const_iterator neighborIterator = verticesToPredict.begin(); neighborIterator != verticesToPredict.end(); neighborIterator++ ) { vertex_t neighbor = *neighborIterator; double predictedValue = this->generateScore( vertex, neighbor ); unsigned int vertexName = this->network.translateIntToExt( vertex ); unsigned int neighborName = this->network.translateIntToExt( neighbor ); cout << vertexName << " " << neighborName << " " << predictedValue << "\n";; } } } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/LinkPredictor.h000066400000000000000000000026101214146114000252430ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef LINKPREDICTOR_H #define LINKPREDICTOR_H #include #include "../WeightedNetwork.h" using std::ostream; using std::vector; class LinkPredictor { private: LinkPredictor& operator=( const LinkPredictor& ); protected: const WeightedNetwork& network; vertex_t vertex; vertex_t neighbor; LinkPredictor(); public: enum Direction { IN, OUT }; LinkPredictor( const WeightedNetwork& ); virtual ~LinkPredictor(); virtual double generateScore( vertex_t, vertex_t ) = 0; void printOutNeighborScores( unsigned int degree ); void printInNeighborScores( unsigned int degree ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/MRLPLinkPredictor.cpp000066400000000000000000000164271214146114000263040ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "MRLPLinkPredictor.h" using std::cerr; MRLPLinkPredictor::MRLPLinkPredictor( const WeightedNetwork& network ) : LinkPredictor( network ) { unsigned long allCounts[graphlets] = {0}; for( vertex_t vertex = 0; vertex < network.vertexCount(); ++vertex ) { const neighbor_set_t& outNeighbors = network.outNeighbors( vertex ); for( neighbor_set_t::const_iterator neighborIterator = outNeighbors.begin(); neighborIterator != outNeighbors.end(); ++neighborIterator ) { const vertex_t neighbor = neighborIterator->first; unsigned long vCounts[graphlets] = {0}; this->edgeProfile( vertex, neighbor, vCounts ); for( size_t i = 0; i < graphlets; ++i ) { allCounts[i] += vCounts[i]; } } } this->bidirectional = network.countMutuals(); this->unidirectional = network.edgeCount() - network.countMutuals() * 2; for( size_t i = 0; i < graphlets; ++i ) { this->counts[i] = (double)allCounts[i] / network.edgeCount(); } } void MRLPLinkPredictor::computeDetailedNeighbors( const neighbor_set_t& inNeighbors, const neighbor_set_t& outNeighbors, DetailedNeighbors& result ) const { neighbor_set_t::const_iterator inIt = inNeighbors.begin(); neighbor_set_t::const_iterator outIt = outNeighbors.begin(); neighbor_set_t::const_iterator inEnd = inNeighbors.end(); neighbor_set_t::const_iterator outEnd = outNeighbors.end(); while( inIt != inEnd && outIt != outEnd ) { if( inIt->first < outIt->first ) { result.inOnly.push_back( inIt->first ); result.either.push_back( inIt->first ); ++inIt; } else if( inIt->first > outIt->first ) { result.outOnly.push_back( outIt->first ); result.either.push_back( outIt->first ); ++outIt; } else { result.both.push_back( inIt->first ); result.either.push_back( inIt->first ); ++inIt; ++outIt; } } while( inIt != inEnd ) { result.inOnly.push_back( inIt->first ); result.either.push_back( inIt->first ); ++inIt; } while( outIt != outEnd ) { result.outOnly.push_back( outIt->first ); result.either.push_back( outIt->first ); ++outIt; } } void MRLPLinkPredictor::edgeProfile( vertex_t v1, vertex_t v2, unsigned long vCounts[] ) { const neighbor_set_t& v1InNeighbors = network.inNeighbors( v1 ); const neighbor_set_t& v1OutNeighbors = network.outNeighbors( v1 ); const neighbor_set_t& v2InNeighbors = network.inNeighbors( v2 ); const neighbor_set_t& v2OutNeighbors = network.outNeighbors( v2 ); unsigned char reciprocal = (graphlets/2) * (char)network.hasEdge( v2, v1 ); DetailedNeighbors v1DetailedNeighbors; DetailedNeighbors v2DetailedNeighbors; this->computeDetailedNeighbors( v1InNeighbors, v1OutNeighbors, v1DetailedNeighbors ); this->computeDetailedNeighbors( v2InNeighbors, v2OutNeighbors, v2DetailedNeighbors ); vCounts[0+reciprocal] += this->intersectionCount( v1DetailedNeighbors.outOnly, v2DetailedNeighbors.inOnly ); vCounts[1+reciprocal] += this->intersectionCount( v1DetailedNeighbors.outOnly, v2DetailedNeighbors.outOnly ); vCounts[2+reciprocal] += this->intersectionCount( v1DetailedNeighbors.outOnly, v2DetailedNeighbors.both ); vCounts[3+reciprocal] += this->intersectionCount( v1DetailedNeighbors.inOnly, v2DetailedNeighbors.inOnly ); vCounts[4+reciprocal] += this->intersectionCount( v1DetailedNeighbors.inOnly, v2DetailedNeighbors.outOnly ); vCounts[5+reciprocal] += this->intersectionCount( v1DetailedNeighbors.inOnly, v2DetailedNeighbors.both ); vCounts[6+reciprocal] += this->intersectionCount( v1DetailedNeighbors.both, v2DetailedNeighbors.inOnly ); vCounts[7+reciprocal] += this->intersectionCount( v1DetailedNeighbors.both, v2DetailedNeighbors.outOnly ); vCounts[8+reciprocal] += this->intersectionCount( v1DetailedNeighbors.both, v2DetailedNeighbors.both ); vCounts[9+reciprocal] += this->differenceCount( v2DetailedNeighbors.inOnly, v1DetailedNeighbors.either ); vCounts[10+reciprocal] += this->differenceCount( v2DetailedNeighbors.outOnly, v1DetailedNeighbors.either ); vCounts[11+reciprocal] += this->differenceCount( v2DetailedNeighbors.both, v1DetailedNeighbors.either ); vCounts[12+reciprocal] += this->differenceCount( v1DetailedNeighbors.inOnly, v2DetailedNeighbors.either ); vCounts[13+reciprocal] += this->differenceCount( v1DetailedNeighbors.outOnly, v2DetailedNeighbors.either ); vCounts[14+reciprocal] += this->differenceCount( v1DetailedNeighbors.both, v2DetailedNeighbors.either ); vCounts[15+reciprocal] += this->network.vertexCount() - this->unionCount( v1DetailedNeighbors.either, v2DetailedNeighbors.either ) - 2; } unsigned long MRLPLinkPredictor::intersectionCount( const vector& v1, const vector& v2 ) const { vector::const_iterator first1 = v1.begin(); vector::const_iterator last1 = v1.end(); vector::const_iterator first2 = v2.begin(); vector::const_iterator last2 = v2.end(); unsigned long count = 0; while( first1 != last1 && first2 != last2 ) { if( *first1 < *first2 ) { ++first1; } else if( *first2 < *first1 ) { ++first2; } else { ++count; ++first1; ++first2; } } return count; } unsigned long MRLPLinkPredictor::differenceCount( const vector& v1, const vector& v2 ) const { vector::const_iterator first1 = v1.begin(); vector::const_iterator last1 = v1.end(); vector::const_iterator first2 = v2.begin(); vector::const_iterator last2 = v2.end(); unsigned long count = 0; while( first1 != last1 && first2 != last2 ) { if( *first1 < *first2 ) { ++count; ++first1; } else if( *first2 < *first1 ) { ++first2; } else { ++first1; ++first2; } } return count; } unsigned long MRLPLinkPredictor::unionCount( const vector& v1, const vector& v2 ) const { vector::const_iterator first1 = v1.begin(); vector::const_iterator last1 = v1.end(); vector::const_iterator first2 = v2.begin(); vector::const_iterator last2 = v2.end(); unsigned long count = 0; while( first1 != last1 && first2 != last2 ) { if( *first1 < *first2 ) { ++count; ++first1; } else if( *first2 < *first1 ) { ++count; ++first2; } else { ++first1; ++first2; } } return count; } MRLPLinkPredictor::~MRLPLinkPredictor() { } double MRLPLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { unsigned long vCounts[graphlets] = {0}; this->edgeProfile( vertex, neighbor, vCounts ); double result = 0; for( size_t i = 0; i < graphlets; ++i ) { result += pow( this->counts[i] - (double)vCounts[i], 2 ); } result = 1 / result; return result; } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/MRLPLinkPredictor.h000066400000000000000000000035161214146114000257440ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef MRLPLINKPREDICTOR_H #define MRLPLINKPREDICTOR_H #include "../WeightedNetwork.h" #include "LinkPredictor.h" struct DetailedNeighbors; class MRLPLinkPredictor : public LinkPredictor { private: const static unsigned long graphlets = 32; unsigned long unidirectional; unsigned long bidirectional; double counts[graphlets]; struct DetailedNeighbors { vector inOnly; vector outOnly; vector both; vector either; }; void computeDetailedNeighbors( const neighbor_set_t&, const neighbor_set_t&, DetailedNeighbors& ) const; unsigned long intersectionCount( const vector&, const vector& ) const; unsigned long differenceCount( const vector&, const vector& ) const; unsigned long unionCount( const vector&, const vector& ) const; void edgeProfile( vertex_t, vertex_t, unsigned long[] ); protected: public: MRLPLinkPredictor( const WeightedNetwork& ); ~MRLPLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/MutualityLinkPredictor.cpp000066400000000000000000000022001214146114000275070ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include "MutualityLinkPredictor.h" MutualityLinkPredictor::MutualityLinkPredictor( const WeightedNetwork& network ) : LinkPredictor( network ) { } MutualityLinkPredictor::~MutualityLinkPredictor() { } double MutualityLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { return this->network.hasEdge( neighbor, vertex ) ? 1 : 0; } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/MutualityLinkPredictor.h000066400000000000000000000021771214146114000271710ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef MUTUALITYLINKPREDICTOR_H #define MUTUALITYLINKPREDICTOR_H #include "../WeightedNetwork.h" #include "LinkPredictor.h" class MutualityLinkPredictor : public LinkPredictor { private: protected: public: MutualityLinkPredictor( const WeightedNetwork& ); ~MutualityLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/OneLinkPredictor.cpp000066400000000000000000000020541214146114000262420ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include "OneLinkPredictor.h" OneLinkPredictor::OneLinkPredictor( const WeightedNetwork& network ) : LinkPredictor( network ) { } OneLinkPredictor::~OneLinkPredictor() { } double OneLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { return 1; } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/OneLinkPredictor.h000066400000000000000000000021411214146114000257040ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef ONELINKPREDICTOR_H #define ONELINKPREDICTOR_H #include "../WeightedNetwork.h" #include "LinkPredictor.h" class OneLinkPredictor : public LinkPredictor { private: protected: public: OneLinkPredictor( const WeightedNetwork& ); ~OneLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/PathCountLinkPredictor.cpp000066400000000000000000000040461214146114000274310ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "PathCountLinkPredictor.h" using std::stack; using std::make_pair; PathCountLinkPredictor::PathCountLinkPredictor( const WeightedNetwork& network, unsigned int l ) : LinkPredictor(network), l(l) { } PathCountLinkPredictor::~PathCountLinkPredictor() { } double PathCountLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { if( this->vertex != vertex ) { this->vertex = vertex; this->scores = vector( this->network.vertexCount() ); stack > search = stack >(); search.push( make_pair( vertex, 0 ) ); while( !search.empty() ) { const pair& searchItem = search.top(); vertex_t searchVertex = searchItem.first; unsigned int distance = searchItem.second; search.pop(); this->scores.at( searchVertex )++; if( distance < l ) { const neighbor_set_t& neighbors = this->network.outNeighbors( searchVertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); neighborIterator++ ) { vertex_t neighbor = neighborIterator->first; search.push( make_pair( neighbor, distance+1 ) ); } } } } return this->scores.at( neighbor ); } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/PathCountLinkPredictor.h000066400000000000000000000024221214146114000270720ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef PATHCOUNTLINKPREDICTOR_H #define PATHCOUNTLINKPREDICTOR_H #include #include "../WeightedNetwork.h" #include "LinkPredictor.h" using std::vector; class PathCountLinkPredictor : public LinkPredictor { private: unsigned int l; vector scores; PathCountLinkPredictor( const WeightedNetwork& ); protected: public: PathCountLinkPredictor( const WeightedNetwork&, unsigned int ); ~PathCountLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/PreferentialAttachmentLinkPredictor.cpp000066400000000000000000000025541214146114000321570ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include "PreferentialAttachmentLinkPredictor.h" PreferentialAttachmentLinkPredictor::PreferentialAttachmentLinkPredictor( const WeightedNetwork& network ) : LinkPredictor(network) { } PreferentialAttachmentLinkPredictor::~PreferentialAttachmentLinkPredictor() { } double PreferentialAttachmentLinkPredictor::generateScore( unsigned int vertex, unsigned int neighbor ) { unsigned int vertexNeighbors = this->network.outNeighbors( vertex ).size(); unsigned int neighborNeighbors = this->network.inNeighbors( neighbor ).size(); return (double)(vertexNeighbors*neighborNeighbors); } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/PreferentialAttachmentLinkPredictor.h000066400000000000000000000006371214146114000316240ustar00rootroot00000000000000#ifndef PREFERENTIALATTACHMENTLINKPREDICTOR_H #define PREFERENTIALATTACHMENTLINKPREDICTOR_H #include "../WeightedNetwork.h" #include "LinkPredictor.h" class PreferentialAttachmentLinkPredictor : public LinkPredictor { private: protected: public: PreferentialAttachmentLinkPredictor( const WeightedNetwork& ); ~PreferentialAttachmentLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/PropFlowLinkPredictor.cpp000066400000000000000000000051041214146114000272700ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include "PropFlowLinkPredictor.h" PropFlowLinkPredictor::PropFlowLinkPredictor( const WeightedNetwork& network, unsigned int l ) : LinkPredictor(network), l(l) { } PropFlowLinkPredictor::~PropFlowLinkPredictor() { } double PropFlowLinkPredictor::generateScore( unsigned int vertex, unsigned int neighbor ) { if( this->vertex != vertex ) { this->vertex = vertex; this->scores = vector( this->network.vertexCount() ); vector found = vector( this->network.vertexCount() ); vector search; found.at( vertex ) = true; search.push_back( vertex ); this->scores.at( vertex ) = 1; for( unsigned int degree = 0; degree < this->l; ++degree ) { vector newSearch; for( vector::const_iterator vertexIterator = search.begin(); vertexIterator != search.end(); ++vertexIterator ) { const vertex_t searchVertex = *vertexIterator; double sourceVertexInput = this->scores.at( searchVertex ); const neighbor_set_t& neighbors = this->network.outNeighbors( searchVertex ); double totalOutput = 0; for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); ++neighborIterator ) { totalOutput += neighborIterator->second; } for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); ++neighborIterator ) { const vertex_t neighbor = neighborIterator->first; double output = neighborIterator->second; double probability = sourceVertexInput * (output / totalOutput); this->scores.at( neighbor ) += probability; if( !found.at( neighbor ) ) { found.at( neighbor ) = true; newSearch.push_back( neighbor ); } } } search.swap( newSearch ); } } return this->scores.at( neighbor ); } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/PropFlowLinkPredictor.h000066400000000000000000000024141214146114000267360ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef PROPFLOWLINKPREDICTOR_H #define PROPFLOWLINKPREDICTOR_H #include #include "../WeightedNetwork.h" #include "LinkPredictor.h" using std::vector; class PropFlowLinkPredictor : public LinkPredictor { private: unsigned int l; vector scores; PropFlowLinkPredictor( const WeightedNetwork& ); protected: public: PropFlowLinkPredictor( const WeightedNetwork&, unsigned int ); ~PropFlowLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/RootedPageRankLinkPredictor.cpp000066400000000000000000000043171214146114000303720ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include "RootedPageRankLinkPredictor.h" #include "../Statistics.h" RootedPageRankLinkPredictor::RootedPageRankLinkPredictor( const WeightedNetwork& network, double alpha ) : LinkPredictor(network), alpha(alpha) { } RootedPageRankLinkPredictor::~RootedPageRankLinkPredictor() { } double RootedPageRankLinkPredictor::generateScore( unsigned int vertex, unsigned int neighbor ) { if( this->vertex != vertex ) { this->vertex = vertex; this->scores = vector( this->network.vertexCount() ); vector oldPageRanks = vector( this->network.vertexCount() ); vertex_t currentVertex = vertex; this->scores.at( currentVertex )++; for( unsigned int step = 1; true; step++ ) { const neighbor_set_t& neighbors = this->network.outNeighbors( currentVertex ); if( neighbors.size() < 1 || (double)rand()/RAND_MAX < this->alpha ) { currentVertex = vertex; } else { vertex_t randomNeighbor = rand() % neighbors.size(); currentVertex = neighbors.at( randomNeighbor ).first; } this->scores.at( currentVertex )++; if( step == 100000 ) { oldPageRanks = this->scores; } else if( step % 100000 == 0 ) { double r = Statistics::sampleCorrelationCoefficient( oldPageRanks, this->scores ); // cerr << r << "\n"; if( r > 0.9999 ) { return this->scores.at( neighbor ); } else { oldPageRanks = this->scores; } } } } return this->scores.at( neighbor ); } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/RootedPageRankLinkPredictor.h000066400000000000000000000024501214146114000300330ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef ROOTEDPAGERANKLINKPREDICTOR_H #define ROOTEDPAGERANKLINKPREDICTOR_H #include #include "../WeightedNetwork.h" #include "LinkPredictor.h" using std::vector; class RootedPageRankLinkPredictor : public LinkPredictor { private: double alpha; vector scores; RootedPageRankLinkPredictor( const WeightedNetwork& ); protected: public: RootedPageRankLinkPredictor( const WeightedNetwork&, double ); ~RootedPageRankLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/ShortestPathCountLinkPredictor.cpp000066400000000000000000000045321214146114000311650ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include "ShortestPathCountLinkPredictor.h" ShortestPathCountLinkPredictor::ShortestPathCountLinkPredictor( const WeightedNetwork& network, unsigned int l ) : LinkPredictor(network), l(l) { } ShortestPathCountLinkPredictor::~ShortestPathCountLinkPredictor() { } double ShortestPathCountLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { if( this->vertex != vertex ) { this->vertex = vertex; this->scores = vector( this->network.vertexCount() ); vector found = vector( this->network.vertexCount() ); vector* oldSearch = new vector(); vector* newSearch = new vector(); found.at( vertex ) = true; newSearch->push_back( vertex ); for( unsigned int degree = 0; degree < this->l; degree++ ) { delete oldSearch; oldSearch = newSearch; newSearch = new vector(); for( vector::const_iterator vertexIterator = oldSearch->begin(); vertexIterator != oldSearch->end(); vertexIterator++ ) { const vertex_t searchVertex = *vertexIterator; const neighbor_set_t& neighbors = this->network.outNeighbors( searchVertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); neighborIterator++ ) { const vertex_t neighbor = neighborIterator->first; this->scores.at( neighbor )++; if( !found.at( neighbor ) ) { found.at( neighbor ) = true; newSearch->push_back( neighbor ); } } } } delete newSearch; delete oldSearch; } return this->scores.at( neighbor ); } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/ShortestPathCountLinkPredictor.h000066400000000000000000000025021214146114000306250ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef SHORTESTPATHCOUNTLINKPREDICTOR_H #define SHORTESTPATHCOUNTLINKPREDICTOR_H #include #include "../WeightedNetwork.h" #include "LinkPredictor.h" using std::vector; class ShortestPathCountLinkPredictor : public LinkPredictor { private: unsigned int l; vector scores; ShortestPathCountLinkPredictor( const WeightedNetwork& ); protected: public: ShortestPathCountLinkPredictor( const WeightedNetwork&, unsigned int ); ~ShortestPathCountLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/SimRankLinkPredictor.cpp000066400000000000000000000022351214146114000270660ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include "SimRankLinkPredictor.h" SimRankLinkPredictor::SimRankLinkPredictor( const WeightedNetwork& network, double C ) : LinkPredictor(network), C(C), scores(network.simRank(C)) { } SimRankLinkPredictor::~SimRankLinkPredictor() { } double SimRankLinkPredictor::generateScore( unsigned int vertex, unsigned int neighbor ) { return this->scores.at( vertex ).at( neighbor ); } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/SimRankLinkPredictor.h000066400000000000000000000024031214146114000265300ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef SIMRANKLINKPREDICTOR_H #define SIMRANKLINKPREDICTOR_H #include #include "../WeightedNetwork.h" #include "LinkPredictor.h" using std::vector; class SimRankLinkPredictor : public LinkPredictor { private: double C; vector > scores; SimRankLinkPredictor( const WeightedNetwork& ); protected: public: SimRankLinkPredictor( const WeightedNetwork&, double ); ~SimRankLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/TriangleLinkPredictor.cpp000066400000000000000000000165501214146114000272740ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "TriangleLinkPredictor.h" using std::cerr; TriangleLinkPredictor::TriangleLinkPredictor( const WeightedNetwork& network, int selection = -1 ) : LinkPredictor( network ), selection( selection ) { if( this->selection > -1 ) { return; } unsigned long allCounts[graphlets] = {0}; for( vertex_t vertex = 0; vertex < network.vertexCount(); ++vertex ) { const neighbor_set_t& outNeighbors = network.outNeighbors( vertex ); for( neighbor_set_t::const_iterator neighborIterator = outNeighbors.begin(); neighborIterator != outNeighbors.end(); ++neighborIterator ) { const vertex_t neighbor = neighborIterator->first; unsigned long vCounts[graphlets] = {0}; this->edgeProfile( vertex, neighbor, vCounts ); for( size_t i = 0; i < graphlets; ++i ) { allCounts[i] += vCounts[i]; } } } for( size_t i = 0; i < graphlets; ++i ) { this->counts[i] = (double)allCounts[i] / network.edgeCount(); } } void TriangleLinkPredictor::computeDetailedNeighbors( const neighbor_set_t& inNeighbors, const neighbor_set_t& outNeighbors, DetailedNeighbors& result ) const { neighbor_set_t::const_iterator inIt = inNeighbors.begin(); neighbor_set_t::const_iterator outIt = outNeighbors.begin(); neighbor_set_t::const_iterator inEnd = inNeighbors.end(); neighbor_set_t::const_iterator outEnd = outNeighbors.end(); while( inIt != inEnd && outIt != outEnd ) { if( inIt->first < outIt->first ) { result.inOnly.push_back( inIt->first ); result.either.push_back( inIt->first ); ++inIt; } else if( inIt->first > outIt->first ) { result.outOnly.push_back( outIt->first ); result.either.push_back( outIt->first ); ++outIt; } else { result.both.push_back( inIt->first ); result.either.push_back( inIt->first ); ++inIt; ++outIt; } } while( inIt != inEnd ) { result.inOnly.push_back( inIt->first ); result.either.push_back( inIt->first ); ++inIt; } while( outIt != outEnd ) { result.outOnly.push_back( outIt->first ); result.either.push_back( outIt->first ); ++outIt; } } void TriangleLinkPredictor::edgeProfile( vertex_t v1, vertex_t v2, unsigned long vCounts[] ) { const neighbor_set_t& v1InNeighbors = network.inNeighbors( v1 ); const neighbor_set_t& v1OutNeighbors = network.outNeighbors( v1 ); const neighbor_set_t& v2InNeighbors = network.inNeighbors( v2 ); const neighbor_set_t& v2OutNeighbors = network.outNeighbors( v2 ); unsigned char reciprocal = (graphlets/2) * (char)network.hasEdge( v2, v1 ); DetailedNeighbors v1DetailedNeighbors; DetailedNeighbors v2DetailedNeighbors; this->computeDetailedNeighbors( v1InNeighbors, v1OutNeighbors, v1DetailedNeighbors ); this->computeDetailedNeighbors( v2InNeighbors, v2OutNeighbors, v2DetailedNeighbors ); vCounts[0+reciprocal] += this->intersectionCount( v1DetailedNeighbors.outOnly, v2DetailedNeighbors.inOnly ); vCounts[1+reciprocal] += this->intersectionCount( v1DetailedNeighbors.outOnly, v2DetailedNeighbors.outOnly ); vCounts[2+reciprocal] += this->intersectionCount( v1DetailedNeighbors.outOnly, v2DetailedNeighbors.both ); vCounts[3+reciprocal] += this->intersectionCount( v1DetailedNeighbors.inOnly, v2DetailedNeighbors.inOnly ); vCounts[4+reciprocal] += this->intersectionCount( v1DetailedNeighbors.inOnly, v2DetailedNeighbors.outOnly ); vCounts[5+reciprocal] += this->intersectionCount( v1DetailedNeighbors.inOnly, v2DetailedNeighbors.both ); vCounts[6+reciprocal] += this->intersectionCount( v1DetailedNeighbors.both, v2DetailedNeighbors.inOnly ); vCounts[7+reciprocal] += this->intersectionCount( v1DetailedNeighbors.both, v2DetailedNeighbors.outOnly ); vCounts[8+reciprocal] += this->intersectionCount( v1DetailedNeighbors.both, v2DetailedNeighbors.both ); vCounts[9+reciprocal] += this->differenceCount( v2DetailedNeighbors.inOnly, v1DetailedNeighbors.either ); vCounts[10+reciprocal] += this->differenceCount( v2DetailedNeighbors.outOnly, v1DetailedNeighbors.either ); vCounts[11+reciprocal] += this->differenceCount( v2DetailedNeighbors.both, v1DetailedNeighbors.either ); vCounts[12+reciprocal] += this->differenceCount( v1DetailedNeighbors.inOnly, v2DetailedNeighbors.either ); vCounts[13+reciprocal] += this->differenceCount( v1DetailedNeighbors.outOnly, v2DetailedNeighbors.either ); vCounts[14+reciprocal] += this->differenceCount( v1DetailedNeighbors.both, v2DetailedNeighbors.either ); vCounts[15+reciprocal] += this->network.vertexCount() - this->unionCount( v1DetailedNeighbors.either, v2DetailedNeighbors.either ) - 2; } unsigned long TriangleLinkPredictor::intersectionCount( const vector& v1, const vector& v2 ) const { vector::const_iterator first1 = v1.begin(); vector::const_iterator last1 = v1.end(); vector::const_iterator first2 = v2.begin(); vector::const_iterator last2 = v2.end(); unsigned long count = 0; while( first1 != last1 && first2 != last2 ) { if( *first1 < *first2 ) { ++first1; } else if( *first2 < *first1 ) { ++first2; } else { ++count; ++first1; ++first2; } } return count; } unsigned long TriangleLinkPredictor::differenceCount( const vector& v1, const vector& v2 ) const { vector::const_iterator first1 = v1.begin(); vector::const_iterator last1 = v1.end(); vector::const_iterator first2 = v2.begin(); vector::const_iterator last2 = v2.end(); unsigned long count = 0; while( first1 != last1 && first2 != last2 ) { if( *first1 < *first2 ) { ++count; ++first1; } else if( *first2 < *first1 ) { ++first2; } else { ++first1; ++first2; } } return count; } unsigned long TriangleLinkPredictor::unionCount( const vector& v1, const vector& v2 ) const { vector::const_iterator first1 = v1.begin(); vector::const_iterator last1 = v1.end(); vector::const_iterator first2 = v2.begin(); vector::const_iterator last2 = v2.end(); unsigned long count = 0; while( first1 != last1 && first2 != last2 ) { if( *first1 < *first2 ) { ++count; ++first1; } else if( *first2 < *first1 ) { ++count; ++first2; } else { ++first1; ++first2; } } return count; } TriangleLinkPredictor::~TriangleLinkPredictor() { } double TriangleLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { unsigned long vCounts[graphlets] = {0}; this->edgeProfile( vertex, neighbor, vCounts ); double result = 0; if( selection > -1 ) { result = vCounts[selection]; } else { for( size_t i = 0; i < graphlets; ++i ) { result += pow( this->counts[i] - (double)vCounts[i], 2 ); } result = 1 / result; } return result; } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/TriangleLinkPredictor.h000066400000000000000000000034711214146114000267370ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef TRIANGLELINKPREDICTOR_H #define TRIANGLELINKPREDICTOR_H #include "../WeightedNetwork.h" #include "LinkPredictor.h" struct DetailedNeighbors; class TriangleLinkPredictor : public LinkPredictor { private: const static unsigned long graphlets = 32; int selection; double counts[graphlets]; struct DetailedNeighbors { vector inOnly; vector outOnly; vector both; vector either; }; void computeDetailedNeighbors( const neighbor_set_t&, const neighbor_set_t&, DetailedNeighbors& ) const; unsigned long intersectionCount( const vector&, const vector& ) const; unsigned long differenceCount( const vector&, const vector& ) const; unsigned long unionCount( const vector&, const vector& ) const; void edgeProfile( vertex_t, vertex_t, unsigned long[] ); protected: public: TriangleLinkPredictor( const WeightedNetwork&, int ); ~TriangleLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/UnweightedPropFlowLinkPredictor.cpp000066400000000000000000000050331214146114000313150ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include "UnweightedPropFlowLinkPredictor.h" using std::cout; UnweightedPropFlowLinkPredictor::UnweightedPropFlowLinkPredictor( const WeightedNetwork& network, unsigned int l ) : LinkPredictor(network), l(l) { } UnweightedPropFlowLinkPredictor::~UnweightedPropFlowLinkPredictor() { } double UnweightedPropFlowLinkPredictor::generateScore( unsigned int vertex, unsigned int neighbor ) { if( this->vertex != vertex ) { this->vertex = vertex; this->scores = vector( this->network.vertexCount() ); vector found = vector( this->network.vertexCount() ); vector oldSearch; found.at( vertex ) = true; oldSearch.push_back( vertex ); this->scores.at( vertex ) = 1; for( unsigned int degree = 0; degree < this->l; degree++ ) { vector newSearch; for( vector::const_iterator vertexIterator = oldSearch.begin(); vertexIterator != oldSearch.end(); vertexIterator++ ) { const vertex_t searchVertex = *vertexIterator; double sourceVertexInput = this->scores.at( searchVertex ); const neighbor_set_t& neighbors = this->network.outNeighbors( searchVertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); neighborIterator++ ) { const vertex_t neighbor = neighborIterator->first; this->scores.at( neighbor ) += sourceVertexInput * ((double)1 / neighbors.size()); if( !found.at( neighbor ) ) { found.at( neighbor ) = true; newSearch.push_back( neighbor ); } } } oldSearch.swap( newSearch ); } } cout << network.translateIntToExt( vertex ) << " " << network.translateIntToExt( neighbor ) << " " << this->scores.at( neighbor ) << "\n"; return this->scores.at( neighbor ); } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/UnweightedPropFlowLinkPredictor.h000066400000000000000000000025101214146114000307570ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef UNWEIGHTEDPROPFLOWLINKPREDICTOR_H #define UNWEIGHTEDPROPFLOWLINKPREDICTOR_H #include #include "../WeightedNetwork.h" #include "LinkPredictor.h" using std::vector; class UnweightedPropFlowLinkPredictor : public LinkPredictor { private: unsigned int l; vector scores; UnweightedPropFlowLinkPredictor( const WeightedNetwork& ); protected: public: UnweightedPropFlowLinkPredictor( const WeightedNetwork&, unsigned int ); ~UnweightedPropFlowLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/VCP3DirectedLinkPredictor.cpp000066400000000000000000000156041214146114000277050ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "VCP3DirectedLinkPredictor.h" using std::cout; using std::cerr; VCP3DirectedLinkPredictor::VCP3DirectedLinkPredictor( const WeightedNetwork& network ) : LinkPredictor( network ) { } VCP3DirectedLinkPredictor::DetailedNeighbors VCP3DirectedLinkPredictor::computeDetailedNeighbors( const neighbor_set_t& inNeighbors, const neighbor_set_t& outNeighbors, vertex_t avoidance ) const { neighbor_set_t::const_iterator inIt = inNeighbors.begin(); neighbor_set_t::const_iterator outIt = outNeighbors.begin(); neighbor_set_t::const_iterator inEnd = inNeighbors.end(); neighbor_set_t::const_iterator outEnd = outNeighbors.end(); DetailedNeighbors result; while( inIt != inEnd && outIt != outEnd ) { if( inIt->first == avoidance ) { ++inIt; continue; } if( outIt->first == avoidance ) { ++outIt; continue; } if( inIt->first < outIt->first ) { result.inOnly.push_back( inIt->first ); result.either.push_back( inIt->first ); ++inIt; } else if( inIt->first > outIt->first ) { result.outOnly.push_back( outIt->first ); result.either.push_back( outIt->first ); ++outIt; } else { result.both.push_back( inIt->first ); result.either.push_back( inIt->first ); ++inIt; ++outIt; } } while( inIt != inEnd ) { if( inIt->first == avoidance ) { ++inIt; continue; } result.inOnly.push_back( inIt->first ); result.either.push_back( inIt->first ); ++inIt; } while( outIt != outEnd ) { if( outIt->first == avoidance ) { ++outIt; continue; } result.outOnly.push_back( outIt->first ); result.either.push_back( outIt->first ); ++outIt; } return result; } void VCP3DirectedLinkPredictor::edgeProfile( vertex_t v1, vertex_t v2, unsigned long vCounts[] ) const { unsigned char reciprocal = (graphlets/2) * (char)network.hasEdge( v2, v1 ); DetailedNeighbors v1DetailedNeighbors = this->computeDetailedNeighbors( network.inNeighbors( v1 ), network.outNeighbors( v1 ), v2 ); DetailedNeighbors v2DetailedNeighbors = this->computeDetailedNeighbors( network.inNeighbors( v2 ), network.outNeighbors( v2 ), v1 ); vCounts[reciprocal] += this->network.vertexCount() - this->unionCount( v1DetailedNeighbors.either, v2DetailedNeighbors.either ) - 2; vCounts[1+reciprocal] += this->differenceCount( v1DetailedNeighbors.outOnly, v2DetailedNeighbors.either ); vCounts[2+reciprocal] += this->differenceCount( v1DetailedNeighbors.inOnly, v2DetailedNeighbors.either ); vCounts[3+reciprocal] += this->differenceCount( v1DetailedNeighbors.both, v2DetailedNeighbors.either ); vCounts[4+reciprocal] += this->differenceCount( v2DetailedNeighbors.outOnly, v1DetailedNeighbors.either ); vCounts[5+reciprocal] += this->intersectionCount( v1DetailedNeighbors.outOnly, v2DetailedNeighbors.outOnly ); vCounts[6+reciprocal] += this->intersectionCount( v1DetailedNeighbors.inOnly, v2DetailedNeighbors.outOnly ); vCounts[7+reciprocal] += this->intersectionCount( v1DetailedNeighbors.both, v2DetailedNeighbors.outOnly ); vCounts[8+reciprocal] += this->differenceCount( v2DetailedNeighbors.inOnly, v1DetailedNeighbors.either ); vCounts[9+reciprocal] += this->intersectionCount( v1DetailedNeighbors.outOnly, v2DetailedNeighbors.inOnly ); vCounts[10+reciprocal] += this->intersectionCount( v1DetailedNeighbors.inOnly, v2DetailedNeighbors.inOnly ); vCounts[11+reciprocal] += this->intersectionCount( v1DetailedNeighbors.both, v2DetailedNeighbors.inOnly ); vCounts[12+reciprocal] += this->differenceCount( v2DetailedNeighbors.both, v1DetailedNeighbors.either ); vCounts[13+reciprocal] += this->intersectionCount( v1DetailedNeighbors.outOnly, v2DetailedNeighbors.both ); vCounts[14+reciprocal] += this->intersectionCount( v1DetailedNeighbors.inOnly, v2DetailedNeighbors.both ); vCounts[15+reciprocal] += this->intersectionCount( v1DetailedNeighbors.both, v2DetailedNeighbors.both ); } unsigned long VCP3DirectedLinkPredictor::intersectionCount( const vector& v1, const vector& v2 ) const { vector::const_iterator first1 = v1.begin(); vector::const_iterator last1 = v1.end(); vector::const_iterator first2 = v2.begin(); vector::const_iterator last2 = v2.end(); unsigned long count = 0; while( first1 != last1 && first2 != last2 ) { if( *first1 < *first2 ) { ++first1; } else if( *first2 < *first1 ) { ++first2; } else { ++count; ++first1; ++first2; } } return count; } unsigned long VCP3DirectedLinkPredictor::differenceCount( const vector& v1, const vector& v2 ) const { vector::const_iterator first1 = v1.begin(); vector::const_iterator last1 = v1.end(); vector::const_iterator first2 = v2.begin(); vector::const_iterator last2 = v2.end(); unsigned long count = 0; while( first1 != last1 && first2 != last2 ) { if( *first1 < *first2 ) { ++count; ++first1; } else if( *first2 < *first1 ) { ++first2; } else { ++first1; ++first2; } } while( first1 != last1 ) { ++count; ++first1; } return count; } unsigned long VCP3DirectedLinkPredictor::unionCount( const vector& v1, const vector& v2 ) const { vector::const_iterator first1 = v1.begin(); vector::const_iterator last1 = v1.end(); vector::const_iterator first2 = v2.begin(); vector::const_iterator last2 = v2.end(); unsigned long count = 0; while( first1 != last1 && first2 != last2 ) { if( *first1 < *first2 ) { ++count; ++first1; } else if( *first2 < *first1 ) { ++count; ++first2; } else { ++count; ++first1; ++first2; } } while( first1 != last1 ) { ++count; ++first1; } while( first2 != last2 ) { ++count; ++first2; } return count; } VCP3DirectedLinkPredictor::~VCP3DirectedLinkPredictor() { } double VCP3DirectedLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { unsigned long vCounts[graphlets] = {0}; this->edgeProfile( vertex, neighbor, vCounts ); cerr << network.translateIntToExt( vertex ) << " " << network.translateIntToExt( neighbor ); for( size_t i = 0; i < graphlets; ++i ) { cerr << " " << vCounts[i]; } cerr << "\n"; return -1; } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/VCP3DirectedLinkPredictor.h000066400000000000000000000035211214146114000273450ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef VCP3DIRECTEDLINKPREDICTOR_H #define VCP3DIRECTEDLINKPREDICTOR_H #include "../WeightedNetwork.h" #include "LinkPredictor.h" struct DetailedNeighbors; class VCP3DirectedLinkPredictor : public LinkPredictor { private: const static unsigned long graphlets = 32; double counts[graphlets]; struct DetailedNeighbors { vector inOnly; vector outOnly; vector both; vector either; }; struct DetailedNeighbors computeDetailedNeighbors( const neighbor_set_t&, const neighbor_set_t&, vertex_t avoidance ) const; unsigned long intersectionCount( const vector&, const vector& ) const; unsigned long differenceCount( const vector&, const vector& ) const; unsigned long unionCount( const vector&, const vector& ) const; void edgeProfile( vertex_t, vertex_t, unsigned long[] ) const; protected: public: VCP3DirectedLinkPredictor( const WeightedNetwork& ); ~VCP3DirectedLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/VCP3UndirectedLinkPredictor.cpp000066400000000000000000000126601214146114000302470ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "VCP3UndirectedLinkPredictor.h" using std::cout; using std::cerr; VCP3UndirectedLinkPredictor::VCP3UndirectedLinkPredictor( const WeightedNetwork& network ) : LinkPredictor( network ) { } VCP3UndirectedLinkPredictor::DetailedNeighbors VCP3UndirectedLinkPredictor::computeDetailedNeighbors( const neighbor_set_t& inNeighbors, const neighbor_set_t& outNeighbors, vertex_t avoidance ) const { neighbor_set_t::const_iterator inIt = inNeighbors.begin(); neighbor_set_t::const_iterator outIt = outNeighbors.begin(); neighbor_set_t::const_iterator inEnd = inNeighbors.end(); neighbor_set_t::const_iterator outEnd = outNeighbors.end(); DetailedNeighbors result; while( inIt != inEnd && outIt != outEnd ) { if( inIt->first == avoidance ) { ++inIt; continue; } if( outIt->first == avoidance ) { ++outIt; continue; } if( inIt->first < outIt->first ) { result.either.push_back( inIt->first ); ++inIt; } else if( inIt->first > outIt->first ) { result.either.push_back( outIt->first ); ++outIt; } else { result.either.push_back( inIt->first ); ++inIt; ++outIt; } } while( inIt != inEnd ) { if( inIt->first == avoidance ) { ++inIt; continue; } result.either.push_back( inIt->first ); ++inIt; } while( outIt != outEnd ) { if( outIt->first == avoidance ) { ++outIt; continue; } result.either.push_back( outIt->first ); ++outIt; } return result; } void VCP3UndirectedLinkPredictor::edgeProfile( vertex_t v1, vertex_t v2, unsigned long vCounts[] ) const { unsigned char reciprocal = (graphlets/2) * (char)network.hasEdge( v2, v1 ); DetailedNeighbors v1DetailedNeighbors = this->computeDetailedNeighbors( network.inNeighbors( v1 ), network.outNeighbors( v1 ), v2 ); DetailedNeighbors v2DetailedNeighbors = this->computeDetailedNeighbors( network.inNeighbors( v2 ), network.outNeighbors( v2 ), v1 ); vCounts[reciprocal] += this->network.vertexCount() - this->unionCount( v1DetailedNeighbors.either, v2DetailedNeighbors.either ) - 2; vCounts[1+reciprocal] += this->differenceCount( v1DetailedNeighbors.either, v2DetailedNeighbors.either ); vCounts[2+reciprocal] += this->differenceCount( v2DetailedNeighbors.either, v1DetailedNeighbors.either ); vCounts[3+reciprocal] += this->intersectionCount( v1DetailedNeighbors.either, v2DetailedNeighbors.either ); } unsigned long VCP3UndirectedLinkPredictor::intersectionCount( const vector& v1, const vector& v2 ) const { vector::const_iterator first1 = v1.begin(); vector::const_iterator last1 = v1.end(); vector::const_iterator first2 = v2.begin(); vector::const_iterator last2 = v2.end(); unsigned long count = 0; while( first1 != last1 && first2 != last2 ) { if( *first1 < *first2 ) { ++first1; } else if( *first2 < *first1 ) { ++first2; } else { ++count; ++first1; ++first2; } } return count; } unsigned long VCP3UndirectedLinkPredictor::differenceCount( const vector& v1, const vector& v2 ) const { vector::const_iterator first1 = v1.begin(); vector::const_iterator last1 = v1.end(); vector::const_iterator first2 = v2.begin(); vector::const_iterator last2 = v2.end(); unsigned long count = 0; while( first1 != last1 && first2 != last2 ) { if( *first1 < *first2 ) { ++count; ++first1; } else if( *first2 < *first1 ) { ++first2; } else { ++first1; ++first2; } } while( first1 != last1 ) { ++count; ++first1; } return count; } unsigned long VCP3UndirectedLinkPredictor::unionCount( const vector& v1, const vector& v2 ) const { vector::const_iterator first1 = v1.begin(); vector::const_iterator last1 = v1.end(); vector::const_iterator first2 = v2.begin(); vector::const_iterator last2 = v2.end(); unsigned long count = 0; while( first1 != last1 && first2 != last2 ) { if( *first1 < *first2 ) { ++count; ++first1; } else if( *first2 < *first1 ) { ++count; ++first2; } else { ++count; ++first1; ++first2; } } while( first1 != last1 ) { ++count; ++first1; } while( first2 != last2 ) { ++count; ++first2; } return count; } VCP3UndirectedLinkPredictor::~VCP3UndirectedLinkPredictor() { } double VCP3UndirectedLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { unsigned long vCounts[graphlets] = {0}; this->edgeProfile( vertex, neighbor, vCounts ); cerr << network.translateIntToExt( vertex ) << " " << network.translateIntToExt( neighbor ); for( size_t i = 0; i < graphlets; ++i ) { cerr << " " << vCounts[i]; } cerr << "\n"; return -1; } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/VCP3UndirectedLinkPredictor.h000066400000000000000000000034071214146114000277130ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef VCP3UNDIRECTEDLINKPREDICTOR_H #define VCP3UNDIRECTEDLINKPREDICTOR_H #include "../WeightedNetwork.h" #include "LinkPredictor.h" struct DetailedNeighbors; class VCP3UndirectedLinkPredictor : public LinkPredictor { private: const static unsigned long graphlets = 8; double counts[graphlets]; struct DetailedNeighbors { vector either; }; struct DetailedNeighbors computeDetailedNeighbors( const neighbor_set_t&, const neighbor_set_t&, vertex_t avoidance ) const; unsigned long intersectionCount( const vector&, const vector& ) const; unsigned long differenceCount( const vector&, const vector& ) const; unsigned long unionCount( const vector&, const vector& ) const; void edgeProfile( vertex_t, vertex_t, unsigned long[] ) const; protected: public: VCP3UndirectedLinkPredictor( const WeightedNetwork& ); ~VCP3UndirectedLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/VCP4DirectedLinkPredictor.cpp000066400000000000000000000414261214146114000277070ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include #include #include "VCP4DirectedLinkPredictor.h" using std::list; using std::set; using std::pair; using std::make_pair; using std::cout; using std::cerr; const size_t VCP4DirectedLinkPredictor::map[graphlets] = {0,1,2,3,1,4,5,6,2,5,7,8,3,6,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,10,14,18,22,11,15,19,23,12,16,20,24,13,17,21,25,58,59,60,61,59,62,63,64,60,63,65,66,61,64,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,26,30,34,38,27,31,35,39,28,32,36,40,29,33,37,41,68,72,76,80,69,73,77,81,70,74,78,82,71,75,79,83,100,101,102,103,101,104,105,106,102,105,107,108,103,106,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,42,46,50,54,43,47,51,55,44,48,52,56,45,49,53,57,84,88,92,96,85,89,93,97,86,90,94,98,87,91,95,99,110,114,118,122,111,115,119,123,112,116,120,124,113,117,121,125,126,127,128,129,127,130,131,132,128,131,133,134,129,132,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,136,140,144,148,137,141,145,149,138,142,146,150,139,143,147,151,200,204,208,212,201,205,209,213,202,206,210,214,203,207,211,215,264,268,272,276,265,269,273,277,266,270,274,278,267,271,275,279,328,332,336,340,329,333,337,341,330,334,338,342,331,335,339,343,152,156,160,164,153,157,161,165,154,158,162,166,155,159,163,167,216,220,224,228,217,221,225,229,218,222,226,230,219,223,227,231,280,284,288,292,281,285,289,293,282,286,290,294,283,287,291,295,344,348,352,356,345,349,353,357,346,350,354,358,347,351,355,359,168,172,176,180,169,173,177,181,170,174,178,182,171,175,179,183,232,236,240,244,233,237,241,245,234,238,242,246,235,239,243,247,296,300,304,308,297,301,305,309,298,302,306,310,299,303,307,311,360,364,368,372,361,365,369,373,362,366,370,374,363,367,371,375,184,188,192,196,185,189,193,197,186,190,194,198,187,191,195,199,248,252,256,260,249,253,257,261,250,254,258,262,251,255,259,263,312,316,320,324,313,317,321,325,314,318,322,326,315,319,323,327,376,380,384,388,377,381,385,389,378,382,386,390,379,383,387,391,392,393,394,395,393,396,397,398,394,397,399,400,395,398,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,402,406,410,414,403,407,411,415,404,408,412,416,405,409,413,417,450,451,452,453,451,454,455,456,452,455,457,458,453,456,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,418,422,426,430,419,423,427,431,420,424,428,432,421,425,429,433,460,464,468,472,461,465,469,473,462,466,470,474,463,467,471,475,492,493,494,495,493,496,497,498,494,497,499,500,495,498,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,434,438,442,446,435,439,443,447,436,440,444,448,437,441,445,449,476,480,484,488,477,481,485,489,478,482,486,490,479,483,487,491,502,506,510,514,503,507,511,515,504,508,512,516,505,509,513,517,518,519,520,521,519,522,523,524,520,523,525,526,521,524,526,527,528,529,530,531,529,532,533,534,530,533,535,536,531,534,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,538,542,546,550,539,543,547,551,540,544,548,552,541,545,549,553,586,587,588,589,587,590,591,592,588,591,593,594,589,592,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,554,558,562,566,555,559,563,567,556,560,564,568,557,561,565,569,596,600,604,608,597,601,605,609,598,602,606,610,599,603,607,611,628,629,630,631,629,632,633,634,630,633,635,636,631,634,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,570,574,578,582,571,575,579,583,572,576,580,584,573,577,581,585,612,616,620,624,613,617,621,625,614,618,622,626,615,619,623,627,638,642,646,650,639,643,647,651,640,644,648,652,641,645,649,653,654,655,656,657,655,658,659,660,656,659,661,662,657,660,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,664,668,672,676,665,669,673,677,666,670,674,678,667,671,675,679,728,732,736,740,729,733,737,741,730,734,738,742,731,735,739,743,792,796,800,804,793,797,801,805,794,798,802,806,795,799,803,807,856,860,864,868,857,861,865,869,858,862,866,870,859,863,867,871,680,684,688,692,681,685,689,693,682,686,690,694,683,687,691,695,744,748,752,756,745,749,753,757,746,750,754,758,747,751,755,759,808,812,816,820,809,813,817,821,810,814,818,822,811,815,819,823,872,876,880,884,873,877,881,885,874,878,882,886,875,879,883,887,696,700,704,708,697,701,705,709,698,702,706,710,699,703,707,711,760,764,768,772,761,765,769,773,762,766,770,774,763,767,771,775,824,828,832,836,825,829,833,837,826,830,834,838,827,831,835,839,888,892,896,900,889,893,897,901,890,894,898,902,891,895,899,903,712,716,720,724,713,717,721,725,714,718,722,726,715,719,723,727,776,780,784,788,777,781,785,789,778,782,786,790,779,783,787,791,840,844,848,852,841,845,849,853,842,846,850,854,843,847,851,855,904,908,912,916,905,909,913,917,906,910,914,918,907,911,915,919,920,921,922,923,921,924,925,926,922,925,927,928,923,926,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,930,934,938,942,931,935,939,943,932,936,940,944,933,937,941,945,978,979,980,981,979,982,983,984,980,983,985,986,981,984,986,987,988,989,990,991,992,993,994,995,996,997,998,999,1000,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,946,950,954,958,947,951,955,959,948,952,956,960,949,953,957,961,988,992,996,1000,989,993,997,1001,990,994,998,1002,991,995,999,1003,1020,1021,1022,1023,1021,1024,1025,1026,1022,1025,1027,1028,1023,1026,1028,1029,1030,1031,1032,1033,1034,1035,1036,1037,1038,1039,1040,1041,1042,1043,1044,1045,962,966,970,974,963,967,971,975,964,968,972,976,965,969,973,977,1004,1008,1012,1016,1005,1009,1013,1017,1006,1010,1014,1018,1007,1011,1015,1019,1030,1034,1038,1042,1031,1035,1039,1043,1032,1036,1040,1044,1033,1037,1041,1045,1046,1047,1048,1049,1047,1050,1051,1052,1048,1051,1053,1054,1049,1052,1054,1055}; VCP4DirectedLinkPredictor::VCP4DirectedLinkPredictor( const WeightedNetwork& network ) : LinkPredictor( network ), connectedPairs( 0 ), amutualPairs( 0 ), mutualPairs( 0 ), unconnectedPairs( 0 ) { // compute the total number of somehow-connected pairs in the graph for( vertex_t i = 0; i < network.vertexCount(); ++i ) { neighbor_set_t::const_iterator outIt = network.outNeighbors( i ).begin(); neighbor_set_t::const_iterator outEnd = network.outNeighbors( i ).end(); neighbor_set_t::const_iterator inIt = network.inNeighbors( i ).begin(); neighbor_set_t::const_iterator inEnd = network.inNeighbors( i ).end(); while( outIt != outEnd && outIt->first <= i ) { ++outIt; } while( inIt != inEnd && inIt->first <= i ) { ++inIt; } while( outIt != outEnd && inIt != inEnd ) { ++connectedPairs; if( outIt->first < inIt->first ) { ++amutualPairs; ++outIt; } else if( outIt->first > inIt->first ) { ++amutualPairs; ++inIt; } else { ++mutualPairs; ++outIt; ++inIt; } } while( outIt != outEnd ) { ++amutualPairs; ++connectedPairs; ++outIt; } while( inIt != inEnd ) { ++amutualPairs; ++connectedPairs; ++inIt; } } unsigned long potentialConnections = (unsigned long)network.vertexCount() * (unsigned long)(network.vertexCount() - 1) / 2; unconnectedPairs = potentialConnections - connectedPairs; } VCP4DirectedLinkPredictor::~VCP4DirectedLinkPredictor() { } double VCP4DirectedLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { vertex_t v1 = vertex; vertex_t v2 = neighbor; unsigned long remapped[elements] = {0}; size_t index = 1024 * (size_t)network.hasEdge( v2, v1 ); unsigned long vertices = 2; unsigned long connections = 0; unsigned long amutuals = 0; unsigned long gaps = 0; // compose ordered list of v3 candidates const neighbor_set_t* v3Neighbors[4] = {&network.inNeighbors( v1 ), &network.outNeighbors( v1 ), &network.inNeighbors( v2 ), &network.outNeighbors( v2 )}; list v3Vertices; for( unsigned int i = 0; i < 4; ++i ) { list::iterator v3VerticesIt = v3Vertices.begin(); list::iterator v3VerticesEnd = v3Vertices.end(); neighbor_set_t::const_iterator neighborsIt = v3Neighbors[i]->begin(); neighbor_set_t::const_iterator neighborsEnd = v3Neighbors[i]->end(); while( v3VerticesIt != v3VerticesEnd && neighborsIt != neighborsEnd ) { if( neighborsIt->first == v1 || neighborsIt->first == v2 ) { ++neighborsIt; continue; } if( *v3VerticesIt < neighborsIt->first ) { ++v3VerticesIt; } else if( neighborsIt->first < *v3VerticesIt ) { v3Vertices.insert( v3VerticesIt, neighborsIt->first ); ++neighborsIt; } else { ++v3VerticesIt; ++neighborsIt; } } while( neighborsIt != neighborsEnd ) { if( neighborsIt->first != v1 && neighborsIt->first != v2 ) { v3Vertices.insert( v3VerticesIt, neighborsIt->first ); } ++neighborsIt; } } // for each v3 vertex in the above list for( list::const_iterator v3It = v3Vertices.begin(); v3It != v3Vertices.end(); ++v3It ) { vertex_t v3 = *v3It; size_t index2 = index; size_t v1v3 = network.hasEdge( v1, v3 ) + 2 * network.hasEdge( v3, v1 ); if( v1v3 ) { index2 += 1 * v1v3; ++connections; } else { ++gaps; } if( v1v3 == 1 || v1v3 == 2 ) { ++amutuals; } size_t v2v3 = network.hasEdge( v2, v3 ) + 2 * network.hasEdge( v3, v2 ); if( v2v3 ) { index2 += 16 * v2v3; ++connections; } else { ++gaps; } if( v2v3 == 1 || v2v3 == 2 ) { ++amutuals; } // compose two ordered lists of v4 candidates: those from v3 and those ONLY from the the neighbors of this v3 vertex const neighbor_set_t* v4Neighbors[2] = {&network.inNeighbors( v3 ), &network.outNeighbors( v3 )}; list v4Vertices; for( unsigned int i = 0; i < 2; ++i ) { list::iterator v4VerticesIt = v4Vertices.begin(); list::iterator v4VerticesEnd = v4Vertices.end(); neighbor_set_t::const_iterator neighborsIt = v4Neighbors[i]->begin(); neighbor_set_t::const_iterator neighborsEnd = v4Neighbors[i]->end(); while( v4VerticesIt != v4VerticesEnd && neighborsIt != neighborsEnd ) { if( neighborsIt->first == v1 || neighborsIt->first == v2 || neighborsIt->first == v3 ) { ++neighborsIt; continue; } if( *v4VerticesIt < neighborsIt->first ) { ++v4VerticesIt; } else if( neighborsIt->first < *v4VerticesIt ) { v4Vertices.insert( v4VerticesIt, neighborsIt->first ); ++neighborsIt; } else { ++v4VerticesIt; ++neighborsIt; } } while( neighborsIt != neighborsEnd ) { if( neighborsIt->first != v1 && neighborsIt->first != v2 && neighborsIt->first != v3 ) { v4Vertices.insert( v4VerticesIt, neighborsIt->first ); } ++neighborsIt; } } list::iterator v4VerticesIt = v4Vertices.begin(); list::iterator v4VerticesEnd = v4Vertices.end(); list::const_iterator v3VerticesIt = v3Vertices.begin(); list::const_iterator v3VerticesEnd = v3Vertices.end(); while( v4VerticesIt != v4VerticesEnd && v3VerticesIt != v3VerticesEnd ) { if( *v4VerticesIt < *v3VerticesIt ) { ++v4VerticesIt; } else if( *v3VerticesIt < *v4VerticesIt ) { ++v3VerticesIt; } else { v4VerticesIt = v4Vertices.erase( v4VerticesIt ); } } // for each v4 vertex in the above list of v4 only vertices vertices += v4Vertices.size(); unsigned long v3v4Connections = 0; for( list::const_iterator v4It = v4Vertices.begin(); v4It != v4Vertices.end(); ++v4It ) { vertex_t v4 = *v4It; size_t index3 = index2; size_t v1v4 = network.hasEdge( v1, v4 ) + 2 * network.hasEdge( v4, v1 ); if( v1v4 ) { index3 += 4 * v1v4; ++v3v4Connections; } else { ++gaps; } if( v1v4 == 1 || v1v4 == 2 ) { ++amutuals; } size_t v2v4 = network.hasEdge( v2, v4 ) + 2 * network.hasEdge( v4, v2 ); if( v2v4 ) { index3 += 64 * v2v4; ++v3v4Connections; } else { ++gaps; } if( v2v4 == 1 || v2v4 == 2 ) { ++amutuals; } size_t v3v4 = network.hasEdge( v3, v4 ) + 2 * network.hasEdge( v4, v3 ); if( v3v4 ) { index3 += 256 * v3v4; ++v3v4Connections; } else { ++gaps; } if( v3v4 == 1 || v3v4 == 2 ) { ++amutuals; } ++remapped[ map[index3] ]; } // for each other v3 vertex if it has a higher index for( list::const_iterator v4It = v3Vertices.begin(); v4It != v3Vertices.end(); ++v4It ) { vertex_t v4 = *v4It; if( v4 <= v3 ) { continue; } size_t index3 = index2; size_t v1v4 = network.hasEdge( v1, v4 ) + 2 * network.hasEdge( v4, v1 ); index3 += 4 * v1v4; size_t v2v4 = network.hasEdge( v2, v4 ) + 2 * network.hasEdge( v4, v2 ); index3 += 64 * v2v4; size_t v3v4 = network.hasEdge( v3, v4 ) + 2 * network.hasEdge( v4, v3 ); if( v3v4 ) { index3 += 256 * v3v4; ++v3v4Connections; } else { ++gaps; } if( v3v4 == 1 || v3v4 == 2 ) { ++amutuals; } ++remapped[ map[index3] ]; } connections += v3v4Connections; // account for triangles and gaps remapped[ map[index2] ] += network.vertexCount() - 2 - (v3Vertices.size() + v4Vertices.size()); remapped[ map[index] ] -= network.vertexCount() - 2 - (v3Vertices.size() + v4Vertices.size()); // go negative for the number of triangles (which contain v3v4 gaps) } vertices += v3Vertices.size(); // account for the least connected substructures remapped[ map[256+index] ] = this->amutualPairs - (amutuals + index/1024); remapped[ map[768+index] ] = this->mutualPairs - (connections - amutuals); remapped[ map[index] ] += this->unconnectedPairs - (gaps + !(bool)(index/1024)) - (2 * (network.vertexCount() - vertices)); // do the remapping and output cerr << network.translateIntToExt( vertex ) << " " << network.translateIntToExt( neighbor ); for( size_t i = 0; i < elements; ++i ) { cerr << " " << remapped[i]; } cerr << "\n"; return -1; } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/VCP4DirectedLinkPredictor.h000066400000000000000000000026011214146114000273440ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef VCP4DIRECTEDLINKPREDICTOR_H #define VCP4DIRECTEDLINKPREDICTOR_H #include "../WeightedNetwork.h" #include "LinkPredictor.h" class VCP4DirectedLinkPredictor : public LinkPredictor { private: const static size_t graphlets = 2048; const static size_t map[graphlets]; const static size_t elements = 1056; unsigned long connectedPairs; unsigned long amutualPairs; unsigned long mutualPairs; unsigned long unconnectedPairs; protected: public: VCP4DirectedLinkPredictor( const WeightedNetwork& ); ~VCP4DirectedLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/VCP4UndirectedLinkPredictor.cpp000066400000000000000000000203011214146114000302370ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include #include #include "VCP4UndirectedLinkPredictor.h" using std::list; using std::set; using std::pair; using std::make_pair; using std::cout; using std::cerr; const size_t VCP4UndirectedLinkPredictor::map[graphlets] = {0,1,1,2,3,4,5,6,3,5,4,6,7,8,8,9,10,11,11,12,13,14,15,16,13,15,14,16,17,18,18,19,20,21,21,22,23,24,25,26,23,25,24,26,27,28,28,29,30,31,31,32,33,34,35,36,33,35,34,36,37,38,38,39}; VCP4UndirectedLinkPredictor::VCP4UndirectedLinkPredictor( const WeightedNetwork& network ) : LinkPredictor( network ), connectedPairs( 0 ), unconnectedPairs( 0 ) { // compute the total number of somehow-connected pairs in the graph for( vertex_t i = 0; i < network.vertexCount(); ++i ) { neighbor_set_t::const_iterator outIt = network.outNeighbors( i ).begin(); neighbor_set_t::const_iterator outEnd = network.outNeighbors( i ).end(); neighbor_set_t::const_iterator inIt = network.inNeighbors( i ).begin(); neighbor_set_t::const_iterator inEnd = network.inNeighbors( i ).end(); while( outIt != outEnd && outIt->first <= i ) { ++outIt; } while( inIt != inEnd && inIt->first <= i ) { ++inIt; } while( outIt != outEnd && inIt != inEnd ) { ++connectedPairs; if( outIt->first < inIt->first ) { ++outIt; } else if( outIt->first > inIt->first ) { ++inIt; } else { ++outIt; ++inIt; } } while( outIt != outEnd ) { ++connectedPairs; ++outIt; } while( inIt != inEnd ) { ++connectedPairs; ++inIt; } } unsigned long potentialConnections = (unsigned long)network.vertexCount() * (unsigned long)(network.vertexCount() - 1) / 2; unconnectedPairs = potentialConnections - connectedPairs; } VCP4UndirectedLinkPredictor::~VCP4UndirectedLinkPredictor() { } double VCP4UndirectedLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { vertex_t v1 = vertex; vertex_t v2 = neighbor; unsigned long remapped[elements] = {0}; size_t index = 32 * (size_t)network.hasEdge( v2, v1 ); unsigned long vertices = 2; unsigned long connections = 0; unsigned long gaps = 0; // compose ordered list of v3 candidates const neighbor_set_t* v3Neighbors[4] = {&network.inNeighbors( v1 ), &network.outNeighbors( v1 ), &network.inNeighbors( v2 ), &network.outNeighbors( v2 )}; list v3Vertices; for( unsigned int i = 0; i < 4; ++i ) { list::iterator v3VerticesIt = v3Vertices.begin(); list::iterator v3VerticesEnd = v3Vertices.end(); neighbor_set_t::const_iterator neighborsIt = v3Neighbors[i]->begin(); neighbor_set_t::const_iterator neighborsEnd = v3Neighbors[i]->end(); while( v3VerticesIt != v3VerticesEnd && neighborsIt != neighborsEnd ) { if( neighborsIt->first == v1 || neighborsIt->first == v2 ) { ++neighborsIt; continue; } if( *v3VerticesIt < neighborsIt->first ) { ++v3VerticesIt; } else if( neighborsIt->first < *v3VerticesIt ) { v3Vertices.insert( v3VerticesIt, neighborsIt->first ); ++neighborsIt; } else { ++v3VerticesIt; ++neighborsIt; } } while( neighborsIt != neighborsEnd ) { if( neighborsIt->first != v1 && neighborsIt->first != v2 ) { v3Vertices.insert( v3VerticesIt, neighborsIt->first ); } ++neighborsIt; } } // for each v3 vertex in the above list for( list::const_iterator v3It = v3Vertices.begin(); v3It != v3Vertices.end(); ++v3It ) { vertex_t v3 = *v3It; size_t index2 = index; if( network.hasEdge( v1, v3 ) || network.hasEdge( v3, v1 ) ) { index2 += 1; ++connections; } else { ++gaps; } if( network.hasEdge( v2, v3 ) || network.hasEdge( v3, v2 ) ) { index2 += 4; ++connections; } else { ++gaps; } // compose two ordered lists of v4 candidates: those from v3 and those ONLY from the the neighbors of this v3 vertex const neighbor_set_t* v4Neighbors[2] = {&network.inNeighbors( v3 ), &network.outNeighbors( v3 )}; list v4Vertices; for( unsigned int i = 0; i < 2; ++i ) { list::iterator v4VerticesIt = v4Vertices.begin(); list::iterator v4VerticesEnd = v4Vertices.end(); neighbor_set_t::const_iterator neighborsIt = v4Neighbors[i]->begin(); neighbor_set_t::const_iterator neighborsEnd = v4Neighbors[i]->end(); while( v4VerticesIt != v4VerticesEnd && neighborsIt != neighborsEnd ) { if( neighborsIt->first == v1 || neighborsIt->first == v2 || neighborsIt->first == v3 ) { ++neighborsIt; continue; } if( *v4VerticesIt < neighborsIt->first ) { ++v4VerticesIt; } else if( neighborsIt->first < *v4VerticesIt ) { v4Vertices.insert( v4VerticesIt, neighborsIt->first ); ++neighborsIt; } else { ++v4VerticesIt; ++neighborsIt; } } while( neighborsIt != neighborsEnd ) { if( neighborsIt->first != v1 && neighborsIt->first != v2 && neighborsIt->first != v3 ) { v4Vertices.insert( v4VerticesIt, neighborsIt->first ); } ++neighborsIt; } } list::iterator v4VerticesIt = v4Vertices.begin(); list::iterator v4VerticesEnd = v4Vertices.end(); list::const_iterator v3VerticesIt = v3Vertices.begin(); list::const_iterator v3VerticesEnd = v3Vertices.end(); while( v4VerticesIt != v4VerticesEnd && v3VerticesIt != v3VerticesEnd ) { if( *v4VerticesIt < *v3VerticesIt ) { ++v4VerticesIt; } else if( *v3VerticesIt < *v4VerticesIt ) { ++v3VerticesIt; } else { v4VerticesIt = v4Vertices.erase( v4VerticesIt ); } } // for each v4 vertex in the above list of v4 only vertices vertices += v4Vertices.size(); for( list::const_iterator v4It = v4Vertices.begin(); v4It != v4Vertices.end(); ++v4It ) { vertex_t v4 = *v4It; size_t index3 = index2; if( network.hasEdge( v1, v4 ) || network.hasEdge( v4, v1 ) ) { index3 += 2; ++connections; } else { ++gaps; } if( network.hasEdge( v2, v4 ) || network.hasEdge( v4, v2 ) ) { index3 += 8; ++connections; } else { ++gaps; } if( network.hasEdge( v3, v4 ) || network.hasEdge( v4, v3 ) ) { index3 += 16; ++connections; } else { ++gaps; } ++remapped[ map[index3] ]; } // for each other v3 vertex if it has a higher index for( list::const_iterator v4It = v3It; v4It != v3Vertices.end(); ++v4It ) { vertex_t v4 = *v4It; if( v3 < v4 ) { size_t index3 = index2; if( network.hasEdge( v1, v4 ) || network.hasEdge( v4, v1 ) ) { index3 += 2; } if( network.hasEdge( v2, v4 ) || network.hasEdge( v4, v2 ) ) { index3 += 8; } if( network.hasEdge( v3, v4 ) || network.hasEdge( v4, v3 ) ) { index3 += 16; ++connections; } else { ++gaps; } ++remapped[ map[index3] ]; } } // account for triangles and gaps remapped[ map[index2] ] += network.vertexCount() - 2 - (v3Vertices.size() + v4Vertices.size()); remapped[ map[index] ] -= network.vertexCount() - 2 - (v3Vertices.size() + v4Vertices.size()); // go negative for the number of triangles (which contain v3v4 gaps) } vertices += v3Vertices.size(); // account for the least connected substructures remapped[ map[16+index] ] = this->connectedPairs - (connections + index/32); remapped[ map[index] ] += this->unconnectedPairs - (gaps + !(bool)(index/32)) - (2 * (network.vertexCount() - vertices)); // do the remapping and output cerr << network.translateIntToExt( vertex ) << " " << network.translateIntToExt( neighbor ); for( size_t i = 0; i < elements; ++i ) { cerr << " " << remapped[i]; } cerr << "\n"; return -1; } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/VCP4UndirectedLinkPredictor.h000066400000000000000000000025141214146114000277120ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef VCP4UNDIRECTEDLINKPREDICTOR_H #define VCP4UNDIRECTEDLINKPREDICTOR_H #include "../WeightedNetwork.h" #include "LinkPredictor.h" class VCP4UndirectedLinkPredictor : public LinkPredictor { private: const static size_t graphlets = 64; const static size_t map[graphlets]; const static size_t elements = 40; unsigned long connectedPairs; unsigned long unconnectedPairs; protected: public: VCP4UndirectedLinkPredictor( const WeightedNetwork& ); ~VCP4UndirectedLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/WeightedRootedPageRankLinkPredictor.cpp000066400000000000000000000047161214146114000320560ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include "WeightedRootedPageRankLinkPredictor.h" #include "../Statistics.h" WeightedRootedPageRankLinkPredictor::WeightedRootedPageRankLinkPredictor( const WeightedNetwork& network, double alpha ) : LinkPredictor(network), alpha(alpha) { } WeightedRootedPageRankLinkPredictor::~WeightedRootedPageRankLinkPredictor() { } double WeightedRootedPageRankLinkPredictor::generateScore( unsigned int vertex, unsigned int neighbor ) { if( this->vertex != vertex ) { this->vertex = vertex; this->scores = vector( this->network.vertexCount() ); vector oldPageRanks = vector( this->network.vertexCount() ); vertex_t currentVertex = vertex; this->scores.at( currentVertex )++; for( unsigned int step = 1; true; step++ ) { const neighbor_set_t& neighbors = this->network.outNeighbors( currentVertex ); if( neighbors.size() < 1 || double(rand())/RAND_MAX < this->alpha ) { currentVertex = vertex; } else { double threshold = (double(rand())/RAND_MAX) * this->network.outVolume( currentVertex ); double total = 0.0; for( neighbor_set_t::const_iterator it = neighbors.begin(); it != neighbors.end(); ++it ) { total += it->second; if( total >= threshold ) { currentVertex = it->first; break; } } } this->scores.at( currentVertex )++; if( step == 100000 ) { oldPageRanks = this->scores; } else if( step % 100000 == 0 ) { double r = Statistics::sampleCorrelationCoefficient( oldPageRanks, this->scores ); // cerr << r << "\n"; if( r > 0.9999 ) { return this->scores.at( neighbor ); } else { oldPageRanks = this->scores; } } } } return this->scores.at( neighbor ); } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/WeightedRootedPageRankLinkPredictor.h000066400000000000000000000025301214146114000315130ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef WEIGHTEDROOTEDPAGERANKLINKPREDICTOR_H #define WEIGHTEDROOTEDPAGERANKLINKPREDICTOR_H #include #include "../WeightedNetwork.h" #include "LinkPredictor.h" using std::vector; class WeightedRootedPageRankLinkPredictor : public LinkPredictor { private: double alpha; vector scores; WeightedRootedPageRankLinkPredictor( const WeightedNetwork& ); protected: public: WeightedRootedPageRankLinkPredictor( const WeightedNetwork&, double ); ~WeightedRootedPageRankLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/WeightedTriangleLinkPredictor.cpp000066400000000000000000000173731214146114000307610ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include #include "WeightedTriangleLinkPredictor.h" using std::min; using std::cerr; WeightedTriangleLinkPredictor::WeightedTriangleLinkPredictor( const WeightedNetwork& network, int selection = -1 ) : LinkPredictor( network ), selection( selection ) { if( this->selection > -1 ) { return; } weight_t allCounts[graphlets] = {0}; for( vertex_t vertex = 0; vertex < network.vertexCount(); ++vertex ) { const neighbor_set_t& outNeighbors = network.outNeighbors( vertex ); for( neighbor_set_t::const_iterator neighborIterator = outNeighbors.begin(); neighborIterator != outNeighbors.end(); ++neighborIterator ) { const vertex_t neighbor = neighborIterator->first; weight_t vCounts[graphlets] = {0}; this->edgeProfile( vertex, neighbor, vCounts ); for( size_t i = 0; i < graphlets; ++i ) { allCounts[i] += vCounts[i]; } } } for( size_t i = 0; i < graphlets; ++i ) { this->counts[i] = (weight_t)allCounts[i] / network.edgeCount(); } } void WeightedTriangleLinkPredictor::computeDetailedNeighbors( const neighbor_set_t& inNeighbors, const neighbor_set_t& outNeighbors, DetailedNeighbors& result ) const { neighbor_set_t::const_iterator inIt = inNeighbors.begin(); neighbor_set_t::const_iterator outIt = outNeighbors.begin(); neighbor_set_t::const_iterator inEnd = inNeighbors.end(); neighbor_set_t::const_iterator outEnd = outNeighbors.end(); while( inIt != inEnd && outIt != outEnd ) { if( inIt->first < outIt->first ) { result.inOnly.push_back( *inIt ); result.either.push_back( *inIt ); ++inIt; } else if( inIt->first > outIt->first ) { result.outOnly.push_back( *outIt ); result.either.push_back( *outIt ); ++outIt; } else { result.both.push_back( *inIt ); result.either.push_back( *inIt ); ++inIt; ++outIt; } } while( inIt != inEnd ) { result.inOnly.push_back( *inIt ); result.either.push_back( *inIt ); ++inIt; } while( outIt != outEnd ) { result.outOnly.push_back( *outIt ); result.either.push_back( *outIt ); ++outIt; } } void WeightedTriangleLinkPredictor::edgeProfile( vertex_t v1, vertex_t v2, weight_t vCounts[] ) { const neighbor_set_t& v1InNeighbors = network.inNeighbors( v1 ); const neighbor_set_t& v1OutNeighbors = network.outNeighbors( v1 ); const neighbor_set_t& v2InNeighbors = network.inNeighbors( v2 ); const neighbor_set_t& v2OutNeighbors = network.outNeighbors( v2 ); unsigned char reciprocal = (graphlets/2) * (char)network.hasEdge( v2, v1 ); DetailedNeighbors v1DetailedNeighbors; DetailedNeighbors v2DetailedNeighbors; this->computeDetailedNeighbors( v1InNeighbors, v1OutNeighbors, v1DetailedNeighbors ); this->computeDetailedNeighbors( v2InNeighbors, v2OutNeighbors, v2DetailedNeighbors ); weight_t w = reciprocal ? network.edgeWeight( v2, v1 ) : 1; vCounts[0+reciprocal] += min( w, this->intersectionCount( v1DetailedNeighbors.outOnly, v2DetailedNeighbors.inOnly ) ); vCounts[1+reciprocal] += min( w, this->intersectionCount( v1DetailedNeighbors.outOnly, v2DetailedNeighbors.outOnly ) ); vCounts[2+reciprocal] += min( w, this->intersectionCount( v1DetailedNeighbors.outOnly, v2DetailedNeighbors.both ) ); vCounts[3+reciprocal] += min( w, this->intersectionCount( v1DetailedNeighbors.inOnly, v2DetailedNeighbors.inOnly ) ); vCounts[4+reciprocal] += min( w, this->intersectionCount( v1DetailedNeighbors.inOnly, v2DetailedNeighbors.outOnly ) ); vCounts[5+reciprocal] += min( w, this->intersectionCount( v1DetailedNeighbors.inOnly, v2DetailedNeighbors.both ) ); vCounts[6+reciprocal] += min( w, this->intersectionCount( v1DetailedNeighbors.both, v2DetailedNeighbors.inOnly ) ); vCounts[7+reciprocal] += min( w, this->intersectionCount( v1DetailedNeighbors.both, v2DetailedNeighbors.outOnly ) ); vCounts[8+reciprocal] += min( w, this->intersectionCount( v1DetailedNeighbors.both, v2DetailedNeighbors.both ) ); vCounts[9+reciprocal] += min( w, this->differenceCount( v2DetailedNeighbors.inOnly, v1DetailedNeighbors.either ) ); vCounts[10+reciprocal] += min( w, this->differenceCount( v2DetailedNeighbors.outOnly, v1DetailedNeighbors.either ) ); vCounts[11+reciprocal] += min( w, this->differenceCount( v2DetailedNeighbors.both, v1DetailedNeighbors.either ) ); vCounts[12+reciprocal] += min( w, this->differenceCount( v1DetailedNeighbors.inOnly, v2DetailedNeighbors.either ) ); vCounts[13+reciprocal] += min( w, this->differenceCount( v1DetailedNeighbors.outOnly, v2DetailedNeighbors.either ) ); vCounts[14+reciprocal] += min( w, this->differenceCount( v1DetailedNeighbors.both, v2DetailedNeighbors.either ) ); vCounts[15+reciprocal] += min( w, this->network.vertexCount() - this->unionCount( v1DetailedNeighbors.either, v2DetailedNeighbors.either ) - 2 ); } weight_t WeightedTriangleLinkPredictor::intersectionCount( const vector& v1, const vector& v2 ) const { vector::const_iterator first1 = v1.begin(); vector::const_iterator last1 = v1.end(); vector::const_iterator first2 = v2.begin(); vector::const_iterator last2 = v2.end(); weight_t count = 0; while( first1 != last1 && first2 != last2 ) { if( first1->first < first2->first ) { ++first1; } else if( first2->first < first1->first ) { ++first2; } else { count += min( first1->second, first2->second ); ++first1; ++first2; } } return count; } weight_t WeightedTriangleLinkPredictor::differenceCount( const vector& v1, const vector& v2 ) const { vector::const_iterator first1 = v1.begin(); vector::const_iterator last1 = v1.end(); vector::const_iterator first2 = v2.begin(); vector::const_iterator last2 = v2.end(); weight_t count = 0; while( first1 != last1 && first2 != last2 ) { if( first1->first < first2->first ) { count += first1->second; ++first1; } else if( first2->first < first1->first ) { ++first2; } else { ++first1; ++first2; } } return count; } weight_t WeightedTriangleLinkPredictor::unionCount( const vector& v1, const vector& v2 ) const { vector::const_iterator first1 = v1.begin(); vector::const_iterator last1 = v1.end(); vector::const_iterator first2 = v2.begin(); vector::const_iterator last2 = v2.end(); weight_t count = 0; while( first1 != last1 && first2 != last2 ) { if( first1->first < first2->first ) { ++count; ++first1; } else if( first2->first < first1->first ) { ++count; ++first2; } else { ++first1; ++first2; } } return count; } WeightedTriangleLinkPredictor::~WeightedTriangleLinkPredictor() { } double WeightedTriangleLinkPredictor::generateScore( vertex_t vertex, vertex_t neighbor ) { weight_t vCounts[graphlets] = {0}; this->edgeProfile( vertex, neighbor, vCounts ); weight_t result = 0; if( selection > -1 ) { result = vCounts[selection]; } else { for( size_t i = 0; i < graphlets; ++i ) { result += pow( this->counts[i] - (double)vCounts[i], 2 ); } result = 1 / result; } return result; } rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/WeightedTriangleLinkPredictor.h000066400000000000000000000035431214146114000304200ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef WEIGHTEDTRIANGLELINKPREDICTOR_H #define WEIGHTEDTRIANGLELINKPREDICTOR_H #include "../WeightedNetwork.h" #include "LinkPredictor.h" struct DetailedNeighbors; class WeightedTriangleLinkPredictor : public LinkPredictor { private: const static unsigned long graphlets = 32; int selection; weight_t counts[graphlets]; struct DetailedNeighbors { vector inOnly; vector outOnly; vector both; vector either; }; void computeDetailedNeighbors( const neighbor_set_t&, const neighbor_set_t&, DetailedNeighbors& ) const; weight_t intersectionCount( const vector&, const vector& ) const; weight_t differenceCount( const vector&, const vector& ) const; weight_t unionCount( const vector&, const vector& ) const; void edgeProfile( vertex_t, vertex_t, weight_t[] ); protected: public: WeightedTriangleLinkPredictor( const WeightedNetwork&, int ); ~WeightedTriangleLinkPredictor(); double generateScore( vertex_t, vertex_t ); }; #endif rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/mains/000077500000000000000000000000001214146114000234315ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/netlib/LinkPredictor/mains/predict.cpp000066400000000000000000000172351214146114000255770ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include #include #include #include "../AdamicAdarLinkPredictor.h" #include "../ClusteringCoefficientLinkPredictor.h" #include "../CommonNeighborLinkPredictor.h" #include "../DistanceLinkPredictor.h" #include "../IDegreeLinkPredictor.h" #include "../IPageRankLinkPredictor.h" #include "../IVolumeLinkPredictor.h" #include "../JaccardCoefficientLinkPredictor.h" #include "../JDegreeLinkPredictor.h" #include "../JPageRankLinkPredictor.h" #include "../JVolumeLinkPredictor.h" #include "../KatzLinkPredictor.h" #include "../LinkPredictor.h" #include "../MutualityLinkPredictor.h" #include "../OneLinkPredictor.h" #include "../PreferentialAttachmentLinkPredictor.h" #include "../PropFlowLinkPredictor.h" #include "../RootedPageRankLinkPredictor.h" #include "../ShortestPathCountLinkPredictor.h" #include "../SimRankLinkPredictor.h" #include "../UnweightedPropFlowLinkPredictor.h" #include "../VCP3DirectedLinkPredictor.h" #include "../VCP3UndirectedLinkPredictor.h" #include "../VCP4DirectedLinkPredictor.h" #include "../VCP4UndirectedLinkPredictor.h" #include "../WeightedRootedPageRankLinkPredictor.h" #include "../../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; using std::ifstream; using std::ios; using std::ios_base; using std::scientific; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options] predictor\n"; cerr << "Options: -h Print this usage message and exit\n"; cerr << " -d DIRECTION The direction of edges to use; must be in {I,O}\n"; cerr << " -f NETWORK File containing the network in which to predict\n"; cerr << " -n DISTANCE Degree of neighborhood in which to predict\n"; cerr << " -c Specifies that node-pairs for prediction are available at standard input\n"; } int main( int argc, char* argv[] ) { char c; bool specifiedPredictions = false; bool outEdges = true; unsigned int degree = 2; ifstream file; while( (c = getopt( argc, argv, "+hcd:f:n:" )) >= 0 ) { if( c == 'd' ) { if( strcmp( optarg, "I" ) == 0 ) { outEdges = false; } } else if( c == 'f' ) { file.open( optarg, ios::in ); } else if( c == 'n' ) { degree = atoi( optarg ); } else if( c == 'c' ) { specifiedPredictions = true; } else if( c == 'h' ) { usage( argv[0] ); exit(0); } } if( argc < optind + 1 ) { usage( argv[0] ); return 1; } const char* predictorName = argv[optind++]; WeightedNetwork network = WeightedNetwork::readNetwork( file ); if( !outEdges ) { network = network.reverseEdges(); } LinkPredictor* predictor = NULL; if( strcmp( predictorName, "AdamicAdar" ) == 0 ) { predictor = new AdamicAdarLinkPredictor( network ); } else if( strcmp( predictorName, "CommonNeighbor" ) == 0 ) { predictor = new CommonNeighborLinkPredictor( network ); } else if( strcmp( predictorName, "ClusteringCoefficient" ) == 0 ) { predictor = new ClusteringCoefficientLinkPredictor( network ); } else if( strcmp( predictorName, "Distance" ) == 0 ) { unsigned int l = argc > optind ? atoi( argv[optind++] ) : 5; predictor = new DistanceLinkPredictor( network, l ); } else if( strcmp( predictorName, "IDegree" ) == 0 ) { predictor = new IDegreeLinkPredictor( network ); } else if( strcmp( predictorName, "IPageRank" ) == 0 ) { double d = argc > optind ? atof( argv[optind++] ) : 0.85; predictor = new IPageRankLinkPredictor( network, d ); } else if( strcmp( predictorName, "IVolume" ) == 0 ) { predictor = new IVolumeLinkPredictor( network ); } else if( strcmp( predictorName, "JaccardCoefficient" ) == 0 ) { predictor = new JaccardCoefficientLinkPredictor( network ); } else if( strcmp( predictorName, "JDegree" ) == 0 ) { predictor = new JDegreeLinkPredictor( network ); } else if( strcmp( predictorName, "JPageRank" ) == 0 ) { double d = argc > optind ? atof( argv[optind++] ) : 0.85; predictor = new JPageRankLinkPredictor( network, d ); } else if( strcmp( predictorName, "JVolume" ) == 0 ) { predictor = new JVolumeLinkPredictor( network ); } else if( strcmp( predictorName, "Katz" ) == 0 ) { unsigned int l = argc > optind ? atoi( argv[optind++] ) : 5; double beta = argc > optind ? atof( argv[optind++] ) : 0.005; predictor = new KatzLinkPredictor( network, l, beta ); } else if( strcmp( predictorName, "Mutuality" ) == 0 ) { predictor = new MutualityLinkPredictor( network ); } else if( strcmp( predictorName, "One" ) == 0 ) { predictor = new OneLinkPredictor( network ); } else if( strcmp( predictorName, "PreferentialAttachment" ) == 0 ) { predictor = new PreferentialAttachmentLinkPredictor( network ); } else if( strcmp( predictorName, "PropFlow" ) == 0 ) { unsigned int l = argc > optind ? atoi( argv[optind++] ) : 5; predictor = new PropFlowLinkPredictor( network, l ); } else if( strcmp( predictorName, "RootedPageRank" ) == 0 ) { double alpha = argc > optind ? atof( argv[optind++] ) : 0.15; predictor = new RootedPageRankLinkPredictor( network, alpha ); } else if( strcmp( predictorName, "ShortestPathCount" ) == 0 ) { unsigned int l = argc > optind ? atoi( argv[optind++] ) : 5; predictor = new ShortestPathCountLinkPredictor( network, l ); } else if( strcmp( predictorName, "SimRank" ) == 0 ) { double C = argc > optind ? atof( argv[optind++ ] ) : 0.8; predictor = new SimRankLinkPredictor( network, C ); } else if( strcmp( predictorName, "UnweightedPropFlow" ) == 0 ) { unsigned int l = argc > optind ? atoi( argv[optind++] ) : 5; predictor = new UnweightedPropFlowLinkPredictor( network, l ); } else if( strcmp( predictorName, "VCP3Directed" ) == 0 ) { predictor = new VCP3DirectedLinkPredictor( network ); } else if( strcmp( predictorName, "VCP3Undirected" ) == 0 ) { predictor = new VCP3UndirectedLinkPredictor( network ); } else if( strcmp( predictorName, "VCP4Directed" ) == 0 ) { predictor = new VCP4DirectedLinkPredictor( network ); } else if( strcmp( predictorName, "VCP4Undirected" ) == 0 ) { predictor = new VCP4UndirectedLinkPredictor( network ); } else if( strcmp( predictorName, "WeightedRootedPageRank" ) == 0 ) { double alpha = argc > optind ? atof( argv[optind++] ) : 5; predictor = new WeightedRootedPageRankLinkPredictor( network, alpha ); } else { cerr << "invalid link predictor: " << predictorName << "\n"; return 1; } cout.precision( 15 ); if( specifiedPredictions ) { vertex_t vertex; vertex_t neighbor; while( cin >> vertex >> neighbor ) { vertex_t internalVertex = network.translateExtToInt( vertex ); vertex_t internalNeighbor = network.translateExtToInt( neighbor ); if( internalVertex != INVALID_VERTEX && internalNeighbor != INVALID_VERTEX ) { cout << vertex << " " << neighbor << " " << predictor->generateScore( internalVertex, internalNeighbor ); } } } else { if( !outEdges ) { predictor->printInNeighborScores( degree ); } else { predictor->printOutNeighborScores( degree ); } } delete predictor; return 0; } rlichtenwalter-LPmade-ba322c3/netlib/Makefile000066400000000000000000000040451214146114000212140ustar00rootroot00000000000000# ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ CONFIG := BIN ifeq ($(CONFIG),DEBUG) CFLAGS := -Wall -Werror -g BINDIR := debug endif ifeq ($(CONFIG),BIN) CFLAGS := -Wall -Werror -O3 -D NDEBUG BINDIR := bin endif CC := g++ NETWORK_BINS := $(patsubst %.cpp,%,$(notdir $(wildcard ./mains/*.cpp))) all: network predictors cp ./LinkPredictor/$(BINDIR)/* $(BINDIR); network: $(addprefix $(BINDIR)/,$(NETWORK_BINS)) predictors: ./LinkPredictor/$(BINDIR)/predict test: $(patsubst %,./test/%.TEST,$(NETWORK_BINS) $(PREDICTOR_BINS)) $(BINDIR)/%: ./mains/%.o WeightedNetwork.o Statistics.o @mkdir -p $(@D); $(CC) $(CFLAGS) $^ -o $@; ./LinkPredictor/$(BINDIR)/predict: ./LinkPredictor/mains/predict.o $(patsubst %.cpp,%.o,$(wildcard ./LinkPredictor/*.cpp)) WeightedNetwork.o @mkdir -p $(@D); $(CC) $(CFLAGS) $^ -o $@; ./Model/$(BINDIR)/%: ./Model/mains/%.o ./Model/%Model.o WeightedNetwork.o @mkdir -p $(@D); $(CC) $(CFLAGS) $^ -o $@; %.o: %.cpp $(CC) $(CFLAGS) -c $< -o $@; ./test/%.TEST: $(BINDIR)/% @if [ -f ./test/$*.RUN ] ; then \ echo -n 'Testing $*...'; \ ./test/$*.RUN $(BINDIR); \ else \ echo 'Testing $*...TEST UNAVAILABLE'; \ fi .PHONY: clean all test network predictors clean: -rm -rf *.o mains/*.o LinkPredictor/mains/*.o $(BINDIR) LinkPredictor/$(BINDIR) LinkPredictor/*.o ./test/*.OUTPUT rlichtenwalter-LPmade-ba322c3/netlib/Statistics.cpp000066400000000000000000000014711214146114000224120ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include "Statistics.h" rlichtenwalter-LPmade-ba322c3/netlib/Statistics.h000066400000000000000000000106751214146114000220650ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef STATISTICS_H #define STATISTICS_H #include #include using std::vector; template class Statistics { private: static double standardDeviation( const vector& x, double xmean ) { double sum = 0; for( std::size_t i = 0; i < x.size(); i++ ) { sum += pow( x.at(i) - xmean, 2 ); } return sqrt( sum / (x.size()-1) ); } static double standardDeviation( const vector >& x, double xmean ) { double sum = 0; for( std::size_t i = 0; i < x.size(); i++ ) { const vector& row = x.at( i ); for( std::size_t j = 0; j < row.size(); j++ ) { sum += pow( row.at( j ) - xmean, 2 ); } } return sqrt( sum / (Statistics::count( x )-1) ); } protected: public: static std::size_t count( const vector& x ) { return x.size(); } static std::size_t count( const vector >& x ) { std::size_t count = 0; for( std::size_t i = 0; i < x.size(); i++ ) { count += x.at(i).size(); } return count; } static T sum( const vector& x ) { T sum = 0; for( std::size_t i = 0; i < x.size(); i++ ) { sum += x.at( i ); } return sum; } static T sum( const vector >& x ) { T sum = 0; for( std::size_t i = 0; i < x.size(); i++ ) { sum += Statistics::mean( x.at( i ) ); } return sum; } static double mean( const vector& x ){ return (double)Statistics::sum(x) / x.size(); } static double mean( const vector >& x ) { return (double)Statistics::sum(x) / Statistics::count(x); } static double standardDeviation( const vector& x ) { double xmean = Statistics::mean( x ); double sum = 0; for( std::size_t i = 0; i < x.size(); i++ ) { sum += pow( x.at(i) - xmean, 2 ); } return sqrt( sum / (x.size()-1) ); } static double standardDeviation( const vector >& x ) { double xmean = Statistics::mean( x ); double sum = 0; for( std::size_t i = 0; i < x.size(); i++ ) { const vector& row = x.at( i ); for( std::size_t j = 0; j < row.size(); j++ ) { sum += pow( row.at( j ) - xmean, 2 ); } } return sqrt( sum / (Statistics::count( x )-1) ); } static double sampleCorrelationCoefficient( const vector& x, const vector& y ){ if( x.size() != y.size() ) { throw "Statistics::r - vector sizes do not match\n"; } double xmean = Statistics::mean( x ); double ymean = Statistics::mean( y ); double xStdDev = Statistics::standardDeviation( x, xmean ); double yStdDev = Statistics::standardDeviation( y, ymean ); double sum = 0; for( std::size_t i = 0; i < x.size(); i++ ) { sum += (x.at( i )-xmean) * (y.at(i)-ymean); } return sum / ((x.size()-1) * xStdDev * yStdDev); } static double sampleCorrelationCoefficient( const vector >& x, const vector >& y ) { if( Statistics::count(x) != Statistics::count(y) ) { throw "Statistics::r - matrix sizes do not match\n"; } double xmean = Statistics::mean( x ); double ymean = Statistics::mean( y ); double xStdDev = Statistics::standardDeviation( x, xmean ); double yStdDev = Statistics::standardDeviation( y, ymean ); double sum = 0; for( std::size_t i = 0; i < x.size(); i++ ) { const vector& xRow = x.at( i ); const vector& yRow = y.at( i ); for( std::size_t j = 0; j < xRow.size(); j++ ) { sum += (xRow.at( j )-xmean) * (yRow.at( j )-ymean); } } return sum / ((Statistics::count(x)-1) * xStdDev * yStdDev); } static void normalize( vector& x ){ T sum = Statistics::sum( x ); for( std::size_t i = 0; i < x.size(); i++ ) { x.at( i ) /= sum; } } }; #endif rlichtenwalter-LPmade-ba322c3/netlib/WeightedNetwork.cpp000066400000000000000000001464171214146114000234040ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include #include #include #include #include #include #include #include #include #include #include "WeightedNetwork.h" #include "Statistics.h" using std::map; using std::make_pair; using std::runtime_error; using std::cout; using std::queue; using std::stack; using std::cerr; using std::streamsize; using std::numeric_limits; ptrdiff_t randomNumberGenerator (ptrdiff_t i) { return rand()%i; } ptrdiff_t (*randomNumberGeneratorPtr)(ptrdiff_t) = randomNumberGenerator; bool neighborCompare( const neighbor_t& neighbor1, const neighbor_t& neighbor2 ) { return neighbor1.first < neighbor2.first; } void WeightedNetwork::compact() { /* unsigned int capacity = 0; unsigned int size = 0; for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { capacity += sizeof(neighbor_t) * ( this->outList.at( vertex ).capacity() + this->inList.at( vertex ).capacity() ); size += sizeof(neighbor_t) * ( this->outList.at( vertex ).size() + this->inList.at( vertex ).size() ); } cerr << "Old capacity: " << capacity << " - Old size: " << size << "\n"; */ vector tempDictionary; tempDictionary.reserve( this->vertexCount() ); for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { tempDictionary.push_back( this->dictionary.at( vertex ) ); neighbor_set_t& outNeighbors = this->outList.at( vertex ); neighbor_set_t tempOutNeighbors = neighbor_set_t(); tempOutNeighbors.reserve( outNeighbors.size() ); for( neighbor_set_t::const_iterator neighborIterator = outNeighbors.begin(); neighborIterator != outNeighbors.end(); neighborIterator++ ) { tempOutNeighbors.push_back( *neighborIterator ); } outNeighbors.swap( tempOutNeighbors ); neighbor_set_t& inNeighbors = this->inList.at( vertex ); neighbor_set_t tempInNeighbors = neighbor_set_t(); tempInNeighbors.reserve( inNeighbors.size() ); for( neighbor_set_t::const_iterator neighborIterator = inNeighbors.begin(); neighborIterator != inNeighbors.end(); neighborIterator++ ) { tempInNeighbors.push_back( *neighborIterator ); } inNeighbors.swap( tempInNeighbors ); } this->dictionary.swap( tempDictionary ); /* capacity = 0; size = 0; for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { capacity += sizeof(neighbor_t) * ( this->outList.at( vertex ).capacity() + this->inList.at( vertex ).capacity() ); size += sizeof(neighbor_t) * ( this->outList.at( vertex ).size() + this->inList.at( vertex ).size() ); } cerr << "New capacity: " << capacity << " - New size: " << size << "\n"; */ } inline void WeightedNetwork::addEdge( vertex_t vertex, vertex_t neighbor, weight_t weight ) { assert( this->outList.at( vertex ).size() == 0 || this->outList.at( vertex ).back().first < neighbor ); assert( this->inList.at( neighbor ).size() == 0 || this->inList.at( neighbor ).back().first < vertex ); this->outList.at( vertex ).push_back( make_pair( neighbor, weight ) ); this->inList.at( neighbor ).push_back( make_pair( vertex, weight ) ); this->numEdges++; } neighbor_set_t WeightedNetwork::intersect( const neighbor_set_t& set1, const neighbor_set_t& set2 ) { neighbor_set_t::const_iterator it1 = set1.begin(); neighbor_set_t::const_iterator it2 = set2.begin(); neighbor_set_t result = neighbor_set_t(); while( it1 != set1.end() && it2 != set2.end() ) { vertex_t v1 = it1->first; vertex_t v2 = it2->first; if( v1 < v2 ) { ++it1; } else if( v1 > v2 ) { ++it2; } else { result.push_back( *it1 ); ++it1; ++it2; } } return result; } WeightedNetwork::WeightedNetwork() : dictionary(), outList(), inList(), numEdges(0) { } WeightedNetwork::WeightedNetwork( const WeightedNetwork& network ) : dictionary(network.dictionary), outList(network.outList), inList(network.inList), numEdges(network.numEdges) { } WeightedNetwork::~WeightedNetwork() { } WeightedNetwork& WeightedNetwork::operator=( const WeightedNetwork& network ) { this->dictionary = network.dictionary; this->outList = network.outList; this->inList = network.inList; this->numEdges = network.numEdges; return *this; } WeightedNetwork WeightedNetwork::readStream( istream& is, const char separator, unsigned int iIndex, unsigned int jIndex, bool ignoreSelfEdges ) { iIndex--; jIndex--; unsigned int maxIndex = jIndex; if( iIndex > jIndex ) { maxIndex = iIndex; } map > tempNetwork = map >(); while( !is.eof() ) { vertex_t vertex = 0; vertex_t neighbor = 0; for( unsigned int index = 0; index <= maxIndex; index++ ) { if( index == iIndex ) { is >> vertex; } else if( index == jIndex ) { is >> neighbor; } if( index < maxIndex ) { is.ignore( numeric_limits::max(), separator ); } } is.ignore( numeric_limits::max(), '\n' ); is.peek(); pair >::iterator,bool> infoPair = tempNetwork.insert( make_pair( vertex, map() ) ); map >::iterator vertexLocation = infoPair.first; infoPair = tempNetwork.insert( make_pair( neighbor, map() ) ); // map >::iterator neighborLocation = infoPair.first; if( vertex != neighbor || !ignoreSelfEdges ) { pair::iterator,bool> neighborInfoPair = vertexLocation->second.insert( make_pair( neighbor, 0 ) ); neighborInfoPair.first->second++; } } WeightedNetwork network; network.dictionary.reserve( tempNetwork.size() ); network.outList = vertex_set_t( tempNetwork.size() ); network.inList = vertex_set_t( tempNetwork.size() ); for( map >::const_iterator tempIt = tempNetwork.begin(); tempIt != tempNetwork.end(); tempIt++ ) { vertex_t name = tempIt->first; network.dictionary.push_back( name ); } vertex_t vertex = 0; for( map >::const_iterator tempIt = tempNetwork.begin(); tempIt != tempNetwork.end(); tempIt++, vertex++ ) { const map& neighborMap = tempIt->second; for( map::const_iterator neighborIterator = neighborMap.begin(); neighborIterator != neighborMap.end(); neighborIterator++ ) { vertex_t neighbor = neighborIterator->first; weight_t weight = neighborIterator->second; network.addEdge( vertex, network.translateExtToInt( neighbor ), weight ); } } network.compact(); return network; } WeightedNetwork WeightedNetwork::readWeightedStream( istream& is, const char separator, unsigned int iIndex, unsigned int jIndex, unsigned int wIndex, bool ignoreSelfEdges ) { iIndex--; jIndex--; wIndex--; unsigned int maxIndex = iIndex; maxIndex = std::max( maxIndex, jIndex ); maxIndex = std::max( maxIndex, wIndex ); map > tempNetwork = map >(); while( !is.eof() ) { vertex_t vertex = 0; vertex_t neighbor = 0; weight_t weight = 0; for( unsigned int index = 0; index <= maxIndex; index++ ) { if( index == iIndex ) { is >> vertex; } else if( index == jIndex ) { is >> neighbor; } else if( index == wIndex ) { is >> weight; } if( index < maxIndex ) { is.ignore( numeric_limits::max(), separator ); } } is.ignore( numeric_limits::max(), '\n' ); is.peek(); pair >::iterator,bool> infoPair = tempNetwork.insert( make_pair( vertex, map() ) ); map >::iterator vertexLocation = infoPair.first; infoPair = tempNetwork.insert( make_pair( neighbor, map() ) ); // map >::iterator neighborLocation = infoPair.first; if( vertex != neighbor || !ignoreSelfEdges ) { pair::iterator,bool> neighborInfoPair = vertexLocation->second.insert( make_pair( neighbor, 0 ) ); neighborInfoPair.first->second += weight; } } WeightedNetwork network; network.dictionary.reserve( tempNetwork.size() ); network.outList = vertex_set_t( tempNetwork.size() ); network.inList = vertex_set_t( tempNetwork.size() ); for( map >::const_iterator tempIt = tempNetwork.begin(); tempIt != tempNetwork.end(); tempIt++ ) { vertex_t name = tempIt->first; network.dictionary.push_back( name ); } vertex_t vertex = 0; for( map >::const_iterator tempIt = tempNetwork.begin(); tempIt != tempNetwork.end(); tempIt++, vertex++ ) { const map& neighborMap = tempIt->second; for( map::const_iterator neighborIterator = neighborMap.begin(); neighborIterator != neighborMap.end(); neighborIterator++ ) { vertex_t neighbor = neighborIterator->first; weight_t weight = neighborIterator->second; network.addEdge( vertex, network.translateExtToInt( neighbor ), weight ); } } network.compact(); return network; } WeightedNetwork WeightedNetwork::readNetwork( istream& is ) { WeightedNetwork network = WeightedNetwork(); is.ignore( numeric_limits::max(), ' ' ); vertex_t vertexCount = 0; is >> vertexCount; network.dictionary.reserve( vertexCount ); network.outList = vertex_set_t( vertexCount ); network.inList = vertex_set_t( vertexCount ); for( vertex_t vertex = 0; vertex < vertexCount; vertex++ ) { vertex_t index; vertex_t name; is >> index >> name; network.dictionary.push_back( name ); } is.ignore( numeric_limits::max(), ' ' ); unsigned int edgeCount = 0; is >> edgeCount; for( unsigned int edge = 0; edge < edgeCount; edge++ ) { vertex_t index1; vertex_t index2; weight_t weight; is >> index1 >> index2 >> weight; network.addEdge( index1, index2, weight ); } network.compact(); return network; } WeightedNetwork WeightedNetwork::combine( const WeightedNetwork& network1, const WeightedNetwork& network2 ) { WeightedNetwork network = WeightedNetwork(); // handle joining dictionaries vector::const_iterator it1 = network1.dictionary.begin(); vector::const_iterator it2 = network2.dictionary.begin(); while( it1 != network1.dictionary.end() || it2 != network2.dictionary.end() ) { if( it1 == network1.dictionary.end() ) { network.dictionary.push_back( *it2 ); it2++; } else if( it2 == network2.dictionary.end() ) { network.dictionary.push_back( *it1 ); it1++; } else if( *it1 > *it2 ) { network.dictionary.push_back( *it2 ); it2++; } else if( *it1 < *it2 ) { network.dictionary.push_back( *it1 ); it1++; } else { network.dictionary.push_back( *it1 ); it1++; it2++; } } // handle joining lists network.outList = vertex_set_t( network.dictionary.size() ); network.inList = vertex_set_t( network.dictionary.size() ); for( vertex_t vertex = 0; vertex < network.dictionary.size(); vertex++ ) { vertex_t name = network.translateIntToExt( vertex ); vertex_t index1 = network1.translateExtToInt( name ); vertex_t index2 = network2.translateExtToInt( name ); if( index1 == INVALID_VERTEX ) { const neighbor_set_t& outNeighbors2 = network2.outList.at( index2 ); for( neighbor_set_t::const_iterator neighborIterator = outNeighbors2.begin(); neighborIterator != outNeighbors2.end(); neighborIterator++ ) { network.addEdge( vertex, network.translateExtToInt( network2.translateIntToExt( neighborIterator->first ) ), neighborIterator->second ); } } else if( index2 == INVALID_VERTEX ) { const neighbor_set_t& outNeighbors1 = network1.outList.at( index1 ); for( neighbor_set_t::const_iterator neighborIterator = outNeighbors1.begin(); neighborIterator != outNeighbors1.end(); neighborIterator++ ) { network.addEdge( vertex, network.translateExtToInt( network1.translateIntToExt( neighborIterator->first ) ), neighborIterator->second ); } } else { const neighbor_set_t& outNeighbors1 = network1.outList.at( index1 ); const neighbor_set_t& outNeighbors2 = network2.outList.at( index2 ); neighbor_set_t::const_iterator it1 = outNeighbors1.begin(); neighbor_set_t::const_iterator it2 = outNeighbors2.begin(); while( it1 != outNeighbors1.end() || it2 != outNeighbors2.end() ) { vertex_t neighborName1 = 0; vertex_t neighborName2 = 0; if( it1 != outNeighbors1.end() ) { neighborName1 = network1.translateIntToExt( it1->first ); } if( it2 != outNeighbors2.end() ) { neighborName2 = network2.translateIntToExt( it2->first ); } if( it1 == outNeighbors1.end() ) { network.addEdge( vertex, network.translateExtToInt( neighborName2 ), it2->second ); it2++; } else if( it2 == outNeighbors2.end() ) { network.addEdge( vertex, network.translateExtToInt( neighborName1 ), it1->second ); it1++; } else if( neighborName1 > neighborName2 ) { network.addEdge( vertex, network.translateExtToInt( neighborName2 ), it2->second ); it2++; } else if( neighborName1 < neighborName2 ) { network.addEdge( vertex, network.translateExtToInt( neighborName1 ), it1->second ); it1++; } else { network.addEdge( vertex, network.translateExtToInt( neighborName1 ), it1->second + it2->second ); it1++; it2++; } } } } network.compact(); return network; } neighbor_set_t WeightedNetwork::commonOutNeighbors( vertex_t vertex, vertex_t neighbor ) const { const neighbor_set_t& vertexNeighbors = this->outNeighbors( vertex ); const neighbor_set_t& neighborNeighbors = this->outNeighbors( neighbor ); return WeightedNetwork::intersect( vertexNeighbors, neighborNeighbors ); } neighbor_set_t WeightedNetwork::commonInNeighbors( vertex_t vertex, vertex_t neighbor ) const { const neighbor_set_t& vertexNeighbors = this->inNeighbors( vertex ); const neighbor_set_t& neighborNeighbors = this->inNeighbors( neighbor ); return WeightedNetwork::intersect( vertexNeighbors, neighborNeighbors ); } void WeightedNetwork::print( ostream& os ) const { os << "*Vertices " << this->vertexCount() << "\n"; for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { os << vertex << " " << this->dictionary.at( vertex ) << "\n"; } os << "*Edges " << this->edgeCount() << "\n"; for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { const neighbor_set_t& outNeighbors = this->outList.at( vertex ); for( neighbor_set_t::const_iterator neighborIterator = outNeighbors.begin(); neighborIterator != outNeighbors.end(); neighborIterator++ ) { vertex_t neighbor = neighborIterator->first; weight_t weight = neighborIterator->second; os << vertex << " " << neighbor << " " << weight << "\n"; } } } void WeightedNetwork::printEdgeList( ostream& os ) const { for( vertex_t vertex = 0; vertex < this->vertexCount(); ++vertex ) { const neighbor_set_t& outNeighbors = this->outList.at( vertex ); for( neighbor_set_t::const_iterator neighborIterator = outNeighbors.begin(); neighborIterator != outNeighbors.end(); ++neighborIterator ) { os << this->translateIntToExt( vertex ) << " " << this->translateIntToExt( neighborIterator->first ) << "\n"; } } } void WeightedNetwork::printAdjacencyList( ostream& os ) const { for( vertex_t vertex = 0; vertex < this->vertexCount(); ++vertex ) { os << this->translateIntToExt( vertex ); const neighbor_set_t& outNeighbors = this->outList.at( vertex ); for( neighbor_set_t::const_iterator neighborIterator = outNeighbors.begin(); neighborIterator != outNeighbors.end(); ++neighborIterator ) { os << " " << this->translateIntToExt( neighborIterator->first ) << "," << neighborIterator->second; } os << "\n"; } } unsigned int WeightedNetwork::vertexCount() const { return this->dictionary.size(); } unsigned int WeightedNetwork::edgeCount() const { return this->numEdges; } const neighbor_set_t& WeightedNetwork::outNeighbors( vertex_t vertex ) const { return this->outList.at( vertex ); } const neighbor_set_t& WeightedNetwork::inNeighbors( vertex_t vertex ) const { return this->inList.at( vertex ); } const vector WeightedNetwork::findOutNeighbors( vertex_t vertex, unsigned int degree ) const { vector found = vector( this->vertexCount() ); vector* oldSearch = new vector(); vector* newSearch = new vector(); found.at( vertex ) = true; newSearch->push_back( vertex ); for( unsigned int currentDegree = 0; currentDegree < degree; currentDegree++ ) { delete oldSearch; oldSearch = newSearch; newSearch = new vector(); for( vector::const_iterator searchIterator = oldSearch->begin(); searchIterator != oldSearch->end(); searchIterator++ ) { const vertex_t searchVertex = *searchIterator; const neighbor_set_t& neighbors = this->outList.at( searchVertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); neighborIterator++ ) { const vertex_t neighborVertex = neighborIterator->first; if( !found.at( neighborVertex ) ) { found.at( neighborVertex ) = true; newSearch->push_back( neighborVertex ); } } } } vector results = vector( newSearch->size() ); for( unsigned int searchVertex = 0; searchVertex < newSearch->size(); searchVertex++ ) { results.at( searchVertex ) = newSearch->at( searchVertex ); } delete newSearch; delete oldSearch; return results; } const vector WeightedNetwork::findInNeighbors( vertex_t vertex, unsigned int degree ) const { vector found = vector( this->vertexCount() ); vector* oldSearch = new vector(); vector* newSearch = new vector(); found.at( vertex ) = true; newSearch->push_back( vertex ); for( unsigned int currentDegree = 0; currentDegree < degree; currentDegree++ ) { delete oldSearch; oldSearch = newSearch; newSearch = new vector(); for( vector::const_iterator searchIterator = oldSearch->begin(); searchIterator != oldSearch->end(); searchIterator++ ) { const vertex_t searchVertex = *searchIterator; const neighbor_set_t& neighbors = this->inList.at( searchVertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); neighborIterator++ ) { const vertex_t neighborVertex = neighborIterator->first; if( !found.at( neighborVertex ) ) { found.at( neighborVertex ) = true; newSearch->push_back( neighborVertex ); } } } } vector results = vector( newSearch->size() ); for( unsigned int searchVertex = 0; searchVertex < newSearch->size(); searchVertex++ ) { results.at( searchVertex ) = newSearch->at( searchVertex ); } delete newSearch; delete oldSearch; return results; } WeightedNetwork WeightedNetwork::thresholdEdges( weight_t threshold ) const { WeightedNetwork network = WeightedNetwork(); network.dictionary = this->dictionary; network.outList = vertex_set_t( this->vertexCount() ); network.inList = vertex_set_t( this->vertexCount() ); for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { const neighbor_set_t& outNeighbors = this->outList.at( vertex ); for( neighbor_set_t::const_iterator neighborIterator = outNeighbors.begin(); neighborIterator != outNeighbors.end(); neighborIterator++ ) { const vertex_t outNeighbor = neighborIterator->first; const weight_t weight = neighborIterator->second; if( weight >= threshold ) { network.addEdge( vertex, outNeighbor, weight ); } } } network.compact(); return network; } WeightedNetwork WeightedNetwork::snowballSample( vertex_t vertex, unsigned int degree ) const { WeightedNetwork network; network.dictionary = this->dictionary; network.outList = vertex_set_t( this->vertexCount() ); network.inList = vertex_set_t( this->vertexCount() ); vector found( this->vertexCount() ); vector search; found.at( vertex ) = true; search.push_back( vertex ); for( unsigned int currentDegree = 0; currentDegree < degree; currentDegree++ ) { vector newSearch; for( vector::const_iterator vertexIterator = search.begin(); vertexIterator != search.end(); vertexIterator++ ) { const vertex_t searchVertex = *vertexIterator; const neighbor_set_t& neighbors = this->outList.at( searchVertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); neighborIterator++ ) { const vertex_t neighbor = neighborIterator->first; const weight_t weight = neighborIterator->second; network.addEdge( searchVertex, neighbor, weight ); if( !found.at( neighbor ) ) { found.at( neighbor ) = true; newSearch.push_back( neighbor ); } } } search.swap( newSearch ); } network.compact(); return network; } WeightedNetwork WeightedNetwork::snowballSampleIn( vertex_t vertex, unsigned int degree ) const { WeightedNetwork network; network.dictionary = this->dictionary; network.outList = vertex_set_t( this->vertexCount() ); network.inList = vertex_set_t( this->vertexCount() ); vector found( this->vertexCount() ); vector search; found.at( vertex ) = true; search.push_back( vertex ); for( unsigned int currentDegree = 0; currentDegree < degree; currentDegree++ ) { vector newSearch; for( vector::const_iterator vertexIterator = search.begin(); vertexIterator != search.end(); vertexIterator++ ) { const vertex_t searchVertex = *vertexIterator; const neighbor_set_t& neighbors = this->inList.at( searchVertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); neighborIterator++ ) { const vertex_t neighbor = neighborIterator->first; const weight_t weight = neighborIterator->second; network.addEdge( neighbor, searchVertex, weight ); if( !found.at( neighbor ) ) { found.at( neighbor ) = true; newSearch.push_back( neighbor ); } } } search.swap( newSearch ); } network.compact(); return network; } WeightedNetwork WeightedNetwork::randomEdgeSample( double percentEdges, unsigned int seed ) const { WeightedNetwork network = WeightedNetwork(); network.dictionary = this->dictionary; network.outList = vertex_set_t( this->vertexCount() ); network.inList = vertex_set_t( this->vertexCount() ); network.numEdges = 0; vector edgeIndices = vector(); edgeIndices.reserve( this->numEdges ); for( unsigned int index = 0; index < this->edgeCount(); index++ ) { edgeIndices.push_back( index ); } srand( seed ); random_shuffle( edgeIndices.begin(), edgeIndices.end(), randomNumberGeneratorPtr ); percentEdges = (100 - percentEdges) / 100.0; unsigned int deletionCount = (unsigned int)(percentEdges*this->numEdges); edgeIndices.resize( deletionCount ); sort( edgeIndices.begin(), edgeIndices.end() ); unsigned int edgeIndex = 0; unsigned int deletionIndex = 0; for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { const neighbor_set_t& neighbors = this->outList.at( vertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); neighborIterator++ ) { if( deletionIndex < edgeIndices.size() && edgeIndex == edgeIndices.at(deletionIndex) ) { deletionIndex++; } else { const vertex_t neighbor = neighborIterator->first; const weight_t weight = neighborIterator->second; network.addEdge( vertex, neighbor, weight ); } edgeIndex++; } } network.compact(); return network; } WeightedNetwork WeightedNetwork::removeIsolates() const { WeightedNetwork network = WeightedNetwork(); for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { if( this->outDegree( vertex ) > 0 || this->inDegree( vertex ) > 0 ) { network.dictionary.push_back( this->translateIntToExt( vertex ) ); } } // network.vertexCount() is now safely up to date since the dictionary has been populated network.outList = vertex_set_t( network.vertexCount() ); network.inList = vertex_set_t( network.vertexCount() ); for( vertex_t vertex = 0; vertex < network.vertexCount(); vertex++ ) { const vertex_t name = this->translateIntToExt( vertex ); const vertex_t oldIndex = this->translateExtToInt( name ); const neighbor_set_t& outNeighbors = this->outList.at( oldIndex ); for( neighbor_set_t::const_iterator neighborIterator = outNeighbors.begin(); neighborIterator != outNeighbors.end(); neighborIterator++ ) { const vertex_t outNeighbor = neighborIterator->first; const weight_t weight = neighborIterator->second; network.addEdge( vertex, network.translateExtToInt( this->translateIntToExt( outNeighbor ) ), weight ); } } network.compact(); return network; } WeightedNetwork WeightedNetwork::undirected() const { WeightedNetwork network = WeightedNetwork(); network.dictionary = this->dictionary; network.outList = vertex_set_t( this->vertexCount() ); network.inList = vertex_set_t( this->vertexCount() ); network.numEdges = 0; for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { const neighbor_set_t& neighbors = this->outList.at( vertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); neighborIterator++ ) { const vertex_t neighbor = neighborIterator->first; const weight_t weight = neighborIterator->second; network.addEdge( vertex, neighbor, weight ); if( !this->hasEdge( neighbor, vertex ) ) { network.addEdge( neighbor, vertex, weight ); } } } network.compact(); return network; } WeightedNetwork WeightedNetwork::reverseEdges() const { WeightedNetwork network = WeightedNetwork(); network.dictionary = this->dictionary; network.outList = this->inList; network.inList = this->outList; network.numEdges = this->numEdges; return network; } WeightedNetwork WeightedNetwork::removeEdges( const WeightedNetwork& network ) const { WeightedNetwork newNetwork; newNetwork.dictionary = this->dictionary; newNetwork.outList = vertex_set_t( this->vertexCount() ); newNetwork.inList = vertex_set_t( this->vertexCount() ); newNetwork.numEdges = 0; for( vertex_t vertex = 0; vertex < this->vertexCount(); ++vertex ) { const neighbor_set_t& neighbors = this->outList.at( vertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); ++neighborIterator ) { const vertex_t neighbor = neighborIterator->first; const weight_t weight = neighborIterator->second; if( !network.hasEdgeExt( this->translateIntToExt( vertex ), this->translateIntToExt( neighbor ) ) ) { newNetwork.addEdge( vertex, neighbor, weight ); } } } newNetwork.compact(); return newNetwork; } WeightedNetwork WeightedNetwork::removeEdges( const vector& edgesToRemove ) const { vector edgeList = edgesToRemove; sort( edgeList.begin(), edgeList.end() ); WeightedNetwork network; network.dictionary = this->dictionary; network.outList = vertex_set_t( this->vertexCount() ); network.inList = vertex_set_t( this->vertexCount() ); network.numEdges = 0; for( vertex_t vertex = 0; vertex < this->vertexCount(); ++vertex ) { const neighbor_set_t& neighbors = this->outList.at( vertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); ++neighborIterator ) { const vertex_t neighbor = neighborIterator->first; const weight_t weight = neighborIterator->second; if( !binary_search( edgeList.begin(), edgeList.end(), make_pair( vertex, neighbor ) ) ) { network.addEdge( vertex, neighbor, weight ); } } } network.compact(); return network; } WeightedNetwork WeightedNetwork::removeAmutuals() const { WeightedNetwork network = WeightedNetwork(); network.dictionary = this->dictionary; network.outList = vertex_set_t( this->vertexCount() ); network.inList = vertex_set_t( this->vertexCount() ); network.numEdges = 0; for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { const neighbor_set_t& neighbors = this->outList.at( vertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); neighborIterator++ ) { const vertex_t neighbor = neighborIterator->first; const weight_t weight = neighborIterator->second; if( this->hasEdge( neighbor, vertex ) ) { network.addEdge( vertex, neighbor, weight ); } } } network.compact(); return network; } bool WeightedNetwork::hasEdge( vertex_t vertex1, vertex_t vertex2 ) const { neighbor_t neighbor = make_pair( vertex2, 0 ); const neighbor_set_t& neighbors = this->outList.at( vertex1 ); return binary_search( neighbors.begin(), neighbors.end(), neighbor, neighborCompare ); } weight_t WeightedNetwork::edgeWeight( vertex_t vertex1, vertex_t vertex2 ) const { neighbor_t neighbor = make_pair( vertex2, 0 ); const neighbor_set_t& neighbors = this->outList.at( vertex1 ); neighbor_set_t::const_iterator it = lower_bound( neighbors.begin(), neighbors.end(), neighbor ); return it == neighbors.end() ? 0 : it->second; } vertex_t WeightedNetwork::maxOutDegreeVertex() const { vertex_t highestVertex = 0; unsigned int highestDegree = 0; for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { unsigned int vertexDegree = this->outDegree( vertex ); if( vertexDegree > highestDegree ) { highestDegree = vertexDegree; highestVertex = vertex; } } return highestVertex; } vertex_t WeightedNetwork::translateExtToInt( vertex_t vertex ) const { vector::const_iterator it = lower_bound( this->dictionary.begin(), this->dictionary.end(), vertex ); if( it == this->dictionary.end() || *it != vertex ) { return INVALID_VERTEX; } else { return it - this->dictionary.begin(); } } vertex_t WeightedNetwork::translateIntToExt( vertex_t vertex ) const { return this->dictionary.at( vertex ); } bool WeightedNetwork::hasEdgeExt( vertex_t vertex1, vertex_t vertex2 ) const { vertex_t vertex = 0; vertex_t neighbor = 0; vector::const_iterator it = lower_bound( this->dictionary.begin(), this->dictionary.end(), vertex1 ); if( it == this->dictionary.end() || *it != vertex1 ) { return false; } else { vertex = it - this->dictionary.begin(); } it = lower_bound( this->dictionary.begin(), this->dictionary.end(), vertex2 ); if( it == this->dictionary.end() || *it != vertex2 ) { return false; } else { neighbor = it - this->dictionary.begin(); } return this->hasEdge( vertex, neighbor ); } weight_t WeightedNetwork::edgeWeightExt( vertex_t vertex1, vertex_t vertex2 ) const { vertex_t vertex = 0; vertex_t neighbor = 0; vector::const_iterator it = lower_bound( this->dictionary.begin(), this->dictionary.end(), vertex1 ); if( it == this->dictionary.end() || *it != vertex1 ) { return false; } else { vertex = it - this->dictionary.begin(); } it = lower_bound( this->dictionary.begin(), this->dictionary.end(), vertex2 ); if( it == this->dictionary.end() || *it != vertex2 ) { return false; } else { neighbor = it - this->dictionary.begin(); } return this->edgeWeight( vertex, neighbor ); } vector WeightedNetwork::wccs() const { vector wccs = vector(); vector found = vector( this->vertexCount() ); for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { if( !found.at( vertex ) ) { vector vertexList; found.at( vertex ) = true; queue search; search.push( vertex ); while( !search.empty() ) { const vertex_t searchVertex = search.front(); search.pop(); vertexList.push_back( searchVertex ); const neighbor_set_t& outNeighbors = this->outList.at( searchVertex ); for( neighbor_set_t::const_iterator neighborIterator = outNeighbors.begin(); neighborIterator != outNeighbors.end(); neighborIterator++ ) { const vertex_t neighborVertex = neighborIterator->first; if( !found.at( neighborVertex ) ) { found.at( neighborVertex ) = true; search.push( neighborVertex ); } } const neighbor_set_t& inNeighbors = this->inList.at( searchVertex ); for( neighbor_set_t::const_iterator neighborIterator = inNeighbors.begin(); neighborIterator != inNeighbors.end(); neighborIterator++ ) { const vertex_t neighborVertex = neighborIterator->first; if( !found.at( neighborVertex ) ) { found.at( neighborVertex ) = true; search.push( neighborVertex ); } } } WeightedNetwork wcc = WeightedNetwork(); sort( vertexList.begin(), vertexList.end() ); wcc.dictionary.reserve( vertexList.size() ); for( vertex_t vListIndex = 0; vListIndex < vertexList.size(); vListIndex++ ) { wcc.dictionary.push_back( this->translateIntToExt( vertexList.at( vListIndex ) ) ); } wcc.outList = vertex_set_t( vertexList.size() ); wcc.inList = vertex_set_t( vertexList.size() ); for( vertex_t vListIndex = 0; vListIndex < vertexList.size(); vListIndex++ ) { const neighbor_set_t& outNeighbors = this->outList.at( vertexList.at( vListIndex ) ); for( neighbor_set_t::const_iterator neighborIterator = outNeighbors.begin(); neighborIterator != outNeighbors.end(); neighborIterator++ ) { const vertex_t neighbor = neighborIterator->first; const weight_t weight = neighborIterator->second; wcc.addEdge( vListIndex, wcc.translateExtToInt( this->translateIntToExt( neighbor ) ), weight ); } } wcc.compact(); wccs.push_back( wcc ); } } return wccs; } vector WeightedNetwork::sccs() const { vector sccs = vector(); unsigned int index = 1; stack searchStack; vector onStack = vector( this->vertexCount() ); vector neighborIndices = vector( this->vertexCount() ); vector indices = vector( this->vertexCount() ); vector lowlinks = vector( this->vertexCount() ); vector callers = vector( this->vertexCount() ); for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { if( indices.at( vertex ) == 0 ) { // not found indices.at( vertex ) = index; lowlinks.at( vertex ) = index; index++; callers.at( vertex ) = INVALID_VERTEX; searchStack.push( vertex ); onStack.at( vertex ) = true; vertex_t searchVertex = vertex; while( true ) { unsigned int& neighborIndex = neighborIndices.at( searchVertex ); if( neighborIndex < this->outDegree( searchVertex ) ) { const vertex_t neighbor = this->outList.at( searchVertex ).at( neighborIndex ).first; neighborIndex++; if( indices.at( neighbor ) == 0 ) { // not found indices.at( neighbor ) = index; lowlinks.at( neighbor ) = index; index++; callers.at( neighbor ) = searchVertex; searchStack.push( neighbor ); onStack.at( neighbor ) = true; searchVertex = neighbor; } else if( onStack.at( neighbor ) == true ) { lowlinks.at( searchVertex ) = std::min( lowlinks.at( searchVertex ), indices.at( neighbor ) ); } } else { if( lowlinks.at( searchVertex ) == indices.at( searchVertex ) ) { vector vertexList; vertex_t sccMember = searchStack.top(); vertexList.push_back( sccMember ); onStack.at( sccMember ) = false; searchStack.pop(); while( sccMember != searchVertex ) { sccMember = searchStack.top(); vertexList.push_back( sccMember ); onStack.at( sccMember ) = false; searchStack.pop(); } WeightedNetwork scc = WeightedNetwork(); sort( vertexList.begin(), vertexList.end() ); scc.dictionary.reserve( vertexList.size() ); for( vertex_t vListIndex = 0; vListIndex < vertexList.size(); vListIndex++ ) { scc.dictionary.push_back( this->translateIntToExt( vertexList.at( vListIndex ) ) ); } scc.outList = vertex_set_t( vertexList.size() ); scc.inList = vertex_set_t( vertexList.size() ); for( vertex_t vListIndex = 0; vListIndex < vertexList.size(); vListIndex++ ) { const neighbor_set_t& outNeighbors = this->outList.at( vertexList.at( vListIndex ) ); for( neighbor_set_t::const_iterator neighborIterator = outNeighbors.begin(); neighborIterator != outNeighbors.end(); neighborIterator++ ) { const vertex_t neighbor = neighborIterator->first; const weight_t weight = neighborIterator->second; if( binary_search( vertexList.begin(), vertexList.end(), neighbor ) ) { scc.addEdge( vListIndex, scc.translateExtToInt( this->translateIntToExt( neighbor ) ), weight ); } } } scc.compact(); sccs.push_back( scc ); } vertex_t caller = callers.at( searchVertex ); if( caller != INVALID_VERTEX ) { lowlinks.at( caller ) = std::min( lowlinks.at( caller ), lowlinks.at( searchVertex ) ); searchVertex = caller; } else { break; } } } } } return sccs; } unsigned int WeightedNetwork::pseudoDiameter() const { vertex_t root = this->maxOutDegreeVertex(); vertex_t farthest = root; int distance = -1; int lastDistance = -2; while( distance > lastDistance ) { lastDistance = distance; distance = -1; root = farthest; vector found = vector( this->vertexCount() ); found.at( root ) = true; vector oldSearch; oldSearch.push_back( root ); while( !oldSearch.empty() ) { ++distance; // distance should start at 0; imagine if the wcc has only one node vector newSearch; for( vector::const_iterator vertexIterator = oldSearch.begin(); vertexIterator != oldSearch.end(); ++vertexIterator ) { const vertex_t searchVertex = *vertexIterator; const neighbor_set_t& neighbors = this->outList.at( searchVertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); ++neighborIterator ) { const vertex_t neighborVertex = neighborIterator->first; if( !found.at( neighborVertex ) ) { found.at( neighborVertex ) = true; newSearch.push_back( neighborVertex ); farthest = neighborVertex; } } } oldSearch.swap( newSearch ); } } return lastDistance; } unsigned int WeightedNetwork::countMutuals() const { unsigned int count = 0; for( vertex_t vertex = 0; vertex < this->vertexCount(); ++vertex ) { const neighbor_set_t& neighbors = this->outList.at( vertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); ++neighborIterator ) { const vertex_t neighborVertex = neighborIterator->first; if( this->hasEdge( neighborVertex, vertex ) ) { ++count; } } } return count / 2; } double WeightedNetwork::freeChoiceMutualityIndex() const { double g = (double)this->vertexCount(); double M = (double)this->countMutuals(); double L = (double)this->edgeCount(); double L2 = 0; for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { L2 += pow( this->outDegree( vertex ), 2 ); } // cerr << "g: " << g << " - M: " << M << " - L: " << L << " - L2: " << L2 << "\n"; double numerator = 2*pow(g - 1, 2)*M - pow(L, 2) + L2; double denominator = L*pow(g - 1, 2) - pow(L, 2) + L2; return numerator / denominator; } unsigned int WeightedNetwork::outDegree( vertex_t vertex ) const { return this->outList.at( vertex ).size(); } unsigned int WeightedNetwork::inDegree( vertex_t vertex ) const { return this->inList.at( vertex ).size(); } weight_t WeightedNetwork::outVolume( vertex_t vertex ) const { weight_t volume = 0.0; const neighbor_set_t& neighbors = this->outList.at( vertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); neighborIterator++ ) { volume += neighborIterator->second; } return volume; } weight_t WeightedNetwork::inVolume( vertex_t vertex ) const { weight_t volume = 0.0; const neighbor_set_t& neighbors = this->inList.at( vertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); neighborIterator++ ) { volume += neighborIterator->second; } return volume; } unsigned int WeightedNetwork::eccentricity( vertex_t vertex ) const { vector found = vector( this->vertexCount() ); vector* oldSearch = new vector(); vector* newSearch = new vector(); found.at( vertex ) = true; newSearch->push_back( vertex ); unsigned int currentDegree = -1; while( !newSearch->empty() ) { delete oldSearch; oldSearch = newSearch; newSearch = new vector(); for( vector::const_iterator searchIterator = oldSearch->begin(); searchIterator != oldSearch->end(); searchIterator++ ) { const vertex_t searchVertex = *searchIterator; const neighbor_set_t& neighbors = this->outList.at( searchVertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); neighborIterator++ ) { const vertex_t neighborVertex = neighborIterator->first; if( !found.at( neighborVertex ) ) { found.at( neighborVertex ) = true; newSearch->push_back( neighborIterator->first ); } } } currentDegree++; } delete newSearch; delete oldSearch; return currentDegree; } double WeightedNetwork::clusteringCoefficient( vertex_t vertex ) const { unsigned int edgeCount = 0; const neighbor_set_t& neighbors = this->outList.at( vertex ); if( neighbors.size() < 2 ) { return 0.0; } for( unsigned int index1 = 0; index1 < neighbors.size(); index1++ ) { const vertex_t neighbor1 = neighbors.at( index1 ).first; for( unsigned int index2 = index1 + 1; index2 < neighbors.size(); index2++ ) { const vertex_t neighbor2 = neighbors.at( index2 ).first; bool edge1 = this->hasEdge( neighbor1, neighbor2 ); bool edge2 = this->hasEdge( neighbor2, neighbor1 ); edgeCount += (unsigned int)edge1 + (unsigned int)edge2; } } return (double)edgeCount / (neighbors.size()*(neighbors.size()-1)); } double WeightedNetwork::clusteringCoefficient() const { double sum = 0; for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { sum += this->clusteringCoefficient( vertex ); } return sum / this->vertexCount(); } vector WeightedNetwork::pageRank( double d ) const { vector oldPageRanks = vector( this->vertexCount() ); vector* pageRanks = new vector( this->vertexCount() ); vector scaledPageRanks = vector( this->vertexCount() ); vector* newPageRanks = new vector( this->vertexCount() ); for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { pageRanks->at( vertex ) = (double)1 / d; } for( unsigned int k = 0; true; k++ ) { for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { // this prevents repetition of costly floating-point divisions scaledPageRanks.at( vertex ) = pageRanks->at( vertex ) / this->outList.at( vertex ).size(); } double sum = 0.0; for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { double& newPageRank = newPageRanks->at( vertex ); newPageRank = 0; const neighbor_set_t& inNeighbors = this->inList.at( vertex ); for( neighbor_set_t::const_iterator neighborIterator = inNeighbors.begin(); neighborIterator != inNeighbors.end(); neighborIterator++ ) { const vertex_t inNeighbor = neighborIterator->first; newPageRank += scaledPageRanks.at( inNeighbor ); } newPageRank *= d; newPageRank += (1-d)/this->vertexCount(); sum += newPageRank; } for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { newPageRanks->at( vertex ) /= sum; } vector* temp = pageRanks; pageRanks = newPageRanks; newPageRanks = temp; // sum = 0; // for( vector::const_iterator it = pageRanks->begin(); it != pageRanks->end(); it++ ) { // sum += *it; // } // convergence test (r at the moment) if( k > 0 ) { double r = Statistics::sampleCorrelationCoefficient( oldPageRanks, *pageRanks ); // cerr << "k=" << k << " - r=" << r << " - sum=" << sum << "\n"; if( r > 0.999 ) { oldPageRanks = *pageRanks; delete pageRanks; delete newPageRanks; return oldPageRanks; } } oldPageRanks = *pageRanks; } } vector > WeightedNetwork::simRank( double C ) const { vertex_t n = this->vertexCount(); vector >* oldSimRanks = new vector >( n, vector(n) ); vector >* simRanks = new vector >( n, vector(n) ); // set initial values; note that vector initialization above gives us initial 0s for( vertex_t vertex = 0; vertex < n; vertex++ ) { oldSimRanks->at( vertex ).at( vertex ) = 1; simRanks->at( vertex ).at( vertex ) = 1; } // perform iterations until convergence for( unsigned int k = 0; true; k++ ) { // cerr << "Iteration " << k << "...\n"; for( vertex_t vertex1 = 0; vertex1 < n; vertex1++ ) { const neighbor_set_t& inNeighbors1 = this->inList.at( vertex1 ); for( vertex_t vertex2 = vertex1+1; vertex2 < n; vertex2++ ) { const neighbor_set_t& inNeighbors2 = this->inList.at( vertex2 ); double& cell = simRanks->at( vertex1 ).at( vertex2 ); cell = 0; if( inNeighbors1.size() > 0 && inNeighbors2.size() > 0 ) { for( neighbor_set_t::const_iterator it1 = inNeighbors1.begin(); it1 != inNeighbors1.end(); it1++ ) { vertex_t neighbor1 = it1->first; const vector& oneDim = oldSimRanks->at( neighbor1 ); for( neighbor_set_t::const_iterator it2 = inNeighbors2.begin(); it2 != inNeighbors2.end(); it2++ ) { vertex_t neighbor2 = it2->first; cell += oneDim.at( neighbor2 ); } } cell = C*cell / (inNeighbors1.size()*inNeighbors2.size()); } simRanks->at( vertex2 ).at( vertex1 ) = cell; } } // convergence test (r at the moment) if( k > 1 ) { double r = Statistics::sampleCorrelationCoefficient( *oldSimRanks, *simRanks ); // cerr << "r=" << r << "\n"; if( r > 0.999 ) { delete oldSimRanks; vector > result = *simRanks; delete simRanks; return result; } } vector >* tempSimRanks = oldSimRanks; oldSimRanks = simRanks; simRanks = tempSimRanks; } } double WeightedNetwork::assortativityCoefficient() const { vector x = vector(); vector y = vector(); for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { const neighbor_set_t& outNeighbors = this->outNeighbors( vertex ); for( neighbor_set_t::const_iterator neighborIterator = outNeighbors.begin(); neighborIterator != outNeighbors.end(); neighborIterator++ ) { x.push_back( outNeighbors.size() ); y.push_back( this->outDegree( neighborIterator->first ) ); } } return Statistics::sampleCorrelationCoefficient( x, y ); } vector WeightedNetwork::betweenness() const { vector C = vector( this->vertexCount(), 0.0 ); for( vertex_t s = 0; s < this->vertexCount(); s++ ) { stack S; vector > P = vector >( this->vertexCount() ); vector sigma = vector( this->vertexCount(), 0 ); vector d = vector( this->vertexCount(), -1 ); queue Q; sigma.at( s ) = 1; d.at( s ) = 0; Q.push( s ); while( !Q.empty() ) { vertex_t v = Q.front(); Q.pop(); S.push( v ); const neighbor_set_t& outNeighbors = this->outNeighbors( v ); for( neighbor_set_t::const_iterator neighborIt = outNeighbors.begin(); neighborIt != outNeighbors.end(); neighborIt++ ) { vertex_t w = neighborIt->first; if( d.at( w ) < 0 ) { Q.push( w ); d.at( w ) = d.at( v ) + 1; } if( d.at( w ) == d.at( v ) + 1 ) { sigma.at( w ) = sigma.at( w ) + sigma.at( v ); P.at( w ).push_back( v ); } } } vector delta = vector( this->vertexCount(), 0 ); while( !S.empty() ) { vertex_t w = S.top(); S.pop(); for( vector::const_iterator it = P.at( w ).begin(); it != P.at( w ).end(); it++ ) { vertex_t v = *it; delta.at( v ) = delta.at( v ) + ((double)sigma.at(v)/(double)sigma.at(w)) * (1 + delta.at(w)); } if( w != s ) { C.at( w ) = C.at( w ) + delta.at( w ); } } } return C; } double WeightedNetwork::closeness( vertex_t vertex ) const { vector distances = vector( this->vertexCount() ); unsigned int found = 0; vector oldSearch; distances.at( vertex ) = 0; oldSearch.push_back( vertex ); for( unsigned int distance = 1; !oldSearch.empty(); distance++ ) { vector newSearch; for( vector::const_iterator vertexIterator = oldSearch.begin(); vertexIterator != oldSearch.end(); vertexIterator++ ) { const vertex_t searchVertex = *vertexIterator; const neighbor_set_t& neighbors = this->outList.at( searchVertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); neighborIterator++ ) { const vertex_t neighbor = neighborIterator->first; if( neighbor != vertex && distances.at( neighbor ) == 0 ) { distances.at( neighbor ) = distance; newSearch.push_back( neighbor ); found++; } } } oldSearch.swap( newSearch ); } double total = 0.0; for( vector::const_iterator distancesIt = distances.begin(); distancesIt != distances.end(); distancesIt++ ) { total += *distancesIt; } return total / found; } vector WeightedNetwork::closeness() const { vector closenesses = vector( this->vertexCount() ); for( vertex_t vertex = 0; vertex < this->vertexCount(); vertex++ ) { closenesses.at( vertex ) = this->closeness( vertex ); } return closenesses; } vector WeightedNetwork::shortestPathLengths( vertex_t vertex ) const { vector found = vector( this->vertexCount() ); vector oldSearch; found.at( vertex ) = 0; oldSearch.push_back( vertex ); unsigned int currentDegree = 1; while( !oldSearch.empty() ) { vector newSearch; for( vector::const_iterator searchIterator = oldSearch.begin(); searchIterator != oldSearch.end(); ++searchIterator ) { const vertex_t searchVertex = *searchIterator; const neighbor_set_t& neighbors = this->outList.at( searchVertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); ++neighborIterator ) { const vertex_t neighborVertex = neighborIterator->first; if( neighborVertex != vertex && found.at( neighborVertex ) == 0 ) { found.at( neighborVertex ) = currentDegree; newSearch.push_back( neighborIterator->first ); } } } ++currentDegree; oldSearch = newSearch; } return found; } vector WeightedNetwork::shortestPathLengths() const { vector lengths; for( vertex_t vertex = 0; vertex < this->vertexCount(); ++vertex ) { vector vertexLengths = this->shortestPathLengths( vertex ); lengths.insert( lengths.end(), vertexLengths.begin(), vertexLengths.end() ); } return lengths; } rlichtenwalter-LPmade-ba322c3/netlib/WeightedNetwork.h000066400000000000000000000106411214146114000230360ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #ifndef WEIGHTEDNETWORK_H #define WEIGHTEDNETWORK_H #include #include #include #include #include using std::vector; using std::pair; using std::istream; using std::ostream; using std::numeric_limits; typedef unsigned int vertex_t; typedef double weight_t; typedef pair edge_t; typedef pair neighbor_t; typedef vector neighbor_set_t; typedef vector vertex_set_t; const vertex_t INVALID_VERTEX = numeric_limits::max(); class WeightedNetwork { private: protected: vector dictionary; vertex_set_t outList; vertex_set_t inList; unsigned int numEdges; void compact(); void addEdge( vertex_t, vertex_t, weight_t ); static neighbor_set_t intersect( const neighbor_set_t&, const neighbor_set_t& ); WeightedNetwork(); public: WeightedNetwork( const WeightedNetwork& ); WeightedNetwork& operator=( const WeightedNetwork& ); ~WeightedNetwork(); static WeightedNetwork readStream( istream&, const char, unsigned int, unsigned int, bool ); static WeightedNetwork readWeightedStream( istream&, const char, unsigned int, unsigned int, unsigned int, bool ); static WeightedNetwork readNetwork( istream& ); static WeightedNetwork combine( const WeightedNetwork& network1, const WeightedNetwork& network2 ); neighbor_set_t commonOutNeighbors( vertex_t, vertex_t ) const; neighbor_set_t commonInNeighbors( vertex_t, vertex_t ) const; void print( ostream& ) const; void printEdgeList( ostream& ) const; void printAdjacencyList( ostream& ) const; unsigned int vertexCount() const; unsigned int edgeCount() const; const neighbor_set_t& outNeighbors( vertex_t ) const; const neighbor_set_t& inNeighbors( vertex_t ) const; const vector findOutNeighbors( vertex_t, unsigned int ) const; const vector findInNeighbors( vertex_t, unsigned int ) const; WeightedNetwork snowballSample( vertex_t, unsigned int ) const; WeightedNetwork snowballSampleIn( vertex_t, unsigned int ) const; WeightedNetwork randomEdgeSample( double, unsigned int ) const; WeightedNetwork undirected() const; WeightedNetwork reverseEdges() const; WeightedNetwork removeEdges( const WeightedNetwork & ) const; WeightedNetwork removeEdges( const vector& ) const; WeightedNetwork thresholdEdges( weight_t ) const; WeightedNetwork removeAmutuals() const; WeightedNetwork removeIsolates() const; bool hasEdge( vertex_t, vertex_t ) const; weight_t edgeWeight( vertex_t, vertex_t ) const; vertex_t maxOutDegreeVertex() const; vector wccs() const; vector sccs() const; unsigned int pseudoDiameter() const; unsigned int countMutuals() const; double freeChoiceMutualityIndex() const; unsigned int outDegree( vertex_t ) const; unsigned int inDegree( vertex_t ) const; weight_t outVolume( vertex_t ) const; weight_t inVolume( vertex_t ) const; vertex_t translateExtToInt( vertex_t ) const; vertex_t translateIntToExt( vertex_t ) const; bool hasEdgeExt( vertex_t, vertex_t ) const; weight_t edgeWeightExt( vertex_t, vertex_t ) const; unsigned int eccentricity( vertex_t vertex ) const; double clusteringCoefficient( vertex_t vertex ) const; double clusteringCoefficient() const; vector pageRank( double ) const; vector > simRank( double ) const; double assortativityCoefficient() const; vector betweenness() const; double closeness( vertex_t ) const; vector closeness() const; vector shortestPathLengths() const; vector shortestPathLengths( vertex_t vertex ) const; }; #endif rlichtenwalter-LPmade-ba322c3/netlib/mains/000077500000000000000000000000001214146114000206605ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/netlib/mains/adjacency_list.cpp000066400000000000000000000026161214146114000243450ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } WeightedNetwork network = WeightedNetwork::readNetwork( cin ); network.printAdjacencyList( cout ); return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/all_pairs.cpp000066400000000000000000000031421214146114000233320ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } WeightedNetwork network = WeightedNetwork::readNetwork( cin ); for( vertex_t v1 = 0; v1 < network.vertexCount(); ++v1 ) { for( vertex_t v2 = 0; v2 < network.vertexCount(); ++v2 ) { if( v1 != v2 ) { cout << network.translateIntToExt( v1 ) << " " << network.translateIntToExt( v2 ) << "\n"; } } } return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/assortativity_coefficient.cpp000066400000000000000000000025721214146114000266550ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } cout << WeightedNetwork::readNetwork( cin ).assortativityCoefficient() << "\n"; return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/average_clustering_coefficient.cpp000066400000000000000000000026141214146114000275760ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; using std::scientific; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } cout << WeightedNetwork::readNetwork( cin ).clusteringCoefficient() << "\n"; return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/betweenness.cpp000066400000000000000000000030761214146114000237140ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; using std::scientific; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } WeightedNetwork network = WeightedNetwork::readNetwork( cin ); vector betweenness = network.betweenness(); for( vector::const_iterator it = betweenness.begin(); it != betweenness.end(); it++ ) { cout << scientific << *it << "\n"; } return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/clustering_coefficient_distribution.cpp000066400000000000000000000030061214146114000306770ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; using std::scientific; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } WeightedNetwork network = WeightedNetwork::readNetwork( cin ); for( vertex_t vertex = 0; vertex < network.vertexCount(); ++vertex ) { cout << network.clusteringCoefficient( vertex ) << "\n"; } return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/clustering_spectrum.cpp000066400000000000000000000040771214146114000254750ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } WeightedNetwork network = WeightedNetwork::readNetwork( cin ); vector sum( 10, 0.0 ); vector count( 10, 0 ); for( vertex_t vertex = 0; vertex < network.vertexCount(); vertex++ ) { unsigned int degree = network.outDegree( vertex ); double clusteringCoefficient = network.clusteringCoefficient( vertex ); if( degree >= sum.size() ) { sum.resize( degree + 1, 0.0 ); count.resize( degree + 1, 0 ); } sum.at( degree ) += clusteringCoefficient; ++count.at( degree ); } for( unsigned int degree = 0; degree < sum.size(); ++degree ) { double degreeSum = sum.at( degree ); unsigned int degreeCount = count.at( degree ); if( degreeCount > 0 ) { double average = degreeSum / degreeCount; cout << degree << " " << average << "\n"; } } return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/combine_networks.cpp000066400000000000000000000034211214146114000247340ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; using std::ifstream; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options] \n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc < optind + 1 ) { usage( argv[0] ); return 1; } const char* filename = argv[optind++]; ifstream file; file.open( filename ); WeightedNetwork network = WeightedNetwork::readNetwork( file ); file.close(); for( int index = 2; index < argc; index++ ) { filename = argv[index]; file.open( filename ); WeightedNetwork toAdd = WeightedNetwork::readNetwork( file ); network = WeightedNetwork::combine( network, toAdd ); file.close(); } network.print( cout ); return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/count_edges.cpp000066400000000000000000000026171214146114000236710ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } WeightedNetwork network = WeightedNetwork::readNetwork( cin ); cout << network.edgeCount() << "\n"; return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/count_mutuals.cpp000066400000000000000000000026221214146114000242700ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } WeightedNetwork network = WeightedNetwork::readNetwork( cin ); cout << network.countMutuals() << "\n"; return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/count_vertices.cpp000066400000000000000000000026211214146114000244210ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } WeightedNetwork network = WeightedNetwork::readNetwork( cin ); cout << network.vertexCount() << "\n"; return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/duplicate.cpp000066400000000000000000000025331214146114000233410ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } WeightedNetwork::readNetwork( cin ).print( cout ); return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/eccentricity_distribution.cpp000066400000000000000000000027501214146114000266540ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } WeightedNetwork network = WeightedNetwork::readNetwork( cin ); for( vertex_t vertex = 0; vertex < network.vertexCount(); vertex++ ) { cout << network.eccentricity( vertex ) << "\n"; } return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/free_choice_mutuality.cpp000066400000000000000000000026361214146114000257430ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } WeightedNetwork network = WeightedNetwork::readNetwork( cin ); cout << network.freeChoiceMutualityIndex() << "\n"; return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/label_predictions.cpp000066400000000000000000000040261214146114000250500ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include #include #include "../WeightedNetwork.h" using std::istream; using std::ifstream; using std::streamsize; using std::numeric_limits; using std::scientific; using std::ios_base; using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options] label_network\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind + 1 ) { usage( argv[0] ); return 1; } const char* filename = argv[optind++]; ifstream file; file.open( filename ); WeightedNetwork network = WeightedNetwork::readNetwork( file ); file.close(); unsigned int vertex = 0; unsigned int neighbor = 0; double score = 0; while( cin.peek() != EOF ) { cin >> vertex >> neighbor >> score; cin.ignore( numeric_limits::max(), '\n' ); if( (int)score != score ) { cout << scientific; } else { cout.unsetf( ios_base::floatfield ); } cout << score << " " << (unsigned int)network.hasEdgeExt( vertex, neighbor ) << "\n"; } return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/largest_scc.cpp000066400000000000000000000033661214146114000236650ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cout; using std::cerr; using std::cin; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } WeightedNetwork network = WeightedNetwork::readNetwork( cin ); vector sccs = network.sccs(); unsigned int maxsize = 0; const WeightedNetwork* largestSCC = NULL; for( vector::const_iterator sccIterator = sccs.begin(); sccIterator != sccs.end(); sccIterator++ ) { if( sccIterator->vertexCount() >= maxsize ) { maxsize = sccIterator->vertexCount(); largestSCC = &(*sccIterator); } } largestSCC->print( cout ); return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/largest_wcc.cpp000066400000000000000000000033611214146114000236640ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cout; using std::cerr; using std::cin; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } WeightedNetwork network = WeightedNetwork::readNetwork( cin ); vector wccs = network.wccs(); unsigned int maxsize = 0; const WeightedNetwork* largestWCC = NULL; for( vector::const_iterator wccIterator = wccs.begin(); wccIterator != wccs.end(); ++wccIterator ) { if( wccIterator->vertexCount() >= maxsize ) { maxsize = wccIterator->vertexCount(); largestWCC = &(*wccIterator); } } largestWCC->print( cout ); return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/out_degree_distribution.cpp000066400000000000000000000027451214146114000263150ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } WeightedNetwork network = WeightedNetwork::readNetwork( cin ); for( vertex_t vertex = 0; vertex < network.vertexCount(); vertex++ ) { cout << network.outDegree( vertex ) << "\n"; } return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/pagerank.cpp000066400000000000000000000031421214146114000231540ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; using std::scientific; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options] d\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind + 1 ) { usage( argv[0] ); return 1; } double d = atof( argv[optind++] ); WeightedNetwork network = WeightedNetwork::readNetwork( cin ); vector pageRanks = network.pageRank( d ); for( vector::const_iterator it = pageRanks.begin(); it != pageRanks.end(); it++ ) { cout << scientific << *it << "\n"; } return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/path_pairs.cpp000066400000000000000000000057721214146114000235310ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options] \n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind + 1 ) { usage( argv[0] ); return 1; } char* spec = argv[optind]; unsigned int currentDistance = 0; unsigned int distance = strlen( spec ); vector direction( distance ); while( currentDistance < distance ) { if( tolower( spec[currentDistance] ) == 'o' ) { direction[currentDistance++] = true; } else if( tolower( spec[currentDistance] ) == 'i' ) { direction[currentDistance++] = false; } else { usage( argv[0] ); return 1; } } WeightedNetwork network = WeightedNetwork::readNetwork( cin ); for( vertex_t v1 = 0; v1 < network.vertexCount(); ++v1 ) { vector found = vector( network.vertexCount() ); vector search; found.at( v1 ) = true; search.push_back( v1 ); for( currentDistance = 0; currentDistance < distance; ++currentDistance ) { vector newSearch; for( vector::const_iterator vertexIterator = search.begin(); vertexIterator != search.end(); ++vertexIterator ) { const vertex_t searchVertex = *vertexIterator; const neighbor_set_t& neighbors = direction[currentDistance] ? network.outNeighbors( searchVertex ) : network.inNeighbors( searchVertex ); for( neighbor_set_t::const_iterator neighborIterator = neighbors.begin(); neighborIterator != neighbors.end(); neighborIterator++ ) { const vertex_t neighbor = neighborIterator->first; if( !found.at( neighbor ) ) { found.at( neighbor ) = true; if( currentDistance == distance - 1 ) { cout << network.translateIntToExt( v1 ) << " " << network.translateIntToExt( neighbor ) << "\n"; } else { newSearch.push_back( neighbor ); } } } } search.swap( newSearch ); } } return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/pseudo_diameter.cpp000066400000000000000000000025571214146114000245460ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } cout << WeightedNetwork::readNetwork( cin ).pseudoDiameter() << "\n"; return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/remove_edges.cpp000066400000000000000000000034131214146114000240310ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include #include #include #include "../WeightedNetwork.h" using std::istream; using std::ifstream; using std::streamsize; using std::make_pair; using std::numeric_limits; using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options] network\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind + 1 ) { usage( argv[0] ); return 1; } const char* filename = argv[optind++]; WeightedNetwork sourceNetwork = WeightedNetwork::readNetwork( cin ); ifstream file; file.open( filename ); WeightedNetwork removalNetwork = WeightedNetwork::readNetwork( file ); file.close(); sourceNetwork.removeEdges( removalNetwork ).print( cout ); return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/remove_isolates.cpp000066400000000000000000000025551214146114000245730ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } WeightedNetwork::readNetwork( cin ).removeIsolates().print( cout ); return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/reverse_edges.cpp000066400000000000000000000026201214146114000242060ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } WeightedNetwork network = WeightedNetwork::readNetwork( cin ); network.reverseEdges().print( cout ); return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/scc_distribution.cpp000066400000000000000000000032761214146114000247430ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cout; using std::cerr; using std::cin; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } WeightedNetwork network = WeightedNetwork::readNetwork( cin ); vector sccs = network.sccs(); for( vector::const_iterator sccIterator = sccs.begin(); sccIterator != sccs.end(); sccIterator++ ) { cout << sccIterator->translateIntToExt( 0 ) << " " << sccIterator->vertexCount() << " " << sccIterator->edgeCount() << " " << sccIterator->pseudoDiameter() << "\n"; } return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/shortest_path_distribution.cpp000066400000000000000000000032231214146114000270520ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } WeightedNetwork network = WeightedNetwork::readNetwork( cin ); for( vertex_t vertex = 0; vertex < network.vertexCount(); ++vertex ) { vector lengths = network.shortestPathLengths( vertex ); for( vector::const_iterator it = lengths.begin(); it != lengths.end(); ++it ) { if( *it != 0 ) { cout << *it << "\n"; } } } return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/simrank.cpp000066400000000000000000000035431214146114000230350ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; using std::ios_base; using std::scientific; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options] C\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind + 1 ) { usage( argv[0] ); return 1; } double C = atof( argv[optind++] ); WeightedNetwork network = WeightedNetwork::readNetwork( cin ); vector > simRanks = network.simRank( C ); for( vector >::const_iterator dim1 = simRanks.begin(); dim1 != simRanks.end(); dim1++ ) { for( vector::const_iterator dim2 = dim1->begin(); dim2 != dim1->end(); dim2++ ) { if( *dim2 != int(*dim2) ) { cout << scientific << *dim2 << "\n"; } else { cout.unsetf( ios_base::floatfield ); cout << *dim2 << "\n"; } } } return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/snowball_sample.cpp000066400000000000000000000030311214146114000245430ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options] source distance\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind + 2 ) { usage( argv[0] ); return 1; } vertex_t vertexName = atoi( argv[optind++] ); vertex_t distance = atoi( argv[optind++] ); WeightedNetwork network = WeightedNetwork::readNetwork( cin ); network.snowballSample( vertexName, distance ).print( cout ); return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/snowball_sample_in.cpp000066400000000000000000000030321214146114000252320ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options] source distance\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind + 2 ) { usage( argv[0] ); return 1; } vertex_t vertexName = atoi( argv[optind++] ); vertex_t distance = atoi( argv[optind++] ); WeightedNetwork network = WeightedNetwork::readNetwork( cin ); network.snowballSampleIn( vertexName, distance ).print( cout ); return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/snowball_sample_size_dist.cpp000066400000000000000000000032541214146114000266270ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; using std::scientific; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options] distance\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind + 1 ) { usage( argv[0] ); return 1; } unsigned int distance = atoi( argv[optind++] ); WeightedNetwork network = WeightedNetwork::readNetwork( cin ); for( vertex_t vertex = 0; vertex < network.vertexCount(); vertex++ ) { WeightedNetwork snowball = network.snowballSample( vertex, distance ).removeIsolates(); cout << snowball.vertexCount() << " " << snowball.edgeCount() << "\n"; } return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/stream_to_network.cpp000066400000000000000000000033541214146114000251370ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options] seperator iindex jindex\n"; cerr << "Options: -h Print this usage message and exit\n"; cerr << " -i Ignore self-edges\n"; } int main( int argc, char* argv[] ) { bool ignoreSelfEdges = false; char c; while( (c = getopt( argc, argv, "+i" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } else if( c == 'i' ) { ignoreSelfEdges = true; } } if( argc != optind + 3 ) { usage( argv[0] ); return 1; } const char separator = argv[optind++][0]; const unsigned int iIndex = atoi( argv[optind++] ); const unsigned int jIndex = atoi( argv[optind++] ); WeightedNetwork network = WeightedNetwork::readStream( cin, separator, iIndex, jIndex, ignoreSelfEdges ); network.print( cout ); return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/threshold_edges.cpp000066400000000000000000000026661214146114000245410ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options] lower_threshold\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind + 1 ) { usage( argv[0] ); return 1; } weight_t threshold = atof( argv[optind++] ); WeightedNetwork::readNetwork( cin ).thresholdEdges( threshold ).print( cout ); return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/wcc_distribution.cpp000066400000000000000000000032751214146114000247460ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cout; using std::cerr; using std::cin; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options]\n"; cerr << "Options: -h Print this usage message and exit\n"; } int main( int argc, char* argv[] ) { char c; while( (c = getopt( argc, argv, "+h" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } } if( argc != optind ) { usage( argv[0] ); return 1; } WeightedNetwork network = WeightedNetwork::readNetwork( cin ); vector wccs = network.wccs(); for( vector::const_iterator wccIterator = wccs.begin(); wccIterator != wccs.end(); wccIterator++ ) { cout << wccIterator->translateIntToExt( 0 ) << " " << wccIterator->vertexCount() << " " << wccIterator->edgeCount() << " " << wccIterator->pseudoDiameter() << "\n"; } return 0; } rlichtenwalter-LPmade-ba322c3/netlib/mains/weighted_stream_to_network.cpp000066400000000000000000000034671214146114000270240ustar00rootroot00000000000000/* ------------------------------------------------ Copyright (C) 2010,2011 by Ryan N. Lichtenwalter Email: rlichtenwalter@gmail.com This file is part of LPmade. LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . ------------------------------------------------ */ #include #include #include "../WeightedNetwork.h" using std::cin; using std::cout; using std::cerr; void usage( const char* binary ) { cerr << "Usage: " << binary << " [options] seperator iindex jindex windex\n"; cerr << "Options: -h Print this usage message and exit\n"; cerr << " -i Ignore self-edges\n"; } int main( int argc, char* argv[] ) { bool ignoreSelfEdges = false; char c; while( (c = getopt( argc, argv, "+i" )) >= 0 ) { if( c == 'h' ) { usage( argv[0] ); return 0; } else if( c == 'i' ) { ignoreSelfEdges = true; } } if( argc != optind + 4 ) { usage( argv[0] ); return 1; } const char separator = argv[optind++][0]; const unsigned int iIndex = atoi( argv[optind++] ); const unsigned int jIndex = atoi( argv[optind++] ); const unsigned int wIndex = atoi( argv[optind++] ); WeightedNetwork network = WeightedNetwork::readWeightedStream( cin, separator, iIndex, jIndex, wIndex, ignoreSelfEdges ); network.print( cout ); return 0; } rlichtenwalter-LPmade-ba322c3/netlib/test/000077500000000000000000000000001214146114000205305ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/netlib/test/adjacency_list.CORRECT000066400000000000000000000000701214146114000245240ustar00rootroot000000000000000 2,1 5,2 7,1 2 3 18,1 4 5 7 4,1 18 3,1 47 2,1 285 47,1 rlichtenwalter-LPmade-ba322c3/netlib/test/adjacency_list.INPUT000066400000000000000000000001551214146114000243260ustar00rootroot00000000000000*Vertices 9 0 0 1 2 2 3 3 4 4 5 5 7 6 18 7 47 8 285 *Edges 8 0 1 1 0 4 2 0 5 1 2 6 1 5 3 1 6 2 1 7 1 1 8 7 1 rlichtenwalter-LPmade-ba322c3/netlib/test/adjacency_list.RUN000077500000000000000000000021221214146114000240720ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/all_pairs.CORRECT000066400000000000000000000000301214146114000235120ustar00rootroot000000000000001 2 1 3 2 1 2 3 3 1 3 2 rlichtenwalter-LPmade-ba322c3/netlib/test/all_pairs.INPUT000066400000000000000000000000561214146114000233200ustar00rootroot00000000000000*Vertices 3 0 1 1 2 2 3 *Edges 2 0 1 1 1 2 3 rlichtenwalter-LPmade-ba322c3/netlib/test/all_pairs.RUN000077500000000000000000000021221214146114000230640ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/assortativity_coefficient.CORRECT000066400000000000000000000000121214146114000270270ustar00rootroot00000000000000-0.258199 rlichtenwalter-LPmade-ba322c3/netlib/test/assortativity_coefficient.INPUT000066400000000000000000000001551214146114000266350ustar00rootroot00000000000000*Vertices 9 0 0 1 2 2 3 3 4 4 5 5 7 6 18 7 47 8 285 *Edges 8 0 1 1 0 4 2 0 5 1 2 6 1 5 3 1 6 2 1 7 1 1 8 7 1 rlichtenwalter-LPmade-ba322c3/netlib/test/assortativity_coefficient.RUN000077500000000000000000000021221214146114000264010ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/average_clustering_coefficient.CORRECT000066400000000000000000000000061214146114000277560ustar00rootroot000000000000000.125 rlichtenwalter-LPmade-ba322c3/netlib/test/average_clustering_coefficient.INPUT000066400000000000000000000001111214146114000275510ustar00rootroot00000000000000*Vertices 4 0 0 1 1 2 2 3 3 *Edges 6 0 1 1 0 2 1 0 3 1 1 2 1 1 3 1 2 1 1 rlichtenwalter-LPmade-ba322c3/netlib/test/average_clustering_coefficient.RUN000077500000000000000000000021221214146114000273250ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/betweenness.CORRECT000066400000000000000000000002021214146114000240670ustar00rootroot000000000000000.000000e+00 2.133333e+01 1.933333e+01 8.000000e+00 2.966667e+01 1.066667e+01 2.333333e+00 2.333333e+00 1.800000e+01 6.333333e+00 rlichtenwalter-LPmade-ba322c3/netlib/test/betweenness.INPUT000066400000000000000000000003201214146114000236660ustar00rootroot00000000000000*Vertices 10 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 *Edges 24 0 1 1 1 0 1 1 2 1 1 3 1 2 1 1 2 4 1 3 1 1 3 5 1 4 2 1 4 6 1 4 7 1 4 8 1 5 3 1 5 8 1 6 4 1 6 9 1 7 4 1 7 9 1 8 4 1 8 5 1 8 9 1 9 6 1 9 7 1 9 8 1 rlichtenwalter-LPmade-ba322c3/netlib/test/betweenness.RUN000077500000000000000000000021221214146114000234400ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/clustering_coefficient_distribution.CORRECT000066400000000000000000000000121214146114000310600ustar00rootroot000000000000000.5 0 0 0 rlichtenwalter-LPmade-ba322c3/netlib/test/clustering_coefficient_distribution.INPUT000066400000000000000000000001111214146114000306560ustar00rootroot00000000000000*Vertices 4 0 0 1 1 2 2 3 3 *Edges 6 0 1 1 0 2 1 0 3 1 1 2 1 1 3 1 2 1 1 rlichtenwalter-LPmade-ba322c3/netlib/test/clustering_coefficient_distribution.RUN000077500000000000000000000021221214146114000304320ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/clustering_spectrum.CORRECT000066400000000000000000000000321214146114000256470ustar00rootroot000000000000001 0 2 0.25 3 0 4 0.333333 rlichtenwalter-LPmade-ba322c3/netlib/test/clustering_spectrum.INPUT000066400000000000000000000002321214146114000254470ustar00rootroot00000000000000*Vertices 9 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 *Edges 16 0 1 1 0 2 1 1 2 1 1 3 1 1 4 1 2 5 3 3 6 1 4 5 1 5 2 1 6 3 1 6 7 1 7 1 1 8 0 1 8 1 7 8 2 1 8 3 1 rlichtenwalter-LPmade-ba322c3/netlib/test/clustering_spectrum.RUN000077500000000000000000000021221214146114000252170ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/combine_networks.CORRECT000066400000000000000000000001131214146114000251160ustar00rootroot00000000000000*Vertices 6 0 1 1 2 2 3 3 4 4 5 5 6 *Edges 5 0 1 1 1 2 1 2 3 3 3 4 1 4 5 3 rlichtenwalter-LPmade-ba322c3/netlib/test/combine_networks.INPUT_1000066400000000000000000000000671214146114000251440ustar00rootroot00000000000000*Vertices 4 0 1 1 2 2 3 3 4 *Edges 3 0 1 1 1 2 1 2 3 1 rlichtenwalter-LPmade-ba322c3/netlib/test/combine_networks.INPUT_2000066400000000000000000000000671214146114000251450ustar00rootroot00000000000000*Vertices 4 0 3 1 4 2 5 3 6 *Edges 3 0 1 2 1 2 1 2 3 3 rlichtenwalter-LPmade-ba322c3/netlib/test/combine_networks.RUN000077500000000000000000000021451214146114000244730ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; $BINDIR/$BASE $DIR/$BASE.INPUT_1 $DIR/$BASE.INPUT_2 > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/count_edges.CORRECT000066400000000000000000000000021214146114000240420ustar00rootroot000000000000003 rlichtenwalter-LPmade-ba322c3/netlib/test/count_edges.INPUT000066400000000000000000000000671214146114000236530ustar00rootroot00000000000000*Vertices 4 0 1 1 2 2 3 3 4 *Edges 3 0 1 1 1 2 1 2 3 1 rlichtenwalter-LPmade-ba322c3/netlib/test/count_edges.RUN000077500000000000000000000021221214146114000234150ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/count_mutuals.CORRECT000066400000000000000000000000021214146114000244450ustar00rootroot000000000000001 rlichtenwalter-LPmade-ba322c3/netlib/test/count_mutuals.INPUT000066400000000000000000000000631214146114000242520ustar00rootroot00000000000000*Vertices 3 0 1 1 2 2 3 *Edges 3 0 1 1 1 2 1 2 1 1 rlichtenwalter-LPmade-ba322c3/netlib/test/count_mutuals.RUN000077500000000000000000000004511214146114000240230ustar00rootroot00000000000000#! /bin/bash DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/count_vertices.CORRECT000066400000000000000000000000021214146114000245770ustar00rootroot000000000000004 rlichtenwalter-LPmade-ba322c3/netlib/test/count_vertices.INPUT000066400000000000000000000000671214146114000244100ustar00rootroot00000000000000*Vertices 4 0 1 1 2 2 3 3 4 *Edges 3 0 1 1 1 2 1 2 3 1 rlichtenwalter-LPmade-ba322c3/netlib/test/count_vertices.RUN000077500000000000000000000021221214146114000241520ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/duplicate.CORRECT000066400000000000000000000001551214146114000235260ustar00rootroot00000000000000*Vertices 9 0 0 1 2 2 3 3 4 4 5 5 7 6 18 7 47 8 285 *Edges 8 0 1 1 0 4 2 0 5 1 2 6 1 5 3 1 6 2 1 7 1 1 8 7 1 rlichtenwalter-LPmade-ba322c3/netlib/test/duplicate.INPUT000066400000000000000000000001551214146114000233240ustar00rootroot00000000000000*Vertices 9 0 0 1 2 2 3 3 4 4 5 5 7 6 18 7 47 8 285 *Edges 8 0 1 1 0 4 2 0 5 1 2 6 1 5 3 1 6 2 1 7 1 1 8 7 1 rlichtenwalter-LPmade-ba322c3/netlib/test/duplicate.RUN000077500000000000000000000021221214146114000230700ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/eccentricity_distribution.CORRECT000066400000000000000000000000241214146114000270330ustar00rootroot000000000000005 4 3 4 3 3 4 4 4 5 rlichtenwalter-LPmade-ba322c3/netlib/test/eccentricity_distribution.INPUT000066400000000000000000000003201214146114000266300ustar00rootroot00000000000000*Vertices 10 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 *Edges 24 0 1 1 1 0 1 1 2 1 1 3 1 2 1 1 2 4 1 3 1 1 3 5 1 4 2 1 4 6 1 4 7 1 4 8 1 5 3 1 5 8 1 6 4 1 6 9 1 7 4 1 7 9 1 8 4 1 8 5 1 8 9 1 9 6 1 9 7 1 9 8 1 rlichtenwalter-LPmade-ba322c3/netlib/test/eccentricity_distribution.RUN000077500000000000000000000021221214146114000264020ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/free_choice_mutuality.CORRECT000066400000000000000000000000111214146114000261130ustar00rootroot000000000000000.333333 rlichtenwalter-LPmade-ba322c3/netlib/test/free_choice_mutuality.INPUT000066400000000000000000000000631214146114000257200ustar00rootroot00000000000000*Vertices 3 0 1 1 2 2 3 *Edges 3 0 1 1 1 2 1 2 1 1 rlichtenwalter-LPmade-ba322c3/netlib/test/free_choice_mutuality.RUN000077500000000000000000000021221214146114000254660ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/label_predictions.CORRECT000066400000000000000000000000551214146114000252350ustar00rootroot000000000000006.000000e-01 0 2.000000e-01 1 7.000000e-01 0 rlichtenwalter-LPmade-ba322c3/netlib/test/label_predictions.INPUT_1000066400000000000000000000000311214146114000252450ustar00rootroot000000000000008 10 0.6 3 5 0.2 4 7 0.7 rlichtenwalter-LPmade-ba322c3/netlib/test/label_predictions.INPUT_2000066400000000000000000000000651214146114000252550ustar00rootroot00000000000000*Vertices 5 0 3 1 4 2 5 3 7 4 9 *Edges 2 0 2 1 0 3 1 rlichtenwalter-LPmade-ba322c3/netlib/test/label_predictions.RUN000077500000000000000000000021471214146114000246070ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT_1 $BINDIR/$BASE $DIR/$BASE.INPUT_2 > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/largest_scc.CORRECT000066400000000000000000000000661214146114000240460ustar00rootroot00000000000000*Vertices 3 0 16 1 17 2 18 *Edges 3 0 1 1 1 2 1 2 0 1 rlichtenwalter-LPmade-ba322c3/netlib/test/largest_scc.INPUT000066400000000000000000000004121214146114000236370ustar00rootroot00000000000000*Vertices 22 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 *Edges 18 0 1 1 2 3 1 3 2 1 4 5 1 6 5 1 8 7 1 8 9 1 10 11 1 11 12 1 13 14 1 14 15 1 15 13 1 16 17 1 17 18 1 18 16 1 18 19 1 19 20 1 20 19 1 rlichtenwalter-LPmade-ba322c3/netlib/test/largest_scc.RUN000077500000000000000000000004511214146114000234120ustar00rootroot00000000000000#! /bin/bash DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/largest_wcc.CORRECT000066400000000000000000000001221214146114000240430ustar00rootroot00000000000000*Vertices 5 0 16 1 17 2 18 3 19 4 20 *Edges 6 0 1 1 1 2 1 2 0 1 2 3 1 3 4 1 4 3 1 rlichtenwalter-LPmade-ba322c3/netlib/test/largest_wcc.INPUT000066400000000000000000000004121214146114000236430ustar00rootroot00000000000000*Vertices 22 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 *Edges 18 0 1 1 2 3 1 3 2 1 4 5 1 6 5 1 8 7 1 8 9 1 10 11 1 11 12 1 13 14 1 14 15 1 15 13 1 16 17 1 17 18 1 18 16 1 18 19 1 19 20 1 20 19 1 rlichtenwalter-LPmade-ba322c3/netlib/test/largest_wcc.RUN000077500000000000000000000004511214146114000234160ustar00rootroot00000000000000#! /bin/bash DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/out_degree_distribution.CORRECT000066400000000000000000000000101214146114000264630ustar00rootroot000000000000003 2 1 0 rlichtenwalter-LPmade-ba322c3/netlib/test/out_degree_distribution.INPUT000066400000000000000000000001111214146114000262630ustar00rootroot00000000000000*Vertices 4 0 0 1 1 2 2 3 3 *Edges 6 0 1 1 0 2 1 0 3 1 1 2 1 1 3 1 2 1 1 rlichtenwalter-LPmade-ba322c3/netlib/test/out_degree_distribution.RUN000077500000000000000000000021221214146114000260370ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/pagerank.CORRECT000066400000000000000000000000641214146114000233430ustar00rootroot000000000000004.924846e-02 3.809763e-01 2.848876e-01 2.848876e-01 rlichtenwalter-LPmade-ba322c3/netlib/test/pagerank.INPUT000066400000000000000000000001111214146114000231320ustar00rootroot00000000000000*Vertices 4 0 0 1 1 2 2 3 3 *Edges 6 0 1 1 0 2 1 0 3 1 1 2 1 1 3 1 2 1 1 rlichtenwalter-LPmade-ba322c3/netlib/test/pagerank.RUN000077500000000000000000000021271214146114000227130ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE 0.85 > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/path_pairs.CORRECT000066400000000000000000000000041214146114000236770ustar00rootroot000000000000004 7 rlichtenwalter-LPmade-ba322c3/netlib/test/path_pairs.INPUT000066400000000000000000000002031214146114000234760ustar00rootroot00000000000000*Vertices 12 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 *Edges 9 0 1 1 1 2 1 2 3 1 4 5 1 6 5 1 6 7 1 9 8 1 10 9 1 10 11 1 rlichtenwalter-LPmade-ba322c3/netlib/test/path_pairs.RUN000077500000000000000000000021261214146114000232540ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE OIO > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/pseudo_diameter.CORRECT000066400000000000000000000000021214146114000247140ustar00rootroot000000000000005 rlichtenwalter-LPmade-ba322c3/netlib/test/pseudo_diameter.INPUT000066400000000000000000000002471214146114000245250ustar00rootroot00000000000000*Vertices 9 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 *Edges 18 0 1 1 0 2 1 0 7 1 1 0 1 1 8 1 2 0 1 2 8 1 3 4 1 3 5 1 3 6 1 3 8 1 4 3 1 5 3 1 6 3 1 7 0 1 8 1 1 8 2 1 8 3 1 rlichtenwalter-LPmade-ba322c3/netlib/test/pseudo_diameter.RUN000077500000000000000000000004511214146114000242720ustar00rootroot00000000000000#! /bin/bash DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/remove_edges.CORRECT000066400000000000000000000000611214146114000242140ustar00rootroot00000000000000*Vertices 4 0 1 1 2 2 3 3 4 *Edges 2 1 2 1 2 3 1 rlichtenwalter-LPmade-ba322c3/netlib/test/remove_edges.INPUT_1000066400000000000000000000000671214146114000242400ustar00rootroot00000000000000*Vertices 4 0 1 1 2 2 3 3 4 *Edges 3 0 1 1 1 2 1 2 3 1 rlichtenwalter-LPmade-ba322c3/netlib/test/remove_edges.INPUT_2000066400000000000000000000000531214146114000242340ustar00rootroot00000000000000*Vertices 4 0 1 1 2 2 3 3 4 *Edges 1 0 1 1 rlichtenwalter-LPmade-ba322c3/netlib/test/remove_edges.RUN000077500000000000000000000021471214146114000235710ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT_1 $BINDIR/$BASE $DIR/$BASE.INPUT_2 > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/remove_isolates.CORRECT000066400000000000000000000001141214146114000247470ustar00rootroot00000000000000*Vertices 7 0 0 1 2 2 4 3 5 4 7 5 47 6 285 *Edges 4 0 1 1 0 3 2 0 4 1 5 2 1 rlichtenwalter-LPmade-ba322c3/netlib/test/remove_isolates.INPUT000066400000000000000000000001411214146114000245450ustar00rootroot00000000000000*Vertices 9 0 0 1 2 2 3 3 4 4 5 5 7 6 18 7 47 8 285 *Edges 6 0 1 1 0 4 2 0 5 1 5 3 1 7 1 1 8 7 1 rlichtenwalter-LPmade-ba322c3/netlib/test/remove_isolates.RUN000077500000000000000000000004511214146114000243210ustar00rootroot00000000000000#! /bin/bash DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/reverse_edges.CORRECT000066400000000000000000000002321214146114000243720ustar00rootroot00000000000000*Vertices 9 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 *Edges 16 0 8 1 1 0 1 1 7 1 1 8 7 2 0 1 2 1 1 2 5 1 2 8 1 3 1 1 3 6 1 3 8 1 4 1 1 5 2 3 5 4 1 6 3 1 7 6 1 rlichtenwalter-LPmade-ba322c3/netlib/test/reverse_edges.INPUT000066400000000000000000000002321214146114000241700ustar00rootroot00000000000000*Vertices 9 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 *Edges 16 0 1 1 0 2 1 1 2 1 1 3 1 1 4 1 2 5 3 3 6 1 4 5 1 5 2 1 6 3 1 6 7 1 7 1 1 8 0 1 8 1 7 8 2 1 8 3 1 rlichtenwalter-LPmade-ba322c3/netlib/test/reverse_edges.RUN000077500000000000000000000004511214146114000237430ustar00rootroot00000000000000#! /bin/bash DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/scc_distribution.CORRECT000066400000000000000000000002071214146114000251210ustar00rootroot000000000000001 1 0 0 0 1 0 0 2 2 2 1 5 1 0 0 4 1 0 0 6 1 0 0 7 1 0 0 9 1 0 0 8 1 0 0 12 1 0 0 11 1 0 0 10 1 0 0 13 3 3 2 19 2 2 1 16 3 3 2 21 1 0 0 rlichtenwalter-LPmade-ba322c3/netlib/test/scc_distribution.INPUT000066400000000000000000000004121214146114000247150ustar00rootroot00000000000000*Vertices 22 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 *Edges 18 0 1 1 2 3 1 3 2 1 4 5 1 6 5 1 8 7 1 8 9 1 10 11 1 11 12 1 13 14 1 14 15 1 15 13 1 16 17 1 17 18 1 18 16 1 18 19 1 19 20 1 20 19 1 rlichtenwalter-LPmade-ba322c3/netlib/test/scc_distribution.RUN000077500000000000000000000004511214146114000244700ustar00rootroot00000000000000#! /bin/bash DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/shortest_path_distribution.CORRECT000066400000000000000000000000241214146114000272350ustar00rootroot000000000000001 2 1 1 1 1 1 1 2 1 rlichtenwalter-LPmade-ba322c3/netlib/test/shortest_path_distribution.INPUT000066400000000000000000000001551214146114000270400ustar00rootroot00000000000000*Vertices 9 0 0 1 2 2 3 3 4 4 5 5 7 6 18 7 47 8 285 *Edges 8 0 1 1 0 4 2 0 5 1 2 6 1 5 3 1 6 2 1 7 1 1 8 7 1 rlichtenwalter-LPmade-ba322c3/netlib/test/shortest_path_distribution.RUN000077500000000000000000000021221214146114000266040ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/simrank.CORRECT000066400000000000000000000002661214146114000232230ustar00rootroot000000000000001 0 0 1.280000e-01 0 0 1 0 4.000000e-01 1.024000e-01 0 0 1 4.096000e-02 3.200000e-01 1.280000e-01 4.000000e-01 4.096000e-02 1 7.168000e-02 0 1.024000e-01 3.200000e-01 7.168000e-02 1 rlichtenwalter-LPmade-ba322c3/netlib/test/simrank.INPUT000066400000000000000000000001161214146114000230130ustar00rootroot00000000000000*Vertices 5 0 0 1 1 2 2 3 3 4 4 *Edges 6 0 1 1 0 3 1 1 2 1 2 0 1 3 4 1 4 3 1 rlichtenwalter-LPmade-ba322c3/netlib/test/simrank.RUN000077500000000000000000000021261214146114000225660ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE 0.8 > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/snowball_sample.CORRECT000066400000000000000000000001571214146114000247400ustar00rootroot00000000000000*Vertices 9 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 *Edges 9 0 1 1 0 2 1 1 2 1 1 3 1 1 4 1 2 5 1 3 6 1 4 5 1 5 2 1 rlichtenwalter-LPmade-ba322c3/netlib/test/snowball_sample.INPUT000066400000000000000000000002611214146114000245320ustar00rootroot00000000000000*Vertices 9 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 *Edges 18 0 1 1 0 2 1 1 2 1 1 3 1 1 4 1 2 5 1 3 6 1 4 5 1 5 2 1 6 3 1 6 7 1 7 1 1 8 0 1 8 1 1 8 2 1 8 3 1 8 4 1 8 5 1 8 6 1 8 7 1rlichtenwalter-LPmade-ba322c3/netlib/test/snowball_sample.RUN000077500000000000000000000021261214146114000243040ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE 0 3 > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/snowball_sample_in.CORRECT000066400000000000000000000001011214146114000254130ustar00rootroot00000000000000*Vertices 5 0 0 1 1 2 2 3 3 4 4 *Edges 4 0 4 1 2 3 1 3 0 1 4 0 1 rlichtenwalter-LPmade-ba322c3/netlib/test/snowball_sample_in.INPUT000066400000000000000000000001071214146114000252170ustar00rootroot00000000000000*Vertices 5 0 0 1 1 2 2 3 3 4 4 *Edges 5 0 1 1 0 4 1 2 3 1 3 0 1 4 0 1 rlichtenwalter-LPmade-ba322c3/netlib/test/snowball_sample_in.RUN000077500000000000000000000004551214146114000247750ustar00rootroot00000000000000#! /bin/bash DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE 0 3 > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/snowball_sample_size_dist.CORRECT000066400000000000000000000000451214146114000270110ustar00rootroot000000000000007 9 7 9 2 0 4 1 3 1 2 0 6 4 7 6 9 17 rlichtenwalter-LPmade-ba322c3/netlib/test/snowball_sample_size_dist.INPUT000066400000000000000000000002611214146114000266070ustar00rootroot00000000000000*Vertices 9 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 *Edges 18 0 1 1 0 2 1 1 2 1 1 3 1 1 4 1 2 5 1 3 6 1 4 5 1 5 2 1 6 3 1 6 7 1 7 1 1 8 0 1 8 1 1 8 2 1 8 3 1 8 4 1 8 5 1 8 6 1 8 7 1rlichtenwalter-LPmade-ba322c3/netlib/test/snowball_sample_size_dist.RUN000077500000000000000000000021241214146114000263570ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE 3 > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/stream_to_network.CORRECT000066400000000000000000000001551214146114000253220ustar00rootroot00000000000000*Vertices 9 0 0 1 2 2 3 3 4 4 5 5 7 6 18 7 47 8 285 *Edges 8 0 1 1 0 4 2 0 5 1 2 6 1 5 3 1 6 2 1 7 1 1 8 7 1 rlichtenwalter-LPmade-ba322c3/netlib/test/stream_to_network.INPUT000066400000000000000000000000521214146114000251140ustar00rootroot0000000000000018 3 7 4 0 7 3 18 0 5 0 5 0 2 285 47 47 2 rlichtenwalter-LPmade-ba322c3/netlib/test/stream_to_network.RUN000077500000000000000000000021321214146114000246650ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE " " 1 2 > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/threshold_edges.CORRECT000066400000000000000000000000671214146114000247210ustar00rootroot00000000000000*Vertices 5 0 0 1 1 2 2 3 3 4 4 *Edges 2 0 3 2.7 0 4 4 rlichtenwalter-LPmade-ba322c3/netlib/test/threshold_edges.INPUT000066400000000000000000000001061214146114000245110ustar00rootroot00000000000000*Vertices 5 0 0 1 1 2 2 3 3 4 4 *Edges 4 0 1 1 0 2 2.2 0 3 2.7 0 4 4 rlichtenwalter-LPmade-ba322c3/netlib/test/threshold_edges.RUN000077500000000000000000000021261214146114000242650ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE 2.5 > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/wcc_distribution.CORRECT000066400000000000000000000001041214146114000251210ustar00rootroot000000000000000 2 1 0 2 2 2 1 4 3 2 0 7 3 2 0 10 3 2 0 13 3 3 2 16 5 6 1 21 1 0 0 rlichtenwalter-LPmade-ba322c3/netlib/test/wcc_distribution.INPUT000066400000000000000000000004121214146114000247210ustar00rootroot00000000000000*Vertices 22 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 *Edges 18 0 1 1 2 3 1 3 2 1 4 5 1 6 5 1 8 7 1 8 9 1 10 11 1 11 12 1 13 14 1 14 15 1 15 13 1 16 17 1 17 18 1 18 16 1 18 19 1 19 20 1 20 19 1 rlichtenwalter-LPmade-ba322c3/netlib/test/wcc_distribution.RUN000077500000000000000000000004511214146114000244740ustar00rootroot00000000000000#! /bin/bash DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/netlib/test/weighted_stream_to_network.CORRECT000066400000000000000000000001661214146114000272040ustar00rootroot00000000000000*Vertices 9 0 0 1 2 2 3 3 4 4 5 5 7 6 18 7 47 8 285 *Edges 8 0 1 2.7 0 4 3.3 0 5 11.4 2 6 9 5 3 0.3 6 2 2 7 1 5 8 7 1 rlichtenwalter-LPmade-ba322c3/netlib/test/weighted_stream_to_network.INPUT000066400000000000000000000001051214146114000267730ustar00rootroot0000000000000018 3 2 7 4 0.3 0 7 11.4 3 18 9 0 5 1.3 0 5 2 0 2 2.7 285 47 1 47 2 5 rlichtenwalter-LPmade-ba322c3/netlib/test/weighted_stream_to_network.RUN000077500000000000000000000021341214146114000265470ustar00rootroot00000000000000#! /bin/bash # ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ DIR=`dirname $0` BASE=`basename $0 .RUN` BINDIR=$1; < $DIR/$BASE.INPUT $BINDIR/$BASE " " 1 2 3 > $DIR/$BASE.OUTPUT diff -q $DIR/$BASE.OUTPUT $DIR/$BASE.CORRECT > /dev/null; if [ $? -eq 0 ] ; then echo 'PASSED'; elif [ $? -eq 1 ] ; then echo 'FAILED'; else echo 'TEST DID NOT COMPLETE' fi rlichtenwalter-LPmade-ba322c3/readme.txt000066400000000000000000000052741214146114000203020ustar00rootroot00000000000000You can find a complete PDF manual in manual/manual.pdf. An abbreviated startup guide is contained below. The procedure for installation is simple. The distribution is constructed such that it is unnecessary to seek and learn how to build and integrate any libraries. The only possible prerequisite for successful operation on supported systems is version 1.5 or higher of the Java Runtime Environment (JRE). We recommend Oracle/Sun Java, but the OpenJDK may work, and the default Java binary path is set to assume a standard Linux OpenJDK distribution. Setting up a working JRE or JDK is only required if your goals include using the bundled non-standard WEKA distribution. Navigate to the directory into which you expanded the gzipped tarball. Here, you will find two files containing the license terms and a file called 'make_all.sh'. To build LPmade and prepare it for use, you must execute 'make_all.sh'. This will perform the following actions within the working directory. -Compile all components of the network and link prediction library. -Compile all evaluation code. -Compile the bundled version of gnuplot and install into the gnuplot subdirectory. After this process completes without errors, you can make use of any of the binaries in the evaluation or network libraries individually or you can use the work flow management scripts. The following series of steps describes the process to quickly use the automated build system for link prediction and related analysis on sample data sets: 1. Follow the instructions above to prepare the system. 2. Navigate to one of the sample networks under 'wd'. 3. Run 'make -j X ' where X is the number of simultaneous jobs to run and target is the build target. For the list of targets, see the manual, but you can try 'sm', 'growth', or 'plots'. The following series of steps describes the process to quickly use the automated build system for link prediction and related analysis on a new network data set: 1. Follow the instructions above to prepare the system. 2. Create a subdirectory under 'wd' with the name of your network . For purposes of example, let us call it 'mynet', so 'wd/mynet'. 3. Create a 'src' subdirectory, 'wd/mynet/src'. Move all necessary source files into this directory. 4. Copy a sample task-specific Makefiles from the condmat or disease-g network. Modify this file so that it indicates how to interpret the raw source data, possibly referencing external scripts, which can go into a subdirectory 'wd/mynet/script'. See the manual for more details if necessary. 5. Change directory to 'wd/mynet'. 6. Type 'make -j X ' where target is the build target. For the list of targets, see the manual, but you can try 'sm', 'growth', or 'plots'. rlichtenwalter-LPmade-ba322c3/wd/000077500000000000000000000000001214146114000167065ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/wd/Makefile.common000066400000000000000000000736321214146114000216500ustar00rootroot00000000000000# ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ ##### # The NEIGHBORHOODS variable controls the geodesic distances at which LPmade performs analysis and predictions. Targets affected by this variable include "sm", "classify", "imbalance", and "plots". In the "sm" and "classify" subdirectories, results will be placed in n$(NEIGHBORHOODS) folders (e.g. n2). Setting the variable to 0 will perform predictions across all neighborhoods placing results in files in n0. ##### NEIGHBORHOODS := 2 ##### # The PREDICTORS variable controls which link prediction methods from the link prediction library are used. Targets affected by this variable include "sm", "classify", and "plots". Currently, the specification of predictors and arguments has strict requirements. Consider the IDegree predictor, the degree of the source node in the prediction. This degree may either be considered in terms of in-edges or out-edges. To predict both, IDegree should be specified as IDegree_D. In directed networks, this will perform predictions IDegree_O and IDegree_I, using both in-edges and out-edges for predictions. In undirected networks, it will only perform one set of predictions since there is no concept of in-edges and out-edges. To perform predictions with only one set of edges in directed networks, you must explicitly remove IDegree_D from the top variable assignment and add IDegree_I or IDegree_O in the directed network conditional block. An example is shown in comments below with PropFlow. All predictor arguments must come after the specification of directionality (D/I/O) and all arguments must be separated form each other and form the predictor name by an underscore (_). The final block is for the addition of predictors that do not accept directionality. Currently this includes only PreferentialAttachment, which is explicitly set to use out-edges of source and in-edges of target in directed networks (degenerating to total degree of both nodes in undirected networks). ##### PREDICTORS := IDegree_D JDegree_D IVolume_D JVolume_D CommonNeighbor_D JaccardCoefficient_D AdamicAdar_D PropFlow_D_5 IPageRank_D_0.85 JPageRank_D_0.85 RootedPageRank_D_0.15 PREDICTORS := $(PREDICTORS) ifeq ($(DIRECTED),1) PREDICTORS := $(shell echo $(PREDICTORS) | sed 's/_D$$/_I/; s/_D /_I /g; s/_D_/_I_/g') $(shell echo $(PREDICTORS) | sed 's/_D$$/_O/; s/_D /_O /g; s/_D_/_O_/g') # PREDICTORS := $(PREDICTORS) PropFlow_O_5 else PREDICTORS := $(shell echo $(PREDICTORS) | sed 's/_D$$//; s/_D / /g; s/_D_/_/g') # PREDICTORS := $(PREDICTORS) PropFlow_5 endif PREDICTORS := $(PREDICTORS) PreferentialAttachment ##### # The features variable and assignments work the same was as the PREDICTORS variable and assignments described above. This variable affects only the "classify" target. The variable specifies that features that should be used in supervised classification of links. ##### FEATURES := IDegree_D JDegree_D IVolume_D JVolume_D CommonNeighbor_D JaccardCoefficient_D AdamicAdar_D PropFlow_D_5 IPageRank_D_0.85 JPageRank_D_0.85 RootedPageRank_D_0.15 ifeq ($(DIRECTED),1) FEATURES := $(shell echo $(FEATURES) | sed 's/_D$$/_I/; s/_D /_I /g; s/_D_/_I_/g') $(shell echo $(FEATURES) | sed 's/_D$$/_O/; s/_D /_O /g; s/_D_/_O_/g') else FEATURES := $(shell echo $(FEATURES) | sed 's/_D$$//; s/_D / /g; s/_D_/_/g') endif FEATURES := $(FEATURES) PreferentialAttachment ##### # This variable allows for link prediction using vertex collocation profile (VCP) computations. Select one or more of VCP3Undirected, VCP3Directed, VCP4Undirected, VCP4Directed. ##### VCP := VCP3Undirected VCP4Undirected ##### # This variable specifies the number of bags that should be used in constructing the supervised classifier. It only affects the "classify" target. Larger numbers lead to increasingly better performance but at higher computational cost and with decreasing gains. In line with the KDD paper "New Perspectives and Methods in Link Prediction" (Lichtenwalter 2010), we use 10 bags. ##### NUM_BAGS := 10 ##### # This variable only affects the "classify" target. Link prediction is inherently a very imbalanced domain with few possible links actually forming. In supervised classification, training sets are much more manageable when the high proportion of negative instances is decreased to allow for faster computation and increased focus on the class boundary region. ##### PERCENT_POSITIVE := 25 ##### # This variable specifies the evaluation metrics to run on prediction outputs and, in some cases, the metrics for which to generate plots. It affects the "sm", "classify", "vcp", and "plots" target. Possible metrics are listed below: # roc auroc pr aupr ef tnp ##### EVALUATION_METRICS := roc auroc aupr ##### # This specifies the maximum memory available to "sort". Increasing values may increase sorting speed. Sort will never exceed this amount of memory consumption instead dumping intermediate results to temporary files. ##### SORT_MEMORY_MAX := 1G ##### # This specifies the maximum memory available to the Java virtual machine when running WEKA. For very large data sets, tens of gigabytes may be required. WEKA will fail if it requires more memory than allowed, so it is safe to set this to physical memory divided by the number of tasks to speculatively run simultaneously in hopes that the memory is sufficient. The fastest way to discover the memory requirements of classification is to first set this to the maximum memory of your machine and to observe the maximum working set size of the WEKA classification process. ##### WEKA_MEMORY_MAX := 1G ##### # This target copies, downloads, or constructs source data depending on task-specific user specifications. If there is no specification for src, then there must be a subdirectory src in the task-specific directory that has the necessary source files. ##### src: $(addprefix ./src/,$(SRC)) ##### # This makes the stream directory and places LPmade generic stream files in that directory based on the source data and PERIOD specifications inside the task-specific Makefile. ##### stream: $(addsuffix .txt.gz,$(addprefix ./stream/,$(PERIODS))) ##### # This makes all possible networks, FEATURE, LABEL, UNSUPERVISED, TEST, and COMPLETE, based on the stream target output and the period settings inside the task-specific Makefile. ##### net: $(addsuffix .net,$(addprefix ./net/,FEATURE LABEL UNSUPERVISED TEST COMPLETE)) ##### # This is the target to compute single-metric unsupervised methods of link prediction. The methods computed are determined by the PREDICTORS variable above. Predictions occur within the NEIGHBORHOODS variable above. ##### sm: $(foreach m,$(EVALUATION_METRICS),$(addsuffix .$(m),$(addprefix ./sm/n,$(foreach n,$(NEIGHBORHOODS),$(addprefix $(n)/,$(foreach p,$(PREDICTORS),$(p))))))) ##### # This is the target to compute supervised classification using bagging and undersampling parameters as specified in the variables above. The WEKA machine learning toolkit is used for its bagging and random forest classification algorithms, but power users may modify the classification rules to use whatever WEKA classifier they like. ##### classify: $(foreach m,$(EVALUATION_METRICS),$(addsuffix .$(m),$(addprefix ./classify/n,$(foreach n,$(NEIGHBORHOODS),$(addprefix $(n)/,result))))) ##### # This target goes beyond simply outputting numbers describing results to instead using the included gnuplot distribution to provide comparative plots of predictor performance. It also provides analytical plots using binning to determine the efficacy of various predictors within their individual score output regions. ##### plots: $(addprefix ./plots/n,$(foreach n,$(NEIGHBORHOODS),$(n)_roc.eps)) $(addprefix ./plots/n,$(foreach n,$(NEIGHBORHOODS),$(n)_pr.eps)) $(addprefix ./plots/,$(foreach p,$(PREDICTORS),$(p)_ef.eps)) ##### # This target computes network statistics on the COMPLETE network as specified in the task-specific Makefile. ##### stats: $(addprefix ./stats/,average_clustering_coefficient.txt free_choice_mutuality.txt scc_stats.txt out_degree_stats.txt pageranks.txt assortativity_coefficient.txt) ##### # This target computes SCC information about network data with increasing edge thresholds and places it in a file in the threshold directory, which the target creates. Currently, the target is set to threshold edges from 1 to 15 where the number is the minimum weight required to maintain the edge in the thresholded network. ##### threshold: $(addprefix ./threshold/,$(foreach t,1 2 3 4 5 6 7 8 9 10 11 12 13 14 15,$(t)_scc_stats.txt)) ##### # This target produces growth plots based on the PERIODS variables specified in the task-specific Makefile. These plots show the trend in various network characteristics with increasing observation time. ##### growth: $(addprefix ./growth/,growth.eps) ##### # This target shows the imbalance properties of the link prediction task across the neighborhoods specified in the NEIGHBORHOODS variable above. ##### imbalance: $(addprefix ./imbalance/,imbalance.eps) ##### # This target allows for link prediction using vertex collocation profile (VCP) computations. ##### vcp: $(foreach m,$(EVALUATION_METRICS),$(foreach vcp,$(VCP),./$(vcp)/n2/result.$(m))) ##### # This target is mostly for demonstration purposes. It will create a folder "params" into which it will place the result of a parameter search over the alpha parameter in the RootedPageRank and WeightedRootedPageRank predictors. This parameter search will be visible to the user in the form of plots produced by gnuplot on the computed data. This demonstration parameter search may be extended by users for parameter searches of other methods by examining the params target directly below as well as the upstream targets farther down in this Makefile. ##### params: $(addprefix ./params/RootedPageRank/,RootedPageRank.eps) $(addprefix ./params/WeightedRootedPageRank/,WeightedRootedPageRank.eps) ##### # The following text contains the core of the architectural automation. It should not be adjusted unless you know exactly what your goal is and how to accomplish it. ##### BAGS := $(shell echo {1..$(NUM_BAGS)}) STREAM_TO_NETWORK := stream_to_network -i "," 2 3 ifeq ($(WEIGHTED_STREAM),1) STREAM_TO_NETWORK := weighted_stream_to_network -i "," 2 3 4 endif ./params/RootedPageRank/RootedPageRank.gpi: ./params/RootedPageRank/RootedPageRank.dat echo 'set datafile separator " "' > $@.tmp; echo 'set term postscript eps enhanced color size 4in,4in blacktext "Helvetica" 18' >> $@.tmp; echo 'set title noenhanced "Rooted PageRank"' >> $@.tmp; echo 'set key on inside top right vertical' >> $@.tmp; echo 'set xlabel "{/Symbol a}"' >> $@.tmp; echo 'set xrange [0:1]' >> $@.tmp; echo 'set xtics 0.1' >> $@.tmp; echo 'set ylabel "AUC Performance"' >> $@.tmp; echo 'set yrange [:1]' >> $@.tmp; echo 'set ytics 0.1' >> $@.tmp; echo 'plot "$<" using 1:2 notitle with linespoints' >> $@.tmp; mv $@.tmp $@; ./params/WeightedRootedPageRank/WeightedRootedPageRank.gpi: ./params/WeightedRootedPageRank/WeightedRootedPageRank.dat echo 'set datafile separator " "' > $@.tmp; echo 'set term postscript eps enhanced color size 4in,4in blacktext "Helvetica" 18' >> $@.tmp; echo 'set title noenhanced "Weighted Rooted PageRank"' >> $@.tmp; echo 'set key on inside top right vertical' >> $@.tmp; echo 'set xlabel "{/Symbol a}"' >> $@.tmp; echo 'set xrange [0:1]' >> $@.tmp; echo 'set xtics 0.1' >> $@.tmp; echo 'set ylabel "AUC Performance"' >> $@.tmp; echo 'set yrange [:1]' >> $@.tmp; echo 'set ytics 0.1' >> $@.tmp; echo 'plot "$<" using 1:2 notitle with linespoints' >> $@.tmp; mv $@.tmp $@; ./params/WeightedRootedPageRank/WeightedRootedPageRank.dat: $(addprefix ./params/WeightedRootedPageRank/,$(foreach p,0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95,$(p).auroc)) rm -f $@; $(foreach p,$^,/bin/echo -n "$(basename $(notdir $(p))) " >> $@; cat $(p) >> $@;) ./params/WeightedRootedPageRank/%.prob.gz: ./net/UNSUPERVISED.net ./net/TEST.net mkdir -p $(@D); $(BIN)/predict -n 3 -f ./net/UNSUPERVISED.net WeightedRootedPageRank O | $(BIN)/label_predictions ./net/TEST.net | gzip > $@.tmp && \ if [ $${PIPESTATUS[0]} -eq 0 -a $${PIPESTATUS[1]} -eq 0 -a $${PIPESTATUS[2]} -eq 0 ]; then mv $@.tmp $@; else exit 1; fi; ./params/RootedPageRank/RootedPageRank.dat: $(addprefix ./params/RootedPageRank/,$(foreach p,0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95,$(p).auroc)) rm -f $@; $(foreach p,$^,/bin/echo -n "$(basename $(notdir $(p))) " >> $@; cat $(p) >> $@;) ./params/RootedPageRank/%.prob.gz: ./net/UNSUPERVISED.net ./net/TEST.net mkdir -p $(@D); $(BIN)/predict -n 3 -f ./net/UNSUPERVISED.net RootedPageRank O | $(BIN)/label_predictions ./net/TEST.net | gzip > $@.tmp && \ if [ $${PIPESTATUS[0]} -eq 0 -a $${PIPESTATUS[1]} -eq 0 -a $${PIPESTATUS[2]} -eq 0 ]; then mv $@.tmp $@; else exit 1; fi; ./net/%.net: $(addsuffix .txt.gz,$(addprefix ./stream/,$(PERIODS))) mkdir -p $(@D); gunzip --stdout $(addsuffix .txt.gz,$(addprefix ./stream/,$($(basename $(@F))))) | $(BIN)/$(STREAM_TO_NETWORK) > $@.tmp; mv $@.tmp $@; ./sm/%.prob.gz: ./net/UNSUPERVISED.net ./net/TEST.net mkdir -p $(@D); $(BIN)/predict -n $(subst sm/n,,$(@D)) -f ./net/UNSUPERVISED.net $(shell echo $(basename $(basename $(@F))) | grep -o '_\(I\|O\)\(_.*\|$$\)' | sed 's/^_\(I\).*/-d \1 /; s/^_\(O\).*/-d \1 /')$(shell echo $(basename $(basename $(@F))) | sed 's/_I_/_/; s/_O_/_/; s/_I$$//; s/_O$$//; s/_/ /g') | $(BIN)/label_predictions ./net/TEST.net | gzip > $@.tmp && \ if [ $${PIPESTATUS[0]} -eq 0 -a $${PIPESTATUS[1]} -eq 0 -a $${PIPESTATUS[2]} -eq 0 ]; then mv $@.tmp $@; else exit 1; fi; ./classify/n%/train.names: $(addprefix ./classify/features/n%/,$(foreach f,$(FEATURES),$(f).prob.gz)) mkdir -p $(@D); echo '0,1' > $@.tmp; echo -e -n '$(subst \n ,\n,$(foreach f,$(FEATURES),$(f): continuous\n))' >> $@.tmp; mv $@.tmp $@; ./classify/n%/test.names: ./classify/n%/train.names cp $< $@; ./classify/features/%.prob.gz: ./net/FEATURE.net ./net/LABEL.net mkdir -p $(@D); $(BIN)/predict -n $(subst classify/features/n,,$(@D)) -f ./net/FEATURE.net $(shell echo $(basename $(basename $(@F))) | grep -o '_\(I\|O\)\(_.*\|$$\)' | sed 's/^_\(I\).*/-d \1 /; s/^_\(O\).*/-d \1 /')$(shell echo $(basename $(basename $(@F))) | sed 's/_I_/_/; s/_O_/_/; s/_I$$//; s/_O$$//; s/_/ /g') | $(BIN)/label_predictions ./net/LABEL.net | gzip > $@.tmp && \ if [ $${PIPESTATUS[0]} -eq 0 -a $${PIPESTATUS[1]} -eq 0 -a $${PIPESTATUS[2]} -eq 0 ]; then mv $@.tmp $@; else exit 1; fi; ./classify/n%/train.data.gz: $(addprefix ./classify/features/n%/,$(foreach f,$(FEATURES),$(f).prob.gz)) mkdir -p $(@D); pr -t -s',' -m $(foreach f,$^,<(< $(f) gunzip | cut -d ' ' -f 1)) <(< $< gunzip | cut -d ' ' -f 2) | gzip > $@.tmp; mv $@.tmp $@; ./classify/n%/test.data.gz: $(addprefix ./sm/n%/,$(foreach f,$(FEATURES),$(f).prob.gz)) mkdir -p $(@D); pr -t -s',' -m $(foreach f,$^,<(< $(f) gunzip | cut -d ' ' -f 1)) <(< $< gunzip | cut -d ' ' -f 2) | gzip > $@.tmp; mv $@.tmp $@; define DISTANCE_TEMPLATE ./VCP%/n$(1)/train.data.gz: ./net/FEATURE.net ./net/LABEL.net mkdir -p $$(@D); pr -t -s',' -m <(($$(BIN)/predict -n $(1) -f ./net/FEATURE.net $$(shell basename $$$$(dirname $$(@D))) 3>&1 1>&2- 2>&3-) 2> /dev/null | cut -d ' ' -f3- | tr ' ' ',') <($$(BIN)/predict -n $(1) -f ./net/FEATURE.net One | $$(BIN)/label_predictions ./net/LABEL.net | cut -d ' ' -f 2) | gzip > $$@.tmp && \ if [ $$$${PIPESTATUS[0]} -eq 0 -a $$$${PIPESTATUS[1]} -eq 0 ]; then mv $$@.tmp $$@; else exit 1; fi; ./VCP%/n$(1)/train.names: ./VCP%/n$(1)/train.data.gz < $$< gunzip | head -n 1 | awk -F ',' 'BEGIN {print "0,1";} {for(i=0;i $$@.tmp; mv $$@.tmp $$@; ./VCP%/n$(1)/test.data.gz: ./net/UNSUPERVISED.net ./net/TEST.net mkdir -p $$(@D); pr -t -s',' -m <(($$(BIN)/predict -n $(1) -f ./net/UNSUPERVISED.net $$(shell basename $$$$(dirname $$(@D))) 3>&1 1>&2- 2>&3-) 2> /dev/null | cut -d ' ' -f3- | tr ' ' ',') <($$(BIN)/predict -n $(1) -f ./net/UNSUPERVISED.net One | $$(BIN)/label_predictions ./net/TEST.net | cut -d ' ' -f 2) | gzip > $$@.tmp && \ if [ $$$${PIPESTATUS[0]} -eq 0 -a $$$${PIPESTATUS[1]} -eq 0 ]; then mv $$@.tmp $$@; else exit 1; fi; ./VCP%/n$(1)/test.names: ./VCP%/n$(1)/test.data.gz < $$< gunzip | head -n 1 | awk -F ',' 'BEGIN {print "0,1";} {for(i=0;i $$@.tmp; mv $$@.tmp $$@; endef $(foreach distance,$(NEIGHBORHOODS),$(eval $(call DISTANCE_TEMPLATE,$(distance)))) define WEKA_TEMPLATE %/result_$(1).names: %/train.names mkdir -p $$(@D); cp $$< $$@; %/result_$(1).data.gz: %/train.data.gz all=$$$$(< $$< gunzip | wc -l) && \ pos=$$$$(< $$< gunzip | grep -c "1$$$$") && \ neg=$$$$(($$$$all-$$$$pos)) && \ count=`awk 'BEGIN {print '$$$$pos'/($$(PERCENT_POSITIVE)/100)-'$$$$pos'}'` && \ percent=`awk 'BEGIN {print '$$$$count'/'$$$$neg'}'` && \ < $$< gunzip | awk 'BEGIN{srand($(1));} {if( $$$$0 ~ /1$$$$/ || ($$$$0 ~ /0$$$$/ && rand() < '$$$$percent') ) {print;}}' | gzip > $$@.tmp && \ if [ $$$${PIPESTATUS[0]} -eq 0 -a $$$${PIPESTATUS[1]} -eq 0 -a $$$${PIPESTATUS[2]} -eq 0 ]; then mv $$@.tmp $$@; else exit 1; fi; %/result_$(1).model: %/result_$(1).names %/result_$(1).data.gz mkdir -p $$(@D); $(JAVA) -Xmx$(WEKA_MEMORY_MAX) -cp $(WEKA) weka.classifiers.trees.RandomForest \ -d $$@.tmp \ -t $$(@D)/result_$(1).names \ -I 10 -K 0 -S $(1); mv $$@.tmp $$@; %/result_$(1).prob.gz: %/result_$(1).model %/test.names %/test.data.gz mkdir -p $$(@D); $(JAVA) -Xmx$(WEKA_MEMORY_MAX) -cp $(WEKA) weka.classifiers.trees.RandomForest \ -l $$(@D)/result_$(1).model \ -T $$(@D)/test.names \ -p 0 -distribution \ | gzip > $$@.tmp && \ if [ $$$${PIPESTATUS[0]} -eq 0 -a $$$${PIPESTATUS[1]} -eq 0 ]; then mv $$@.tmp $$@; else exit 1; fi; endef $(foreach bag,$(BAGS),$(eval $(call WEKA_TEMPLATE,$(bag)))) %/result.prob.gz: $(foreach bag,$(BAGS),%/result_$(bag).prob.gz) mkdir -p $(@D); pr -t -s' ' -m $(foreach bag,$(BAGS),<(< $(@D)/result_$(bag).prob.gz gunzip | cut -d ' ' -f 1) ) <(< $(@D)/result_1.prob.gz gunzip | cut -d ' ' -f 2) | awk -F ' ' '{sum=0; for(i=1;i<=$(NUM_BAGS);i++){sum+=$$i} print sum/$(NUM_BAGS)" "$$NF;}' | gzip > $@.tmp && \ if [ $${PIPESTATUS[0]} -eq 0 -a $${PIPESTATUS[1]} -eq 0 ]; then mv $@.tmp $@; else exit 1; fi; %.sprob.gz: %.prob.gz < $< gunzip | sort -S $(SORT_MEMORY_MAX) -T $(TEMP) -k1gr | gzip > $@.tmp && \ if [ $${PIPESTATUS[0]} -eq 0 -a $${PIPESTATUS[1]} -eq 0 -a $${PIPESTATUS[2]} -eq 0 ]; then mv $@.tmp $@; else exit 1; fi; %.roc: %.sprob.gz all=`< $< gunzip | wc -l` && \ pos=`< $< gunzip | grep -c "1$$"` && \ neg=`echo "$$all-$$pos" | bc` && \ < $< gunzip | $(METRICS)/roc $$neg $$pos | sed 's/\(.\...\)..../\1/g' | uniq > $@.tmp && \ if [ $${PIPESTATUS[0]} -eq 0 -a $${PIPESTATUS[1]} -eq 0 -a $${PIPESTATUS[2]} -eq 0 -a $${PIPESTATUS[3]} -eq 0 ]; then mv $@.tmp $@; else exit 1; fi; %.pr: %.sprob.gz pos=`< $< gunzip | grep -c "1$$"` && \ < $< gunzip | $(METRICS)/pr 1 $$pos | sed 's/\(.\...\)......./\1/g' | uniq > $@.tmp && \ if [ $${PIPESTATUS[0]} -eq 0 -a $${PIPESTATUS[1]} -eq 0 -a $${PIPESTATUS[2]} -eq 0 -a $${PIPESTATUS[3]} -eq 0 ]; then mv $@.tmp $@; else exit 1; fi; %.auroc: %.sprob.gz < $< gunzip | $(METRICS)/$(subst .,,$(suffix $(@F))) > $@.tmp && \ if [ $${PIPESTATUS[0]} -eq 0 -a $${PIPESTATUS[1]} -eq 0 ]; then mv $@.tmp $@; else exit 1; fi; %.aupr: %.sprob.gz pos=$$(< $< gunzip | grep -c '1$$') && \ < $< gunzip | $(METRICS)/$(subst .,,$(suffix $(@F))) 0.001 $$pos > $@.tmp && \ if [ $${PIPESTATUS[0]} -eq 0 -a $${PIPESTATUS[1]} -eq 0 ]; then mv $@.tmp $@; else exit 1; fi; %.tnp: %.sprob.gz all=`< $< gunzip | wc -l` && \ < $< gunzip | $(METRICS)/topnprec $$all > $@.tmp && \ if [ $${PIPESTATUS[0]} -eq 0 -a $${PIPESTATUS[1]} -eq 0 ]; then mv $@.tmp $@; else exit 1; fi; %.ef: %.sprob.gz all=`< $< gunzip | wc -l` && \ < $< gunzip | $(METRICS)/$(subst .,,$(suffix $(@F))) 10 $$all > $@.tmp && \ if [ $${PIPESTATUS[0]} -eq 0 -a $${PIPESTATUS[1]} -eq 0 ]; then mv $@.tmp $@; else exit 1; fi; ./plots/%_roc.gpi: $(addprefix ./sm/%/,$(foreach p,$(PREDICTORS),$(p).roc)) mkdir -p plots; echo 'set datafile separator " "' > $@.tmp; echo 'set term postscript eps enhanced color size 4in,4in blacktext "Helvetica" 18' >> $@.tmp; echo 'set title noenhanced "$(notdir $(CURDIR)) $(subst _roc,,$(basename $(@F)))"' >> $@.tmp; echo 'set key on inside bottom right vertical' >> $@.tmp; echo 'set xlabel "False Positive Rate"' >> $@.tmp; echo 'set xrange [0:1]' >> $@.tmp; echo 'set xtics 0.1' >> $@.tmp; echo 'set ylabel "True Positive Rate"' >> $@.tmp; echo 'set yrange [0:1]' >> $@.tmp; echo 'set ytics 0.1' >> $@.tmp; echo 'plot x notitle with lines lt 2 linecolor 0 $(foreach p,$(PREDICTORS),,"sm/$(subst _roc,,$(basename $(@F)))/$(p).roc" using 1:2 title "$(subst _, ,$(p))" with linespoints)' >> $@.tmp; mv $@.tmp $@; ./plots/%_pr.gpi: $(addprefix ./sm/%/,$(foreach p,$(PREDICTORS),$(p).pr)) mkdir -p plots; echo 'set datafile separator " "' > $@.tmp; echo 'set term postscript eps enhanced color size 4in,4in blacktext "Helvetica" 18' >> $@.tmp; echo 'set title noenhanced "$(notdir $(CURDIR)) $(subst _roc,,$(basename $(@F)))"' >> $@.tmp; echo 'set key on inside bottom right vertical' >> $@.tmp; echo 'set xlabel "False Positive Rate"' >> $@.tmp; echo 'set xrange [0:1]' >> $@.tmp; echo 'set xtics 0.1' >> $@.tmp; echo 'set ylabel "True Positive Rate"' >> $@.tmp; echo 'set yrange [0:1]' >> $@.tmp; echo 'set ytics 0.1' >> $@.tmp; echo 'plot x notitle with lines lt 2 linecolor 0 $(foreach p,$(PREDICTORS),,"sm/$(subst _pr,,$(basename $(@F)))/$(p).pr" using 1:2 title "$(subst _, ,$(p))" with linespoints)' >> $@.tmp; mv $@.tmp $@; ./plots/%_tnp.gpi: $(addprefix ./sm/%/,$(foreach p,$(PREDICTORS),$(p).tnp)) mkdir -p plots; echo 'set datafile separator " "' > $@.tmp; echo 'set term postscript eps enhanced color size 4in,4in blacktext "Helvetica" 18' >> $@.tmp; echo 'set title noenhanced "$(notdir $(CURDIR)) $(subst _tnp,,$(basename $(@F)))"' >> $@.tmp; echo 'set key on inside bottom right vertical' >> $@.tmp; echo 'set xlabel "Top-X Proportion"' >> $@.tmp; echo 'set xrange [0:0.2]' >> $@.tmp; echo 'set xtics 0.1' >> $@.tmp; echo 'set yrange [0:]' >> $@.tmp; echo 'set ylabel "Precision"' >> $@.tmp; echo 'plot x notitle with lines lt 2 linecolor 0 $(foreach p,$(PREDICTORS),,"sm/$(subst _tnp,,$(basename $(@F)))/$(p).tnp" using 1:2 title "$(subst _, ,$(p))" with linespoints)' >> $@.tmp; mv $@.tmp $@; ./plots/%_ef.gpi: $(addprefix ./sm/n,$(foreach n,$(NEIGHBORHOODS),$(n)/%.ef)) mkdir -p plots; echo 'set datafile separator " "' > $@.tmp; echo 'set term postscript eps enhanced color size 4in,4in blacktext "Helvetica" 18' >> $@.tmp; echo 'set title noenhanced "$(notdir $(CURDIR)) $(subst _ef,,$(basename $(@F)))"' >> $@.tmp; echo 'set key on inside bottom right vertical' >> $@.tmp; echo 'set xlabel "Score"' >> $@.tmp; echo 'set log x' >> $@.tmp; echo 'set ylabel "True Positive Rate"' >> $@.tmp; echo 'set log y' >> $@.tmp; echo 'plot 0 notitle $(foreach n,$(NEIGHBORHOODS),,"sm/n$(n)/$(subst _ef,,$(basename $(@F))).ef" using 1:2 title "$(subst _, ,n=$(n))" with linespoints)' >> $@.tmp; mv $@.tmp $@; ./imbalance/%.counts: ./net/UNSUPERVISED.net ./net/TEST.net mkdir -p imbalance; rm -f $(@D)/pipe$(basename $(@F)); mkfifo $(@D)/pipe$(basename $(@F)); (wc -l > $(@D)/$(basename $(@F)).all) < $(@D)/pipe$(basename $(@F)) & $(BIN)/predict -n $(basename $(@F)) -f ./net/UNSUPERVISED.net One O | $(BIN)/label_predictions ./net/TEST.net | tee $(@D)/pipe$(basename $(@F)) | grep -c "1$$" | cat > $(@D)/$(basename $(@F)).pos; /bin/echo -n '$(basename $(@F)) ' > $@.tmp; paste -d ' ' $(@D)/$(basename $(@F)).all $(@D)/$(basename $(@F)).pos >> $@.tmp; mv $@.tmp $@; rm -f $(@D)/pipe$(basename $(@F)) $(@D)/$(basename $(@F)).all $(@D)/$(basename $(@F)).pos ./imbalance/imbalance.dat: $(addprefix ./imbalance/,$(foreach n,$(NEIGHBORHOODS),$(n).counts)) cat ./imbalance/* | awk -F ' ' '{if( $$3 > 0 ){print $$1" "$$2" "$$3" "$$2-$$3" "($$2-$$3)/$$3;}}' > $@.tmp; mv $@.tmp $@; ./imbalance/imbalance.gpi: ./imbalance/imbalance.dat mkdir -p imbalance; echo 'set datafile separator " "' > $@.tmp; echo 'set term postscript eps enhanced color size 4in,4in blacktext "Helvetica" 18' >> $@.tmp; echo 'set notitle' >> $@.tmp; echo 'set key on inside bottom center vertical' >> $@.tmp; echo 'set xlabel "Geodesic Distance"' >> $@.tmp; echo 'set xtics 1' >> $@.tmp; echo 'set logscale y' >> $@.tmp; echo 'set yrange [1:]' >> $@.tmp; echo 'set format y "10^{%L}"' >> $@.tmp; /bin/echo -n 'plot "$<" using 1:4 title "(-) Count" with linespoints, ' >> $@.tmp; /bin/echo -n '"$<" using 1:3 title "(+) Count" with linespoints, ' >> $@.tmp; echo '"$<" using 1:5 title "Imbalance Ratio" with linespoints' >> $@.tmp; mv $@.tmp $@; %.eps: %.gpi < $< $(GNUPLOT) > $@.tmp; mv $@.tmp $@; ./stats/average_clustering_coefficient.txt: ./net/COMPLETE.net mkdir -p $(@D); < $< $(BIN)/average_clustering_coefficient > $@.tmp; mv $@.tmp $@; ./stats/free_choice_mutuality.txt: ./net/COMPLETE.net mkdir -p $(@D); < $< $(BIN)/free_choice_mutuality > $@.tmp; mv $@.tmp $@; ./stats/out_degree_distribution.txt: ./net/COMPLETE.net mkdir -p $(@D); < $< $(BIN)/out_degree_distribution > $@.tmp; mv $@.tmp $@; ./stats/scc_distribution.txt: ./net/COMPLETE.net mkdir -p $(@D); < $< $(BIN)/scc_distribution > $@.tmp; mv $@.tmp $@; ./stats/pageranks.txt: ./net/COMPLETE.net mkdir -p $(@D); < $< $(BIN)/pagerank 0.85 > $@.tmp; mv $@.tmp $@; ./stats/assortativity_coefficient.txt: ./net/COMPLETE.net mkdir -p $(@D); < $< $(BIN)/assortativity_coefficient > $@.tmp; mv $@.tmp $@; %_stats.txt: %_distribution.txt mkdir -p $(@D); < $< sort -g | awk '{sum+=$$0; array[NR]=$$0} END{for(x=1;x<=NR;x++){sumsq+=((array[x]-(sum/NR))^2);} print NR" "array[1]" "(array[int((NR+1)/2)]+array[int((NR+2)/2)])/2" "array[NR]" "sum/NR" "sqrt(sumsq/NR);}' > $@.tmp; mv $@.tmp $@; ./threshold/%_scc_distribution.txt: ./net/COMPLETE.net mkdir -p $(@D); < $< $(BIN)/threshold_edges $(subst _scc_distribution.txt,,$(@F)) | $(BIN)/scc_distribution > $@.tmp; mv $@.tmp $@; INDEX := 1 define SPAN_TEMPLATE SPAN$(INDEX) := $(SPAN$(shell echo $$(($(INDEX)-1)))) $(1) INDEX := $(shell echo $$(($(INDEX)+1))) SPANS += SPAN$(INDEX) endef $(foreach period,$(PERIODS),$(eval $(call SPAN_TEMPLATE,$(period)))) define SPAN_NET_TEMPLATE ./growth/$(1).net: $(addsuffix .txt.gz,$(addprefix ./stream/,$($(1)))) gunzip --stdout $$^ | $(BIN)/stream_to_network "," 2 3 > $$@.tmp && \ if [ $$$${PIPESTATUS[0]} -eq 0 -a $$$${PIPESTATUS[1]} -eq 0 ]; then mv $$@.tmp $$@; else exit 1; fi; endef $(foreach span,$(SPANS),$(eval $(call SPAN_NET_TEMPLATE,$(span)))) define SPAN_STATS_TEMPLATE ./growth/$(1).stats: ./growth/$(1).net mkdir -p $$(@D); /bin/echo -n $$$$(< $$< $(BIN)/count_vertices)' ' > $$@.tmp; /bin/echo -n $$$$(< $$< $(BIN)/count_edges)' ' >> $$@.tmp; /bin/echo -n $$$$(< $$< $(BIN)/average_clustering_coefficient)' ' >> $$@.tmp; /bin/echo -n $$$$(< $$< $(BIN)/assortativity_coefficient)' ' >> $$@.tmp; /bin/echo -n $$$$(< $$< $(BIN)/scc_distribution | wc -l)' ' >> $$@.tmp; echo $$$$(< $$< $(BIN)/scc_distribution | awk -F ' ' 'BEGIN{MAX=-1} {if($$$$2>MAX){MAX=$$$$2;}} END{print MAX;}') >> $$@.tmp mv $$@.tmp $$@; endef $(foreach span,$(SPANS),$(eval $(call SPAN_STATS_TEMPLATE,$(span)))) ./growth/growth.dat: $(addprefix ./growth/,$(foreach period,$(PERIODS),$(subst PERIOD,SPAN,$(period).stats))) cat $^ | awk -F ' ' '{for(c=1;c<=NF;c++){vector[NR,c]=$$c;}} END{for(r=1;r<=NR;r++){printf "%d",r;for(c=1;c<=NF;c++){printf " %f",vector[r,c]/vector[NR,c];}printf "\n";}}' > $@.tmp && \ if [ $${PIPESTATUS[0]} -eq 0 -a $${PIPESTATUS[1]} -eq 0 ]; then mv $@.tmp $@; else exit 1; fi; ./growth/growth.gpi: ./growth/growth.dat echo 'set datafile separator " "' > $@.tmp; echo 'set term postscript eps enhanced color size 4in,4in blacktext "Helvetica" 18' >> $@.tmp; echo 'set title noenhanced "Network Growth Characteristics"' >> $@.tmp; echo 'set key on inside bottom right vertical' >> $@.tmp; echo 'set xlabel "Time (in equal-width periods)"' >> $@.tmp; echo 'set xrange [1:$(shell echo $$(($$(< $< wc -l)-1)))]' >> $@.tmp; echo 'set xtics 1' >> $@.tmp; echo 'set ylabel "Value Relative to Final Value"' >> $@.tmp; echo 'set yrange [0:2]' >> $@.tmp; echo 'set ytics 0.25' >> $@.tmp; /bin/echo -n 'plot "$<" using 1:2 notitle "Vertex Count" with points linecolor 1, ' >> $@.tmp; /bin/echo -n '"$<" using 1:2 title "Vertex Count" smooth csplines with lines linecolor 1, ' >> $@.tmp; /bin/echo -n '"$<" using 1:3 notitle "Edge Count" with points linecolor 2, ' >> $@.tmp; /bin/echo -n '"$<" using 1:3 title "Edge Count" smooth csplines with lines linecolor 2, ' >> $@.tmp; /bin/echo -n '"$<" using 1:4 notitle "Average Clustering Coefficient" with points linecolor 3, ' >> $@.tmp; /bin/echo -n '"$<" using 1:4 title "Average Clustering Coefficient" smooth csplines with lines linecolor 3, ' >> $@.tmp; /bin/echo -n '"$<" using 1:5 notitle "Assortativity Coefficient" with points linecolor 4, ' >> $@.tmp; /bin/echo -n '"$<" using 1:5 title "Assortativity Coefficient" smooth csplines with lines linecolor 4, ' >> $@.tmp; /bin/echo -n '"$<" using 1:6 notitle "Number of SCCs" with points linecolor 5, ' >> $@.tmp; /bin/echo -n '"$<" using 1:6 title "Number of SCCs" smooth csplines with lines linecolor 5, ' >> $@.tmp; /bin/echo -n '"$<" using 1:7 notitle "Size of LSCC" with points linecolor 6, ' >> $@.tmp; echo '"$<" using 1:7 title "Size of LSCC" smooth csplines with lines linecolor 6' >> $@.tmp; mv $@.tmp $@; .SECONDARY: .PHONY: clean src stream net sm classify plots stats threshold growth imbalance params clean: for i in $(find . -name *.tmp) ; do rm -f $$i; done; rlichtenwalter-LPmade-ba322c3/wd/Makefile.options000066400000000000000000000042221214146114000220400ustar00rootroot00000000000000# ------------------------------------------------ # Copyright (C) 2010,2011 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ ##### # The JAVA variable defines the path to the java binary. The default is to use the java in /usr/bin/java, but this version is not the Oracle/Sun JRE and may not support 64 bit operations. We suggest using the Oracle/Sun JRE. ##### JAVA := /usr/bin/java ##### # This provides the path to the temporary directory that the user wishes to use. The default is /tmp, but this can be changed to accomodate larger temporary directory locations in case this is needed. ##### TEMP := /tmp ##### # As of LPmade version 1.1.2, the bash shell in non-POSIX mode is assumed. This relaxation of constraints from pure POSIX was to allow for process substitution in Makefile.common. Process substitution is a much cleaner alternative to named pipes. Additionally, previous versions of LPmade were not purely POSIX compliant anyhow as they used the PIPESTATUS bash variable. This variable should only be changed by users who are very comfortable with shells, scripting, and GNU make. ##### SHELL := /bin/bash ##### # The options passed to the shell by GNU make. ##### export SHELLOPTS := errexit ##### # These are internal paths. These should not be modified by users of LPmade unless there is some specific reason to change the internal relative directory structure. ##### BIN := ../../netlib/bin METRICS := ../../metrics GNUPLOT := ../../gnuplot/bin/gnuplot WEKA := ../../weka/weka.jar rlichtenwalter-LPmade-ba322c3/wd/condmat/000077500000000000000000000000001214146114000203335ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/wd/condmat/Makefile000066400000000000000000000033521214146114000217760ustar00rootroot00000000000000# ------------------------------------------------ # Copyright (C) 2010 by Ryan N. Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ include ../Makefile.options SRC := cond-mat.hg2.gz DIRECTED := 0 WEIGHTED_STREAM := 1 SRC2STREAM := ./script/src_to_stream.pl PERIOD1 := 1995 PERIOD2 := 1996 PERIOD3 := 1997 PERIOD4 := 1998 PERIOD5 := 1999 PERIOD6 := 2000 FEATURE := $(addprefix PERIOD,$(shell echo {1..4})) LABEL := PERIOD5 UNSUPERVISED := $(addprefix PERIOD,$(shell echo {1..5})) TEST := PERIOD6 COMPLETE := $(addprefix PERIOD,$(shell echo {1..6})) PERIODS := $(addprefix PERIOD,$(shell echo {1..6})) empty := space := $(empty) $(empty) define PERIOD_STREAM_TEMPLATE ./stream/$(1).txt.gz: $(addprefix ./src/,$(SRC)) mkdir -p $$(@D); < $$< gunzip | $(SRC2STREAM) | grep "^\($(subst $(space),\|,$($(1)))\)-" | sort -t ',' -k1n | gzip > $$@.tmp; mv $$@.tmp $$@; endef $(foreach period,$(PERIODS),$(eval $(call PERIOD_STREAM_TEMPLATE,$(period)))) include ../Makefile.common ./src/%.hg2.gz: mkdir -p $(@D); wget -O - http://www.nd.edu/~rlichten/linkpred/$(@F) > $@.tmp; mv $@.tmp $@; rlichtenwalter-LPmade-ba322c3/wd/condmat/script/000077500000000000000000000000001214146114000216375ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/wd/condmat/script/src_to_stream.pl000077500000000000000000000016161214146114000250470ustar00rootroot00000000000000#! /usr/bin/perl use strict; use warnings FATAL => 'all'; my $processingEdges = 0; while( my $line = ) { chomp( $line ); if( $line =~ m/\*Hyperedges:.*/ ) { $processingEdges = 1; next; } if( $processingEdges == 1 ) { processEdge( $line ); } } sub processEdge { my ($line) = @_; $line =~ m/(\d+) (\d+) \[ (.*) \]/; my $time = $1; my $edgeCount = $2; my $edgeList = $3; my @edgeArray = split( / /, $edgeList ); foreach my $i (0..$#edgeArray) { foreach my $j ($i+1..$#edgeArray) { my ($sec,$min,$hour,$mday,$mon,$year,$wday,$yday,$isdst) = gmtime( $time ); $year += 1900; $mon += 1; printf STDOUT "$year-%02d-%02d %02d:%02d:%02d,$edgeArray[$i],$edgeArray[$j],%.6f\n", $mon, $mday, $hour, $min, $sec, 1 / ($edgeCount-1); printf STDOUT "$year-%02d-%02d %02d:%02d:%02d,$edgeArray[$j],$edgeArray[$i],%.6f\n", $mon, $mday, $hour, $min, $sec, 1 / ($edgeCount-1); } } } rlichtenwalter-LPmade-ba322c3/wd/condmat/src/000077500000000000000000000000001214146114000211225ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/wd/condmat/src/cond-mat.hg2.gz000066400000000000000000013721541214146114000236620ustar00rootroot00000000000000H:cond-mat.hg2\ɶ )(@ 3W\9iU*ʼn-@ڍoxsIf,OS޴L纘7Sk/u%SU^+tU;UV6qX^onCtߜiaSݯ:}նk{SmYRvͩ{o_Qwjj/k4Jo7F}ոk6뵺0xt9x6_nsmOj_:QO:q/~F/bjsi-M56N-A3nZqs)>͠y~k{ʶjκk۴C,ٜ*Wnz3lbD˯,\Ji \곹=~eb6Ӧʎ;5ߛbyʷZS|閩Z;OXE[LM_1jY_yn4k0J~oT?} &gםf4UүiZOMY׿[lf]n_W3ε~O6S7V>ܫyʽzQ̍Mkkg5mn_^Xjt?j5^{3O@76uPh_M6AY:ze䲒{]!=׻sմ}su8lCyƾZZKo~, m_ǭO먩(Y;:Qk=G F1v6juLҠN[g$g5k6e-j߿anks1m;=qО 5 Y0!~Z~%\WLѯæC|*nZ%IߛnZne2؁j6M_n=w4)FVdVC7#bfYͽX 6/ keHFj2F[KF"rh4^ڌɦ:3 ز4UWMOfZ($:ZtPu:,q+]ݲ6I%Ǐz'i6KJj_);TAh˥$c]?u*j@tI!*ڹLyiV.e%+a1(AѶRuuDSȜ*٠O94ޝdpy$ 42V@sSzV{5vv^V'nZ c>LWkqO2\[;F$YЖV\qf^I %Bn'}Â~N )?&}iYO蝤tk[J\W*[B)J71N͉?,vu_SZ=Mu++K*^N$]Zu%HB_d#|CR4uo( 2Ay85P͒o<-} L9ɮ=vTDUiNZ4R^펫&hjKXd,zh:4<37S 6rIWKy"Y7I*!7Of~scgqGcET[^.Kߥ}v4ߣz!1,^W*dz$EHxd<`>ɝBAE Z"A_HIW"?P6F]B>JCK$o1$uu b\"q{/4l =RSa $-/ Ik/IjLTu_C9eb%gkD=BrvS5jᾒ4y6i85#!T%t"9Ax,H֢k%G. 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Lichtenwalter # Email: rlichtenwalter@gmail.com # # This file is part of LPmade. # LPmade is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. LPmade is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with LPmade. If not, see . # ------------------------------------------------ include ../Makefile.options SRC := disease-g.txt.gz WEIGHTED_STREAM := 1 DIRECTED := 0 SRC2STREAM := ./script/src_to_stream.pl PERIOD1 := 1 PERIOD2 := 2 PERIOD3 := 3 PERIOD4 := 4 PERIOD5 := 5 PERIOD6 := 6 PERIOD7 := 7 PERIOD8 := 8 PERIOD9 := 9 PERIOD10:= 10 FEATURE := $(addprefix PERIOD,$(shell echo {1..8})) LABEL := PERIOD9 UNSUPERVISED := $(addprefix PERIOD,$(shell echo {1..9})) TEST := PERIOD10 COMPLETE := $(addprefix PERIOD,$(shell echo {1..10})) PERIODS := $(addprefix PERIOD,$(shell echo {1..10})) empty := space := $(empty) $(empty) define PERIOD_STREAM_TEMPLATE ./stream/$(1).txt.gz: $(addprefix ./src/,$(SRC)) mkdir -p $$(@D); < $$< gunzip | $(SRC2STREAM) | grep "^\($(subst $(space),\|,$($(1)))\)," | gzip > $$@.tmp; mv $$@.tmp $$@; endef $(foreach period,$(PERIODS),$(eval $(call PERIOD_STREAM_TEMPLATE,$(period)))) include ../Makefile.common ./src/%.txt.gz: mkdir -p $(@D); wget -O - http://www.nd.edu/~rlichten/linkpred/$(@F) > $@.tmp; mv $@.tmp $@; rlichtenwalter-LPmade-ba322c3/wd/disease-g/script/000077500000000000000000000000001214146114000220535ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/wd/disease-g/script/src_to_stream.pl000077500000000000000000000027121214146114000252610ustar00rootroot00000000000000#! /usr/bin/perl use strict; use warnings FATAL => 'all'; srand(1); my %vertex_hash = (); while( my $line = ) { chomp( $line ); my ($d1,$d2,$weight) = split( /\s+/, $line ); $d1 =~ s/^0+//; $d2 =~ s/^0+//; # add vertices if( !defined( $vertex_hash{$d1} ) ) { $vertex_hash{$d1} = {}; } if( !defined( $vertex_hash{$d2} ) ) { $vertex_hash{$d2} = {}; } # determine unset weights if( $weight eq "f" ) { my $count = 0; $weight = 0; my $neighbor_hash_ref = $vertex_hash{$d1}; foreach my $neighbor (keys(%{$neighbor_hash_ref})) { $count++; $weight += $neighbor_hash_ref->{$neighbor}; } $neighbor_hash_ref = $vertex_hash{$d2}; foreach my $neighbor (keys(%{$neighbor_hash_ref})) { $count++; $weight += $neighbor_hash_ref->{$neighbor}; } $weight /= $count; } # add edge with weight if( !defined( $vertex_hash{$d1}->{$d2} ) ) { $vertex_hash{$d1}->{$d2} = $weight; } if( !defined( $vertex_hash{$d2}->{$d1} ) ) { $vertex_hash{$d2}->{$d1} = $weight; } } foreach my $vertex (sort(keys(%vertex_hash))) { my $neighbor_hash_ref = $vertex_hash{$vertex}; foreach my $neighbor (sort(keys(%{$neighbor_hash_ref}))) { if( $vertex lt $neighbor ) { my $weight = $neighbor_hash_ref->{$neighbor}; my $random = rand() * 10; foreach my $check (1..10) { if( $random <= $check ) { print STDOUT "$check,$vertex,$neighbor,$weight\n"; print STDOUT "$check,$neighbor,$vertex,$weight\n"; last; } } } } } rlichtenwalter-LPmade-ba322c3/wd/disease-g/src/000077500000000000000000000000001214146114000213365ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/wd/disease-g/src/disease-g.txt.gz000066400000000000000000001374661214146114000244000ustar00rootroot00000000000000Kdisease.txtu}WY /S21 m{GYY;ZyJ߼c z?uHq^H3uzܽ5\m2s:ZZgZPz6*uj|( 7<ӎQ1԰Y,Ryg>K -^WC\ )XcX|LU=Kڳ5]8^yǗpTqk Y3or>wU EL]t`lX/؆5> 1HnՊ!-KP"kU/Ŷ꣟Dfb#^q+ -8b-~p6@&]BD|^,3?xGGgaǍmp񞪱 #a$WPy6A9=x5fرu%8YfBoX hr}u}+(})ώAD#44H ;J'༯XM*G4\Do? h6 uг_q#&Q'iby0t_U;ܾM͏w 'uv56k}C3AbbCм>e\ƘUiC,~ ޷ IGBs8۰)jW6w>AlF4qhͣ*3di&M[8J sm_5K46?^ZiMT9HYo~#`_/Cv{\3$;bggCt~)xF(1 €OIzg=-;1[{@_҉8AM={"X{""-o]'Aě6(G*r˽Om%ϣm3mgb^b`]' &6fp}伷 YBE/Ւ9/$851sߌɇX}1g5L&Ï7O՟I=b1cp ԉK)#U<ڇe,ރAD^c=iݔL8/*Y_f0x[SJl~G-D"x.s08dqťp8S<{6Q2 n'<$oQP]zF\epMȠ M*g->"}A1}u:VNGwHNSsLčTy3fESK;r`{cc 4|>=He1h9ai0&gHU l.gCNmC{#~ା5Hc.FoNhpq I-ت{Od[߳*y1gW?WP3Z|)N\BxEvT1 eF "p3!$ǜM3)?%RN 7?/՗כ9=yIN*h1T RmbƯW$$f<$tR)3'/Ů3!;n@S@ hPu9}}CȌǕ_~$33F+Čh?&"$;U_E5lq&U] tp&hA!ow`_>c4O;a{V-b[~o߾~hnUj(baD-jony-C4J ;(mC\?s2 s*1N-nѭ`Ծ3v-oԫ (X1$* .t[~]r;0g l] ~a21_q lUzJs$-)d2}Ѻ11x/.c1o"I}~xl~a}fB=|͆M411c XlK€1Gsչ+wd@!>nn,F{sQ\j\׎~@`pcwv;֩}oP`?s`4v<]6"!f{+K* 04afC[i7 f$dV ^[sD.^[漢="$~'nӻ\~ʊXņ H 3%"*եkkv䯠d-TU 7#`&4?f.Y%!0/:S$3kvAA. k EHv,/7$6Uox}03fP$A@-Pм?lJA'!$MM9h&@b}ĞkbsT1]0gp9-࿚rMȐqE_įlpNMuѧزb bsJf)pbkPd+ik\!qf3$֘H:CrqžU1"MxEw+il H6y!B3tC"cAXΛxR[986s[@P"NxUМ\~/vXujKAUC8Vf֥q®zѸ*՜GM#Rnv@aܤPbo@G/f/Ŏ)dgem#2v G :pƂ"Ky\5ҋBg@Ҽ&™:Ff c-5 A+h"&dGv؈ b [SMܤLMcjtLlAJbʛj!Y_ͻh49bRDI1ƍ)x8V Cdc"H{qRoo/.A wF@q_CM6%W_ CgB7D/IMŗV3]jS3qw*> n`,[a) \sp6aD~qӘ $wV>j#OSP[c ,D㞌7cqp^.n#d>@7^HAM+?(x\@\gtEs;| MG >j_3 ڏ 8_M7AA'=0Yqܥxݱ+h9A`Apn>E+@8A屶`h?;NP]Or,sx>D'. 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HO3ϴ'GmM#>?b6?ϛP\hclm@2ӿ <$ƀE[rRJ. 6gvzΣ{}m-gg:(3SRusjh{H""yPcc۷Nvmf3yJ5Cvpuam 惩:\^}>?4 |y?' -ljW)UvP a:ny" O.'߳Hm /EY4lN?ۓOre;i+yRv1PEN\=v/%Xprd^Αab`lZҏ! $ĴZm p*hii4;No5.lծN 3TJJ~S^)lcP^ߴd)klYLkQagN+x\k畽5?͜OUpΆ7WHeZDl9;5EN*?Sۆ`h%%~}_XG_],:ou'T(] egFyURt\:*J*F/R\͎4/-,_ϳmt==' sm `=e?vX_Eɼ_B33h9)`l_QrԷOR2=Ү_tq˿;_}G{߬y!V|]y639rlichtenwalter-LPmade-ba322c3/weka/000077500000000000000000000000001214146114000172235ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/weka/COPYING000066400000000000000000000430711214146114000202630ustar00rootroot00000000000000 GNU GENERAL PUBLIC LICENSE Version 2, June 1991 Copyright (C) 1989, 1991 Free Software Foundation, Inc. 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The licenses for most software are designed to take away your freedom to share and change it. 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If the software is modified by someone else and passed on, we want its recipients to know that what they have is not the original, so that any problems introduced by others will not reflect on the original authors' reputations. Finally, any free program is threatened constantly by software patents. We wish to avoid the danger that redistributors of a free program will individually obtain patent licenses, in effect making the program proprietary. To prevent this, we have made it clear that any patent must be licensed for everyone's free use or not licensed at all. The precise terms and conditions for copying, distribution and modification follow. GNU GENERAL PUBLIC LICENSE TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION 0. This License applies to any program or other work which contains a notice placed by the copyright holder saying it may be distributed under the terms of this General Public License. The "Program", below, refers to any such program or work, and a "work based on the Program" means either the Program or any derivative work under copyright law: that is to say, a work containing the Program or a portion of it, either verbatim or with modifications and/or translated into another language. (Hereinafter, translation is included without limitation in the term "modification".) Each licensee is addressed as "you". Activities other than copying, distribution and modification are not covered by this License; they are outside its scope. The act of running the Program is not restricted, and the output from the Program is covered only if its contents constitute a work based on the Program (independent of having been made by running the Program). Whether that is true depends on what the Program does. 1. You may copy and distribute verbatim copies of the Program's source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice and disclaimer of warranty; keep intact all the notices that refer to this License and to the absence of any warranty; and give any other recipients of the Program a copy of this License along with the Program. You may charge a fee for the physical act of transferring a copy, and you may at your option offer warranty protection in exchange for a fee. 2. 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IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. END OF TERMS AND CONDITIONS Appendix: How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively convey the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. Copyright (C) 19yy This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. Also add information on how to contact you by electronic and paper mail. If the program is interactive, make it output a short notice like this when it starts in an interactive mode: Gnomovision version 69, Copyright (C) 19yy name of author Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, the commands you use may be called something other than `show w' and `show c'; they could even be mouse-clicks or menu items--whatever suits your program. You should also get your employer (if you work as a programmer) or your school, if any, to sign a "copyright disclaimer" for the program, if necessary. Here is a sample; alter the names: Yoyodyne, Inc., hereby disclaims all copyright interest in the program `Gnomovision' (which makes passes at compilers) written by James Hacker. , 1 April 1989 Ty Coon, President of Vice This General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Library General Public License instead of this License. rlichtenwalter-LPmade-ba322c3/weka/src/000077500000000000000000000000001214146114000200125ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/weka/src/weka/000077500000000000000000000000001214146114000207415ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/000077500000000000000000000000001214146114000234405ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/AbstractAssociator.java000066400000000000000000000105701214146114000301010ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * Associator.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.associations; import weka.core.Capabilities; import weka.core.CapabilitiesHandler; import weka.core.Instances; import weka.core.RevisionHandler; import weka.core.SerializedObject; import weka.core.Utils; import java.io.Serializable; /** * Abstract scheme for learning associations. All schemes for learning * associations implemement this class * * @author Eibe Frank (eibe@cs.waikato.ac.nz) * @version $Revision: 1.1 $ */ public abstract class AbstractAssociator implements Cloneable, Associator, Serializable, CapabilitiesHandler, RevisionHandler { /** for serialization */ private static final long serialVersionUID = -3017644543382432070L; /** * Creates a new instance of a associator given it's class name and * (optional) arguments to pass to it's setOptions method. If the * associator implements OptionHandler and the options parameter is * non-null, the associator will have it's options set. * * @param associatorName the fully qualified class name of the associator * @param options an array of options suitable for passing to setOptions. May * be null. * @return the newly created associator, ready for use. * @exception Exception if the associator name is invalid, or the options * supplied are not acceptable to the associator */ public static Associator forName(String associatorName, String [] options) throws Exception { return (Associator)Utils.forName(Associator.class, associatorName, options); } /** * Creates a deep copy of the given associator using serialization. * * @param model the associator to copy * @return a deep copy of the associator * @exception Exception if an error occurs */ public static Associator makeCopy(Associator model) throws Exception { return (Associator) new SerializedObject(model).getObject(); } /** * Creates copies of the current associator. Note that this method * now uses Serialization to perform a deep copy, so the Associator * object must be fully Serializable. Any currently built model will * now be copied as well. * * @param model an example associator to copy * @param num the number of associators copies to create. * @return an array of associators. * @exception Exception if an error occurs */ public static Associator[] makeCopies(Associator model, int num) throws Exception { if (model == null) { throw new Exception("No model associator set"); } Associator [] associators = new Associator [num]; SerializedObject so = new SerializedObject(model); for(int i = 0; i < associators.length; i++) { associators[i] = (Associator) so.getObject(); } return associators; } /** * Returns the Capabilities of this associator. Derived associators have to * override this method to enable capabilities. * * @return the capabilities of this object * @see Capabilities */ public Capabilities getCapabilities() { return new Capabilities(this); } /** * runs the associator with the given commandline options * * @param associator the associator to run * @param options the commandline options */ protected static void runAssociator(Associator associator, String[] options) { try { System.out.println( AssociatorEvaluation.evaluate(associator, options)); } catch (Exception e) { if ( (e.getMessage() != null) && (e.getMessage().indexOf("General options") == -1) ) e.printStackTrace(); else System.err.println(e.getMessage()); } } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/Apriori.java000066400000000000000000001347251214146114000257240ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * alo0ng with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * Apriori.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.associations; import weka.core.AttributeStats; import weka.core.Capabilities; import weka.core.FastVector; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.SelectedTag; import weka.core.Tag; import weka.core.TechnicalInformation; import weka.core.TechnicalInformationHandler; import weka.core.Utils; import weka.core.Capabilities.Capability; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; import weka.filters.Filter; import weka.filters.unsupervised.attribute.Remove; import java.util.Enumeration; import java.util.Hashtable; /** * Class implementing an Apriori-type algorithm. Iteratively reduces the minimum support until it finds the required number of rules with the given minimum confidence.
* The algorithm has an option to mine class association rules. It is adapted as explained in the second reference.
*
* For more information see:
*
* R. Agrawal, R. Srikant: Fast Algorithms for Mining Association Rules in Large Databases. In: 20th International Conference on Very Large Data Bases, 478-499, 1994.
*
* Bing Liu, Wynne Hsu, Yiming Ma: Integrating Classification and Association Rule Mining. In: Fourth International Conference on Knowledge Discovery and Data Mining, 80-86, 1998. *

* * BibTeX: * 

 * @inproceedings{Agrawal1994,
 *    author = {R. Agrawal and R. Srikant},
 *    booktitle = {20th International Conference on Very Large Data Bases},
 *    pages = {478-499},
 *    publisher = {Morgan Kaufmann, Los Altos, CA},
 *    title = {Fast Algorithms for Mining Association Rules in Large Databases},
 *    year = {1994}
 * }
 * 
 * @inproceedings{Liu1998,
 *    author = {Bing Liu and Wynne Hsu and Yiming Ma},
 *    booktitle = {Fourth International Conference on Knowledge Discovery and Data Mining},
 *    pages = {80-86},
 *    publisher = {AAAI Press},
 *    title = {Integrating Classification and Association Rule Mining},
 *    year = {1998}
 * }
 *
*

* * Valid options are:

* * 

 -N <required number of rules output>
 *  The required number of rules. (default = 10)
* * 
 -T <0=confidence | 1=lift | 2=leverage | 3=Conviction>
 *  The metric type by which to rank rules. (default = confidence)
* * 
 -C <minimum metric score of a rule>
 *  The minimum confidence of a rule. (default = 0.9)
* * 
 -D <delta for minimum support>
 *  The delta by which the minimum support is decreased in
 *  each iteration. (default = 0.05)
* * 
 -U <upper bound for minimum support>
 *  Upper bound for minimum support. (default = 1.0)
* * 
 -M <lower bound for minimum support>
 *  The lower bound for the minimum support. (default = 0.1)
* * 
 -S <significance level>
 *  If used, rules are tested for significance at
 *  the given level. Slower. (default = no significance testing)
* * 
 -I
 *  If set the itemsets found are also output. (default = no)
* * 
 -R
 *  Remove columns that contain all missing values (default = no)
* * 
 -V
 *  Report progress iteratively. (default = no)
* * 
 -A
 *  If set class association rules are mined. (default = no)
* * 
 -c <the class index>
 *  The class index. (default = last)
* * * @author Eibe Frank (eibe@cs.waikato.ac.nz) * @author Mark Hall (mhall@cs.waikato.ac.nz) * @author Stefan Mutter (mutter@cs.waikato.ac.nz) * @version $Revision: 1.31 $ */ public class Apriori extends AbstractAssociator implements OptionHandler, CARuleMiner, TechnicalInformationHandler { /** for serialization */ static final long serialVersionUID = 3277498842319212687L; /** The minimum support. */ protected double m_minSupport; /** The upper bound on the support */ protected double m_upperBoundMinSupport; /** The lower bound for the minimum support. */ protected double m_lowerBoundMinSupport; /** Metric type: Confidence */ protected static final int CONFIDENCE = 0; /** Metric type: Lift */ protected static final int LIFT = 1; /** Metric type: Leverage */ protected static final int LEVERAGE = 2; /** Metric type: Conviction */ protected static final int CONVICTION = 3; /** Metric types. */ public static final Tag [] TAGS_SELECTION = { new Tag(CONFIDENCE, "Confidence"), new Tag(LIFT, "Lift"), new Tag(LEVERAGE, "Leverage"), new Tag(CONVICTION, "Conviction") }; /** The selected metric type. */ protected int m_metricType = CONFIDENCE; /** The minimum metric score. */ protected double m_minMetric; /** The maximum number of rules that are output. */ protected int m_numRules; /** Delta by which m_minSupport is decreased in each iteration. */ protected double m_delta; /** Significance level for optional significance test. */ protected double m_significanceLevel; /** Number of cycles used before required number of rules was one. */ protected int m_cycles; /** The set of all sets of itemsets L. */ protected FastVector m_Ls; /** The same information stored in hash tables. */ protected FastVector m_hashtables; /** The list of all generated rules. */ protected FastVector[] m_allTheRules; /** The instances (transactions) to be used for generating the association rules. */ protected Instances m_instances; /** Output itemsets found? */ protected boolean m_outputItemSets; /** Remove columns with all missing values */ protected boolean m_removeMissingCols; /** Report progress iteratively */ protected boolean m_verbose; /** Only the class attribute of all Instances.*/ protected Instances m_onlyClass; /** The class index. */ protected int m_classIndex; /** Flag indicating whether class association rules are mined. */ protected boolean m_car; /** * Returns a string describing this associator * @return a description of the evaluator suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Class implementing an Apriori-type algorithm. Iteratively reduces " + "the minimum support until it finds the required number of rules with " + "the given minimum confidence.\n" + "The algorithm has an option to mine class association rules. It is " + "adapted as explained in the second reference.\n\n" + "For more information see:\n\n" + getTechnicalInformation().toString(); } /** * Returns an instance of a TechnicalInformation object, containing * detailed information about the technical background of this class, * e.g., paper reference or book this class is based on. * * @return the technical information about this class */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; TechnicalInformation additional; result = new TechnicalInformation(Type.INPROCEEDINGS); result.setValue(Field.AUTHOR, "R. Agrawal and R. Srikant"); result.setValue(Field.TITLE, "Fast Algorithms for Mining Association Rules in Large Databases"); result.setValue(Field.BOOKTITLE, "20th International Conference on Very Large Data Bases"); result.setValue(Field.YEAR, "1994"); result.setValue(Field.PAGES, "478-499"); result.setValue(Field.PUBLISHER, "Morgan Kaufmann, Los Altos, CA"); additional = result.add(Type.INPROCEEDINGS); additional.setValue(Field.AUTHOR, "Bing Liu and Wynne Hsu and Yiming Ma"); additional.setValue(Field.TITLE, "Integrating Classification and Association Rule Mining"); additional.setValue(Field.BOOKTITLE, "Fourth International Conference on Knowledge Discovery and Data Mining"); additional.setValue(Field.YEAR, "1998"); additional.setValue(Field.PAGES, "80-86"); additional.setValue(Field.PUBLISHER, "AAAI Press"); return result; } /** * Constructor that allows to sets default values for the * minimum confidence and the maximum number of rules * the minimum confidence. */ public Apriori() { resetOptions(); } /** * Resets the options to the default values. */ public void resetOptions() { m_removeMissingCols = false; m_verbose = false; m_delta = 0.05; m_minMetric = 0.90; m_numRules = 10; m_lowerBoundMinSupport = 0.1; m_upperBoundMinSupport = 1.0; m_significanceLevel = -1; m_outputItemSets = false; m_car = false; m_classIndex = -1; } /** * Removes columns that are all missing from the data * @param instances the instances * @return a new set of instances with all missing columns removed * @throws Exception if something goes wrong */ protected Instances removeMissingColumns(Instances instances) throws Exception { int numInstances = instances.numInstances(); StringBuffer deleteString = new StringBuffer(); int removeCount = 0; boolean first = true; int maxCount = 0; for (int i=0;i maxCount) { maxCount = counts[Utils.maxIndex(counts)]; } } if (as.missingCount == numInstances) { if (first) { deleteString.append((i+1)); first = false; } else { deleteString.append(","+(i+1)); } removeCount++; } } if (m_verbose) { System.err.println("Removed : "+removeCount+" columns with all missing " +"values."); } if (m_upperBoundMinSupport == 1.0 && maxCount != numInstances) { m_upperBoundMinSupport = (double)maxCount / (double)numInstances; if (m_verbose) { System.err.println("Setting upper bound min support to : " +m_upperBoundMinSupport); } } if (deleteString.toString().length() > 0) { Remove af = new Remove(); af.setAttributeIndices(deleteString.toString()); af.setInvertSelection(false); af.setInputFormat(instances); Instances newInst = Filter.useFilter(instances, af); return newInst; } return instances; } /** * Returns default capabilities of the classifier. * * @return the capabilities of this classifier */ public Capabilities getCapabilities() { Capabilities result = super.getCapabilities(); // attributes result.enable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.MISSING_VALUES); // class (can handle a nominal class if CAR rules are selected). This result.enable(Capability.NO_CLASS); result.enable(Capability.NOMINAL_CLASS); result.enable(Capability.MISSING_CLASS_VALUES); return result; } /** * Method that generates all large itemsets with a minimum support, and from * these all association rules with a minimum confidence. * * @param instances the instances to be used for generating the associations * @throws Exception if rules can't be built successfully */ public void buildAssociations(Instances instances) throws Exception { double[] confidences, supports; int[] indices; FastVector[] sortedRuleSet; int necSupport=0; instances = new Instances(instances); if (m_removeMissingCols) { instances = removeMissingColumns(instances); } if(m_car && m_metricType != CONFIDENCE) throw new Exception("For CAR-Mining metric type has to be confidence!"); // only set class index if CAR is requested if (m_car) { if (m_classIndex == -1 ) { instances.setClassIndex(instances.numAttributes()-1); } else if (m_classIndex <= instances.numAttributes() && m_classIndex > 0) { instances.setClassIndex(m_classIndex - 1); } else { throw new Exception("Invalid class index."); } } // can associator handle the data? getCapabilities().testWithFail(instances); m_cycles = 0; if(m_car){ //m_instances does not contain the class attribute m_instances = LabeledItemSet.divide(instances,false); //m_onlyClass contains only the class attribute m_onlyClass = LabeledItemSet.divide(instances,true); } else m_instances = instances; if(m_car && m_numRules == Integer.MAX_VALUE){ // Set desired minimum support m_minSupport = m_lowerBoundMinSupport; } else{ // Decrease minimum support until desired number of rules found. m_minSupport = m_upperBoundMinSupport - m_delta; m_minSupport = (m_minSupport < m_lowerBoundMinSupport) ? m_lowerBoundMinSupport : m_minSupport; } do { // Reserve space for variables m_Ls = new FastVector(); m_hashtables = new FastVector(); m_allTheRules = new FastVector[6]; m_allTheRules[0] = new FastVector(); m_allTheRules[1] = new FastVector(); m_allTheRules[2] = new FastVector(); if (m_metricType != CONFIDENCE || m_significanceLevel != -1) { m_allTheRules[3] = new FastVector(); m_allTheRules[4] = new FastVector(); m_allTheRules[5] = new FastVector(); } sortedRuleSet = new FastVector[6]; sortedRuleSet[0] = new FastVector(); sortedRuleSet[1] = new FastVector(); sortedRuleSet[2] = new FastVector(); if (m_metricType != CONFIDENCE || m_significanceLevel != -1) { sortedRuleSet[3] = new FastVector(); sortedRuleSet[4] = new FastVector(); sortedRuleSet[5] = new FastVector(); } if(!m_car){ // Find large itemsets and rules findLargeItemSets(); if (m_significanceLevel != -1 || m_metricType != CONFIDENCE) findRulesBruteForce(); else findRulesQuickly(); } else{ findLargeCarItemSets(); findCarRulesQuickly(); } // Sort rules according to their support /*supports = new double[m_allTheRules[2].size()]; for (int i = 0; i < m_allTheRules[2].size(); i++) supports[i] = (double)((AprioriItemSet)m_allTheRules[1].elementAt(i)).support(); indices = Utils.stableSort(supports); for (int i = 0; i < m_allTheRules[2].size(); i++) { sortedRuleSet[0].addElement(m_allTheRules[0].elementAt(indices[i])); sortedRuleSet[1].addElement(m_allTheRules[1].elementAt(indices[i])); sortedRuleSet[2].addElement(m_allTheRules[2].elementAt(indices[i])); if (m_metricType != CONFIDENCE || m_significanceLevel != -1) { sortedRuleSet[3].addElement(m_allTheRules[3].elementAt(indices[i])); sortedRuleSet[4].addElement(m_allTheRules[4].elementAt(indices[i])); sortedRuleSet[5].addElement(m_allTheRules[5].elementAt(indices[i])); } }*/ int j = m_allTheRules[2].size()-1; supports = new double[m_allTheRules[2].size()]; for (int i = 0; i < (j+1); i++) supports[j-i] = ((double)((ItemSet)m_allTheRules[1].elementAt(j-i)).support())*(-1); indices = Utils.stableSort(supports); for (int i = 0; i < (j+1); i++) { sortedRuleSet[0].addElement(m_allTheRules[0].elementAt(indices[j-i])); sortedRuleSet[1].addElement(m_allTheRules[1].elementAt(indices[j-i])); sortedRuleSet[2].addElement(m_allTheRules[2].elementAt(indices[j-i])); if (m_metricType != CONFIDENCE || m_significanceLevel != -1) { sortedRuleSet[3].addElement(m_allTheRules[3].elementAt(indices[j-i])); sortedRuleSet[4].addElement(m_allTheRules[4].elementAt(indices[j-i])); sortedRuleSet[5].addElement(m_allTheRules[5].elementAt(indices[j-i])); } } // Sort rules according to their confidence m_allTheRules[0].removeAllElements(); m_allTheRules[1].removeAllElements(); m_allTheRules[2].removeAllElements(); if (m_metricType != CONFIDENCE || m_significanceLevel != -1) { m_allTheRules[3].removeAllElements(); m_allTheRules[4].removeAllElements(); m_allTheRules[5].removeAllElements(); } confidences = new double[sortedRuleSet[2].size()]; int sortType = 2 + m_metricType; for (int i = 0; i < sortedRuleSet[2].size(); i++) confidences[i] = ((Double)sortedRuleSet[sortType].elementAt(i)).doubleValue(); indices = Utils.stableSort(confidences); for (int i = sortedRuleSet[0].size() - 1; (i >= (sortedRuleSet[0].size() - m_numRules)) && (i >= 0); i--) { m_allTheRules[0].addElement(sortedRuleSet[0].elementAt(indices[i])); m_allTheRules[1].addElement(sortedRuleSet[1].elementAt(indices[i])); m_allTheRules[2].addElement(sortedRuleSet[2].elementAt(indices[i])); if (m_metricType != CONFIDENCE || m_significanceLevel != -1) { m_allTheRules[3].addElement(sortedRuleSet[3].elementAt(indices[i])); m_allTheRules[4].addElement(sortedRuleSet[4].elementAt(indices[i])); m_allTheRules[5].addElement(sortedRuleSet[5].elementAt(indices[i])); } } if (m_verbose) { if (m_Ls.size() > 1) { System.out.println(toString()); } } if(m_minSupport == m_lowerBoundMinSupport || m_minSupport - m_delta > m_lowerBoundMinSupport) m_minSupport -= m_delta; else m_minSupport = m_lowerBoundMinSupport; necSupport = Math.round((float)((m_minSupport * (double)m_instances.numInstances())+0.5)); m_cycles++; } while ((m_allTheRules[0].size() < m_numRules) && (Utils.grOrEq(m_minSupport, m_lowerBoundMinSupport)) /* (necSupport >= lowerBoundNumInstancesSupport)*/ /* (Utils.grOrEq(m_minSupport, m_lowerBoundMinSupport)) */ && (necSupport >= 1)); m_minSupport += m_delta; } /** * Method that mines all class association rules with minimum support and * with a minimum confidence. * @return an sorted array of FastVector (confidence depended) containing the rules and metric information * @param data the instances for which class association rules should be mined * @throws Exception if rules can't be built successfully */ public FastVector[] mineCARs(Instances data) throws Exception{ m_car = true; buildAssociations(data); return m_allTheRules; } /** * Gets the instances without the class atrribute. * * @return the instances without the class attribute. */ public Instances getInstancesNoClass() { return m_instances; } /** * Gets only the class attribute of the instances. * * @return the class attribute of all instances. */ public Instances getInstancesOnlyClass() { return m_onlyClass; } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { String string1 = "\tThe required number of rules. (default = " + m_numRules + ")", string2 = "\tThe minimum confidence of a rule. (default = " + m_minMetric + ")", string3 = "\tThe delta by which the minimum support is decreased in\n", string4 = "\teach iteration. (default = " + m_delta + ")", string5 = "\tThe lower bound for the minimum support. (default = " + m_lowerBoundMinSupport + ")", string6 = "\tIf used, rules are tested for significance at\n", string7 = "\tthe given level. Slower. (default = no significance testing)", string8 = "\tIf set the itemsets found are also output. (default = no)", string9 = "\tIf set class association rules are mined. (default = no)", string10 = "\tThe class index. (default = last)", stringType = "\tThe metric type by which to rank rules. (default = " +"confidence)"; FastVector newVector = new FastVector(11); newVector.addElement(new Option(string1, "N", 1, "-N ")); newVector.addElement(new Option(stringType, "T", 1, "-T <0=confidence | 1=lift | " +"2=leverage | 3=Conviction>")); newVector.addElement(new Option(string2, "C", 1, "-C ")); newVector.addElement(new Option(string3 + string4, "D", 1, "-D ")); newVector.addElement(new Option("\tUpper bound for minimum support. " +"(default = 1.0)", "U", 1, "-U ")); newVector.addElement(new Option(string5, "M", 1, "-M ")); newVector.addElement(new Option(string6 + string7, "S", 1, "-S ")); newVector.addElement(new Option(string8, "I", 0, "-I")); newVector.addElement(new Option("\tRemove columns that contain " +"all missing values (default = no)" , "R", 0, "-R")); newVector.addElement(new Option("\tReport progress iteratively. (default " +"= no)", "V", 0, "-V")); newVector.addElement(new Option(string9, "A", 0, "-A")); newVector.addElement(new Option(string10, "c", 1, "-c ")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -N <required number of rules output>
   *  The required number of rules. (default = 10)
* * 
 -T <0=confidence | 1=lift | 2=leverage | 3=Conviction>
   *  The metric type by which to rank rules. (default = confidence)
* * 
 -C <minimum metric score of a rule>
   *  The minimum confidence of a rule. (default = 0.9)
* * 
 -D <delta for minimum support>
   *  The delta by which the minimum support is decreased in
   *  each iteration. (default = 0.05)
* * 
 -U <upper bound for minimum support>
   *  Upper bound for minimum support. (default = 1.0)
* * 
 -M <lower bound for minimum support>
   *  The lower bound for the minimum support. (default = 0.1)
* * 
 -S <significance level>
   *  If used, rules are tested for significance at
   *  the given level. Slower. (default = no significance testing)
* * 
 -I
   *  If set the itemsets found are also output. (default = no)
* * 
 -R
   *  Remove columns that contain all missing values (default = no)
* * 
 -V
   *  Report progress iteratively. (default = no)
* * 
 -A
   *  If set class association rules are mined. (default = no)
* * 
 -c <the class index>
   *  The class index. (default = last)
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { resetOptions(); String numRulesString = Utils.getOption('N', options), minConfidenceString = Utils.getOption('C', options), deltaString = Utils.getOption('D', options), maxSupportString = Utils.getOption('U', options), minSupportString = Utils.getOption('M', options), significanceLevelString = Utils.getOption('S', options), classIndexString = Utils.getOption('c',options); String metricTypeString = Utils.getOption('T', options); if (metricTypeString.length() != 0) { setMetricType(new SelectedTag(Integer.parseInt(metricTypeString), TAGS_SELECTION)); } if (numRulesString.length() != 0) { m_numRules = Integer.parseInt(numRulesString); } if (classIndexString.length() != 0) { if (classIndexString.equalsIgnoreCase("last")) { m_classIndex = -1; } else if (classIndexString.equalsIgnoreCase("first")) { m_classIndex = 0; } else { m_classIndex = Integer.parseInt(classIndexString); } } if (minConfidenceString.length() != 0) { m_minMetric = (new Double(minConfidenceString)).doubleValue(); } if (deltaString.length() != 0) { m_delta = (new Double(deltaString)).doubleValue(); } if (maxSupportString.length() != 0) { setUpperBoundMinSupport((new Double(maxSupportString)).doubleValue()); } if (minSupportString.length() != 0) { m_lowerBoundMinSupport = (new Double(minSupportString)).doubleValue(); } if (significanceLevelString.length() != 0) { m_significanceLevel = (new Double(significanceLevelString)).doubleValue(); } m_outputItemSets = Utils.getFlag('I', options); m_car = Utils.getFlag('A', options); m_verbose = Utils.getFlag('V', options); setRemoveAllMissingCols(Utils.getFlag('R', options)); } /** * Gets the current settings of the Apriori object. * * @return an array of strings suitable for passing to setOptions */ public String [] getOptions() { String [] options = new String [20]; int current = 0; if (m_outputItemSets) { options[current++] = "-I"; } if (getRemoveAllMissingCols()) { options[current++] = "-R"; } options[current++] = "-N"; options[current++] = "" + m_numRules; options[current++] = "-T"; options[current++] = "" + m_metricType; options[current++] = "-C"; options[current++] = "" + m_minMetric; options[current++] = "-D"; options[current++] = "" + m_delta; options[current++] = "-U"; options[current++] = "" + m_upperBoundMinSupport; options[current++] = "-M"; options[current++] = "" + m_lowerBoundMinSupport; options[current++] = "-S"; options[current++] = "" + m_significanceLevel; if (m_car) options[current++] = "-A"; if (m_verbose) options[current++] = "-V"; options[current++] = "-c"; options[current++] = "" + m_classIndex; while (current < options.length) { options[current++] = ""; } return options; } /** * Outputs the size of all the generated sets of itemsets and the rules. * * @return a string representation of the model */ public String toString() { StringBuffer text = new StringBuffer(); if (m_Ls.size() <= 1) return "\nNo large itemsets and rules found!\n"; text.append("\nApriori\n=======\n\n"); text.append("Minimum support: " + Utils.doubleToString(m_minSupport,2) + " (" + ((int)(m_minSupport * (double)m_instances.numInstances()+0.5)) + " instances)" + '\n'); text.append("Minimum metric <"); switch(m_metricType) { case CONFIDENCE: text.append("confidence>: "); break; case LIFT: text.append("lift>: "); break; case LEVERAGE: text.append("leverage>: "); break; case CONVICTION: text.append("conviction>: "); break; } text.append(Utils.doubleToString(m_minMetric,2)+'\n'); if (m_significanceLevel != -1) text.append("Significance level: "+ Utils.doubleToString(m_significanceLevel,2)+'\n'); text.append("Number of cycles performed: " + m_cycles+'\n'); text.append("\nGenerated sets of large itemsets:\n"); if(!m_car){ for (int i = 0; i < m_Ls.size(); i++) { text.append("\nSize of set of large itemsets L("+(i+1)+"): "+ ((FastVector)m_Ls.elementAt(i)).size()+'\n'); if (m_outputItemSets) { text.append("\nLarge Itemsets L("+(i+1)+"):\n"); for (int j = 0; j < ((FastVector)m_Ls.elementAt(i)).size(); j++) text.append(((AprioriItemSet)((FastVector)m_Ls.elementAt(i)).elementAt(j)). toString(m_instances)+"\n"); } } text.append("\nBest rules found:\n\n"); for (int i = 0; i < m_allTheRules[0].size(); i++) { text.append(Utils.doubleToString((double)i+1, (int)(Math.log(m_numRules)/Math.log(10)+1),0)+ ". " + ((AprioriItemSet)m_allTheRules[0].elementAt(i)). toString(m_instances) + " ==> " + ((AprioriItemSet)m_allTheRules[1].elementAt(i)). toString(m_instances) +" conf:("+ Utils.doubleToString(((Double)m_allTheRules[2]. elementAt(i)).doubleValue(),2)+")"); if (m_metricType != CONFIDENCE || m_significanceLevel != -1) { text.append((m_metricType == LIFT ? " <" : "")+" lift:("+ Utils.doubleToString(((Double)m_allTheRules[3]. elementAt(i)).doubleValue(),2) +")"+(m_metricType == LIFT ? ">" : "")); text.append((m_metricType == LEVERAGE ? " <" : "")+" lev:("+ Utils.doubleToString(((Double)m_allTheRules[4]. elementAt(i)).doubleValue(),2) +")"); text.append(" ["+ (int)(((Double)m_allTheRules[4].elementAt(i)) .doubleValue() * (double)m_instances.numInstances()) +"]"+(m_metricType == LEVERAGE ? ">" : "")); text.append((m_metricType == CONVICTION ? " <" : "")+" conv:("+ Utils.doubleToString(((Double)m_allTheRules[5]. elementAt(i)).doubleValue(),2) +")"+(m_metricType == CONVICTION ? ">" : "")); } text.append('\n'); } } else{ for (int i = 0; i < m_Ls.size(); i++) { text.append("\nSize of set of large itemsets L("+(i+1)+"): "+ ((FastVector)m_Ls.elementAt(i)).size()+'\n'); if (m_outputItemSets) { text.append("\nLarge Itemsets L("+(i+1)+"):\n"); for (int j = 0; j < ((FastVector)m_Ls.elementAt(i)).size(); j++){ text.append(((ItemSet)((FastVector)m_Ls.elementAt(i)).elementAt(j)). toString(m_instances)+"\n"); text.append(((LabeledItemSet)((FastVector)m_Ls.elementAt(i)).elementAt(j)).m_classLabel+" "); text.append(((LabeledItemSet)((FastVector)m_Ls.elementAt(i)).elementAt(j)).support()+"\n"); } } } text.append("\nBest rules found:\n\n"); for (int i = 0; i < m_allTheRules[0].size(); i++) { text.append(Utils.doubleToString((double)i+1, (int)(Math.log(m_numRules)/Math.log(10)+1),0)+ ". " + ((ItemSet)m_allTheRules[0].elementAt(i)). toString(m_instances) + " ==> " + ((ItemSet)m_allTheRules[1].elementAt(i)). toString(m_onlyClass) +" conf:("+ Utils.doubleToString(((Double)m_allTheRules[2]. elementAt(i)).doubleValue(),2)+")"); text.append('\n'); } } return text.toString(); } /** * Returns the metric string for the chosen metric type * @return a string describing the used metric for the interestingness of a class association rule */ public String metricString() { switch(m_metricType) { case LIFT: return "lif"; case LEVERAGE: return "leverage"; case CONVICTION: return "conviction"; default: return "conf"; } } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String removeAllMissingColsTipText() { return "Remove columns with all missing values."; } /** * Remove columns containing all missing values. * @param r true if cols are to be removed. */ public void setRemoveAllMissingCols(boolean r) { m_removeMissingCols = r; } /** * Returns whether columns containing all missing values are to be removed * @return true if columns are to be removed. */ public boolean getRemoveAllMissingCols() { return m_removeMissingCols; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String upperBoundMinSupportTipText() { return "Upper bound for minimum support. Start iteratively decreasing " +"minimum support from this value."; } /** * Get the value of upperBoundMinSupport. * * @return Value of upperBoundMinSupport. */ public double getUpperBoundMinSupport() { return m_upperBoundMinSupport; } /** * Set the value of upperBoundMinSupport. * * @param v Value to assign to upperBoundMinSupport. */ public void setUpperBoundMinSupport(double v) { m_upperBoundMinSupport = v; } /** * Sets the class index * @param index the class index */ public void setClassIndex(int index){ m_classIndex = index; } /** * Gets the class index * @return the index of the class attribute */ public int getClassIndex(){ return m_classIndex; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String classIndexTipText() { return "Index of the class attribute. If set to -1, the last attribute is taken as class attribute."; } /** * Sets class association rule mining * @param flag if class association rules are mined, false otherwise */ public void setCar(boolean flag){ m_car = flag; } /** * Gets whether class association ruels are mined * @return true if class association rules are mined, false otherwise */ public boolean getCar(){ return m_car; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String carTipText() { return "If enabled class association rules are mined instead of (general) association rules."; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String lowerBoundMinSupportTipText() { return "Lower bound for minimum support."; } /** * Get the value of lowerBoundMinSupport. * * @return Value of lowerBoundMinSupport. */ public double getLowerBoundMinSupport() { return m_lowerBoundMinSupport; } /** * Set the value of lowerBoundMinSupport. * * @param v Value to assign to lowerBoundMinSupport. */ public void setLowerBoundMinSupport(double v) { m_lowerBoundMinSupport = v; } /** * Get the metric type * * @return the type of metric to use for ranking rules */ public SelectedTag getMetricType() { return new SelectedTag(m_metricType, TAGS_SELECTION); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String metricTypeTipText() { return "Set the type of metric by which to rank rules. Confidence is " +"the proportion of the examples covered by the premise that are also " +"covered by the consequence(Class association rules can only be mined using confidence). Lift is confidence divided by the " +"proportion of all examples that are covered by the consequence. This " +"is a measure of the importance of the association that is independent " +"of support. Leverage is the proportion of additional examples covered " +"by both the premise and consequence above those expected if the " +"premise and consequence were independent of each other. The total " +"number of examples that this represents is presented in brackets " +"following the leverage. Conviction is " +"another measure of departure from independence. Conviction is given " +"by "; } /** * Set the metric type for ranking rules * * @param d the type of metric */ public void setMetricType (SelectedTag d) { if (d.getTags() == TAGS_SELECTION) { m_metricType = d.getSelectedTag().getID(); } if (m_significanceLevel != -1 && m_metricType != CONFIDENCE) { m_metricType = CONFIDENCE; } if (m_metricType == CONFIDENCE) { setMinMetric(0.9); } if (m_metricType == LIFT || m_metricType == CONVICTION) { setMinMetric(1.1); } if (m_metricType == LEVERAGE) { setMinMetric(0.1); } } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String minMetricTipText() { return "Minimum metric score. Consider only rules with scores higher than " +"this value."; } /** * Get the value of minConfidence. * * @return Value of minConfidence. */ public double getMinMetric() { return m_minMetric; } /** * Set the value of minConfidence. * * @param v Value to assign to minConfidence. */ public void setMinMetric(double v) { m_minMetric = v; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String numRulesTipText() { return "Number of rules to find."; } /** * Get the value of numRules. * * @return Value of numRules. */ public int getNumRules() { return m_numRules; } /** * Set the value of numRules. * * @param v Value to assign to numRules. */ public void setNumRules(int v) { m_numRules = v; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String deltaTipText() { return "Iteratively decrease support by this factor. Reduces support " +"until min support is reached or required number of rules has been " +"generated."; } /** * Get the value of delta. * * @return Value of delta. */ public double getDelta() { return m_delta; } /** * Set the value of delta. * * @param v Value to assign to delta. */ public void setDelta(double v) { m_delta = v; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String significanceLevelTipText() { return "Significance level. Significance test (confidence metric only)."; } /** * Get the value of significanceLevel. * * @return Value of significanceLevel. */ public double getSignificanceLevel() { return m_significanceLevel; } /** * Set the value of significanceLevel. * * @param v Value to assign to significanceLevel. */ public void setSignificanceLevel(double v) { m_significanceLevel = v; } /** * Sets whether itemsets are output as well * @param flag true if itemsets are to be output as well */ public void setOutputItemSets(boolean flag){ m_outputItemSets = flag; } /** * Gets whether itemsets are output as well * @return true if itemsets are output as well */ public boolean getOutputItemSets(){ return m_outputItemSets; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String outputItemSetsTipText() { return "If enabled the itemsets are output as well."; } /** * Sets verbose mode * @param flag true if algorithm should be run in verbose mode */ public void setVerbose(boolean flag){ m_verbose = flag; } /** * Gets whether algorithm is run in verbose mode * @return true if algorithm is run in verbose mode */ public boolean getVerbose(){ return m_verbose; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String verboseTipText() { return "If enabled the algorithm will be run in verbose mode."; } /** * Method that finds all large itemsets for the given set of instances. * * @throws Exception if an attribute is numeric */ private void findLargeItemSets() throws Exception { FastVector kMinusOneSets, kSets; Hashtable hashtable; int necSupport, necMaxSupport,i = 0; // Find large itemsets // minimum support necSupport = (int)(m_minSupport * (double)m_instances.numInstances()+0.5); necMaxSupport = (int)(m_upperBoundMinSupport * (double)m_instances.numInstances()+0.5); kSets = AprioriItemSet.singletons(m_instances); AprioriItemSet.upDateCounters(kSets,m_instances); kSets = AprioriItemSet.deleteItemSets(kSets, necSupport, necMaxSupport); if (kSets.size() == 0) return; do { m_Ls.addElement(kSets); kMinusOneSets = kSets; kSets = AprioriItemSet.mergeAllItemSets(kMinusOneSets, i, m_instances.numInstances()); hashtable = AprioriItemSet.getHashtable(kMinusOneSets, kMinusOneSets.size()); m_hashtables.addElement(hashtable); kSets = AprioriItemSet.pruneItemSets(kSets, hashtable); AprioriItemSet.upDateCounters(kSets, m_instances); kSets = AprioriItemSet.deleteItemSets(kSets, necSupport, necMaxSupport); i++; } while (kSets.size() > 0); } /** * Method that finds all association rules and performs significance test. * * @throws Exception if an attribute is numeric */ private void findRulesBruteForce() throws Exception { FastVector[] rules; // Build rules for (int j = 1; j < m_Ls.size(); j++) { FastVector currentItemSets = (FastVector)m_Ls.elementAt(j); Enumeration enumItemSets = currentItemSets.elements(); while (enumItemSets.hasMoreElements()) { AprioriItemSet currentItemSet = (AprioriItemSet)enumItemSets.nextElement(); //AprioriItemSet currentItemSet = new AprioriItemSet((ItemSet)enumItemSets.nextElement()); rules=currentItemSet.generateRulesBruteForce(m_minMetric,m_metricType, m_hashtables,j+1, m_instances.numInstances(), m_significanceLevel); for (int k = 0; k < rules[0].size(); k++) { m_allTheRules[0].addElement(rules[0].elementAt(k)); m_allTheRules[1].addElement(rules[1].elementAt(k)); m_allTheRules[2].addElement(rules[2].elementAt(k)); m_allTheRules[3].addElement(rules[3].elementAt(k)); m_allTheRules[4].addElement(rules[4].elementAt(k)); m_allTheRules[5].addElement(rules[5].elementAt(k)); } } } } /** * Method that finds all association rules. * * @throws Exception if an attribute is numeric */ private void findRulesQuickly() throws Exception { FastVector[] rules; // Build rules for (int j = 1; j < m_Ls.size(); j++) { FastVector currentItemSets = (FastVector)m_Ls.elementAt(j); Enumeration enumItemSets = currentItemSets.elements(); while (enumItemSets.hasMoreElements()) { AprioriItemSet currentItemSet = (AprioriItemSet)enumItemSets.nextElement(); //AprioriItemSet currentItemSet = new AprioriItemSet((ItemSet)enumItemSets.nextElement()); rules = currentItemSet.generateRules(m_minMetric, m_hashtables, j + 1); for (int k = 0; k < rules[0].size(); k++) { m_allTheRules[0].addElement(rules[0].elementAt(k)); m_allTheRules[1].addElement(rules[1].elementAt(k)); m_allTheRules[2].addElement(rules[2].elementAt(k)); } } } } /** * * Method that finds all large itemsets for class association rules for the given set of instances. * @throws Exception if an attribute is numeric */ private void findLargeCarItemSets() throws Exception { FastVector kMinusOneSets, kSets; Hashtable hashtable; int necSupport, necMaxSupport,i = 0; // Find large itemsets // minimum support double nextMinSupport = m_minSupport*(double)m_instances.numInstances(); double nextMaxSupport = m_upperBoundMinSupport*(double)m_instances.numInstances(); if((double)Math.rint(nextMinSupport) == nextMinSupport){ necSupport = (int) nextMinSupport; } else{ necSupport = Math.round((float)(nextMinSupport+0.5)); } if((double)Math.rint(nextMaxSupport) == nextMaxSupport){ necMaxSupport = (int) nextMaxSupport; } else{ necMaxSupport = Math.round((float)(nextMaxSupport+0.5)); } //find item sets of length one kSets = LabeledItemSet.singletons(m_instances,m_onlyClass); LabeledItemSet.upDateCounters(kSets, m_instances,m_onlyClass); //check if a item set of lentgh one is frequent, if not delete it kSets = LabeledItemSet.deleteItemSets(kSets, necSupport, necMaxSupport); if (kSets.size() == 0) return; do { m_Ls.addElement(kSets); kMinusOneSets = kSets; kSets = LabeledItemSet.mergeAllItemSets(kMinusOneSets, i, m_instances.numInstances()); hashtable = LabeledItemSet.getHashtable(kMinusOneSets, kMinusOneSets.size()); kSets = LabeledItemSet.pruneItemSets(kSets, hashtable); LabeledItemSet.upDateCounters(kSets, m_instances,m_onlyClass); kSets = LabeledItemSet.deleteItemSets(kSets, necSupport, necMaxSupport); i++; } while (kSets.size() > 0); } /** * Method that finds all class association rules. * * @throws Exception if an attribute is numeric */ private void findCarRulesQuickly() throws Exception { FastVector[] rules; // Build rules for (int j = 0; j < m_Ls.size(); j++) { FastVector currentLabeledItemSets = (FastVector)m_Ls.elementAt(j); Enumeration enumLabeledItemSets = currentLabeledItemSets.elements(); while (enumLabeledItemSets.hasMoreElements()) { LabeledItemSet currentLabeledItemSet = (LabeledItemSet)enumLabeledItemSets.nextElement(); rules = currentLabeledItemSet.generateRules(m_minMetric,false); for (int k = 0; k < rules[0].size(); k++) { m_allTheRules[0].addElement(rules[0].elementAt(k)); m_allTheRules[1].addElement(rules[1].elementAt(k)); m_allTheRules[2].addElement(rules[2].elementAt(k)); } } } } /** * returns all the rules * * @return all the rules * @see #m_allTheRules */ public FastVector[] getAllTheRules() { return m_allTheRules; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.31 $"); } /** * Main method. * * @param args the commandline options */ public static void main(String[] args) { runAssociator(new Apriori(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/AprioriItemSet.java000066400000000000000000000433321214146114000272100ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * AprioriItemSet.java * Copyright (C) 2004 University of Waikato, Hamilton, New Zealand * */ package weka.associations; import weka.core.ContingencyTables; import weka.core.FastVector; import weka.core.Instances; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import java.io.Serializable; import java.util.Enumeration; import java.util.Hashtable; /** * Class for storing a set of items. Item sets are stored in a lexicographic * order, which is determined by the header information of the set of instances * used for generating the set of items. All methods in this class assume that * item sets are stored in lexicographic order. * The class provides methods that are used in the Apriori algorithm to construct * association rules. * * @author Eibe Frank (eibe@cs.waikato.ac.nz) * @author Stefan Mutter (mutter@cs.waikato.ac.nz) * @version $Revision: 1.6 $ */ public class AprioriItemSet extends ItemSet implements Serializable, RevisionHandler { /** for serialization */ static final long serialVersionUID = 7684467755712672058L; /** * Constructor * * @param totalTrans the total number of transactions in the data */ public AprioriItemSet(int totalTrans) { super(totalTrans); } /** * Outputs the confidence for a rule. * * @param premise the premise of the rule * @param consequence the consequence of the rule * @return the confidence on the training data */ public static double confidenceForRule(AprioriItemSet premise, AprioriItemSet consequence) { return (double)consequence.m_counter/(double)premise.m_counter; } /** * Outputs the lift for a rule. Lift is defined as:
* confidence / prob(consequence) * * @param premise the premise of the rule * @param consequence the consequence of the rule * @param consequenceCount how many times the consequence occurs independent * of the premise * @return the lift on the training data */ public double liftForRule(AprioriItemSet premise, AprioriItemSet consequence, int consequenceCount) { double confidence = confidenceForRule(premise, consequence); return confidence / ((double)consequenceCount / (double)m_totalTransactions); } /** * Outputs the leverage for a rule. Leverage is defined as:
* prob(premise & consequence) - (prob(premise) * prob(consequence)) * * @param premise the premise of the rule * @param consequence the consequence of the rule * @param premiseCount how many times the premise occurs independent * of the consequent * @param consequenceCount how many times the consequence occurs independent * of the premise * @return the leverage on the training data */ public double leverageForRule(AprioriItemSet premise, AprioriItemSet consequence, int premiseCount, int consequenceCount) { double coverageForItemSet = (double)consequence.m_counter / (double)m_totalTransactions; double expectedCoverageIfIndependent = ((double)premiseCount / (double)m_totalTransactions) * ((double)consequenceCount / (double)m_totalTransactions); double lev = coverageForItemSet - expectedCoverageIfIndependent; return lev; } /** * Outputs the conviction for a rule. Conviction is defined as:
* prob(premise) * prob(!consequence) / prob(premise & !consequence) * * @param premise the premise of the rule * @param consequence the consequence of the rule * @param premiseCount how many times the premise occurs independent * of the consequent * @param consequenceCount how many times the consequence occurs independent * of the premise * @return the conviction on the training data */ public double convictionForRule(AprioriItemSet premise, AprioriItemSet consequence, int premiseCount, int consequenceCount) { double num = (double)premiseCount * (double)(m_totalTransactions - consequenceCount) / (double)m_totalTransactions; double denom = ((premiseCount - consequence.m_counter)+1); if (num < 0 || denom < 0) { System.err.println("*** "+num+" "+denom); System.err.println("premis count: "+premiseCount+" consequence count "+consequenceCount+" total trans "+m_totalTransactions); } return num / denom; } /** * Generates all rules for an item set. * * @param minConfidence the minimum confidence the rules have to have * @param hashtables containing all(!) previously generated * item sets * @param numItemsInSet the size of the item set for which the rules * are to be generated * @return all the rules with minimum confidence for the given item set */ public FastVector[] generateRules(double minConfidence, FastVector hashtables, int numItemsInSet) { FastVector premises = new FastVector(),consequences = new FastVector(), conf = new FastVector(); FastVector[] rules = new FastVector[3], moreResults; AprioriItemSet premise, consequence; Hashtable hashtable = (Hashtable)hashtables.elementAt(numItemsInSet - 2); // Generate all rules with one item in the consequence. for (int i = 0; i < m_items.length; i++) if (m_items[i] != -1) { premise = new AprioriItemSet(m_totalTransactions); consequence = new AprioriItemSet(m_totalTransactions); premise.m_items = new int[m_items.length]; consequence.m_items = new int[m_items.length]; consequence.m_counter = m_counter; for (int j = 0; j < m_items.length; j++) consequence.m_items[j] = -1; System.arraycopy(m_items, 0, premise.m_items, 0, m_items.length); premise.m_items[i] = -1; consequence.m_items[i] = m_items[i]; premise.m_counter = ((Integer)hashtable.get(premise)).intValue(); premises.addElement(premise); consequences.addElement(consequence); conf.addElement(new Double(confidenceForRule(premise, consequence))); } rules[0] = premises; rules[1] = consequences; rules[2] = conf; pruneRules(rules, minConfidence); // Generate all the other rules moreResults = moreComplexRules(rules, numItemsInSet, 1, minConfidence, hashtables); if (moreResults != null) for (int i = 0; i < moreResults[0].size(); i++) { rules[0].addElement(moreResults[0].elementAt(i)); rules[1].addElement(moreResults[1].elementAt(i)); rules[2].addElement(moreResults[2].elementAt(i)); } return rules; } /** * Generates all significant rules for an item set. * * @param minMetric the minimum metric (confidence, lift, leverage, * improvement) the rules have to have * @param metricType (confidence=0, lift, leverage, improvement) * @param hashtables containing all(!) previously generated * item sets * @param numItemsInSet the size of the item set for which the rules * are to be generated * @param numTransactions * @param significanceLevel the significance level for testing the rules * @return all the rules with minimum metric for the given item set * @exception Exception if something goes wrong */ public final FastVector[] generateRulesBruteForce(double minMetric, int metricType, FastVector hashtables, int numItemsInSet, int numTransactions, double significanceLevel) throws Exception { FastVector premises = new FastVector(),consequences = new FastVector(), conf = new FastVector(), lift = new FastVector(), lev = new FastVector(), conv = new FastVector(); FastVector[] rules = new FastVector[6]; AprioriItemSet premise, consequence; Hashtable hashtableForPremise, hashtableForConsequence; int numItemsInPremise, help, max, consequenceUnconditionedCounter; double[][] contingencyTable = new double[2][2]; double metric, chiSquared; // Generate all possible rules for this item set and test their // significance. max = (int)Math.pow(2, numItemsInSet); for (int j = 1; j < max; j++) { numItemsInPremise = 0; help = j; while (help > 0) { if (help % 2 == 1) numItemsInPremise++; help /= 2; } if (numItemsInPremise < numItemsInSet) { hashtableForPremise = (Hashtable)hashtables.elementAt(numItemsInPremise-1); hashtableForConsequence = (Hashtable)hashtables.elementAt(numItemsInSet-numItemsInPremise-1); premise = new AprioriItemSet(m_totalTransactions); consequence = new AprioriItemSet(m_totalTransactions); premise.m_items = new int[m_items.length]; consequence.m_items = new int[m_items.length]; consequence.m_counter = m_counter; help = j; for (int i = 0; i < m_items.length; i++) if (m_items[i] != -1) { if (help % 2 == 1) { premise.m_items[i] = m_items[i]; consequence.m_items[i] = -1; } else { premise.m_items[i] = -1; consequence.m_items[i] = m_items[i]; } help /= 2; } else { premise.m_items[i] = -1; consequence.m_items[i] = -1; } premise.m_counter = ((Integer)hashtableForPremise.get(premise)).intValue(); consequenceUnconditionedCounter = ((Integer)hashtableForConsequence.get(consequence)).intValue(); if (metricType == 0) { contingencyTable[0][0] = (double)(consequence.m_counter); contingencyTable[0][1] = (double)(premise.m_counter - consequence.m_counter); contingencyTable[1][0] = (double)(consequenceUnconditionedCounter - consequence.m_counter); contingencyTable[1][1] = (double)(numTransactions - premise.m_counter - consequenceUnconditionedCounter + consequence.m_counter); chiSquared = ContingencyTables.chiSquared(contingencyTable, false); metric = confidenceForRule(premise, consequence); if ((!(metric < minMetric)) && (!(chiSquared > significanceLevel))) { premises.addElement(premise); consequences.addElement(consequence); conf.addElement(new Double(metric)); lift.addElement(new Double(liftForRule(premise, consequence, consequenceUnconditionedCounter))); lev.addElement(new Double(leverageForRule(premise, consequence, premise.m_counter, consequenceUnconditionedCounter))); conv.addElement(new Double(convictionForRule(premise, consequence, premise.m_counter, consequenceUnconditionedCounter))); } } else { double tempConf = confidenceForRule(premise, consequence); double tempLift = liftForRule(premise, consequence, consequenceUnconditionedCounter); double tempLev = leverageForRule(premise, consequence, premise.m_counter, consequenceUnconditionedCounter); double tempConv = convictionForRule(premise, consequence, premise.m_counter, consequenceUnconditionedCounter); switch(metricType) { case 1: metric = tempLift; break; case 2: metric = tempLev; break; case 3: metric = tempConv; break; default: throw new Exception("ItemSet: Unknown metric type!"); } if (!(metric < minMetric)) { premises.addElement(premise); consequences.addElement(consequence); conf.addElement(new Double(tempConf)); lift.addElement(new Double(tempLift)); lev.addElement(new Double(tempLev)); conv.addElement(new Double(tempConv)); } } } } rules[0] = premises; rules[1] = consequences; rules[2] = conf; rules[3] = lift; rules[4] = lev; rules[5] = conv; return rules; } /** * Subtracts an item set from another one. * * @param toSubtract the item set to be subtracted from this one. * @return an item set that only contains items form this item sets that * are not contained by toSubtract */ public final AprioriItemSet subtract(AprioriItemSet toSubtract) { AprioriItemSet result = new AprioriItemSet(m_totalTransactions); result.m_items = new int[m_items.length]; for (int i = 0; i < m_items.length; i++) if (toSubtract.m_items[i] == -1) result.m_items[i] = m_items[i]; else result.m_items[i] = -1; result.m_counter = 0; return result; } /** * Generates rules with more than one item in the consequence. * * @param rules all the rules having (k-1)-item sets as consequences * @param numItemsInSet the size of the item set for which the rules * are to be generated * @param numItemsInConsequence the value of (k-1) * @param minConfidence the minimum confidence a rule has to have * @param hashtables the hashtables containing all(!) previously generated * item sets * @return all the rules having (k)-item sets as consequences */ private final FastVector[] moreComplexRules(FastVector[] rules, int numItemsInSet, int numItemsInConsequence, double minConfidence, FastVector hashtables) { AprioriItemSet newPremise; FastVector[] result, moreResults; FastVector newConsequences, newPremises = new FastVector(), newConf = new FastVector(); Hashtable hashtable; if (numItemsInSet > numItemsInConsequence + 1) { hashtable = (Hashtable)hashtables.elementAt(numItemsInSet - numItemsInConsequence - 2); newConsequences = mergeAllItemSets(rules[1], numItemsInConsequence - 1, m_totalTransactions); Enumeration enu = newConsequences.elements(); while (enu.hasMoreElements()) { AprioriItemSet current = (AprioriItemSet)enu.nextElement(); current.m_counter = m_counter; newPremise = subtract(current); newPremise.m_counter = ((Integer)hashtable.get(newPremise)).intValue(); newPremises.addElement(newPremise); newConf.addElement(new Double(confidenceForRule(newPremise, current))); } result = new FastVector[3]; result[0] = newPremises; result[1] = newConsequences; result[2] = newConf; pruneRules(result, minConfidence); moreResults = moreComplexRules(result,numItemsInSet,numItemsInConsequence+1, minConfidence, hashtables); if (moreResults != null) for (int i = 0; i < moreResults[0].size(); i++) { result[0].addElement(moreResults[0].elementAt(i)); result[1].addElement(moreResults[1].elementAt(i)); result[2].addElement(moreResults[2].elementAt(i)); } return result; } else return null; } /** * Returns the contents of an item set as a string. * * @param instances contains the relevant header information * @return string describing the item set */ public final String toString(Instances instances) { return super.toString(instances); } /** * Converts the header info of the given set of instances into a set * of item sets (singletons). The ordering of values in the header file * determines the lexicographic order. * * @param instances the set of instances whose header info is to be used * @return a set of item sets, each containing a single item * @exception Exception if singletons can't be generated successfully */ public static FastVector singletons(Instances instances) throws Exception { FastVector setOfItemSets = new FastVector(); ItemSet current; for (int i = 0; i < instances.numAttributes(); i++) { if (instances.attribute(i).isNumeric()) throw new Exception("Can't handle numeric attributes!"); for (int j = 0; j < instances.attribute(i).numValues(); j++) { current = new AprioriItemSet(instances.numInstances()); current.m_items = new int[instances.numAttributes()]; for (int k = 0; k < instances.numAttributes(); k++) current.m_items[k] = -1; current.m_items[i] = j; setOfItemSets.addElement(current); } } return setOfItemSets; } /** * Merges all item sets in the set of (k-1)-item sets * to create the (k)-item sets and updates the counters. * * @param itemSets the set of (k-1)-item sets * @param size the value of (k-1) * @param totalTrans the total number of transactions in the data * @return the generated (k)-item sets */ public static FastVector mergeAllItemSets(FastVector itemSets, int size, int totalTrans) { FastVector newVector = new FastVector(); ItemSet result; int numFound, k; for (int i = 0; i < itemSets.size(); i++) { ItemSet first = (ItemSet)itemSets.elementAt(i); out: for (int j = i+1; j < itemSets.size(); j++) { ItemSet second = (ItemSet)itemSets.elementAt(j); result = new AprioriItemSet(totalTrans); result.m_items = new int[first.m_items.length]; // Find and copy common prefix of size 'size' numFound = 0; k = 0; while (numFound < size) { if (first.m_items[k] == second.m_items[k]) { if (first.m_items[k] != -1) numFound++; result.m_items[k] = first.m_items[k]; } else break out; k++; } // Check difference while (k < first.m_items.length) { if ((first.m_items[k] != -1) && (second.m_items[k] != -1)) break; else { if (first.m_items[k] != -1) result.m_items[k] = first.m_items[k]; else result.m_items[k] = second.m_items[k]; } k++; } if (k == first.m_items.length) { result.m_counter = 0; newVector.addElement(result); } } } return newVector; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.6 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/Associator.java000066400000000000000000000032431214146114000264140ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * Associator.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.associations; import weka.core.Instances; import weka.core.Capabilities; public interface Associator { /** * Generates an associator. Must initialize all fields of the associator * that are not being set via options (ie. multiple calls of buildAssociator * must always lead to the same result). Must not change the dataset * in any way. * * @param data set of instances serving as training data * @exception Exception if the associator has not been * generated successfully */ void buildAssociations(Instances data) throws Exception; /** * Returns the Capabilities of this associator. Derived associators have to * override this method to enable capabilities. * * @return the capabilities of this object * @see Capabilities */ Capabilities getCapabilities(); }rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/AssociatorEvaluation.java000066400000000000000000000206201214146114000304420ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * AssociatorEvaluation.java * Copyright (C) 2006 University of Waikato, Hamilton, New Zealand */ package weka.associations; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import weka.core.Utils; import weka.core.Drawable; import weka.core.converters.ConverterUtils.DataSource; import java.util.Enumeration; import java.io.BufferedWriter; import java.io.FileWriter; /** * Class for evaluating Associaters. * * @author fracpete (fracpete at waikato dot ac dot nz) * @version $Revision: 1.5 $ */ public class AssociatorEvaluation implements RevisionHandler { /** the result string */ protected StringBuffer m_Result; /** * default constructor */ public AssociatorEvaluation() { super(); m_Result = new StringBuffer(); } /** * Generates an option string to output on the commandline. * * @param associator the associator to generate the string for * @return the option string */ protected static String makeOptionString(Associator associator) { StringBuffer text; text = new StringBuffer(); // general options text.append("\nGeneral options:\n\n"); text.append("-t \n"); text.append("\tThe name of the training file.\n"); text.append("-g \n"); text.append("\tOutputs the graph representation (if supported) of the associator to a file.\n"); // associator specific options, if any if (associator instanceof OptionHandler) { text.append( "\nOptions specific to " + associator.getClass().getName().replaceAll(".*\\.", "") + ":\n\n"); Enumeration enm = ((OptionHandler) associator).listOptions(); while (enm.hasMoreElements()) { Option option = (Option) enm.nextElement(); text.append(option.synopsis() + "\n"); text.append(option.description() + "\n"); } } return text.toString(); } /** * Evaluates an associator with the options given in an array of strings. * * @param associatorString class of associator as a string * @param options the array of string containing the options * @throws Exception if model could not be evaluated successfully * @return a string describing the results */ public static String evaluate(String associatorString, String[] options) throws Exception { Associator associator; // Create associator try { associator = (Associator) Class.forName(associatorString).newInstance(); } catch (Exception e) { throw new Exception("Can't find class with name " + associatorString + '.'); } return evaluate(associator, options); } /** * Evaluates the associator with the given commandline options and returns * the evaluation string. * * @param associator the Associator to evaluate * @param options the commandline options * @return the generated output string * @throws Exception if evaluation fails */ public static String evaluate(Associator associator, String[] options) throws Exception { String trainFileString = ""; String graphFileName = ""; AssociatorEvaluation eval; DataSource loader; // help? if (Utils.getFlag('h', options)) throw new Exception("\nHelp requested.\n" + makeOptionString(associator)); try { // general options trainFileString = Utils.getOption('t', options); if (trainFileString.length() == 0) throw new Exception("No training file given!"); loader = new DataSource(trainFileString); graphFileName = Utils.getOption('g', options); // associator specific options if (associator instanceof OptionHandler) { ((OptionHandler) associator).setOptions(options); } // left-over options? Utils.checkForRemainingOptions(options); } catch (Exception e) { throw new Exception( "\nWeka exception: " + e.getMessage() + "\n" + makeOptionString(associator)); } // load file and build associations eval = new AssociatorEvaluation(); String results = eval.evaluate(associator, new Instances(loader.getDataSet())); // If associator is drawable output string describing graph if ((associator instanceof Drawable) && (graphFileName.length() != 0)) { BufferedWriter writer = new BufferedWriter(new FileWriter(graphFileName)); writer.write(((Drawable) associator).graph()); writer.newLine(); writer.flush(); writer.close(); } return results; } /** * Evaluates the associator with the given commandline options and returns * the evaluation string. * * @param associator the Associator to evaluate * @param data the data to run the associator with * @return the generated output string * @throws Exception if evaluation fails */ public String evaluate(Associator associator, Instances data) throws Exception { long startTime; long endTime; // build associations startTime = System.currentTimeMillis(); associator.buildAssociations(data); endTime = System.currentTimeMillis(); m_Result = new StringBuffer(associator.toString()); m_Result.append("\n=== Evaluation ===\n\n"); m_Result.append("Elapsed time: " + (((double) (endTime - startTime)) / 1000) + "s"); m_Result.append("\n"); return m_Result.toString(); } /** * Tests whether the current evaluation object is equal to another * evaluation object * * @param obj the object to compare against * @return true if the two objects are equal */ public boolean equals(Object obj) { if ((obj == null) || !(obj.getClass().equals(this.getClass()))) return false; AssociatorEvaluation cmp = (AssociatorEvaluation) obj; // TODO: better comparison??? String associatingResults1 = m_Result.toString().replaceAll("Elapsed time.*", ""); String associatingResults2 = cmp.m_Result.toString().replaceAll("Elapsed time.*", ""); if (!associatingResults1.equals(associatingResults2)) return false; return true; } /** * returns a summary string of the evaluation with a no title * * @return the summary string */ public String toSummaryString() { return toSummaryString(""); } /** * returns a summary string of the evaluation with a default title * * @param title the title to print before the result * @return the summary string */ public String toSummaryString(String title) { StringBuffer result; result = new StringBuffer(title); if (title.length() != 0) result.append("\n"); result.append(m_Result); return result.toString(); } /** * returns the current result * * @return the currently stored result * @see #toSummaryString() */ public String toString() { return toSummaryString(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.5 $"); } /** * A test method for this class. Just extracts the first command line * argument as an associator class name and calls evaluate. * * @param args an array of command line arguments, the first of which * must be the class name of an associator. */ public static void main(String[] args) { try { if (args.length == 0) { throw new Exception( "The first argument must be the class name of a kernel"); } String associator = args[0]; args[0] = ""; System.out.println(evaluate(associator, args)); } catch (Exception ex) { ex.printStackTrace(); System.err.println(ex.getMessage()); } } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/CARuleMiner.java000066400000000000000000000046571214146114000264250ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * CARuleMiner.java * Copyright (C) 2004 University of Waikato, Hamilton, New Zealand * */ package weka.associations; import weka.core.FastVector; import weka.core.Instances; import weka.core.OptionHandler; /** * Interface for learning class association rules. All schemes for learning * class association rules implemement this interface. * * @author Stefan Mutter (mutter@cs.waikato.ac.nz) * @version $Revision: 1.3 $ */ public interface CARuleMiner extends OptionHandler { /** * Method for mining class association rules. * Must initialize all fields of the CARuleMiner that are not being set via options (ie. multiple calls of mineCARs * must always lead to the same result). Must not change the dataset in any way. * @param data the insatnces for which class association rules are mined * @throws Exception throws exception if class association rules cannot be mined * @return class association rules and their scoring metric in an FastVector array */ public FastVector[] mineCARs(Instances data) throws Exception; /** * Gets the instances without the class attribute * @return the instances withoput the class attribute */ public Instances getInstancesNoClass(); /** * Gets the class attribute and its values for all instances * @return the class attribute and its values for all instances */ public Instances getInstancesOnlyClass(); /** * Gets name of the scoring metric used for car mining * @return string containing the name of the scoring metric */ public String metricString(); /** * Sets the class index for the class association rule miner * @param index the class index */ public void setClassIndex(int index); } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/CaRuleGeneration.java000066400000000000000000000214411214146114000274740ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * CaRuleGeneration.java * Copyright (C) 2004 University of Waikato, Hamilton, New Zealand * */ package weka.associations; import weka.core.Attribute; import weka.core.FastVector; import weka.core.Instances; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import weka.core.UnassignedClassException; import java.io.Serializable; import java.util.Hashtable; import java.util.TreeSet; /** * Class implementing the rule generation procedure of the predictive apriori algorithm for class association rules. * * For association rules in gerneral the method is described in: * T. Scheffer (2001). Finding Association Rules That Trade Support * Optimally against Confidence. Proc of the 5th European Conf. * on Principles and Practice of Knowledge Discovery in Databases (PKDD'01), * pp. 424-435. Freiburg, Germany: Springer-Verlag.

* * The implementation follows the paper expect for adding a rule to the output of the * n best rules. A rule is added if: * the expected predictive accuracy of this rule is among the n best and it is * not subsumed by a rule with at least the same expected predictive accuracy * (out of an unpublished manuscript from T. Scheffer). * * @author Stefan Mutter (mutter@cs.waikato.ac.nz) * @version $Revision: 1.4 $ */ public class CaRuleGeneration extends RuleGeneration implements Serializable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = 3065752149646517703L; /** * Constructor * @param itemSet the item set that forms the premise of the rule */ public CaRuleGeneration(ItemSet itemSet){ super(itemSet); } /** * Generates all rules for an item set. The item set is the premise. * @param numRules the number of association rules the use wants to mine. * This number equals the size n of the list of the * best rules. * @param midPoints the mid points of the intervals * @param priors Hashtable that contains the prior probabilities * @param expectation the minimum value of the expected predictive accuracy * that is needed to get into the list of the best rules * @param instances the instances for which association rules are generated * @param best the list of the n best rules. * The list is implemented as a TreeSet * @param genTime the maximum time of generation * @return all the rules with minimum confidence for the given item set */ public TreeSet generateRules(int numRules, double[] midPoints, Hashtable priors, double expectation, Instances instances, TreeSet best, int genTime) { boolean redundant = false; FastVector consequences = new FastVector(); ItemSet premise; RuleItem current = null, old = null; Hashtable hashtable; m_change = false; m_midPoints = midPoints; m_priors = priors; m_best = best; m_expectation = expectation; m_count = genTime; m_instances = instances; //create rule body premise =null; premise = new ItemSet(m_totalTransactions); int[] premiseItems = new int[m_items.length]; System.arraycopy(m_items, 0, premiseItems, 0, m_items.length); premise.setItem(premiseItems); premise.setCounter(m_counter); consequences = singleConsequence(instances); //create n best rules do{ if(premise == null || consequences.size() == 0) return m_best; m_minRuleCount = 1; while(expectation((double)m_minRuleCount,premise.counter(),m_midPoints,m_priors) <= m_expectation){ m_minRuleCount++; if(m_minRuleCount > premise.counter()) return m_best; } redundant = false; //create possible heads FastVector allRuleItems = new FastVector(); int h = 0; while(h < consequences.size()){ RuleItem dummie = new RuleItem(); m_count++; current = dummie.generateRuleItem(premise,(ItemSet)consequences.elementAt(h),instances,m_count,m_minRuleCount,m_midPoints,m_priors); if(current != null) allRuleItems.addElement(current); h++; } //update best for(h =0; h< allRuleItems.size();h++){ current = (RuleItem)allRuleItems.elementAt(h); if(m_best.size() < numRules){ m_change =true; redundant = removeRedundant(current); } else{ m_expectation = ((RuleItem)(m_best.first())).accuracy(); if(current.accuracy() > m_expectation){ boolean remove = m_best.remove(m_best.first()); m_change = true; redundant = removeRedundant(current); m_expectation = ((RuleItem)(m_best.first())).accuracy(); while(expectation((double)m_minRuleCount, (current.premise()).counter(),m_midPoints,m_priors) < m_expectation){ m_minRuleCount++; if(m_minRuleCount > (current.premise()).counter()) break; } } } } }while(redundant); return m_best; } /** * Methods that decides whether or not rule a subsumes rule b. * The defintion of subsumption is: * Rule a subsumes rule b, if a subsumes b * AND * a has got least the same expected predictive accuracy as b. * @param a an association rule stored as a RuleItem * @param b an association rule stored as a RuleItem * @return true if rule a subsumes rule b or false otherwise. */ public static boolean aSubsumesB(RuleItem a, RuleItem b){ if(!a.consequence().equals(b.consequence())) return false; if(a.accuracy() < b.accuracy()) return false; for(int k = 0; k < ((a.premise()).items()).length;k++){ if((a.premise()).itemAt(k) != (b.premise()).itemAt(k)){ if(((a.premise()).itemAt(k) != -1 && (b.premise()).itemAt(k) != -1) || (b.premise()).itemAt(k) == -1) return false; } /*if(a.m_consequence.m_items[k] != b.m_consequence.m_items[k]){ if((a.m_consequence.m_items[k] != -1 && b.m_consequence.m_items[k] != -1) || a.m_consequence.m_items[k] == -1) return false; }*/ } return true; } /** * Converts the header info of the given set of instances into a set * of item sets (singletons). The ordering of values in the header file * determines the lexicographic order. * * @param instances the set of instances whose header info is to be used * @return a set of item sets, each containing a single item * @exception Exception if singletons can't be generated successfully */ public static FastVector singletons(Instances instances) throws Exception { FastVector setOfItemSets = new FastVector(); ItemSet current; if(instances.classIndex() == -1) throw new UnassignedClassException("Class index is negative (not set)!"); Attribute att = instances.classAttribute(); for (int i = 0; i < instances.numAttributes(); i++) { if (instances.attribute(i).isNumeric()) throw new Exception("Can't handle numeric attributes!"); if(i != instances.classIndex()){ for (int j = 0; j < instances.attribute(i).numValues(); j++) { current = new ItemSet(instances.numInstances()); int[] currentItems = new int[instances.numAttributes()]; for (int k = 0; k < instances.numAttributes(); k++) currentItems[k] = -1; currentItems[i] = j; current.setItem(currentItems); setOfItemSets.addElement(current); } } } return setOfItemSets; } /** * generates a consequence of length 1 for a class association rule. * @param instances the instances under consideration * @return FastVector with consequences of length 1 */ public static FastVector singleConsequence(Instances instances){ ItemSet consequence; FastVector consequences = new FastVector(); for (int j = 0; j < (instances.classAttribute()).numValues(); j++) { consequence = new ItemSet(instances.numInstances()); int[] consequenceItems = new int[instances.numAttributes()]; consequence.setItem(consequenceItems); for (int k = 0; k < instances.numAttributes(); k++) consequence.setItemAt(-1,k); consequence.setItemAt(j,instances.classIndex()); consequences.addElement(consequence); } return consequences; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.4 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/CheckAssociator.java000066400000000000000000001546251214146114000273650ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * CheckAssociator.java * Copyright (C) 2006 University of Waikato, Hamilton, New Zealand * */ package weka.associations; import weka.core.Attribute; import weka.core.CheckScheme; import weka.core.FastVector; import weka.core.Instances; import weka.core.MultiInstanceCapabilitiesHandler; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import weka.core.SerializationHelper; import weka.core.TestInstances; import weka.core.Utils; import weka.core.WeightedInstancesHandler; import java.util.Enumeration; import java.util.Random; import java.util.Vector; /** * Class for examining the capabilities and finding problems with * associators. If you implement an associators using the WEKA.libraries, * you should run the checks on it to ensure robustness and correct * operation. Passing all the tests of this object does not mean * bugs in the associators don't exist, but this will help find some * common ones.

* * Typical usage:

* java weka.associations.CheckAssociator -W associator_name * -- associator_options

* * CheckAssociator reports on the following: *

    *
  • Associator abilities *
      *
    • Possible command line options to the associators
      • *
    • Whether the associators can predict nominal, numeric, string, * date or relational class attributes.
      • *
    • Whether the associators can handle numeric predictor attributes
      • *
    • Whether the associators can handle nominal predictor attributes
      • *
    • Whether the associators can handle string predictor attributes
      • *
    • Whether the associators can handle date predictor attributes
      • *
    • Whether the associators can handle relational predictor attributes
      • *
    • Whether the associators can handle multi-instance data
      • *
    • Whether the associators can handle missing predictor values
      • *
    • Whether the associators can handle missing class values
      • *
    • Whether a nominal associators only handles 2 class problems
      • *
    • Whether the associators can handle instance weights
      • *
    *
    • *
  • Correct functioning *
      *
    • Correct initialisation during buildAssociations (i.e. no result * changes when buildAssociations called repeatedly)
      • *
    • Whether the associators alters the data pased to it * (number of instances, instance order, instance weights, etc)
      • *
    *
    • *
  • Degenerate cases *
      *
    • building associators with zero training instances
      • *
    • all but one predictor attribute values missing
      • *
    • all predictor attribute values missing
      • *
    • all but one class values missing
      • *
    • all class values missing
      • *
    *
    • *
* Running CheckAssociator with the debug option set will output the * training dataset for any failed tests.

* * The weka.associations.AbstractAssociatorTest uses this * class to test all the associators. Any changes here, have to be * checked in that abstract test class, too.

* * Valid options are:

* * 

 -D
 *  Turn on debugging output.
* * 
 -S
 *  Silent mode - prints nothing to stdout.
* * 
 -N <num>
 *  The number of instances in the datasets (default 20).
* * 
 -nominal <num>
 *  The number of nominal attributes (default 2).
* * 
 -nominal-values <num>
 *  The number of values for nominal attributes (default 1).
* * 
 -numeric <num>
 *  The number of numeric attributes (default 1).
* * 
 -string <num>
 *  The number of string attributes (default 1).
* * 
 -date <num>
 *  The number of date attributes (default 1).
* * 
 -relational <num>
 *  The number of relational attributes (default 1).
* * 
 -num-instances-relational <num>
 *  The number of instances in relational/bag attributes (default 10).
* * 
 -words <comma-separated-list>
 *  The words to use in string attributes.
* * 
 -word-separators <chars>
 *  The word separators to use in string attributes.
* * 
 -W
 *  Full name of the associator analysed.
 *  eg: weka.associations.Apriori
 *  (default weka.associations.Apriori)
* * 
 
 * Options specific to associator weka.associations.Apriori:
 *
* * 
 -N <required number of rules output>
 *  The required number of rules. (default = 10)
* * 
 -T <0=confidence | 1=lift | 2=leverage | 3=Conviction>
 *  The metric type by which to rank rules. (default = confidence)
* * 
 -C <minimum metric score of a rule>
 *  The minimum confidence of a rule. (default = 0.9)
* * 
 -D <delta for minimum support>
 *  The delta by which the minimum support is decreased in
 *  each iteration. (default = 0.05)
* * 
 -U <upper bound for minimum support>
 *  Upper bound for minimum support. (default = 1.0)
* * 
 -M <lower bound for minimum support>
 *  The lower bound for the minimum support. (default = 0.1)
* * 
 -S <significance level>
 *  If used, rules are tested for significance at
 *  the given level. Slower. (default = no significance testing)
* * 
 -I
 *  If set the itemsets found are also output. (default = no)
* * 
 -R
 *  Remove columns that contain all missing values (default = no)
* * 
 -V
 *  Report progress iteratively. (default = no)
* * 
 -A
 *  If set class association rules are mined. (default = no)
* * 
 -c <the class index>
 *  The class index. (default = last)
* * * Options after -- are passed to the designated associator.

* * @author Len Trigg (trigg@cs.waikato.ac.nz) * @author FracPete (fracpete at waikato dot ac dot nz) * @version $Revision: 1.7 $ * @see TestInstances */ public class CheckAssociator extends CheckScheme implements RevisionHandler { /* * Note about test methods: * - methods return array of booleans * - first index: success or not * - second index: acceptable or not (e.g., Exception is OK) * * FracPete (fracpete at waikato dot ac dot nz) */ /** a "dummy" class type */ public final static int NO_CLASS = -1; /*** The associator to be examined */ protected Associator m_Associator = new weka.associations.Apriori(); /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector result = new Vector(); Enumeration en = super.listOptions(); while (en.hasMoreElements()) result.addElement(en.nextElement()); result.addElement(new Option( "\tFull name of the associator analysed.\n" +"\teg: weka.associations.Apriori\n" + "\t(default weka.associations.Apriori)", "W", 1, "-W")); if ((m_Associator != null) && (m_Associator instanceof OptionHandler)) { result.addElement(new Option("", "", 0, "\nOptions specific to associator " + m_Associator.getClass().getName() + ":")); Enumeration enu = ((OptionHandler)m_Associator).listOptions(); while (enu.hasMoreElements()) result.addElement(enu.nextElement()); } return result.elements(); } /** * Parses a given list of options. * * Valid options are:

* * 

 -D
   *  Turn on debugging output.
* * 
 -S
   *  Silent mode - prints nothing to stdout.
* * 
 -N <num>
   *  The number of instances in the datasets (default 20).
* * 
 -nominal <num>
   *  The number of nominal attributes (default 2).
* * 
 -nominal-values <num>
   *  The number of values for nominal attributes (default 1).
* * 
 -numeric <num>
   *  The number of numeric attributes (default 1).
* * 
 -string <num>
   *  The number of string attributes (default 1).
* * 
 -date <num>
   *  The number of date attributes (default 1).
* * 
 -relational <num>
   *  The number of relational attributes (default 1).
* * 
 -num-instances-relational <num>
   *  The number of instances in relational/bag attributes (default 10).
* * 
 -words <comma-separated-list>
   *  The words to use in string attributes.
* * 
 -word-separators <chars>
   *  The word separators to use in string attributes.
* * 
 -W
   *  Full name of the associator analysed.
   *  eg: weka.associations.Apriori
   *  (default weka.associations.Apriori)
* * 
 
   * Options specific to associator weka.associations.Apriori:
   *
* * 
 -N <required number of rules output>
   *  The required number of rules. (default = 10)
* * 
 -T <0=confidence | 1=lift | 2=leverage | 3=Conviction>
   *  The metric type by which to rank rules. (default = confidence)
* * 
 -C <minimum metric score of a rule>
   *  The minimum confidence of a rule. (default = 0.9)
* * 
 -D <delta for minimum support>
   *  The delta by which the minimum support is decreased in
   *  each iteration. (default = 0.05)
* * 
 -U <upper bound for minimum support>
   *  Upper bound for minimum support. (default = 1.0)
* * 
 -M <lower bound for minimum support>
   *  The lower bound for the minimum support. (default = 0.1)
* * 
 -S <significance level>
   *  If used, rules are tested for significance at
   *  the given level. Slower. (default = no significance testing)
* * 
 -I
   *  If set the itemsets found are also output. (default = no)
* * 
 -R
   *  Remove columns that contain all missing values (default = no)
* * 
 -V
   *  Report progress iteratively. (default = no)
* * 
 -A
   *  If set class association rules are mined. (default = no)
* * 
 -c <the class index>
   *  The class index. (default = last)
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { String tmpStr; super.setOptions(options); tmpStr = Utils.getOption('W', options); if (tmpStr.length() == 0) tmpStr = weka.associations.Apriori.class.getName(); setAssociator( (Associator) forName( "weka.associations", Associator.class, tmpStr, Utils.partitionOptions(options))); } /** * Gets the current settings of the CheckAssociator. * * @return an array of strings suitable for passing to setOptions */ public String[] getOptions() { Vector result; String[] options; int i; result = new Vector(); options = super.getOptions(); for (i = 0; i < options.length; i++) result.add(options[i]); if (getAssociator() != null) { result.add("-W"); result.add(getAssociator().getClass().getName()); } if ((m_Associator != null) && (m_Associator instanceof OptionHandler)) options = ((OptionHandler) m_Associator).getOptions(); else options = new String[0]; if (options.length > 0) { result.add("--"); for (i = 0; i < options.length; i++) result.add(options[i]); } return (String[]) result.toArray(new String[result.size()]); } /** * Begin the tests, reporting results to System.out */ public void doTests() { if (getAssociator() == null) { println("\n=== No associator set ==="); return; } println("\n=== Check on Associator: " + getAssociator().getClass().getName() + " ===\n"); // Start tests m_ClasspathProblems = false; println("--> Checking for interfaces"); canTakeOptions(); boolean weightedInstancesHandler = weightedInstancesHandler()[0]; boolean multiInstanceHandler = multiInstanceHandler()[0]; println("--> Associator tests"); declaresSerialVersionUID(); println("--> no class attribute"); testsWithoutClass(weightedInstancesHandler, multiInstanceHandler); println("--> with class attribute"); testsPerClassType(Attribute.NOMINAL, weightedInstancesHandler, multiInstanceHandler); testsPerClassType(Attribute.NUMERIC, weightedInstancesHandler, multiInstanceHandler); testsPerClassType(Attribute.DATE, weightedInstancesHandler, multiInstanceHandler); testsPerClassType(Attribute.STRING, weightedInstancesHandler, multiInstanceHandler); testsPerClassType(Attribute.RELATIONAL, weightedInstancesHandler, multiInstanceHandler); } /** * Set the associator to test. * * @param newAssociator the Associator to use. */ public void setAssociator(Associator newAssociator) { m_Associator = newAssociator; } /** * Get the associator being tested * * @return the associator being tested */ public Associator getAssociator() { return m_Associator; } /** * Run a battery of tests for a given class attribute type * * @param classType true if the class attribute should be numeric * @param weighted true if the associator says it handles weights * @param multiInstance true if the associator is a multi-instance associator */ protected void testsPerClassType(int classType, boolean weighted, boolean multiInstance) { boolean PNom = canPredict(true, false, false, false, false, multiInstance, classType)[0]; boolean PNum = canPredict(false, true, false, false, false, multiInstance, classType)[0]; boolean PStr = canPredict(false, false, true, false, false, multiInstance, classType)[0]; boolean PDat = canPredict(false, false, false, true, false, multiInstance, classType)[0]; boolean PRel; if (!multiInstance) PRel = canPredict(false, false, false, false, true, multiInstance, classType)[0]; else PRel = false; if (PNom || PNum || PStr || PDat || PRel) { if (weighted) instanceWeights(PNom, PNum, PStr, PDat, PRel, multiInstance, classType); if (classType == Attribute.NOMINAL) canHandleNClasses(PNom, PNum, PStr, PDat, PRel, multiInstance, 4); if (!multiInstance) { canHandleClassAsNthAttribute(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, 0); canHandleClassAsNthAttribute(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, 1); } canHandleZeroTraining(PNom, PNum, PStr, PDat, PRel, multiInstance, classType); boolean handleMissingPredictors = canHandleMissing(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, true, false, 20)[0]; if (handleMissingPredictors) canHandleMissing(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, true, false, 100); boolean handleMissingClass = canHandleMissing(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, false, true, 20)[0]; if (handleMissingClass) canHandleMissing(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, false, true, 100); correctBuildInitialisation(PNom, PNum, PStr, PDat, PRel, multiInstance, classType); datasetIntegrity(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, handleMissingPredictors, handleMissingClass); } } /** * Run a battery of tests without a class * * @param weighted true if the associator says it handles weights * @param multiInstance true if the associator is a multi-instance associator */ protected void testsWithoutClass(boolean weighted, boolean multiInstance) { boolean PNom = canPredict(true, false, false, false, false, multiInstance, NO_CLASS)[0]; boolean PNum = canPredict(false, true, false, false, false, multiInstance, NO_CLASS)[0]; boolean PStr = canPredict(false, false, true, false, false, multiInstance, NO_CLASS)[0]; boolean PDat = canPredict(false, false, false, true, false, multiInstance, NO_CLASS)[0]; boolean PRel; if (!multiInstance) PRel = canPredict(false, false, false, false, true, multiInstance, NO_CLASS)[0]; else PRel = false; if (PNom || PNum || PStr || PDat || PRel) { if (weighted) instanceWeights(PNom, PNum, PStr, PDat, PRel, multiInstance, NO_CLASS); canHandleZeroTraining(PNom, PNum, PStr, PDat, PRel, multiInstance, NO_CLASS); boolean handleMissingPredictors = canHandleMissing(PNom, PNum, PStr, PDat, PRel, multiInstance, NO_CLASS, true, false, 20)[0]; if (handleMissingPredictors) canHandleMissing(PNom, PNum, PStr, PDat, PRel, multiInstance, NO_CLASS, true, false, 100); correctBuildInitialisation(PNom, PNum, PStr, PDat, PRel, multiInstance, NO_CLASS); datasetIntegrity(PNom, PNum, PStr, PDat, PRel, multiInstance, NO_CLASS, handleMissingPredictors, false); } } /** * Checks whether the scheme can take command line options. * * @return index 0 is true if the associator can take options */ protected boolean[] canTakeOptions() { boolean[] result = new boolean[2]; print("options..."); if (m_Associator instanceof OptionHandler) { println("yes"); if (m_Debug) { println("\n=== Full report ==="); Enumeration enu = ((OptionHandler)m_Associator).listOptions(); while (enu.hasMoreElements()) { Option option = (Option) enu.nextElement(); print(option.synopsis() + "\n" + option.description() + "\n"); } println("\n"); } result[0] = true; } else { println("no"); result[0] = false; } return result; } /** * Checks whether the scheme says it can handle instance weights. * * @return true if the associator handles instance weights */ protected boolean[] weightedInstancesHandler() { boolean[] result = new boolean[2]; print("weighted instances associator..."); if (m_Associator instanceof WeightedInstancesHandler) { println("yes"); result[0] = true; } else { println("no"); result[0] = false; } return result; } /** * Checks whether the scheme handles multi-instance data. * * @return true if the associator handles multi-instance data */ protected boolean[] multiInstanceHandler() { boolean[] result = new boolean[2]; print("multi-instance associator..."); if (m_Associator instanceof MultiInstanceCapabilitiesHandler) { println("yes"); result[0] = true; } else { println("no"); result[0] = false; } return result; } /** * tests for a serialVersionUID. Fails in case the scheme doesn't declare * a UID. * * @return index 0 is true if the scheme declares a UID */ protected boolean[] declaresSerialVersionUID() { boolean[] result = new boolean[2]; print("serialVersionUID..."); result[0] = !SerializationHelper.needsUID(m_Associator.getClass()); if (result[0]) println("yes"); else println("no"); return result; } /** * Checks basic prediction of the scheme, for simple non-troublesome * datasets. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NOMINAL, NUMERIC, etc.) * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable */ protected boolean[] canPredict( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType) { print("basic predict"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); FastVector accepts = new FastVector(); accepts.addElement("any"); accepts.addElement("unary"); accepts.addElement("binary"); accepts.addElement("nominal"); accepts.addElement("numeric"); accepts.addElement("string"); accepts.addElement("date"); accepts.addElement("relational"); accepts.addElement("multi-instance"); accepts.addElement("not in classpath"); int numTrain = getNumInstances(), numClasses = 2, missingLevel = 0; boolean predictorMissing = false, classMissing = false; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, missingLevel, predictorMissing, classMissing, numTrain, numClasses, accepts); } /** * Checks whether nominal schemes can handle more than two classes. * If a scheme is only designed for two-class problems it should * throw an appropriate exception for multi-class problems. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param numClasses the number of classes to test * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable */ protected boolean[] canHandleNClasses( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int numClasses) { print("more than two class problems"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, Attribute.NOMINAL); print("..."); FastVector accepts = new FastVector(); accepts.addElement("number"); accepts.addElement("class"); int numTrain = getNumInstances(), missingLevel = 0; boolean predictorMissing = false, classMissing = false; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, Attribute.NOMINAL, missingLevel, predictorMissing, classMissing, numTrain, numClasses, accepts); } /** * Checks whether the scheme can handle class attributes as Nth attribute. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param classIndex the index of the class attribute (0-based, -1 means last attribute) * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable * @see TestInstances#CLASS_IS_LAST */ protected boolean[] canHandleClassAsNthAttribute( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType, int classIndex) { if (classIndex == TestInstances.CLASS_IS_LAST) print("class attribute as last attribute"); else print("class attribute as " + (classIndex + 1) + ". attribute"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); FastVector accepts = new FastVector(); int numTrain = getNumInstances(), numClasses = 2, missingLevel = 0; boolean predictorMissing = false, classMissing = false; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, classIndex, missingLevel, predictorMissing, classMissing, numTrain, numClasses, accepts); } /** * Checks whether the scheme can handle zero training instances. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable */ protected boolean[] canHandleZeroTraining( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType) { print("handle zero training instances"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); FastVector accepts = new FastVector(); accepts.addElement("train"); accepts.addElement("value"); int numTrain = 0, numClasses = 2, missingLevel = 0; boolean predictorMissing = false, classMissing = false; return runBasicTest( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, missingLevel, predictorMissing, classMissing, numTrain, numClasses, accepts); } /** * Checks whether the scheme correctly initialises models when * buildAssociations is called. This test calls buildAssociations with * one training dataset. buildAssociations is then called on a training * set with different structure, and then again with the original training * set. If the equals method of the AssociatorEvaluation class returns * false, this is noted as incorrect build initialisation. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @return index 0 is true if the test was passed */ protected boolean[] correctBuildInitialisation( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType) { boolean[] result = new boolean[2]; print("correct initialisation during buildAssociations"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); int numTrain = getNumInstances(), numClasses = 2, missingLevel = 0; boolean predictorMissing = false, classMissing = false; Instances train1 = null; Instances train2 = null; Associator associator = null; AssociatorEvaluation evaluation1A = null; AssociatorEvaluation evaluation1B = null; AssociatorEvaluation evaluation2 = null; int stage = 0; try { // Make two train sets with different numbers of attributes train1 = makeTestDataset(42, numTrain, nominalPredictor ? getNumNominal() : 0, numericPredictor ? getNumNumeric() : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, multiInstance); train2 = makeTestDataset(84, numTrain, nominalPredictor ? getNumNominal() + 1 : 0, numericPredictor ? getNumNumeric() + 1 : 0, stringPredictor ? getNumString() + 1 : 0, datePredictor ? getNumDate() + 1 : 0, relationalPredictor ? getNumRelational() + 1 : 0, numClasses, classType, multiInstance); if (missingLevel > 0) { addMissing(train1, missingLevel, predictorMissing, classMissing); addMissing(train2, missingLevel, predictorMissing, classMissing); } associator = AbstractAssociator.makeCopies(getAssociator(), 1)[0]; evaluation1A = new AssociatorEvaluation(); evaluation1B = new AssociatorEvaluation(); evaluation2 = new AssociatorEvaluation(); } catch (Exception ex) { throw new Error("Error setting up for tests: " + ex.getMessage()); } try { stage = 0; evaluation1A.evaluate(associator, train1); stage = 1; evaluation2.evaluate(associator, train2); stage = 2; evaluation1B.evaluate(associator, train1); stage = 3; if (!evaluation1A.equals(evaluation1B)) { if (m_Debug) { println("\n=== Full report ===\n" + evaluation1A.toSummaryString("\nFirst buildAssociations()") + "\n\n"); println( evaluation1B.toSummaryString("\nSecond buildAssociations()") + "\n\n"); } throw new Exception("Results differ between buildAssociations calls"); } println("yes"); result[0] = true; if (false && m_Debug) { println("\n=== Full report ===\n" + evaluation1A.toSummaryString("\nFirst buildAssociations()") + "\n\n"); println( evaluation1B.toSummaryString("\nSecond buildAssociations()") + "\n\n"); } } catch (Exception ex) { println("no"); result[0] = false; if (m_Debug) { println("\n=== Full Report ==="); print("Problem during building"); switch (stage) { case 0: print(" of dataset 1"); break; case 1: print(" of dataset 2"); break; case 2: print(" of dataset 1 (2nd build)"); break; case 3: print(", comparing results from builds of dataset 1"); break; } println(": " + ex.getMessage() + "\n"); println("here are the datasets:\n"); println("=== Train1 Dataset ===\n" + train1.toString() + "\n"); println("=== Train2 Dataset ===\n" + train2.toString() + "\n"); } } return result; } /** * Checks basic missing value handling of the scheme. If the missing * values cause an exception to be thrown by the scheme, this will be * recorded. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param predictorMissing true if the missing values may be in * the predictors * @param classMissing true if the missing values may be in the class * @param missingLevel the percentage of missing values * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable */ protected boolean[] canHandleMissing( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType, boolean predictorMissing, boolean classMissing, int missingLevel) { if (missingLevel == 100) print("100% "); print("missing"); if (predictorMissing) { print(" predictor"); if (classMissing) print(" and"); } if (classMissing) print(" class"); print(" values"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); FastVector accepts = new FastVector(); accepts.addElement("missing"); accepts.addElement("value"); accepts.addElement("train"); int numTrain = getNumInstances(), numClasses = 2; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, missingLevel, predictorMissing, classMissing, numTrain, numClasses, accepts); } /** * Checks whether the associator can handle instance weights. * This test compares the associator performance on two datasets * that are identical except for the training weights. If the * results change, then the associator must be using the weights. It * may be possible to get a false positive from this test if the * weight changes aren't significant enough to induce a change * in associator performance (but the weights are chosen to minimize * the likelihood of this). * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @return index 0 true if the test was passed */ protected boolean[] instanceWeights( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType) { print("associator uses instance weights"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); int numTrain = 2*getNumInstances(), numClasses = 2, missingLevel = 0; boolean predictorMissing = false, classMissing = false; boolean[] result = new boolean[2]; Instances train = null; Associator [] associators = null; AssociatorEvaluation evaluationB = null; AssociatorEvaluation evaluationI = null; boolean evalFail = false; try { train = makeTestDataset(42, numTrain, nominalPredictor ? getNumNominal() + 1 : 0, numericPredictor ? getNumNumeric() + 1 : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, multiInstance); if (missingLevel > 0) addMissing(train, missingLevel, predictorMissing, classMissing); associators = AbstractAssociator.makeCopies(getAssociator(), 2); evaluationB = new AssociatorEvaluation(); evaluationI = new AssociatorEvaluation(); evaluationB.evaluate(associators[0], train); } catch (Exception ex) { throw new Error("Error setting up for tests: " + ex.getMessage()); } try { // Now modify instance weights and re-built/test for (int i = 0; i < train.numInstances(); i++) { train.instance(i).setWeight(0); } Random random = new Random(1); for (int i = 0; i < train.numInstances() / 2; i++) { int inst = Math.abs(random.nextInt()) % train.numInstances(); int weight = Math.abs(random.nextInt()) % 10 + 1; train.instance(inst).setWeight(weight); } evaluationI.evaluate(associators[1], train); if (evaluationB.equals(evaluationI)) { // println("no"); evalFail = true; throw new Exception("evalFail"); } println("yes"); result[0] = true; } catch (Exception ex) { println("no"); result[0] = false; if (m_Debug) { println("\n=== Full Report ==="); if (evalFail) { println("Results don't differ between non-weighted and " + "weighted instance models."); println("Here are the results:\n"); println(evaluationB.toSummaryString("\nboth methods\n")); } else { print("Problem during building"); println(": " + ex.getMessage() + "\n"); } println("Here is the dataset:\n"); println("=== Train Dataset ===\n" + train.toString() + "\n"); println("=== Train Weights ===\n"); for (int i = 0; i < train.numInstances(); i++) { println(" " + (i + 1) + " " + train.instance(i).weight()); } } } return result; } /** * Checks whether the scheme alters the training dataset during * building. If the scheme needs to modify the data it should take * a copy of the training data. Currently checks for changes to header * structure, number of instances, order of instances, instance weights. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param predictorMissing true if we know the associator can handle * (at least) moderate missing predictor values * @param classMissing true if we know the associator can handle * (at least) moderate missing class values * @return index 0 is true if the test was passed */ protected boolean[] datasetIntegrity( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType, boolean predictorMissing, boolean classMissing) { print("associator doesn't alter original datasets"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); int numTrain = getNumInstances(), numClasses = 2, missingLevel = 20; boolean[] result = new boolean[2]; Instances train = null; Associator associator = null; try { train = makeTestDataset(42, numTrain, nominalPredictor ? getNumNominal() : 0, numericPredictor ? getNumNumeric() : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, multiInstance); if (missingLevel > 0) addMissing(train, missingLevel, predictorMissing, classMissing); associator = AbstractAssociator.makeCopies(getAssociator(), 1)[0]; } catch (Exception ex) { throw new Error("Error setting up for tests: " + ex.getMessage()); } try { Instances trainCopy = new Instances(train); associator.buildAssociations(trainCopy); compareDatasets(train, trainCopy); println("yes"); result[0] = true; } catch (Exception ex) { println("no"); result[0] = false; if (m_Debug) { println("\n=== Full Report ==="); print("Problem during building"); println(": " + ex.getMessage() + "\n"); println("Here is the dataset:\n"); println("=== Train Dataset ===\n" + train.toString() + "\n"); } } return result; } /** * Runs a text on the datasets with the given characteristics. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param missingLevel the percentage of missing values * @param predictorMissing true if the missing values may be in * the predictors * @param classMissing true if the missing values may be in the class * @param numTrain the number of instances in the training set * @param numClasses the number of classes * @param accepts the acceptable string in an exception * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable */ protected boolean[] runBasicTest(boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType, int missingLevel, boolean predictorMissing, boolean classMissing, int numTrain, int numClasses, FastVector accepts) { return runBasicTest( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, TestInstances.CLASS_IS_LAST, missingLevel, predictorMissing, classMissing, numTrain, numClasses, accepts); } /** * Runs a text on the datasets with the given characteristics. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param classIndex the attribute index of the class * @param missingLevel the percentage of missing values * @param predictorMissing true if the missing values may be in * the predictors * @param classMissing true if the missing values may be in the class * @param numTrain the number of instances in the training set * @param numClasses the number of classes * @param accepts the acceptable string in an exception * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable */ protected boolean[] runBasicTest(boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType, int classIndex, int missingLevel, boolean predictorMissing, boolean classMissing, int numTrain, int numClasses, FastVector accepts) { boolean[] result = new boolean[2]; Instances train = null; Associator associator = null; try { train = makeTestDataset(42, numTrain, nominalPredictor ? getNumNominal() : 0, numericPredictor ? getNumNumeric() : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, classIndex, multiInstance); if (missingLevel > 0) addMissing(train, missingLevel, predictorMissing, classMissing); associator = AbstractAssociator.makeCopies(getAssociator(), 1)[0]; } catch (Exception ex) { ex.printStackTrace(); throw new Error("Error setting up for tests: " + ex.getMessage()); } try { associator.buildAssociations(train); println("yes"); result[0] = true; } catch (Exception ex) { boolean acceptable = false; String msg; if (ex.getMessage() == null) msg = ""; else msg = ex.getMessage().toLowerCase(); if (msg.indexOf("not in classpath") > -1) m_ClasspathProblems = true; for (int i = 0; i < accepts.size(); i++) { if (msg.indexOf((String)accepts.elementAt(i)) >= 0) { acceptable = true; } } println("no" + (acceptable ? " (OK error message)" : "")); result[1] = acceptable; if (m_Debug) { println("\n=== Full Report ==="); print("Problem during building"); println(": " + ex.getMessage() + "\n"); if (!acceptable) { if (accepts.size() > 0) { print("Error message doesn't mention "); for (int i = 0; i < accepts.size(); i++) { if (i != 0) { print(" or "); } print('"' + (String)accepts.elementAt(i) + '"'); } } println("here is the dataset:\n"); println("=== Train Dataset ===\n" + train.toString() + "\n"); } } } return result; } /** * Make a simple set of instances, which can later be modified * for use in specific tests. * * @param seed the random number seed * @param numInstances the number of instances to generate * @param numNominal the number of nominal attributes * @param numNumeric the number of numeric attributes * @param numString the number of string attributes * @param numDate the number of date attributes * @param numRelational the number of relational attributes * @param numClasses the number of classes (if nominal class) * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param multiInstance whether the dataset should a multi-instance dataset * @return the test dataset * @throws Exception if the dataset couldn't be generated * @see #process(Instances) */ protected Instances makeTestDataset(int seed, int numInstances, int numNominal, int numNumeric, int numString, int numDate, int numRelational, int numClasses, int classType, boolean multiInstance) throws Exception { return makeTestDataset( seed, numInstances, numNominal, numNumeric, numString, numDate, numRelational, numClasses, classType, TestInstances.CLASS_IS_LAST, multiInstance); } /** * Make a simple set of instances with variable position of the class * attribute, which can later be modified for use in specific tests. * * @param seed the random number seed * @param numInstances the number of instances to generate * @param numNominal the number of nominal attributes * @param numNumeric the number of numeric attributes * @param numString the number of string attributes * @param numDate the number of date attributes * @param numRelational the number of relational attributes * @param numClasses the number of classes (if nominal class) * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param classIndex the index of the class (0-based, -1 as last) * @param multiInstance whether the dataset should a multi-instance dataset * @return the test dataset * @throws Exception if the dataset couldn't be generated * @see TestInstances#CLASS_IS_LAST * @see #process(Instances) */ protected Instances makeTestDataset(int seed, int numInstances, int numNominal, int numNumeric, int numString, int numDate, int numRelational, int numClasses, int classType, int classIndex, boolean multiInstance) throws Exception { TestInstances dataset = new TestInstances(); dataset.setSeed(seed); dataset.setNumInstances(numInstances); dataset.setNumNominal(numNominal); dataset.setNumNumeric(numNumeric); dataset.setNumString(numString); dataset.setNumDate(numDate); dataset.setNumRelational(numRelational); dataset.setNumClasses(numClasses); if (classType == NO_CLASS) { dataset.setClassType(Attribute.NOMINAL); // ignored dataset.setClassIndex(TestInstances.NO_CLASS); } else { dataset.setClassType(classType); dataset.setClassIndex(classIndex); } dataset.setNumClasses(numClasses); dataset.setMultiInstance(multiInstance); dataset.setWords(getWords()); dataset.setWordSeparators(getWordSeparators()); return process(dataset.generate()); } /** * Print out a short summary string for the dataset characteristics * * @param nominalPredictor true if nominal predictor attributes are present * @param numericPredictor true if numeric predictor attributes are present * @param stringPredictor true if string predictor attributes are present * @param datePredictor true if date predictor attributes are present * @param relationalPredictor true if relational predictor attributes are present * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) */ protected void printAttributeSummary(boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType) { String str = ""; if (numericPredictor) str += " numeric"; if (nominalPredictor) { if (str.length() > 0) str += " &"; str += " nominal"; } if (stringPredictor) { if (str.length() > 0) str += " &"; str += " string"; } if (datePredictor) { if (str.length() > 0) str += " &"; str += " date"; } if (relationalPredictor) { if (str.length() > 0) str += " &"; str += " relational"; } str += " predictors)"; switch (classType) { case Attribute.NUMERIC: str = " (numeric class," + str; break; case Attribute.NOMINAL: str = " (nominal class," + str; break; case Attribute.STRING: str = " (string class," + str; break; case Attribute.DATE: str = " (date class," + str; break; case Attribute.RELATIONAL: str = " (relational class," + str; break; case NO_CLASS: str = " (no class," + str; break; } print(str); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.7 $"); } /** * Test method for this class * * @param args the commandline parameters */ public static void main(String [] args) { runCheck(new CheckAssociator(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/FilteredAssociator.java000066400000000000000000000327531214146114000301030ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * FilteredAssociator.java * Copyright (C) 2007 University of Waikato, Hamilton, New Zealand * */ package weka.associations; import weka.core.Capabilities; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.Utils; import weka.core.Capabilities.Capability; import weka.filters.Filter; import weka.filters.MultiFilter; import java.util.Enumeration; import java.util.Vector; /** * Class for running an arbitrary associator on data that has been passed through an arbitrary filter. Like the associator, the structure of the filter is based exclusively on the training data and test instances will be processed by the filter without changing their structure. *

* * Valid options are:

* * 

 -F <filter specification>
 *  Full class name of filter to use, followed
 *  by filter options.
 *  eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"
 *  (default: weka.filters.MultiFilter with
 *  weka.filters.unsupervised.attribute.ReplaceMissingValues)
* * 
 -c <the class index>
 *  The class index.
 *  (default: -1, i.e. unset)
* * 
 -W
 *  Full name of base associator.
 *  (default: weka.associations.Apriori)
* * 
 
 * Options specific to associator weka.associations.Apriori:
 *
* * 
 -N <required number of rules output>
 *  The required number of rules. (default = 10)
* * 
 -T <0=confidence | 1=lift | 2=leverage | 3=Conviction>
 *  The metric type by which to rank rules. (default = confidence)
* * 
 -C <minimum metric score of a rule>
 *  The minimum confidence of a rule. (default = 0.9)
* * 
 -D <delta for minimum support>
 *  The delta by which the minimum support is decreased in
 *  each iteration. (default = 0.05)
* * 
 -U <upper bound for minimum support>
 *  Upper bound for minimum support. (default = 1.0)
* * 
 -M <lower bound for minimum support>
 *  The lower bound for the minimum support. (default = 0.1)
* * 
 -S <significance level>
 *  If used, rules are tested for significance at
 *  the given level. Slower. (default = no significance testing)
* * 
 -I
 *  If set the itemsets found are also output. (default = no)
* * 
 -R
 *  Remove columns that contain all missing values (default = no)
* * 
 -V
 *  Report progress iteratively. (default = no)
* * 
 -A
 *  If set class association rules are mined. (default = no)
* * 
 -c <the class index>
 *  The class index. (default = last)
* * * @author Len Trigg (trigg@cs.waikato.ac.nz) * @author FracPete (fracpete at waikato dot ac dot nz) * @version $Revision: 1.4 $ */ public class FilteredAssociator extends SingleAssociatorEnhancer { /** for serialization */ static final long serialVersionUID = -4523450618538717400L; /** The filter */ protected Filter m_Filter; /** The instance structure of the filtered instances */ protected Instances m_FilteredInstances; /** The class index. */ protected int m_ClassIndex; /** * Default constructor. */ public FilteredAssociator() { m_Associator = new Apriori(); m_Filter = new MultiFilter(); ((MultiFilter) m_Filter).setFilters(new Filter[]{ new weka.filters.unsupervised.attribute.ReplaceMissingValues()}); m_ClassIndex = -1; } /** * Returns a string describing this Associator * * @return a description of the Associator suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Class for running an arbitrary associator on data that has been passed " + "through an arbitrary filter. Like the associator, the structure of the filter " + "is based exclusively on the training data and test instances will be processed " + "by the filter without changing their structure."; } /** * String describing default associator. * * @return the default associator classname */ protected String defaultAssociatorString() { return Apriori.class.getName(); } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector result = new Vector(); result.addElement(new Option( "\tFull class name of filter to use, followed\n" + "\tby filter options.\n" + "\teg: \"weka.filters.unsupervised.attribute.Remove -V -R 1,2\"\n" + "\t(default: weka.filters.MultiFilter with\n" + "\tweka.filters.unsupervised.attribute.ReplaceMissingValues)", "F", 1, "-F ")); result.addElement(new Option( "\tThe class index.\n" + "\t(default: -1, i.e. unset)", "c", 1, "-c ")); Enumeration enm = super.listOptions(); while (enm.hasMoreElements()) result.addElement(enm.nextElement()); return result.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -F <filter specification>
   *  Full class name of filter to use, followed
   *  by filter options.
   *  eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"
   *  (default: weka.filters.MultiFilter with
   *  weka.filters.unsupervised.attribute.ReplaceMissingValues)
* * 
 -c <the class index>
   *  The class index.
   *  (default: -1, i.e. unset)
* * 
 -W
   *  Full name of base associator.
   *  (default: weka.associations.Apriori)
* * 
 
   * Options specific to associator weka.associations.Apriori:
   *
* * 
 -N <required number of rules output>
   *  The required number of rules. (default = 10)
* * 
 -T <0=confidence | 1=lift | 2=leverage | 3=Conviction>
   *  The metric type by which to rank rules. (default = confidence)
* * 
 -C <minimum metric score of a rule>
   *  The minimum confidence of a rule. (default = 0.9)
* * 
 -D <delta for minimum support>
   *  The delta by which the minimum support is decreased in
   *  each iteration. (default = 0.05)
* * 
 -U <upper bound for minimum support>
   *  Upper bound for minimum support. (default = 1.0)
* * 
 -M <lower bound for minimum support>
   *  The lower bound for the minimum support. (default = 0.1)
* * 
 -S <significance level>
   *  If used, rules are tested for significance at
   *  the given level. Slower. (default = no significance testing)
* * 
 -I
   *  If set the itemsets found are also output. (default = no)
* * 
 -R
   *  Remove columns that contain all missing values (default = no)
* * 
 -V
   *  Report progress iteratively. (default = no)
* * 
 -A
   *  If set class association rules are mined. (default = no)
* * 
 -c <the class index>
   *  The class index. (default = last)
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { String tmpStr; tmpStr = Utils.getOption('F', options); if (tmpStr.length() > 0) { String[] filterSpec = Utils.splitOptions(tmpStr); if (filterSpec.length == 0) throw new IllegalArgumentException("Invalid filter specification string"); String filterName = filterSpec[0]; filterSpec[0] = ""; setFilter((Filter) Utils.forName(Filter.class, filterName, filterSpec)); } else { setFilter(new weka.filters.supervised.attribute.Discretize()); } tmpStr = Utils.getOption('c', options); if (tmpStr.length() > 0) { if (tmpStr.equalsIgnoreCase("last")) { setClassIndex(0); } else if (tmpStr.equalsIgnoreCase("first")) { setClassIndex(1); } else { setClassIndex(Integer.parseInt(tmpStr)); } } else { setClassIndex(-1); } super.setOptions(options); } /** * Gets the current settings of the Associator. * * @return an array of strings suitable for passing to setOptions */ public String[] getOptions() { Vector result; int i; String[] options; result = new Vector(); result.add("-F"); result.add("" + getFilterSpec()); result.add("-c"); result.add("" + getClassIndex()); options = super.getOptions(); for (i = 0; i < options.length; i++) result.add(options[i]); return result.toArray(new String[result.size()]); } /** * Returns the tip text for this property * * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String filterTipText() { return "The filter to be used."; } /** * Sets the filter * * @param value the filter with all options set. */ public void setFilter(Filter value) { m_Filter = value; } /** * Gets the filter used. * * @return the current filter */ public Filter getFilter() { return m_Filter; } /** * Returns the tip text for this property * * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String classIndexTipText() { return "Index of the class attribute. If set to -1, the last attribute is taken as class attribute."; } /** * Sets the class index * * @param value the class index */ public void setClassIndex(int value){ m_ClassIndex = value; } /** * Gets the class index * * @return the index of the class attribute */ public int getClassIndex(){ return m_ClassIndex; } /** * Gets the filter specification string, which contains the class name of * the filter and any options to the filter * * @return the filter string. */ protected String getFilterSpec() { Filter c = getFilter(); if (c instanceof OptionHandler) return c.getClass().getName() + " " + Utils.joinOptions(((OptionHandler)c).getOptions()); else return c.getClass().getName(); } /** * Returns default capabilities of the associator. * * @return the capabilities of this associator */ public Capabilities getCapabilities() { Capabilities result; if (getFilter() == null) result = super.getCapabilities(); else result = getFilter().getCapabilities(); result.enable(Capability.NO_CLASS); // set dependencies for (Capability cap: Capability.values()) result.enableDependency(cap); return result; } /** * Build the associator on the filtered data. * * @param data the training data * @throws Exception if the Associator could not be built successfully */ public void buildAssociations(Instances data) throws Exception { if (m_Associator == null) throw new Exception("No base associator has been set!"); // create copy and set class-index data = new Instances(data); if (getClassIndex() == 0) { data.setClassIndex(data.numAttributes() - 1); } else { data.setClassIndex(getClassIndex() - 1); } if (getClassIndex() != -1) { // remove instances with missing class data.deleteWithMissingClass(); } m_Filter.setInputFormat(data); // filter capabilities are checked here data = Filter.useFilter(data, m_Filter); // can associator handle the data? getAssociator().getCapabilities().testWithFail(data); m_FilteredInstances = data.stringFreeStructure(); m_Associator.buildAssociations(data); } /** * Output a representation of this associator * * @return a representation of this associator */ public String toString() { String result; if (m_FilteredInstances == null) { result = "FilteredAssociator: No model built yet."; } else { result = "FilteredAssociator using " + getAssociatorSpec() + " on data filtered through " + getFilterSpec() + "\n\nFiltered Header\n" + m_FilteredInstances.toString() + "\n\nAssociator Model\n" + m_Associator.toString(); } return result; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.4 $"); } /** * Main method for running this class. * * @param args commandline arguments, use "-h" for full list */ public static void main(String[] args) { runAssociator(new FilteredAssociator(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/GeneralizedSequentialPatterns.java000066400000000000000000000440621214146114000323160ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * GeneralizedSequentialPatterns.java * Copyright (C) 2007 Sebastian Beer * */ package weka.associations; import weka.associations.gsp.Element; import weka.associations.gsp.Sequence; import weka.core.Attribute; import weka.core.Capabilities; import weka.core.FastVector; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.TechnicalInformation; import weka.core.TechnicalInformationHandler; import weka.core.Utils; import weka.core.Capabilities.Capability; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; import java.text.SimpleDateFormat; import java.util.Date; import java.util.Enumeration; import java.util.Vector; /** * Class implementing a GSP algorithm for discovering sequential patterns in a sequential data set.
* The attribute identifying the distinct data sequences contained in the set can be determined by the respective option. Furthermore, the set of output results can be restricted by specifying one or more attributes that have to be contained in each element/itemset of a sequence.
*
* For further information see:
*
* Ramakrishnan Srikant, Rakesh Agrawal (1996). Mining Sequential Patterns: Generalizations and Performance Improvements. *

* * BibTeX: * 

 * @proceedings{Srikant1996,
 *    author = {Ramakrishnan Srikant and Rakesh Agrawal},
 *    booktitle = {Advances in Database Technology EDBT '96},
 *    publisher = {Springer},
 *    title = {Mining Sequential Patterns: Generalizations and Performance Improvements},
 *    year = {1996}
 * }
 *
*

* * Valid options are:

* * 

 -D
 *  If set, algorithm is run in debug mode and
 *  may output additional info to the console
* * 
 -S <minimum support threshold>
 *  The miminum support threshold.
 *  (default: 0.9)
* * 
 -I <attribute number representing the data sequence ID
 *  The attribute number representing the data sequence ID.
 *  (default: 0)
* * 
 -F <attribute numbers used for result filtering
 *  The attribute numbers used for result filtering.
 *  (default: -1)
* * * @author Sebastian Beer * @version $Revision: 1.3 $ */ public class GeneralizedSequentialPatterns extends AbstractAssociator implements OptionHandler, TechnicalInformationHandler { /** for serialization */ private static final long serialVersionUID = -4119691320812254676L; /** the minimum support threshold */ protected double m_MinSupport; /** number indicating the attribute holding the data sequence ID */ protected int m_DataSeqID; /** original sequential data set to be used for sequential patterns extraction */ protected Instances m_OriginalDataSet; /** all generated frequent sequences, i.e. sequential patterns */ protected FastVector m_AllSequentialPatterns; /** number of cycles performed until termination */ protected int m_Cycles; /** String indicating the starting time of an cycle. */ protected String m_CycleStart; /** String indicating the ending time of an cycle. */ protected String m_CycleEnd; /** String indicating the starting time of the algorithm. */ protected String m_AlgorithmStart; /** String containing the attribute numbers that are used for result * filtering; -1 means no filtering */ protected String m_FilterAttributes; /** Vector containing the attribute numbers that are used for result * filtering; -1 means no filtering */ protected FastVector m_FilterAttrVector; /** Whether the classifier is run in debug mode. */ protected boolean m_Debug = false; /** * Constructor. */ public GeneralizedSequentialPatterns() { resetOptions(); } /** * Returns global information about the algorithm. * * @return the global information */ public String globalInfo() { return "Class implementing a GSP algorithm for discovering sequential " + "patterns in a sequential data set.\n" + "The attribute identifying the distinct data sequences contained in " + "the set can be determined by the respective option. Furthermore, the " + "set of output results can be restricted by specifying one or more " + "attributes that have to be contained in each element/itemset of a " + "sequence.\n\n" + "For further information see:\n\n" + getTechnicalInformation().toString(); } /** * Returns TechnicalInformation about the paper related to the algorithm. * * @return the TechnicalInformation */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation paper = new TechnicalInformation(Type.PROCEEDINGS); paper.setValue(Field.AUTHOR, "Ramakrishnan Srikant and Rakesh Agrawal"); paper.setValue(Field.TITLE, "Mining Sequential Patterns: Generalizations and Performance Improvements"); paper.setValue(Field.BOOKTITLE, "Advances in Database Technology EDBT '96"); paper.setValue(Field.YEAR, "1996"); paper.setValue(Field.PUBLISHER, "Springer"); return paper; } /** * Returns an enumeration of the available options. * * @return the available options */ public Enumeration listOptions() { Vector result = new Vector(); result.addElement(new Option( "\tIf set, algorithm is run in debug mode and\n" + "\tmay output additional info to the console", "D", 0, "-D")); result.addElement(new Option( "\tThe miminum support threshold.\n" + "\t(default: 0.9)", "S", 1, "-S ")); result.addElement(new Option( "\tThe attribute number representing the data sequence ID.\n" + "\t(default: 0)", "I", 1, "-I * * Valid options are:

* * 

 -D
   *  If set, algorithm is run in debug mode and
   *  may output additional info to the console
* * 
 -S <minimum support threshold>
   *  The miminum support threshold.
   *  (default: 0.9)
* * 
 -I <attribute number representing the data sequence ID
   *  The attribute number representing the data sequence ID.
   *  (default: 0)
* * 
 -F <attribute numbers used for result filtering
   *  The attribute numbers used for result filtering.
   *  (default: -1)
* * * @param options the Array containing the options */ public void setOptions(String[] options) throws Exception { String tmpStr; resetOptions(); setDebug(Utils.getFlag('D', options)); tmpStr = Utils.getOption('S', options); if (tmpStr.length() != 0) setMinSupport(Double.parseDouble(tmpStr)); tmpStr = Utils.getOption('I', options); if (tmpStr.length() != 0) setDataSeqID(Integer.parseInt(tmpStr)); tmpStr = Utils.getOption('F', options); if (tmpStr.length() != 0) setFilterAttributes(tmpStr); } /** * Returns an Array containing the current options settings. * * @return the Array containing the settings */ public String[] getOptions() { Vector result; result = new Vector(); if (getDebug()) result.add("-D"); result.add("-S"); result.add("" + getMinSupport()); result.add("-I"); result.add("" + getDataSeqID()); result.add("-F"); result.add(getFilterAttributes()); return result.toArray(new String[result.size()]); } /** * Resets the algorithm's options to the default values. */ protected void resetOptions() { m_MinSupport = 0.9; m_DataSeqID = 0; m_FilterAttributes = "-1"; } /** * Returns the Capabilities of the algorithm. * * @return the Capabilities */ public Capabilities getCapabilities() { Capabilities result = super.getCapabilities(); result.enable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.NO_CLASS); return result; } /** * Extracts all sequential patterns out of a given sequential data set and * prints out the results. * * @param data the original data set */ public void buildAssociations(Instances data) throws Exception { // can associator handle the data? getCapabilities().testWithFail(data); m_AllSequentialPatterns = new FastVector(); m_Cycles = 0; m_FilterAttrVector = new FastVector(); m_AlgorithmStart = getTimeAndDate(); m_OriginalDataSet = new Instances(data); extractFilterAttributes(m_FilterAttributes); findFrequentSequences(); } /** * Calculates the total number of extracted frequent sequences. * * @return the total number of frequent sequences */ protected int calcFreqSequencesTotal() { int total = 0; Enumeration allSeqPatternsEnum = m_AllSequentialPatterns.elements(); while (allSeqPatternsEnum.hasMoreElements()) { FastVector kSequences = (FastVector) allSeqPatternsEnum.nextElement(); total += kSequences.size(); } return total; } /** * Extracts the data sequences out of the original data set according to * their sequence id attribute, which is removed after extraction. * * @param originalDataSet the original data set * @param dataSeqID the squence ID to use * @return set of distinct data sequences */ protected FastVector extractDataSequences (Instances originalDataSet, int dataSeqID) { FastVector dataSequences = new FastVector(); int firstInstance = 0; int lastInstance = 0; Attribute seqIDAttribute = originalDataSet.attribute(dataSeqID); for (int i = 0; i < seqIDAttribute.numValues(); i++) { double sequenceID = originalDataSet.instance(firstInstance).value(dataSeqID); while (lastInstance < originalDataSet.numInstances() && sequenceID == originalDataSet.instance(lastInstance).value(dataSeqID)) { lastInstance++; } Instances dataSequence = new Instances(originalDataSet, firstInstance, (lastInstance)-firstInstance); dataSequence.deleteAttributeAt(dataSeqID); dataSequences.addElement(dataSequence); firstInstance = lastInstance; } return dataSequences; } /** * Parses a given String containing attribute numbers which are used for * result filtering. * * @param attrNumbers the String of attribute numbers */ public void extractFilterAttributes(String attrNumbers) { String numbers = attrNumbers.trim(); while (!numbers.equals("")) { int commaLoc = numbers.indexOf(','); if (commaLoc != -1) { String number = numbers.substring(0, commaLoc); numbers = numbers.substring(commaLoc + 1).trim(); m_FilterAttrVector.addElement(Integer.decode(number)); } else { m_FilterAttrVector.addElement(Integer.decode(numbers)); break; } } } /** * The actual method for extracting frequent sequences. * * @throws CloneNotSupportedException */ protected void findFrequentSequences() throws CloneNotSupportedException { m_CycleStart = getTimeAndDate(); Instances originalDataSet = m_OriginalDataSet; FastVector dataSequences = extractDataSequences(m_OriginalDataSet, m_DataSeqID); long minSupportCount = Math.round(m_MinSupport * dataSequences.size()); FastVector kMinusOneSequences; FastVector kSequences; originalDataSet.deleteAttributeAt(0); FastVector oneElements = Element.getOneElements(originalDataSet); m_Cycles = 1; kSequences = Sequence.oneElementsToSequences(oneElements); Sequence.updateSupportCount(kSequences, dataSequences); kSequences = Sequence.deleteInfrequentSequences(kSequences, minSupportCount); m_CycleEnd = getTimeAndDate(); if (kSequences.size() == 0) { return; } while (kSequences.size() > 0) { m_CycleStart = getTimeAndDate(); m_AllSequentialPatterns.addElement(kSequences.copy()); kMinusOneSequences = kSequences; kSequences = Sequence.aprioriGen(kMinusOneSequences); Sequence.updateSupportCount(kSequences, dataSequences); kSequences = Sequence.deleteInfrequentSequences(kSequences, minSupportCount); m_CycleEnd = getTimeAndDate(); if (getDebug()) System.out.println( "Cycle " + m_Cycles + " from " + m_CycleStart + " to " + m_CycleEnd); m_Cycles++; } } /** * Returns the dataSeqID option tip text for the Weka GUI. * * @return the option tip text */ public String dataSeqIDTipText() { return "The attribute number representing the data sequence ID."; } /** * Returns the attribute representing the data sequence ID. * * @return the data sequence ID */ public int getDataSeqID() { return m_DataSeqID; } /** * Sets the attribute representing the data sequence ID. * * @param value the data sequence ID to set */ public void setDataSeqID(int value) { m_DataSeqID = value; } /** * Returns the filterAttributes option tip text for the Weka GUI. * * @return the option tip text */ public String filterAttributesTipText() { return "The attribute numbers (eg \"0, 1\") used for result filtering; only " + "sequences containing the specified attributes in each of their " + "elements/itemsets will be output; -1 prints all."; } /** * Returns the String containing the attributes which are used for output * filtering. * * @return the String containing the attributes */ public String getFilterAttributes() { return m_FilterAttributes; } /** * Sets the String containing the attributes which are used for output * filtering. * * @param value the String containing the attributes */ public void setFilterAttributes(String value) { m_FilterAttributes = value; } /** * Returns the minimum support option tip text for the Weka GUI. * * @return the option tip text */ public String minSupportTipText() { return "Minimum support threshold."; } /** * Returns the minimum support threshold. * * @return the minimum support threshold */ public double getMinSupport() { return m_MinSupport; } /** * Sets the minimum support threshold. * * @param value the minimum support threshold */ public void setMinSupport(double value) { m_MinSupport = value; } /** * Set debugging mode. * * @param value true if debug output should be printed */ public void setDebug(boolean value) { m_Debug = value; } /** * Get whether debugging is turned on. * * @return true if debugging output is on */ public boolean getDebug() { return m_Debug; } /** * Returns the tip text for this property * * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String debugTipText() { return "If set to true, algorithm may output additional info to the console."; } /** * Returns the current time and date. * * @return the time and date */ protected String getTimeAndDate() { SimpleDateFormat dateFormat; dateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss"); return dateFormat.format(new Date()); } /** * Returns the time/date string the algorithm was started * * @return the time and date the algorithm was started */ public String getAlgorithmStart() { return m_AlgorithmStart; } /** * Returns the time/date string the cycle was started * * @return the time and date the cycle was started */ public String getCycleStart() { return m_CycleStart; } /** * Returns the time/date string the cycle ended * * @return the time and date the cycle ended */ public String getCycleEnd() { return m_CycleEnd; } /** * Returns a String containing the result information of the algorithm. * * @return the String containing the result information */ public String toString() { StringBuffer result = new StringBuffer(); result.append("GeneralizedSequentialPatterns\n"); result.append("=============================\n\n"); result.append("Number of cycles performed: " + (m_Cycles-1) + "\n"); result.append("Total number of frequent sequences: " + calcFreqSequencesTotal() + "\n\n"); result.append("Frequent Sequences Details (filtered):\n\n"); for (int i = 0; i < m_AllSequentialPatterns.size(); i++) { result.append("- " + (i+1) + "-sequences\n\n"); FastVector kSequences = (FastVector) m_AllSequentialPatterns.elementAt(i); result.append(Sequence.setOfSequencesToString(kSequences, m_OriginalDataSet, m_FilterAttrVector) + "\n"); } return result.toString(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.3 $"); } /** * Main method. * * @param args commandline options, use -h for help */ public static void main(String[] args) { runAssociator(new GeneralizedSequentialPatterns(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/HotSpot.java000066400000000000000000001240051214146114000257050ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * alo0ng with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * HotSpot.java * Copyright (C) 2008 Pentaho Corporation * */ package weka.associations; import java.util.PriorityQueue; import java.util.HashMap; import java.util.ArrayList; import java.util.Vector; import java.util.Enumeration; import java.io.Serializable; import weka.core.Instances; import weka.core.Instance; import weka.core.Attribute; import weka.core.Utils; import weka.core.OptionHandler; import weka.core.Option; import weka.core.SingleIndex; import weka.core.Drawable; import weka.core.Capabilities.Capability; import weka.core.Capabilities; import weka.core.CapabilitiesHandler; import weka.core.RevisionHandler; import weka.core.RevisionUtils; /** * HotSpot learns a set of rules (displayed in a tree-like structure) that maximize/minimize a target variable/value of interest. With a nominal target, one might want to look for segments of the data where there is a high probability of a minority value occuring (given the constraint of a minimum support). For a numeric target, one might be interested in finding segments where this is higher on average than in the whole data set. For example, in a health insurance scenario, find which health insurance groups are at the highest risk (have the highest claim ratio), or, which groups have the highest average insurance payout. *

* * Valid options are:

* * 

 -c <num | first | last>
 *  The target index. (default = last)
* * 
 -V <num | first | last>
 *  The target value (nominal target only, default = first)
* * 
 -L
 *  Minimize rather than maximize.
* * 
 -S <num>
 *  Minimum value count (nominal target)/segment size (numeric target).
 *  Values between 0 and 1 are 
 *  interpreted as a percentage of 
 *  the total population; values > 1 are 
 *  interpreted as an absolute number of 
 *  instances (default = 0.3)
* * 
 -M <num>
 *  Maximum branching factor (default = 2)
* * 
 -I <num>
 *  Minimum improvement in target value in order 
 *  to add a new branch/test (default = 0.01 (1%))
* * 
 -D
 *  Output debugging info (duplicate rule lookup 
 *  hash table stats)
* * * @author Mark Hall (mhall{[at]}pentaho{[dot]}org * @version $Revision: 1.6 $ */ public class HotSpot implements Associator, OptionHandler, RevisionHandler, CapabilitiesHandler, Drawable, Serializable { static final long serialVersionUID = 42972325096347677L; /** index of the target attribute */ protected SingleIndex m_targetSI = new SingleIndex("last"); protected int m_target; /** Support as a fraction of the total training set */ protected double m_support; /** Support as an instance count */ private int m_supportCount; /** The global value of the attribute of interest (mean or probability) */ protected double m_globalTarget; /** The minimum improvement necessary to justify adding a test */ protected double m_minImprovement; /** Actual global support of the target value (discrete target only) */ protected int m_globalSupport; /** For discrete target, the index of the value of interest */ protected SingleIndex m_targetIndexSI = new SingleIndex("first"); protected int m_targetIndex; /** At each level of the tree consider at most this number extensions */ protected int m_maxBranchingFactor; /** Number of instances in the full data */ protected int m_numInstances; /** The head of the tree */ protected HotNode m_head; /** Header of the training data */ protected Instances m_header; /** Debugging stuff */ protected int m_lookups = 0; protected int m_insertions = 0; protected int m_hits = 0; protected boolean m_debug; /** Minimize, rather than maximize the target */ protected boolean m_minimize; /** Error messages relating to too large/small support values */ protected String m_errorMessage; /** Rule lookup table */ protected HashMap m_ruleLookup; /** * Constructor */ public HotSpot() { resetOptions(); } /** * Returns a string describing this classifier * @return a description of the classifier suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "HotSpot learns a set of rules (displayed in a tree-like structure) " + "that maximize/minimize a target variable/value of interest. " + "With a nominal target, one might want to look for segments of the " + "data where there is a high probability of a minority value occuring (" + "given the constraint of a minimum support). For a numeric target, " + "one might be interested in finding segments where this is higher " + "on average than in the whole data set. For example, in a health " + "insurance scenario, find which health insurance groups are at " + "the highest risk (have the highest claim ratio), or, which groups " + "have the highest average insurance payout."; } /** * Returns default capabilities of HotSpot * * @return the capabilities of HotSpot */ public Capabilities getCapabilities() { Capabilities result = new Capabilities(this); // attributes result.enable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.NUMERIC_ATTRIBUTES); result.enable(Capability.MISSING_VALUES); // class result.enable(Capability.NUMERIC_CLASS); result.enable(Capability.NOMINAL_CLASS); return result; } /** * Hash key class for sets of attribute, value tests */ protected class HotSpotHashKey { // split values, one for each attribute (0 indicates att not used). // for nominal indexes, 1 is added so that 0 can indicate not used. protected double[] m_splitValues; // 0 = not used, 1 = "<=", 2 = "=", 3 = ">" protected byte[] m_testTypes; protected boolean m_computed = false; protected int m_key; public HotSpotHashKey(double[] splitValues, byte[] testTypes) { m_splitValues = splitValues.clone(); m_testTypes = testTypes.clone(); } public boolean equals(Object b) { if ((b == null) || !(b.getClass().equals(this.getClass()))) { return false; } HotSpotHashKey comp = (HotSpotHashKey)b; boolean ok = true; for (int i = 0; i < m_splitValues.length; i++) { if (m_splitValues[i] != comp.m_splitValues[i] || m_testTypes[i] != comp.m_testTypes[i]) { ok = false; break; } } return ok; } public int hashCode() { if (m_computed) { return m_key; } else { int hv = 0; for (int i = 0; i < m_splitValues.length; i++) { hv += (m_splitValues[i] * 5 * i); hv += (m_testTypes[i] * i * 3); } m_computed = true; m_key = hv; } return m_key; } } /** * Build the tree * * @param instances the training instances * @throws Exception if something goes wrong */ public void buildAssociations(Instances instances) throws Exception { m_errorMessage = null; m_targetSI.setUpper(instances.numAttributes() - 1); m_target = m_targetSI.getIndex(); Instances inst = new Instances(instances); inst.setClassIndex(m_target); inst.deleteWithMissingClass(); // can associator handle the data? getCapabilities().testWithFail(inst); if (inst.attribute(m_target).isNominal()) { m_targetIndexSI.setUpper(inst.attribute(m_target).numValues() - 1); m_targetIndex = m_targetIndexSI.getIndex(); } else { m_targetIndexSI.setUpper(1); // just to stop this SingleIndex from moaning } if (m_support <= 0) { throw new Exception("Support must be greater than zero."); } m_numInstances = inst.numInstances(); if (m_support >= 1) { m_supportCount = (int)m_support; m_support = m_support / (double)m_numInstances; } m_supportCount = (int)Math.floor((m_support * m_numInstances) + 0.5d); // m_supportCount = (int)(m_support * m_numInstances); if (m_supportCount < 1) { m_supportCount = 1; } m_header = new Instances(inst, 0); if (inst.attribute(m_target).isNumeric()) { if (m_supportCount > m_numInstances) { m_errorMessage = "Error: support set to more instances than there are in the data!"; return; } m_globalTarget = inst.meanOrMode(m_target); } else { double[] probs = new double[inst.attributeStats(m_target).nominalCounts.length]; for (int i = 0; i < probs.length; i++) { probs[i] = (double)inst.attributeStats(m_target).nominalCounts[i]; } m_globalSupport = (int)probs[m_targetIndex]; // check that global support is greater than min support if (m_globalSupport < m_supportCount) { m_errorMessage = "Error: minimum support " + m_supportCount + " is too high. Target value " + m_header.attribute(m_target).value(m_targetIndex) + " has support " + m_globalSupport + "."; } Utils.normalize(probs); m_globalTarget = probs[m_targetIndex]; /* System.err.println("Global target " + m_globalTarget); System.err.println("Min support count " + m_supportCount); */ } m_ruleLookup = new HashMap(); double[] splitVals = new double[m_header.numAttributes()]; byte[] tests = new byte[m_header.numAttributes()]; m_head = new HotNode(inst, m_globalTarget, splitVals, tests); // m_head = new HotNode(inst, m_globalTarget); } /** * Return the tree as a string * * @return a String containing the tree */ public String toString() { StringBuffer buff = new StringBuffer(); buff.append("\nHot Spot\n========"); if (m_errorMessage != null) { buff.append("\n\n" + m_errorMessage + "\n\n"); return buff.toString(); } if (m_head == null) { buff.append("No model built!"); return buff.toString(); } buff.append("\nTotal population: "); buff.append("" + m_numInstances + " instances"); buff.append("\nTarget attribute: " + m_header.attribute(m_target).name()); if (m_header.attribute(m_target).isNominal()) { buff.append("\nTarget value: " + m_header.attribute(m_target).value(m_targetIndex)); buff.append(" [value count in total population: " + m_globalSupport + " instances (" + Utils.doubleToString((m_globalTarget * 100.0), 2) + "%)]"); buff.append("\nMinimum value count for segments: "); } else { buff.append("\nMinimum segment size: "); } buff.append("" + m_supportCount + " instances (" + Utils.doubleToString((m_support * 100.0), 2) + "% of total population)"); buff.append("\nMaximum branching factor: " + m_maxBranchingFactor); buff.append("\nMinimum improvement in target: " + Utils.doubleToString((m_minImprovement * 100.0), 2) + "%"); buff.append("\n\n"); buff.append(m_header.attribute(m_target).name()); if (m_header.attribute(m_target).isNumeric()) { buff.append(" (" + Utils.doubleToString(m_globalTarget, 4) + ")"); } else { buff.append("=" + m_header.attribute(m_target).value(m_targetIndex) + " ("); buff.append("" + Utils.doubleToString((m_globalTarget * 100.0), 2) + "% ["); buff.append("" + m_globalSupport + "/" + m_numInstances + "])"); } m_head.dumpTree(0, buff); buff.append("\n"); if (m_debug) { buff.append("\n=== Duplicate rule lookup hashtable stats ===\n"); buff.append("Insertions: "+ m_insertions); buff.append("\nLookups : "+ m_lookups); buff.append("\nHits: "+ m_hits); buff.append("\n"); } return buff.toString(); } public String graph() throws Exception { System.err.println("Here"); m_head.assignIDs(-1); StringBuffer text = new StringBuffer(); text.append("digraph HotSpot {\n"); text.append("rankdir=LR;\n"); text.append("N0 [label=\"" + m_header.attribute(m_target).name()); if (m_header.attribute(m_target).isNumeric()) { text.append("\\n(" + Utils.doubleToString(m_globalTarget, 4) + ")"); } else { text.append("=" + m_header.attribute(m_target).value(m_targetIndex) + "\\n("); text.append("" + Utils.doubleToString((m_globalTarget * 100.0), 2) + "% ["); text.append("" + m_globalSupport + "/" + m_numInstances + "])"); } text.append("\" shape=plaintext]\n"); m_head.graphHotSpot(text); text.append("}\n"); return text.toString(); } /** * Inner class representing a node/leaf in the tree */ protected class HotNode implements Serializable { /** * An inner class holding data on a particular attribute value test */ protected class HotTestDetails implements Comparable, Serializable { public double m_merit; public int m_support; public int m_subsetSize; public int m_splitAttIndex; public double m_splitValue; public boolean m_lessThan; public HotTestDetails(int attIndex, double splitVal, boolean lessThan, int support, int subsetSize, double merit) { m_merit = merit; m_splitAttIndex = attIndex; m_splitValue = splitVal; m_lessThan = lessThan; m_support = support; m_subsetSize = subsetSize; } // reverse order for maximize as PriorityQueue has the least element at the head public int compareTo(HotTestDetails comp) { int result = 0; if (m_minimize) { if (m_merit == comp.m_merit) { // larger support is better if (m_support == comp.m_support) { } else if (m_support > comp.m_support) { result = -1; } else { result = 1; } } else if (m_merit < comp.m_merit) { result = -1; } else { result = 1; } } else { if (m_merit == comp.m_merit) { // larger support is better if (m_support == comp.m_support) { } else if (m_support > comp.m_support) { result = -1; } else { result = 1; } } else if (m_merit < comp.m_merit) { result = 1; } else { result = -1; } } return result; } } // the instances at this node protected Instances m_insts; // the value (to beat) of the target for these instances protected double m_targetValue; // child nodes protected HotNode[] m_children; protected HotTestDetails[] m_testDetails; public int m_id; /** * Constructor * * @param insts the instances at this node * @param targetValue the target value * @param splitVals the values of attributes split on so far down this branch * @param tests the types of tests corresponding to the split values (<=, =, >) */ public HotNode(Instances insts, double targetValue, double[] splitVals, byte[] tests) { m_insts = insts; m_targetValue = targetValue; PriorityQueue splitQueue = new PriorityQueue(); // Consider each attribute for (int i = 0; i < m_insts.numAttributes(); i++) { if (i != m_target) { if (m_insts.attribute(i).isNominal()) { evaluateNominal(i, splitQueue); } else { evaluateNumeric(i, splitQueue); } } } if (splitQueue.size() > 0) { int queueSize = splitQueue.size(); // count how many of the potential children are unique ArrayList newCandidates = new ArrayList(); ArrayList keyList = new ArrayList(); for (int i = 0; i < queueSize; i++) { if (newCandidates.size() < m_maxBranchingFactor) { HotTestDetails temp = splitQueue.poll(); double[] newSplitVals = splitVals.clone(); byte[] newTests = tests.clone(); newSplitVals[temp.m_splitAttIndex] = temp.m_splitValue + 1; newTests[temp.m_splitAttIndex] = (m_header.attribute(temp.m_splitAttIndex).isNominal()) ? (byte)2 // == : (temp.m_lessThan) ? (byte)1 : (byte)3; HotSpotHashKey key = new HotSpotHashKey(newSplitVals, newTests); m_lookups++; if (!m_ruleLookup.containsKey(key)) { // insert it into the hash table m_ruleLookup.put(key, ""); newCandidates.add(temp); keyList.add(key); m_insertions++; } else { m_hits++; } } else { break; } } m_children = new HotNode[(newCandidates.size() < m_maxBranchingFactor) ? newCandidates.size() : m_maxBranchingFactor]; // save the details of the tests at this node m_testDetails = new HotTestDetails[m_children.length]; for (int i = 0; i < m_children.length; i++) { m_testDetails[i] = newCandidates.get(i); } // save memory splitQueue = null; newCandidates = null; m_insts = new Instances(m_insts, 0); // process the children for (int i = 0; i < m_children.length; i++) { Instances subset = subset(insts, m_testDetails[i]); HotSpotHashKey tempKey = keyList.get(i); m_children[i] = new HotNode(subset, m_testDetails[i].m_merit, tempKey.m_splitValues, tempKey.m_testTypes); } } } /** * Create a subset of instances that correspond to the supplied test details * * @param insts the instances to create the subset from * @param test the details of the split */ private Instances subset(Instances insts, HotTestDetails test) { Instances sub = new Instances(insts, insts.numInstances()); for (int i = 0; i < insts.numInstances(); i++) { Instance temp = insts.instance(i); if (!temp.isMissing(test.m_splitAttIndex)) { if (insts.attribute(test.m_splitAttIndex).isNominal()) { if (temp.value(test.m_splitAttIndex) == test.m_splitValue) { sub.add(temp); } } else { if (test.m_lessThan) { if (temp.value(test.m_splitAttIndex) <= test.m_splitValue) { sub.add(temp); } } else { if (temp.value(test.m_splitAttIndex) > test.m_splitValue) { sub.add(temp); } } } } } sub.compactify(); return sub; } /** * Evaluate a numeric attribute for a potential split * * @param attIndex the index of the attribute * @param pq the priority queue of candidtate splits */ private void evaluateNumeric(int attIndex, PriorityQueue pq) { Instances tempInsts = m_insts; tempInsts.sort(attIndex); // target sums/counts double targetLeft = 0; double targetRight = 0; int numMissing = 0; // count missing values and sum/counts for the initial right subset for (int i = tempInsts.numInstances() - 1; i >= 0; i--) { if (!tempInsts.instance(i).isMissing(attIndex)) { targetRight += (tempInsts.attribute(m_target).isNumeric()) ? (tempInsts.instance(i).value(m_target)) : ((tempInsts.instance(i).value(m_target) == m_targetIndex) ? 1 : 0); } else { numMissing++; } } // are there still enough instances? if (tempInsts.numInstances() - numMissing <= m_supportCount) { return; } double bestMerit = 0.0; double bestSplit = 0.0; double bestSupport = 0.0; double bestSubsetSize = 0; boolean lessThan = true; // denominators double leftCount = 0; double rightCount = tempInsts.numInstances() - numMissing; /* targetRight = (tempInsts.attribute(m_target).isNumeric()) ? tempInsts.attributeStats(m_target).numericStats.sum : tempInsts.attributeStats(m_target).nominalCounts[m_targetIndex]; */ // targetRight = tempInsts.attributeStats(attIndexnominalCounts[m_targetIndex]; // consider all splits for (int i = 0; i < tempInsts.numInstances() - numMissing; i++) { Instance inst = tempInsts.instance(i); if (tempInsts.attribute(m_target).isNumeric()) { targetLeft += inst.value(m_target); targetRight -= inst.value(m_target); } else { if ((int)inst.value(m_target) == m_targetIndex) { targetLeft++; targetRight--; } } leftCount++; rightCount--; // move to the end of any ties if (i < tempInsts.numInstances() - 1 && inst.value(attIndex) == tempInsts.instance(i + 1).value(attIndex)) { continue; } // evaluate split if (tempInsts.attribute(m_target).isNominal()) { if (targetLeft >= m_supportCount) { double delta = (m_minimize) ? (bestMerit - (targetLeft / leftCount)) : ((targetLeft / leftCount) - bestMerit); // if (targetLeft / leftCount > bestMerit) { if (delta > 0) { bestMerit = targetLeft / leftCount; bestSplit = inst.value(attIndex); bestSupport = targetLeft; bestSubsetSize = leftCount; lessThan = true; // } else if (targetLeft / leftCount == bestMerit) { } else if (delta == 0) { // break ties in favour of higher support if (targetLeft > bestSupport) { bestMerit = targetLeft / leftCount; bestSplit = inst.value(attIndex); bestSupport = targetLeft; bestSubsetSize = leftCount; lessThan = true; } } } if (targetRight >= m_supportCount) { double delta = (m_minimize) ? (bestMerit - (targetRight / rightCount)) : ((targetRight / rightCount) - bestMerit); // if (targetRight / rightCount > bestMerit) { if (delta > 0) { bestMerit = targetRight / rightCount; bestSplit = inst.value(attIndex); bestSupport = targetRight; bestSubsetSize = rightCount; lessThan = false; // } else if (targetRight / rightCount == bestMerit) { } else if (delta == 0) { // break ties in favour of higher support if (targetRight > bestSupport) { bestMerit = targetRight / rightCount; bestSplit = inst.value(attIndex); bestSupport = targetRight; bestSubsetSize = rightCount; lessThan = false; } } } } else { if (leftCount >= m_supportCount) { double delta = (m_minimize) ? (bestMerit - (targetLeft / leftCount)) : ((targetLeft / leftCount) - bestMerit); // if (targetLeft / leftCount > bestMerit) { if (delta > 0) { bestMerit = targetLeft / leftCount; bestSplit = inst.value(attIndex); bestSupport = leftCount; bestSubsetSize = leftCount; lessThan = true; // } else if (targetLeft / leftCount == bestMerit) { } else if (delta == 0) { // break ties in favour of higher support if (leftCount > bestSupport) { bestMerit = targetLeft / leftCount; bestSplit = inst.value(attIndex); bestSupport = leftCount; bestSubsetSize = leftCount; lessThan = true; } } } if (rightCount >= m_supportCount) { double delta = (m_minimize) ? (bestMerit - (targetRight / rightCount)) : ((targetRight / rightCount) - bestMerit); // if (targetRight / rightCount > bestMerit) { if (delta > 0) { bestMerit = targetRight / rightCount; bestSplit = inst.value(attIndex); bestSupport = rightCount; bestSubsetSize = rightCount; lessThan = false; // } else if (targetRight / rightCount == bestMerit) { } else if (delta == 0) { // break ties in favour of higher support if (rightCount > bestSupport) { bestMerit = targetRight / rightCount; bestSplit = inst.value(attIndex); bestSupport = rightCount; bestSubsetSize = rightCount; lessThan = false; } } } } } double delta = (m_minimize) ? m_targetValue - bestMerit : bestMerit - m_targetValue; // Have we found a candidate split? if (bestSupport > 0 && (delta / m_targetValue >= m_minImprovement)) { /* System.err.println("Evaluating " + tempInsts.attribute(attIndex).name()); System.err.println("Merit : " + bestMerit); System.err.println("Support : " + bestSupport); */ // double suppFraction = bestSupport / m_numInstances; HotTestDetails newD = new HotTestDetails(attIndex, bestSplit, lessThan, (int)bestSupport, (int)bestSubsetSize, bestMerit); pq.add(newD); } } /** * Evaluate a nominal attribute for a potential split * * @param attIndex the index of the attribute * @param pq the priority queue of candidtate splits */ private void evaluateNominal(int attIndex, PriorityQueue pq) { int[] counts = m_insts.attributeStats(attIndex).nominalCounts; boolean ok = false; // only consider attribute values that result in subsets that meet/exceed min support for (int i = 0; i < m_insts.attribute(attIndex).numValues(); i++) { if (counts[i] >= m_supportCount) { ok = true; break; } } if (ok) { double[] subsetMerit = new double[m_insts.attribute(attIndex).numValues()]; for (int i = 0; i < m_insts.numInstances(); i++) { Instance temp = m_insts.instance(i); if (!temp.isMissing(attIndex)) { int attVal = (int)temp.value(attIndex); if (m_insts.attribute(m_target).isNumeric()) { subsetMerit[attVal] += temp.value(m_target); } else { subsetMerit[attVal] += ((int)temp.value(m_target) == m_targetIndex) ? 1.0 : 0; } } } // add to queue if it meets min support and exceeds the merit for the full set for (int i = 0; i < m_insts.attribute(attIndex).numValues(); i++) { // does the subset based on this value have enough instances, and, furthermore, // does the target value (nominal only) occur enough times to exceed min support if (counts[i] >= m_supportCount && ((m_insts.attribute(m_target).isNominal()) ? (subsetMerit[i] >= m_supportCount) // nominal only test : true)) { double merit = subsetMerit[i] / counts[i]; //subsetMerit[i][1]; double delta = (m_minimize) ? m_targetValue - merit : merit - m_targetValue; if (delta / m_targetValue >= m_minImprovement) { double support = (m_insts.attribute(m_target).isNominal()) ? subsetMerit[i] : counts[i]; HotTestDetails newD = new HotTestDetails(attIndex, (double)i, false, (int)support, counts[i], merit); pq.add(newD); } } } } } public int assignIDs(int lastID) { int currentLastID = lastID + 1; m_id = currentLastID; if (m_children != null) { for (int i = 0; i < m_children.length; i++) { currentLastID = m_children[i].assignIDs(currentLastID); } } return currentLastID; } private void addNodeDetails(StringBuffer buff, int i, String spacer) { buff.append(m_header.attribute(m_testDetails[i].m_splitAttIndex).name()); if (m_header.attribute(m_testDetails[i].m_splitAttIndex).isNumeric()) { if (m_testDetails[i].m_lessThan) { buff.append(" <= "); } else { buff.append(" > "); } buff.append(Utils.doubleToString(m_testDetails[i].m_splitValue, 4)); } else { buff.append(" = " + m_header. attribute(m_testDetails[i].m_splitAttIndex). value((int)m_testDetails[i].m_splitValue)); } if (m_header.attribute(m_target).isNumeric()) { buff.append(spacer + "(" + Utils.doubleToString(m_testDetails[i].m_merit, 4) + " [" + m_testDetails[i].m_support + "])"); } else { buff.append(spacer + "(" + Utils.doubleToString((m_testDetails[i].m_merit * 100.0), 2) + "% [" + m_testDetails[i].m_support + "/" + m_testDetails[i].m_subsetSize + "])"); } } private void graphHotSpot(StringBuffer text) { if (m_children != null) { for (int i = 0; i < m_children.length; i++) { text.append("N" + m_children[i].m_id); text.append(" [label=\""); addNodeDetails(text, i, "\\n"); text.append("\" shape=plaintext]\n"); m_children[i].graphHotSpot(text); text.append("N" + m_id + "->" + "N" + m_children[i].m_id + "\n"); } } } /** * Traverse the tree to create a string description * * @param depth the depth at this point in the tree * @param buff the string buffer to append node details to */ protected void dumpTree(int depth, StringBuffer buff) { if (m_children == null) { // buff.append("\n"); } else { for (int i = 0; i < m_children.length; i++) { buff.append("\n "); for (int j = 0; j < depth; j++) { buff.append("| "); } addNodeDetails(buff, i, " "); m_children[i].dumpTree(depth + 1, buff); } } } } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String targetTipText() { return "The target attribute of interest."; } /** * Set the target index * * @param target the target index as a string (1-based) */ public void setTarget(String target) { m_targetSI.setSingleIndex(target); } /** * Get the target index as a string * * @return the target index (1-based) */ public String getTarget() { return m_targetSI.getSingleIndex(); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String targetIndexTipText() { return "The value of the target (nominal attributes only) of interest."; } /** * For a nominal target, set the index of the value of interest (1-based) * * @param index the index of the nominal value of interest */ public void setTargetIndex(String index) { m_targetIndexSI.setSingleIndex(index); } /** * For a nominal target, get the index of the value of interest (1-based) * * @return the index of the nominal value of interest */ public String getTargetIndex() { return m_targetIndexSI.getSingleIndex(); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String minimizeTargetTipText() { return "Minimize rather than maximize the target."; } /** * Set whether to minimize the target rather than maximize * * @param m true if target is to be minimized */ public void setMinimizeTarget(boolean m) { m_minimize = m; } /** * Get whether to minimize the target rather than maximize * * @return true if target is to be minimized */ public boolean getMinimizeTarget() { return m_minimize; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String supportTipText() { return "The minimum support. Values between 0 and 1 are interpreted " + "as a percentage of the total population; values > 1 are " + "interpreted as an absolute number of instances"; } /** * Get the minimum support * * @return the minimum support */ public double getSupport() { return m_support; } /** * Set the minimum support * * @param s the minimum support */ public void setSupport(double s) { m_support = s; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String maxBranchingFactorTipText() { return "Maximum branching factor. The maximum number of children " + "to consider extending each node with."; } /** * Set the maximum branching factor * * @param b the maximum branching factor */ public void setMaxBranchingFactor(int b) { m_maxBranchingFactor = b; } /** * Get the maximum branching factor * * @return the maximum branching factor */ public int getMaxBranchingFactor() { return m_maxBranchingFactor; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String minImprovementTipText() { return "Minimum improvement in target value in order to " + "consider adding a new branch/test"; } /** * Set the minimum improvement in the target necessary to add a test * * @param i the minimum improvement */ public void setMinImprovement(double i) { m_minImprovement = i; } /** * Get the minimum improvement in the target necessary to add a test * * @return the minimum improvement */ public double getMinImprovement() { return m_minImprovement; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String debugTipText() { return "Output debugging info (duplicate rule lookup hash table stats)."; } /** * Set whether debugging info is output * * @param d true to output debugging info */ public void setDebug(boolean d) { m_debug = d; } /** * Get whether debugging info is output * * @return true if outputing debugging info */ public boolean getDebug() { return m_debug; } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector newVector = new Vector(); newVector.addElement(new Option("\tThe target index. (default = last)", "c", 1, "-c ")); newVector.addElement(new Option("\tThe target value (nominal target only, default = first)", "V", 1, "-V ")); newVector.addElement(new Option("\tMinimize rather than maximize.", "L", 0, "-L")); newVector.addElement(new Option("\tMinimum value count (nominal target)/segment size " + "(numeric target)." +"\n\tValues between 0 and 1 are " + "\n\tinterpreted as a percentage of " + "\n\tthe total population; values > 1 are " + "\n\tinterpreted as an absolute number of " +"\n\tinstances (default = 0.3)", "-S", 1, "-S ")); newVector.addElement(new Option("\tMaximum branching factor (default = 2)", "-M", 1, "-M ")); newVector.addElement(new Option("\tMinimum improvement in target value in order " + "\n\tto add a new branch/test (default = 0.01 (1%))", "-I", 1, "-I ")); newVector.addElement(new Option("\tOutput debugging info (duplicate rule lookup " + "\n\thash table stats)", "-D", 0, "-D")); return newVector.elements(); } /** * Reset options to their defaults */ public void resetOptions() { m_support = 0.33; m_minImprovement = 0.01; // 1% m_maxBranchingFactor = 2; m_minimize = false; m_debug = false; setTarget("last"); setTargetIndex("first"); m_errorMessage = null; } /** * Parses a given list of options.

* * Valid options are:

* * 

 -c <num | first | last>
   *  The target index. (default = last)
* * 
 -V <num | first | last>
   *  The target value (nominal target only, default = first)
* * 
 -L
   *  Minimize rather than maximize.
* * 
 -S <num>
   *  Minimum value count (nominal target)/segment size (numeric target).
   *  Values between 0 and 1 are 
   *  interpreted as a percentage of 
   *  the total population; values > 1 are 
   *  interpreted as an absolute number of 
   *  instances (default = 0.3)
* * 
 -M <num>
   *  Maximum branching factor (default = 2)
* * 
 -I <num>
   *  Minimum improvement in target value in order 
   *  to add a new branch/test (default = 0.01 (1%))
* * 
 -D
   *  Output debugging info (duplicate rule lookup 
   *  hash table stats)
* * * @param options the list of options as an array of strings * @exception Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { resetOptions(); String tempString = Utils.getOption('c', options); if (tempString.length() != 0) { setTarget(tempString); } tempString = Utils.getOption('V', options); if (tempString.length() != 0) { setTargetIndex(tempString); } setMinimizeTarget(Utils.getFlag('L', options)); tempString = Utils.getOption('S', options); if (tempString.length() != 0) { setSupport(Double.parseDouble(tempString)); } tempString = Utils.getOption('M', options); if (tempString.length() != 0) { setMaxBranchingFactor(Integer.parseInt(tempString)); } tempString = Utils.getOption('I', options); if (tempString.length() != 0) { setMinImprovement(Double.parseDouble(tempString)); } setDebug(Utils.getFlag('D', options)); } /** * Gets the current settings of HotSpot. * * @return an array of strings suitable for passing to setOptions */ public String [] getOptions() { String[] options = new String[12]; int current = 0; options[current++] = "-c"; options[current++] = getTarget(); options[current++] = "-V"; options[current++] = getTargetIndex(); if (getMinimizeTarget()) { options[current++] = "-L"; } options[current++] = "-S"; options[current++] = "" + getSupport(); options[current++] = "-M"; options[current++] = "" + getMaxBranchingFactor(); options[current++] = "-I"; options[current++] = "" + getMinImprovement(); if (getDebug()) { options[current++] = "-D"; } while (current < options.length) { options[current++] = ""; } return options; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.6 $"); } /** * Returns the type of graph this scheme * represents. * @return Drawable.TREE */ public int graphType() { return Drawable.TREE; } /** * Main method for testing this class. * * @param args the options */ public static void main(String[] args) { try { HotSpot h = new HotSpot(); AbstractAssociator.runAssociator(new HotSpot(), args); } catch (Exception ex) { ex.printStackTrace(); } } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/ItemSet.java000066400000000000000000000277401214146114000256670ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * ItemSet.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.associations; import weka.core.FastVector; import weka.core.Instance; import weka.core.Instances; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import java.io.Serializable; import java.util.Enumeration; import java.util.Hashtable; /** * Class for storing a set of items. Item sets are stored in a lexicographic * order, which is determined by the header information of the set of instances * used for generating the set of items. All methods in this class assume that * item sets are stored in lexicographic order. * The class provides the general methods used for item sets in class - and * standard association rule mining. * * @author Eibe Frank (eibe@cs.waikato.ac.nz) * @version $Revision: 1.13 $ */ public class ItemSet implements Serializable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = 2724000045282835791L; /** The items stored as an array of of ints. */ protected int[] m_items; /** Counter for how many transactions contain this item set. */ protected int m_counter; /** The total number of transactions */ protected int m_totalTransactions; /** * Constructor * @param totalTrans the total number of transactions in the data */ public ItemSet(int totalTrans) { m_totalTransactions = totalTrans; } /** * Constructor * @param totalTrans the total number of transactions in the data * @param array the attribute values encoded in an int array */ public ItemSet(int totalTrans, int[] array){ m_totalTransactions = totalTrans; m_items = array; m_counter =1; } /** Contsructor * @param array the item set represented as an int array */ public ItemSet(int[] array){ m_items = array; m_counter = 0; } /** * Checks if an instance contains an item set. * * @param instance the instance to be tested * @return true if the given instance contains this item set */ public boolean containedBy(Instance instance) { for (int i = 0; i < instance.numAttributes(); i++) if (m_items[i] > -1) { if (instance.isMissing(i)) return false; if (m_items[i] != (int)instance.value(i)) return false; } return true; } /** Deletes all item sets that don't have minimum support. * @return the reduced set of item sets * @param maxSupport the maximum support * @param itemSets the set of item sets to be pruned * @param minSupport the minimum number of transactions to be covered */ public static FastVector deleteItemSets(FastVector itemSets, int minSupport, int maxSupport) { FastVector newVector = new FastVector(itemSets.size()); for (int i = 0; i < itemSets.size(); i++) { ItemSet current = (ItemSet)itemSets.elementAt(i); if ((current.m_counter >= minSupport) && (current.m_counter <= maxSupport)) newVector.addElement(current); } return newVector; } /** * Tests if two item sets are equal. * * @param itemSet another item set * @return true if this item set contains the same items as the given one */ public boolean equals(Object itemSet) { if ((itemSet == null) || !(itemSet.getClass().equals(this.getClass()))) { return false; } if (m_items.length != ((ItemSet)itemSet).m_items.length) return false; for (int i = 0; i < m_items.length; i++) if (m_items[i] != ((ItemSet)itemSet).m_items[i]) return false; return true; } /** * Return a hashtable filled with the given item sets. * * @param itemSets the set of item sets to be used for filling the hash table * @param initialSize the initial size of the hashtable * @return the generated hashtable */ public static Hashtable getHashtable(FastVector itemSets, int initialSize) { Hashtable hashtable = new Hashtable(initialSize); for (int i = 0; i < itemSets.size(); i++) { ItemSet current = (ItemSet)itemSets.elementAt(i); hashtable.put(current, new Integer(current.m_counter)); } return hashtable; } /** * Produces a hash code for a item set. * * @return a hash code for a set of items */ public int hashCode() { long result = 0; for (int i = m_items.length-1; i >= 0; i--) result += (i * m_items[i]); return (int)result; } /** Merges all item sets in the set of (k-1)-item sets * to create the (k)-item sets and updates the counters. * @return the generated (k)-item sets * @param totalTrans thetotal number of transactions * @param itemSets the set of (k-1)-item sets * @param size the value of (k-1) */ public static FastVector mergeAllItemSets(FastVector itemSets, int size, int totalTrans) { FastVector newVector = new FastVector(); ItemSet result; int numFound, k; for (int i = 0; i < itemSets.size(); i++) { ItemSet first = (ItemSet)itemSets.elementAt(i); out: for (int j = i+1; j < itemSets.size(); j++) { ItemSet second = (ItemSet)itemSets.elementAt(j); result = new ItemSet(totalTrans); result.m_items = new int[first.m_items.length]; // Find and copy common prefix of size 'size' numFound = 0; k = 0; while (numFound < size) { if (first.m_items[k] == second.m_items[k]) { if (first.m_items[k] != -1) numFound++; result.m_items[k] = first.m_items[k]; } else break out; k++; } // Check difference while (k < first.m_items.length) { if ((first.m_items[k] != -1) && (second.m_items[k] != -1)) break; else { if (first.m_items[k] != -1) result.m_items[k] = first.m_items[k]; else result.m_items[k] = second.m_items[k]; } k++; } if (k == first.m_items.length) { result.m_counter = 0; newVector.addElement(result); } } } return newVector; } /** * Prunes a set of (k)-item sets using the given (k-1)-item sets. * * @param toPrune the set of (k)-item sets to be pruned * @param kMinusOne the (k-1)-item sets to be used for pruning * @return the pruned set of item sets */ public static FastVector pruneItemSets(FastVector toPrune, Hashtable kMinusOne) { FastVector newVector = new FastVector(toPrune.size()); int help, j; for (int i = 0; i < toPrune.size(); i++) { ItemSet current = (ItemSet)toPrune.elementAt(i); for (j = 0; j < current.m_items.length; j++) if (current.m_items[j] != -1) { help = current.m_items[j]; current.m_items[j] = -1; if (kMinusOne.get(current) == null) { current.m_items[j] = help; break; } else{ current.m_items[j] = help; } } if (j == current.m_items.length) newVector.addElement(current); } return newVector; } /** * Prunes a set of rules. * * @param rules a two-dimensional array of lists of item sets. The first list * of item sets contains the premises, the second one the consequences. * @param minConfidence the minimum confidence the rules have to have */ public static void pruneRules(FastVector[] rules, double minConfidence) { FastVector newPremises = new FastVector(rules[0].size()), newConsequences = new FastVector(rules[1].size()), newConf = new FastVector(rules[2].size()); for (int i = 0; i < rules[0].size(); i++) if (!(((Double)rules[2].elementAt(i)).doubleValue() < minConfidence)) { newPremises.addElement(rules[0].elementAt(i)); newConsequences.addElement(rules[1].elementAt(i)); newConf.addElement(rules[2].elementAt(i)); } rules[0] = newPremises; rules[1] = newConsequences; rules[2] = newConf; } /** * Converts the header info of the given set of instances into a set * of item sets (singletons). The ordering of values in the header file * determines the lexicographic order. * * @param instances the set of instances whose header info is to be used * @return a set of item sets, each containing a single item * @exception Exception if singletons can't be generated successfully */ public static FastVector singletons(Instances instances) throws Exception { FastVector setOfItemSets = new FastVector(); ItemSet current; for (int i = 0; i < instances.numAttributes(); i++) { if (instances.attribute(i).isNumeric()) throw new Exception("Can't handle numeric attributes!"); for (int j = 0; j < instances.attribute(i).numValues(); j++) { current = new ItemSet(instances.numInstances()); current.m_items = new int[instances.numAttributes()]; for (int k = 0; k < instances.numAttributes(); k++) current.m_items[k] = -1; current.m_items[i] = j; setOfItemSets.addElement(current); } } return setOfItemSets; } /** * Outputs the support for an item set. * * @return the support */ public int support() { return m_counter; } /** * Returns the contents of an item set as a string. * * @param instances contains the relevant header information * @return string describing the item set */ public String toString(Instances instances) { StringBuffer text = new StringBuffer(); for (int i = 0; i < instances.numAttributes(); i++) if (m_items[i] != -1) { text.append(instances.attribute(i).name()+'='); text.append(instances.attribute(i).value(m_items[i])+' '); } text.append(m_counter); return text.toString(); } /** * Updates counter of item set with respect to given transaction. * * @param instance the instance to be used for ubdating the counter */ public void upDateCounter(Instance instance) { if (containedBy(instance)) m_counter++; } /** * Updates counters for a set of item sets and a set of instances. * * @param itemSets the set of item sets which are to be updated * @param instances the instances to be used for updating the counters */ public static void upDateCounters(FastVector itemSets, Instances instances) { for (int i = 0; i < instances.numInstances(); i++) { Enumeration enu = itemSets.elements(); while (enu.hasMoreElements()) ((ItemSet)enu.nextElement()).upDateCounter(instances.instance(i)); } } /** Gets the counter * @return the counter */ public int counter(){ return m_counter; } /** Gest the item set as an int array * @return int array represneting an item set */ public int[] items(){ return m_items; } /** Gest the index of the value of the specified attribute * @param k the attribute index * @return the index of the attribute value */ public int itemAt(int k){ return m_items[k]; } /** Sets the counter * @param count the counter */ public void setCounter(int count){ m_counter = count; } /** Sets an item sets * @param items an int array representing an item set */ public void setItem(int[] items){ m_items = items; } /** Sets the index of an attribute value * @param value the inex of the attribute value * @param k the index of the attribute */ public void setItemAt(int value, int k){ m_items[k] = value; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.13 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/LabeledItemSet.java000066400000000000000000000312701214146114000271310ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * LabeledItemSet.java * Copyright (C) 2004 University of Waikato, Hamilton, New Zealand * */ package weka.associations; import weka.core.FastVector; import weka.core.Instance; import weka.core.Instances; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import java.io.Serializable; import java.util.Enumeration; import java.util.Hashtable; /** * Class for storing a set of items together with a class label. Item sets are stored in a lexicographic * order, which is determined by the header information of the set of instances * used for generating the set of items. All methods in this class assume that * item sets are stored in lexicographic order. * The class provides the methods used for item sets in class association rule mining. * Because every item set knows its class label the training set can be splitted up virtually. * * @author Stefan Mutter (mutter@cs.waikato.ac.nz) * @version $Revision: 1.5 $ */ public class LabeledItemSet extends ItemSet implements Serializable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = 4158771925518299903L; /** The class label. */ protected int m_classLabel; /** The support of the rule. */ protected int m_ruleSupCounter; /** * Constructor * * @param totalTrans the total number of transactions * @param classLabel the class lebel */ public LabeledItemSet(int totalTrans, int classLabel){ super(totalTrans); m_classLabel = classLabel; } /** * Deletes all item sets that don't have minimum support and have more than maximum support * @return the reduced set of item sets * @param maxSupport the maximum support * @param itemSets the set of item sets to be pruned * @param minSupport the minimum number of transactions to be covered */ public static FastVector deleteItemSets(FastVector itemSets, int minSupport, int maxSupport) { FastVector newVector = new FastVector(itemSets.size()); for (int i = 0; i < itemSets.size(); i++) { LabeledItemSet current = (LabeledItemSet)itemSets.elementAt(i); if ((current.m_ruleSupCounter >= minSupport) && (current.m_ruleSupCounter <= maxSupport)) newVector.addElement(current); } return newVector; } /** * Tests if two item sets are equal. * * @param itemSet another item set * @return true if this item set contains the same items as the given one */ public final boolean equals(Object itemSet) { if (!(this.equalCondset(itemSet))) return false; if(m_classLabel != ((LabeledItemSet)itemSet).m_classLabel) return false; return true; } /** * Compares two item sets * @param itemSet an item set * @return true if the the item sets are equal, false otherwise */ public final boolean equalCondset(Object itemSet) { if ((itemSet == null) || !(itemSet.getClass().equals(this.getClass()))) { return false; } if (m_items.length != ((ItemSet)itemSet).items().length) return false; for (int i = 0; i < m_items.length; i++) if (m_items[i] != ((ItemSet)itemSet).itemAt(i)) return false; return true; } /** * Return a hashtable filled with the given item sets. * * @param itemSets the set of item sets to be used for filling the hash table * @param initialSize the initial size of the hashtable * @return the generated hashtable */ public static Hashtable getHashtable(FastVector itemSets, int initialSize) { Hashtable hashtable = new Hashtable(initialSize); for (int i = 0; i < itemSets.size(); i++) { LabeledItemSet current = (LabeledItemSet)itemSets.elementAt(i); hashtable.put(current, new Integer(current.m_classLabel)); } return hashtable; } /** * Merges all item sets in the set of (k-1)-item sets * to create the (k)-item sets and updates the counters. * @return the generated (k)-item sets * @param totalTrans the total number of transactions * @param itemSets the set of (k-1)-item sets * @param size the value of (k-1) */ public static FastVector mergeAllItemSets(FastVector itemSets, int size, int totalTrans) { FastVector newVector = new FastVector(); LabeledItemSet result; int numFound, k; for (int i = 0; i < itemSets.size(); i++) { LabeledItemSet first = (LabeledItemSet)itemSets.elementAt(i); out: for (int j = i+1; j < itemSets.size(); j++) { LabeledItemSet second = (LabeledItemSet)itemSets.elementAt(j); while(first.m_classLabel != second.m_classLabel){ j++; if(j == itemSets.size()) break out; second = (LabeledItemSet)itemSets.elementAt(j); } result = new LabeledItemSet(totalTrans,first.m_classLabel); result.m_items = new int[first.m_items.length]; // Find and copy common prefix of size 'size' numFound = 0; k = 0; while (numFound < size) { if (first.m_items[k] == second.m_items[k]) { if (first.m_items[k] != -1) numFound++; result.m_items[k] = first.m_items[k]; } else break out; k++; } // Check difference while (k < first.m_items.length) { if ((first.m_items[k] != -1) && (second.m_items[k] != -1)) break; else { if (first.m_items[k] != -1) result.m_items[k] = first.m_items[k]; else result.m_items[k] = second.m_items[k]; } k++; } if (k == first.m_items.length) { result.m_ruleSupCounter = 0; result.m_counter = 0; newVector.addElement(result); } } } return newVector; } /** * Splits the class attribute away. Depending on the invert flag, the instances without class attribute or only the class attribute of all instances is returned * @param instances the instances * @param invert flag; if true only the class attribute remains, otherweise the class attribute is the only attribute that is deleted. * @throws Exception exception if instances cannot be splitted * @return Instances without the class attribute or instances with only the class attribute */ public static Instances divide(Instances instances, boolean invert) throws Exception{ Instances newInstances = new Instances(instances); if(instances.classIndex() < 0) throw new Exception("For class association rule mining a class attribute has to be specified."); if(invert){ for(int i=0;i * Class implementing the predictive apriori algorithm to mine association rules.
* It searches with an increasing support threshold for the best 'n' rules concerning a support-based corrected confidence value.
*
* For more information see:
*
* Tobias Scheffer: Finding Association Rules That Trade Support Optimally against Confidence. In: 5th European Conference on Principles of Data Mining and Knowledge Discovery, 424-435, 2001.
*
* The implementation follows the paper expect for adding a rule to the output of the 'n' best rules. A rule is added if:
* the expected predictive accuracy of this rule is among the 'n' best and it is not subsumed by a rule with at least the same expected predictive accuracy (out of an unpublished manuscript from T. Scheffer). *

* * BibTeX: * 

 * @inproceedings{Scheffer2001,
 *    author = {Tobias Scheffer},
 *    booktitle = {5th European Conference on Principles of Data Mining and Knowledge Discovery},
 *    pages = {424-435},
 *    publisher = {Springer},
 *    title = {Finding Association Rules That Trade Support Optimally against Confidence},
 *    year = {2001}
 * }
 *
*

* * Valid options are:

* * 

 -N <required number of rules output>
 *  The required number of rules. (default = 100)
* * 
 -A
 *  If set class association rules are mined. (default = no)
* * 
 -c <the class index>
 *  The class index. (default = last)
* * * @author Stefan Mutter (mutter@cs.waikato.ac.nz) * @version $Revision: 1.14 $ */ public class PredictiveApriori extends AbstractAssociator implements OptionHandler, CARuleMiner, TechnicalInformationHandler { /** for serialization */ static final long serialVersionUID = 8109088846865075341L; /** The minimum support. */ protected int m_premiseCount; /** The maximum number of rules that are output. */ protected int m_numRules; /** The number of rules created for the prior estimation. */ protected static final int m_numRandRules = 1000; /** The number of intervals used for the prior estimation. */ protected static final int m_numIntervals = 100; /** The set of all sets of itemsets. */ protected FastVector m_Ls; /** The same information stored in hash tables. */ protected FastVector m_hashtables; /** The list of all generated rules. */ protected FastVector[] m_allTheRules; /** The instances (transactions) to be used for generating the association rules. */ protected Instances m_instances; /** The hashtable containing the prior probabilities. */ protected Hashtable m_priors; /** The mid points of the intervals used for the prior estimation. */ protected double[] m_midPoints; /** The expected predictive accuracy a rule needs to be a candidate for the output. */ protected double m_expectation; /** The n best rules. */ protected TreeSet m_best; /** Flag keeping track if the list of the n best rules has changed. */ protected boolean m_bestChanged; /** Counter for the time of generation for an association rule. */ protected int m_count; /** The prior estimator. */ protected PriorEstimation m_priorEstimator; /** The class index. */ protected int m_classIndex; /** Flag indicating whether class association rules are mined. */ protected boolean m_car; /** * Returns a string describing this associator * @return a description of the evaluator suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Class implementing the predictive apriori algorithm to mine " + "association rules.\n" + "It searches with an increasing support threshold for the best 'n' " + "rules concerning a support-based corrected confidence value.\n\n" + "For more information see:\n\n" + getTechnicalInformation().toString() + "\n\n" + "The implementation follows the paper expect for adding a rule to the " + "output of the 'n' best rules. A rule is added if:\n" + "the expected predictive accuracy of this rule is among the 'n' best " + "and it is not subsumed by a rule with at least the same expected " + "predictive accuracy (out of an unpublished manuscript from T. " + "Scheffer)."; } /** * Returns an instance of a TechnicalInformation object, containing * detailed information about the technical background of this class, * e.g., paper reference or book this class is based on. * * @return the technical information about this class */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.INPROCEEDINGS); result.setValue(Field.AUTHOR, "Tobias Scheffer"); result.setValue(Field.TITLE, "Finding Association Rules That Trade Support Optimally against Confidence"); result.setValue(Field.BOOKTITLE, "5th European Conference on Principles of Data Mining and Knowledge Discovery"); result.setValue(Field.YEAR, "2001"); result.setValue(Field.PAGES, "424-435"); result.setValue(Field.PUBLISHER, "Springer"); return result; } /** * Constructor that allows to sets default values for the * minimum confidence and the maximum number of rules * the minimum confidence. */ public PredictiveApriori() { resetOptions(); } /** * Resets the options to the default values. */ public void resetOptions() { m_numRules = 105; m_premiseCount = 1; m_best = new TreeSet(); m_bestChanged = false; m_expectation = 0; m_count = 1; m_car = false; m_classIndex = -1; m_priors = new Hashtable(); } /** * Returns default capabilities of the classifier. * * @return the capabilities of this classifier */ public Capabilities getCapabilities() { Capabilities result = super.getCapabilities(); // attributes result.enable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.MISSING_VALUES); // class result.enable(Capability.NOMINAL_CLASS); result.enable(Capability.MISSING_CLASS_VALUES); return result; } /** * Method that generates all large itemsets with a minimum support, and from * these all association rules. * * @param instances the instances to be used for generating the associations * @throws Exception if rules can't be built successfully */ public void buildAssociations(Instances instances) throws Exception { int temp = m_premiseCount, exactNumber = m_numRules-5; m_premiseCount = 1; m_best = new TreeSet(); m_bestChanged = false; m_expectation = 0; m_count = 1; m_instances = new Instances(instances); if (m_classIndex == -1) m_instances.setClassIndex(m_instances.numAttributes()-1); else if (m_classIndex < m_instances.numAttributes() && m_classIndex >= 0) m_instances.setClassIndex(m_classIndex); else throw new Exception("Invalid class index."); // can associator handle the data? getCapabilities().testWithFail(m_instances); //prior estimation m_priorEstimator = new PriorEstimation(m_instances,m_numRandRules,m_numIntervals,m_car); m_priors = m_priorEstimator.estimatePrior(); m_midPoints = m_priorEstimator.getMidPoints(); m_Ls = new FastVector(); m_hashtables = new FastVector(); for(int i =1; i < m_instances.numAttributes();i++){ m_bestChanged = false; if(!m_car){ // find large item sets findLargeItemSets(i); //find association rules (rule generation procedure) findRulesQuickly(); } else{ findLargeCarItemSets(i); findCaRulesQuickly(); } if(m_bestChanged){ temp =m_premiseCount; while(RuleGeneration.expectation(m_premiseCount, m_premiseCount,m_midPoints,m_priors) <= m_expectation){ m_premiseCount++; if(m_premiseCount > m_instances.numInstances()) break; } } if(m_premiseCount > m_instances.numInstances()){ // Reserve space for variables m_allTheRules = new FastVector[3]; m_allTheRules[0] = new FastVector(); m_allTheRules[1] = new FastVector(); m_allTheRules[2] = new FastVector(); int k = 0; while(m_best.size()>0 && exactNumber > 0){ m_allTheRules[0].insertElementAt((ItemSet)((RuleItem)m_best.last()).premise(),k); m_allTheRules[1].insertElementAt((ItemSet)((RuleItem)m_best.last()).consequence(),k); m_allTheRules[2].insertElementAt(new Double(((RuleItem)m_best.last()).accuracy()),k); m_best.remove(m_best.last()); k++; exactNumber--; } return; } if(temp != m_premiseCount && m_Ls.size() > 0){ FastVector kSets = (FastVector)m_Ls.lastElement(); m_Ls.removeElementAt(m_Ls.size()-1); kSets = ItemSet.deleteItemSets(kSets, m_premiseCount,Integer.MAX_VALUE); m_Ls.addElement(kSets); } } // Reserve space for variables m_allTheRules = new FastVector[3]; m_allTheRules[0] = new FastVector(); m_allTheRules[1] = new FastVector(); m_allTheRules[2] = new FastVector(); int k = 0; while(m_best.size()>0 && exactNumber > 0){ m_allTheRules[0].insertElementAt((ItemSet)((RuleItem)m_best.last()).premise(),k); m_allTheRules[1].insertElementAt((ItemSet)((RuleItem)m_best.last()).consequence(),k); m_allTheRules[2].insertElementAt(new Double(((RuleItem)m_best.last()).accuracy()),k); m_best.remove(m_best.last()); k++; exactNumber--; } } /** * Method that mines the n best class association rules. * @return an sorted array of FastVector (depending on the expected predictive accuracy) containing the rules and metric information * @param data the instances for which class association rules should be mined * @throws Exception if rules can't be built successfully */ public FastVector[] mineCARs(Instances data) throws Exception{ m_car = true; m_best = new TreeSet(); m_premiseCount = 1; m_bestChanged = false; m_expectation = 0; m_count = 1; buildAssociations(data); FastVector[] allCARRules = new FastVector[3]; allCARRules[0] = new FastVector(); allCARRules[1] = new FastVector(); allCARRules[2] = new FastVector(); for(int k =0; k < m_allTheRules[0].size();k++){ int[] newPremiseArray = new int[m_instances.numAttributes()-1]; int help = 0; for(int j = 0;j < m_instances.numAttributes();j++){ if(j != m_instances.classIndex()){ newPremiseArray[help] = ((ItemSet)m_allTheRules[0].elementAt(k)).itemAt(j); help++; } } ItemSet newPremise = new ItemSet(m_instances.numInstances(), newPremiseArray); newPremise.setCounter (((ItemSet)m_allTheRules[0].elementAt(k)).counter()); allCARRules[0].addElement(newPremise); int[] newConsArray = new int[1]; newConsArray[0] =((ItemSet)m_allTheRules[1].elementAt(k)).itemAt(m_instances.classIndex()); ItemSet newCons = new ItemSet(m_instances.numInstances(), newConsArray); newCons.setCounter(((ItemSet)m_allTheRules[1].elementAt(k)).counter()); allCARRules[1].addElement(newCons); allCARRules[2].addElement(m_allTheRules[2].elementAt(k)); } return allCARRules; } /** * Gets the instances without the class attribute * @return instances without class attribute */ public Instances getInstancesNoClass() { Instances noClass = null; try{ noClass = LabeledItemSet.divide(m_instances,false); } catch(Exception e){ e.printStackTrace(); System.out.println("\n"+e.getMessage()); } //System.out.println(noClass); return noClass; } /** * Gets the class attribute of all instances * @return Instances containing only the class attribute */ public Instances getInstancesOnlyClass() { Instances onlyClass = null; try{ onlyClass = LabeledItemSet.divide(m_instances,true); } catch(Exception e){ e.printStackTrace(); System.out.println("\n"+e.getMessage()); } return onlyClass; } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { String string1 = "\tThe required number of rules. (default = " + (m_numRules-5) + ")", string2 = "\tIf set class association rules are mined. (default = no)", string3 = "\tThe class index. (default = last)"; FastVector newVector = new FastVector(3); newVector.addElement(new Option(string1, "N", 1, "-N ")); newVector.addElement(new Option(string2, "A", 0, "-A")); newVector.addElement(new Option(string3, "c", 1, "-c ")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -N <required number of rules output>
   *  The required number of rules. (default = 100)
* * 
 -A
   *  If set class association rules are mined. (default = no)
* * 
 -c <the class index>
   *  The class index. (default = last)
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { resetOptions(); String numRulesString = Utils.getOption('N', options); if (numRulesString.length() != 0) m_numRules = Integer.parseInt(numRulesString)+5; else m_numRules = Integer.MAX_VALUE; String classIndexString = Utils.getOption('c',options); if (classIndexString.length() != 0) m_classIndex = Integer.parseInt(classIndexString); m_car = Utils.getFlag('A', options); } /** * Gets the current settings of the PredictiveApriori object. * * @return an array of strings suitable for passing to setOptions */ public String [] getOptions() { Vector result; result = new Vector(); result.add("-N"); result.add("" + (m_numRules-5)); if (m_car) result.add("-A"); result.add("-c"); result.add("" + m_classIndex); return (String[]) result.toArray(new String[result.size()]); } /** * Outputs the association rules. * * @return a string representation of the model */ public String toString() { StringBuffer text = new StringBuffer(); if (m_allTheRules[0].size() == 0) return "\nNo large itemsets and rules found!\n"; text.append("\nPredictiveApriori\n===================\n\n"); text.append("\nBest rules found:\n\n"); for (int i = 0; i < m_allTheRules[0].size(); i++) { text.append(Utils.doubleToString((double)i+1, (int)(Math.log(m_numRules)/Math.log(10)+1),0)+ ". " + ((ItemSet)m_allTheRules[0].elementAt(i)). toString(m_instances) + " ==> " + ((ItemSet)m_allTheRules[1].elementAt(i)). toString(m_instances) +" acc:("+ Utils.doubleToString(((Double)m_allTheRules[2]. elementAt(i)).doubleValue(),5)+")"); text.append('\n'); } return text.toString(); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String numRulesTipText() { return "Number of rules to find."; } /** * Get the value of the number of required rules. * * @return Value of the number of required rules. */ public int getNumRules() { return m_numRules-5; } /** * Set the value of required rules. * * @param v Value to assign to number of required rules. */ public void setNumRules(int v) { m_numRules = v+5; } /** * Sets the class index * @param index the index of the class attribute */ public void setClassIndex(int index){ m_classIndex = index; } /** * Gets the index of the class attribute * @return the index of the class attribute */ public int getClassIndex(){ return m_classIndex; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String classIndexTipText() { return "Index of the class attribute.\n If set to -1, the last attribute will be taken as the class attribute."; } /** * Sets class association rule mining * @param flag if class association rules are mined, false otherwise */ public void setCar(boolean flag){ m_car = flag; } /** * Gets whether class association ruels are mined * @return true if class association rules are mined, false otherwise */ public boolean getCar(){ return m_car; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String carTipText() { return "If enabled class association rules are mined instead of (general) association rules."; } /** * Returns the metric string for the chosen metric type. * Predictive apriori uses the estimated predictive accuracy. * Therefore the metric string is "acc". * @return string "acc" */ public String metricString() { return "acc"; } /** * Method that finds all large itemsets for the given set of instances. * * @param index the instances to be used * @throws Exception if an attribute is numeric */ private void findLargeItemSets(int index) throws Exception { FastVector kMinusOneSets, kSets = new FastVector(); Hashtable hashtable; int i = 0; // Find large itemsets //of length 1 if(index == 1){ kSets = ItemSet.singletons(m_instances); ItemSet.upDateCounters(kSets, m_instances); kSets = ItemSet.deleteItemSets(kSets, m_premiseCount,Integer.MAX_VALUE); if (kSets.size() == 0) return; m_Ls.addElement(kSets); } //of length > 1 if(index >1){ if(m_Ls.size() > 0) kSets = (FastVector)m_Ls.lastElement(); m_Ls.removeAllElements(); i = index-2; kMinusOneSets = kSets; kSets = ItemSet.mergeAllItemSets(kMinusOneSets, i, m_instances.numInstances()); hashtable = ItemSet.getHashtable(kMinusOneSets, kMinusOneSets.size()); m_hashtables.addElement(hashtable); kSets = ItemSet.pruneItemSets(kSets, hashtable); ItemSet.upDateCounters(kSets, m_instances); kSets = ItemSet.deleteItemSets(kSets, m_premiseCount,Integer.MAX_VALUE); if(kSets.size() == 0) return; m_Ls.addElement(kSets); } } /** * Method that finds all association rules. * * @throws Exception if an attribute is numeric */ private void findRulesQuickly() throws Exception { RuleGeneration currentItemSet; // Build rules for (int j = 0; j < m_Ls.size(); j++) { FastVector currentItemSets = (FastVector)m_Ls.elementAt(j); Enumeration enumItemSets = currentItemSets.elements(); while (enumItemSets.hasMoreElements()) { currentItemSet = new RuleGeneration((ItemSet)enumItemSets.nextElement()); m_best = currentItemSet.generateRules(m_numRules-5, m_midPoints,m_priors,m_expectation, m_instances,m_best,m_count); m_count = currentItemSet.m_count; if(!m_bestChanged && currentItemSet.m_change) m_bestChanged = true; //update minimum expected predictive accuracy to get into the n best if(m_best.size() >= m_numRules-5) m_expectation = ((RuleItem)m_best.first()).accuracy(); else m_expectation =0; } } } /** * Method that finds all large itemsets for class association rule mining for the given set of instances. * @param index the size of the large item sets * @throws Exception if an attribute is numeric */ private void findLargeCarItemSets(int index) throws Exception { FastVector kMinusOneSets, kSets = new FastVector(); Hashtable hashtable; int i = 0; // Find large itemsets if(index == 1){ kSets = CaRuleGeneration.singletons(m_instances); ItemSet.upDateCounters(kSets, m_instances); kSets = ItemSet.deleteItemSets(kSets, m_premiseCount,Integer.MAX_VALUE); if (kSets.size() == 0) return; m_Ls.addElement(kSets); } if(index >1){ if(m_Ls.size() > 0) kSets = (FastVector)m_Ls.lastElement(); m_Ls.removeAllElements(); i = index-2; kMinusOneSets = kSets; kSets = ItemSet.mergeAllItemSets(kMinusOneSets, i, m_instances.numInstances()); hashtable = ItemSet.getHashtable(kMinusOneSets, kMinusOneSets.size()); m_hashtables.addElement(hashtable); kSets = ItemSet.pruneItemSets(kSets, hashtable); ItemSet.upDateCounters(kSets, m_instances); kSets = ItemSet.deleteItemSets(kSets, m_premiseCount,Integer.MAX_VALUE); if(kSets.size() == 0) return; m_Ls.addElement(kSets); } } /** * Method that finds all class association rules. * * @throws Exception if an attribute is numeric */ private void findCaRulesQuickly() throws Exception { CaRuleGeneration currentLItemSet; // Build rules for (int j = 0; j < m_Ls.size(); j++) { FastVector currentItemSets = (FastVector)m_Ls.elementAt(j); Enumeration enumItemSets = currentItemSets.elements(); while (enumItemSets.hasMoreElements()) { currentLItemSet = new CaRuleGeneration((ItemSet)enumItemSets.nextElement()); m_best = currentLItemSet.generateRules(m_numRules-5, m_midPoints,m_priors,m_expectation, m_instances,m_best,m_count); m_count = currentLItemSet.count(); if(!m_bestChanged && currentLItemSet.change()) m_bestChanged = true; if(m_best.size() == m_numRules-5) m_expectation = ((RuleItem)m_best.first()).accuracy(); else m_expectation = 0; } } } /** * returns all the rules * * @return all the rules * @see #m_allTheRules */ public FastVector[] getAllTheRules() { return m_allTheRules; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.14 $"); } /** * Main method. * * @param args the commandline parameters */ public static void main(String[] args) { runAssociator(new PredictiveApriori(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/PriorEstimation.java000066400000000000000000000441521214146114000274410ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * PriorEstimation.java * Copyright (C) 2004 University of Waikato, Hamilton, New Zealand * */ package weka.associations; import weka.core.Instances; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import weka.core.SpecialFunctions; import weka.core.Utils; import java.io.Serializable; import java.util.Hashtable; import java.util.Random; /** * Class implementing the prior estimattion of the predictive apriori algorithm * for mining association rules. * * Reference: T. Scheffer (2001). Finding Association Rules That Trade Support * Optimally against Confidence. Proc of the 5th European Conf. * on Principles and Practice of Knowledge Discovery in Databases (PKDD'01), * pp. 424-435. Freiburg, Germany: Springer-Verlag.

* * @author Stefan Mutter (mutter@cs.waikato.ac.nz) * @version $Revision: 1.7 $ */ public class PriorEstimation implements Serializable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = 5570863216522496271L; /** The number of rnadom rules. */ protected int m_numRandRules; /** The number of intervals. */ protected int m_numIntervals; /** The random seed used for the random rule generation step. */ protected static final int SEED = 0; /** The maximum number of attributes for which a prior can be estimated. */ protected static final int MAX_N = 1024; /** The random number generator. */ protected Random m_randNum; /** The instances for which association rules are mined. */ protected Instances m_instances; /** Flag indicating whether standard association rules or class association rules are mined. */ protected boolean m_CARs; /** Hashtable to store the confidence values of randomly generated rules. */ protected Hashtable m_distribution; /** Hashtable containing the estimated prior probabilities. */ protected Hashtable m_priors; /** Sums up the confidences of all rules with a certain length. */ protected double m_sum; /** The mid points of the discrete intervals in which the interval [0,1] is divided. */ protected double[] m_midPoints; /** * Constructor * * @param instances the instances to be used for generating the associations * @param numRules the number of random rules used for generating the prior * @param numIntervals the number of intervals to discretise [0,1] * @param car flag indicating whether standard or class association rules are mined */ public PriorEstimation(Instances instances,int numRules,int numIntervals,boolean car) { m_instances = instances; m_CARs = car; m_numRandRules = numRules; m_numIntervals = numIntervals; m_randNum = m_instances.getRandomNumberGenerator(SEED); } /** * Calculates the prior distribution. * * @exception Exception if prior can't be estimated successfully */ public final void generateDistribution() throws Exception{ boolean jump; int i,maxLength = m_instances.numAttributes(), count =0,count1=0, ruleCounter; int [] itemArray; m_distribution = new Hashtable(maxLength*m_numIntervals); RuleItem current; ItemSet generate; if(m_instances.numAttributes() == 0) throw new Exception("Dataset has no attributes!"); if(m_instances.numAttributes() >= MAX_N) throw new Exception("Dataset has to many attributes for prior estimation!"); if(m_instances.numInstances() == 0) throw new Exception("Dataset has no instances!"); for (int h = 0; h < maxLength; h++) { if (m_instances.attribute(h).isNumeric()) throw new Exception("Can't handle numeric attributes!"); } if(m_numIntervals == 0 || m_numRandRules == 0) throw new Exception("Prior initialisation impossible"); //calculate mid points for the intervals midPoints(); //create random rules of length i and measure their support and if support >0 their confidence for(i = 1;i <= maxLength; i++){ m_sum = 0; int j = 0; count = 0; count1 = 0; while(j < m_numRandRules){ count++; jump =false; if(!m_CARs){ itemArray = randomRule(maxLength,i,m_randNum); current = splitItemSet(m_randNum.nextInt(i), itemArray); } else{ itemArray = randomCARule(maxLength,i,m_randNum); current = addCons(itemArray); } int [] ruleItem = new int[maxLength]; for(int k =0; k < itemArray.length;k++){ if(current.m_premise.m_items[k] != -1) ruleItem[k] = current.m_premise.m_items[k]; else if(current.m_consequence.m_items[k] != -1) ruleItem[k] = current.m_consequence.m_items[k]; else ruleItem[k] = -1; } ItemSet rule = new ItemSet(ruleItem); updateCounters(rule); ruleCounter = rule.m_counter; if(ruleCounter > 0) jump =true; updateCounters(current.m_premise); j++; if(jump){ buildDistribution((double)ruleCounter/(double)current.m_premise.m_counter, (double)i); } } //normalize if(m_sum > 0){ for(int w = 0; w < m_midPoints.length;w++){ String key = (String.valueOf(m_midPoints[w])).concat(String.valueOf((double)i)); Double oldValue = (Double)m_distribution.remove(key); if(oldValue == null){ m_distribution.put(key,new Double(1.0/m_numIntervals)); m_sum += 1.0/m_numIntervals; } else m_distribution.put(key,oldValue); } for(int w = 0; w < m_midPoints.length;w++){ double conf =0; String key = (String.valueOf(m_midPoints[w])).concat(String.valueOf((double)i)); Double oldValue = (Double)m_distribution.remove(key); if(oldValue != null){ conf = oldValue.doubleValue() / m_sum; m_distribution.put(key,new Double(conf)); } } } else{ for(int w = 0; w < m_midPoints.length;w++){ String key = (String.valueOf(m_midPoints[w])).concat(String.valueOf((double)i)); m_distribution.put(key,new Double(1.0/m_numIntervals)); } } } } /** * Constructs an item set of certain length randomly. * This method is used for standard association rule mining. * @param maxLength the number of attributes of the instances * @param actualLength the number of attributes that should be present in the item set * @param randNum the random number generator * @return a randomly constructed item set in form of an int array */ public final int[] randomRule(int maxLength, int actualLength, Random randNum){ int[] itemArray = new int[maxLength]; for(int k =0;k < itemArray.length;k++) itemArray[k] = -1; int help =actualLength; if(help == maxLength){ help = 0; for(int h = 0; h < itemArray.length; h++){ itemArray[h] = m_randNum.nextInt((m_instances.attribute(h)).numValues()); } } while(help > 0){ int mark = randNum.nextInt(maxLength); if(itemArray[mark] == -1){ help--; itemArray[mark] = m_randNum.nextInt((m_instances.attribute(mark)).numValues()); } } return itemArray; } /** * Constructs an item set of certain length randomly. * This method is used for class association rule mining. * @param maxLength the number of attributes of the instances * @param actualLength the number of attributes that should be present in the item set * @param randNum the random number generator * @return a randomly constructed item set in form of an int array */ public final int[] randomCARule(int maxLength, int actualLength, Random randNum){ int[] itemArray = new int[maxLength]; for(int k =0;k < itemArray.length;k++) itemArray[k] = -1; if(actualLength == 1) return itemArray; int help =actualLength-1; if(help == maxLength-1){ help = 0; for(int h = 0; h < itemArray.length; h++){ if(h != m_instances.classIndex()){ itemArray[h] = m_randNum.nextInt((m_instances.attribute(h)).numValues()); } } } while(help > 0){ int mark = randNum.nextInt(maxLength); if(itemArray[mark] == -1 && mark != m_instances.classIndex()){ help--; itemArray[mark] = m_randNum.nextInt((m_instances.attribute(mark)).numValues()); } } return itemArray; } /** * updates the distribution of the confidence values. * For every confidence value the interval to which it belongs is searched * and the confidence is added to the confidence already found in this * interval. * @param conf the confidence of the randomly created rule * @param length the legnth of the randomly created rule */ public final void buildDistribution(double conf, double length){ double mPoint = findIntervall(conf); String key = (String.valueOf(mPoint)).concat(String.valueOf(length)); m_sum += conf; Double oldValue = (Double)m_distribution.remove(key); if(oldValue != null) conf = conf + oldValue.doubleValue(); m_distribution.put(key,new Double(conf)); } /** * searches the mid point of the interval a given confidence value falls into * @param conf the confidence of a rule * @return the mid point of the interval the confidence belongs to */ public final double findIntervall(double conf){ if(conf == 1.0) return m_midPoints[m_midPoints.length-1]; int end = m_midPoints.length-1; int start = 0; while (Math.abs(end-start) > 1) { int mid = (start + end) / 2; if (conf > m_midPoints[mid]) start = mid+1; if (conf < m_midPoints[mid]) end = mid-1; if(conf == m_midPoints[mid]) return m_midPoints[mid]; } if(Math.abs(conf-m_midPoints[start]) <= Math.abs(conf-m_midPoints[end])) return m_midPoints[start]; else return m_midPoints[end]; } /** * calculates the numerator and the denominator of the prior equation * @param weighted indicates whether the numerator or the denominator is calculated * @param mPoint the mid Point of an interval * @return the numerator or denominator of the prior equation */ public final double calculatePriorSum(boolean weighted, double mPoint){ double distr, sum =0, max = logbinomialCoefficient(m_instances.numAttributes(),(int)m_instances.numAttributes()/2); for(int i = 1; i <= m_instances.numAttributes(); i++){ if(weighted){ String key = (String.valueOf(mPoint)).concat(String.valueOf((double)i)); Double hashValue = (Double)m_distribution.get(key); if(hashValue !=null) distr = hashValue.doubleValue(); else distr = 0; //distr = 1.0/m_numIntervals; if(distr != 0){ double addend = Utils.log2(distr) - max + Utils.log2((Math.pow(2,i)-1)) + logbinomialCoefficient(m_instances.numAttributes(),i); sum = sum + Math.pow(2,addend); } } else{ double addend = Utils.log2((Math.pow(2,i)-1)) - max + logbinomialCoefficient(m_instances.numAttributes(),i); sum = sum + Math.pow(2,addend); } } return sum; } /** * Method that calculates the base 2 logarithm of a binomial coefficient * @param upperIndex upper Inedx of the binomial coefficient * @param lowerIndex lower index of the binomial coefficient * @return the base 2 logarithm of the binomial coefficient */ public static final double logbinomialCoefficient(int upperIndex, int lowerIndex){ double result =1.0; if(upperIndex == lowerIndex || lowerIndex == 0) return result; result = SpecialFunctions.log2Binomial((double)upperIndex, (double)lowerIndex); return result; } /** * Method to estimate the prior probabilities * @throws Exception throws exception if the prior cannot be calculated * @return a hashtable containing the prior probabilities */ public final Hashtable estimatePrior() throws Exception{ double distr, prior, denominator, mPoint; Hashtable m_priors = new Hashtable(m_numIntervals); denominator = calculatePriorSum(false,1.0); generateDistribution(); for(int i = 0; i < m_numIntervals; i++){ mPoint = m_midPoints[i]; prior = calculatePriorSum(true,mPoint) / denominator; m_priors.put(new Double(mPoint), new Double(prior)); } return m_priors; } /** * split the interval [0,1] into a predefined number of intervals and calculates their mid points */ public final void midPoints(){ m_midPoints = new double[m_numIntervals]; for(int i = 0; i < m_numIntervals; i++) m_midPoints[i] = midPoint(1.0/m_numIntervals, i); } /** * calculates the mid point of an interval * @param size the size of each interval * @param number the number of the interval. * The intervals are numbered from 0 to m_numIntervals. * @return the mid point of the interval */ public double midPoint(double size, int number){ return (size * (double)number) + (size / 2.0); } /** * returns an ordered array of all mid points * @return an ordered array of doubles conatining all midpoints */ public final double[] getMidPoints(){ return m_midPoints; } /** * splits an item set into premise and consequence and constructs therefore * an association rule. The length of the premise is given. The attributes * for premise and consequence are chosen randomly. The result is a RuleItem. * @param premiseLength the length of the premise * @param itemArray a (randomly generated) item set * @return a randomly generated association rule stored in a RuleItem */ public final RuleItem splitItemSet (int premiseLength, int[] itemArray){ int[] cons = new int[m_instances.numAttributes()]; System.arraycopy(itemArray, 0, cons, 0, itemArray.length); int help = premiseLength; while(help > 0){ int mark = m_randNum.nextInt(itemArray.length); if(cons[mark] != -1){ help--; cons[mark] =-1; } } if(premiseLength == 0) for(int i =0; i < itemArray.length;i++) itemArray[i] = -1; else for(int i =0; i < itemArray.length;i++) if(cons[i] != -1) itemArray[i] = -1; ItemSet premise = new ItemSet(itemArray); ItemSet consequence = new ItemSet(cons); RuleItem current = new RuleItem(); current.m_premise = premise; current.m_consequence = consequence; return current; } /** * generates a class association rule out of a given premise. * It randomly chooses a class label as consequence. * @param itemArray the (randomly constructed) premise of the class association rule * @return a class association rule stored in a RuleItem */ public final RuleItem addCons (int[] itemArray){ ItemSet premise = new ItemSet(itemArray); int[] cons = new int[itemArray.length]; for(int i =0;i < itemArray.length;i++) cons[i] = -1; cons[m_instances.classIndex()] = m_randNum.nextInt((m_instances.attribute(m_instances.classIndex())).numValues()); ItemSet consequence = new ItemSet(cons); RuleItem current = new RuleItem(); current.m_premise = premise; current.m_consequence = consequence; return current; } /** * updates the support count of an item set * @param itemSet the item set */ public final void updateCounters(ItemSet itemSet){ for (int i = 0; i < m_instances.numInstances(); i++) itemSet.upDateCounter(m_instances.instance(i)); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.7 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/RuleGeneration.java000066400000000000000000000314601214146114000272320ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * RuleGeneration.java * Copyright (C) 2004 University of Waikato, Hamilton, New Zealand * */ package weka.associations; import weka.core.FastVector; import weka.core.Instances; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import weka.core.Statistics; import weka.core.Utils; import java.io.Serializable; import java.util.Hashtable; import java.util.TreeSet; /** * Class implementing the rule generation procedure of the predictive apriori algorithm. * * Reference: T. Scheffer (2001). Finding Association Rules That Trade Support * Optimally against Confidence. Proc of the 5th European Conf. * on Principles and Practice of Knowledge Discovery in Databases (PKDD'01), * pp. 424-435. Freiburg, Germany: Springer-Verlag.

* * The implementation follows the paper expect for adding a rule to the output of the * n best rules. A rule is added if: * the expected predictive accuracy of this rule is among the n best and it is * not subsumed by a rule with at least the same expected predictive accuracy * (out of an unpublished manuscript from T. Scheffer). * * @author Stefan Mutter (mutter@cs.waikato.ac.nz) * @version $Revision: 1.4 $ */ public class RuleGeneration implements Serializable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = -8927041669872491432L; /** The items stored as an array of of integer. */ protected int[] m_items; /** Counter for how many transactions contain this item set. */ protected int m_counter; /** The total number of transactions */ protected int m_totalTransactions; /** Flag indicating whether the list fo the best rules has changed. */ protected boolean m_change = false; /** The minimum expected predictive accuracy that is needed to be a candidate for the list of the best rules. */ protected double m_expectation; /** Threshold. If the support of the premise is higher the binomial distrubution is approximated by a normal one. */ protected static final int MAX_N = 300; /** The minimum support a rule needs to be a candidate for the list of the best rules. */ protected int m_minRuleCount; /** Sorted array of the mied points of the intervals used for prior estimation. */ protected double[] m_midPoints; /** Hashtable conatining the estimated prior probabilities. */ protected Hashtable m_priors; /** The list of the actual n best rules. */ protected TreeSet m_best; /** Integer indicating the generation time of a rule. */ protected int m_count; /** The instances. */ protected Instances m_instances; /** * Constructor * @param itemSet item set for that rules should be generated. * The item set will form the premise of the rules. */ public RuleGeneration(ItemSet itemSet){ m_totalTransactions = itemSet.m_totalTransactions; m_counter = itemSet.m_counter; m_items = itemSet.m_items; } /** * calculates the probability using a binomial distribution. * If the support of the premise is too large this distribution * is approximated by a normal distribution. * @param accuracy the accuracy value * @param ruleCount the support of the whole rule * @param premiseCount the support of the premise * @return the probability value */ public static final double binomialDistribution(double accuracy, double ruleCount, double premiseCount){ double mu, sigma; if(premiseCount < MAX_N) return Math.pow(2,(Utils.log2(Math.pow(accuracy,ruleCount))+Utils.log2(Math.pow((1.0-accuracy),(premiseCount-ruleCount)))+PriorEstimation.logbinomialCoefficient((int)premiseCount,(int)ruleCount))); else{ mu = premiseCount * accuracy; sigma = Math.sqrt((premiseCount * (1.0 - accuracy))*accuracy); return Statistics.normalProbability(((ruleCount+0.5)-mu)/(sigma*Math.sqrt(2))); } } /** * calculates the expected predctive accuracy of a rule * @param ruleCount the support of the rule * @param premiseCount the premise support of the rule * @param midPoints array with all mid points * @param priors hashtable containing the prior probabilities * @return the expected predictive accuracy */ public static final double expectation(double ruleCount, int premiseCount,double[] midPoints, Hashtable priors){ double numerator = 0, denominator = 0; for(int i = 0;i < midPoints.length; i++){ Double actualPrior = (Double)priors.get(new Double(midPoints[i])); if(actualPrior != null){ if(actualPrior.doubleValue() != 0){ double addend = actualPrior.doubleValue() * binomialDistribution(midPoints[i], ruleCount, (double)premiseCount); denominator += addend; numerator += addend*midPoints[i]; } } } if(denominator <= 0 || Double.isNaN(denominator)) System.out.println("RuleItem denominator: "+denominator); if(numerator <= 0 || Double.isNaN(numerator)) System.out.println("RuleItem numerator: "+numerator); return numerator/denominator; } /** * Generates all rules for an item set. The item set is the premise. * @param numRules the number of association rules the use wants to mine. * This number equals the size n of the list of the * best rules. * @param midPoints the mid points of the intervals * @param priors Hashtable that contains the prior probabilities * @param expectation the minimum value of the expected predictive accuracy * that is needed to get into the list of the best rules * @param instances the instances for which association rules are generated * @param best the list of the n best rules. * The list is implemented as a TreeSet * @param genTime the maximum time of generation * @return all the rules with minimum confidence for the given item set */ public TreeSet generateRules(int numRules, double[] midPoints, Hashtable priors, double expectation, Instances instances,TreeSet best,int genTime) { boolean redundant = false; FastVector consequences = new FastVector(), consequencesMinusOne = new FastVector(); ItemSet premise; int s = 0; RuleItem current = null, old; Hashtable hashtable; m_change = false; m_midPoints = midPoints; m_priors = priors; m_best = best; m_expectation = expectation; m_count = genTime; m_instances = instances; //create rule body premise =null; premise = new ItemSet(m_totalTransactions); premise.m_items = new int[m_items.length]; System.arraycopy(m_items, 0, premise.m_items, 0, m_items.length); premise.m_counter = m_counter; do{ m_minRuleCount = 1; while(expectation((double)m_minRuleCount,premise.m_counter,m_midPoints,m_priors) <= m_expectation){ m_minRuleCount++; if(m_minRuleCount > premise.m_counter) return m_best; } redundant = false; for(int i = 0; i < instances.numAttributes();i++){ if(i == 0){ for(int j = 0; j < m_items.length;j++) if(m_items[j] == -1) consequences = singleConsequence(instances, j,consequences); if(premise == null || consequences.size() == 0) return m_best; } FastVector allRuleItems = new FastVector(); int index = 0; do { int h = 0; while(h < consequences.size()){ RuleItem dummie = new RuleItem(); current = dummie.generateRuleItem(premise,(ItemSet)consequences.elementAt(h),instances,m_count,m_minRuleCount,m_midPoints,m_priors); if(current != null){ allRuleItems.addElement(current); h++; } else consequences.removeElementAt(h); } if(index == i) break; consequencesMinusOne = consequences; consequences = ItemSet.mergeAllItemSets(consequencesMinusOne, index, instances.numInstances()); hashtable = ItemSet.getHashtable(consequencesMinusOne, consequencesMinusOne.size()); consequences = ItemSet.pruneItemSets(consequences, hashtable); index++; } while (consequences.size() > 0); for(int h = 0;h < allRuleItems.size();h++){ current = (RuleItem)allRuleItems.elementAt(h); m_count++; if(m_best.size() < numRules){ m_change =true; redundant = removeRedundant(current); } else{ if(current.accuracy() > m_expectation){ m_expectation = ((RuleItem)(m_best.first())).accuracy(); boolean remove = m_best.remove(m_best.first()); m_change = true; redundant = removeRedundant(current); m_expectation = ((RuleItem)(m_best.first())).accuracy(); while(expectation((double)m_minRuleCount, (current.premise()).m_counter,m_midPoints,m_priors) < m_expectation){ m_minRuleCount++; if(m_minRuleCount > (current.premise()).m_counter) break; } } } } } }while(redundant); return m_best; } /** * Methods that decides whether or not rule a subsumes rule b. * The defintion of subsumption is: * Rule a subsumes rule b, if a subsumes b * AND * a has got least the same expected predictive accuracy as b. * @param a an association rule stored as a RuleItem * @param b an association rule stored as a RuleItem * @return true if rule a subsumes rule b or false otherwise. */ public static boolean aSubsumesB(RuleItem a, RuleItem b){ if(a.m_accuracy < b.m_accuracy) return false; for(int k = 0; k < a.premise().m_items.length;k++){ if(a.premise().m_items[k] != b.premise().m_items[k]){ if((a.premise().m_items[k] != -1 && b.premise().m_items[k] != -1) || b.premise().m_items[k] == -1) return false; } if(a.consequence().m_items[k] != b.consequence().m_items[k]){ if((a.consequence().m_items[k] != -1 && b.consequence().m_items[k] != -1) || a.consequence().m_items[k] == -1) return false; } } return true; } /** * generates a consequence of length 1 for an association rule. * @param instances the instances under consideration * @param attNum an item that does not occur in the premise * @param consequences FastVector that possibly already contains other consequences of length 1 * @return FastVector with consequences of length 1 */ public static FastVector singleConsequence(Instances instances, int attNum, FastVector consequences){ ItemSet consequence; for (int i = 0; i < instances.numAttributes(); i++) { if( i == attNum){ for (int j = 0; j < instances.attribute(i).numValues(); j++) { consequence = new ItemSet(instances.numInstances()); consequence.m_items = new int[instances.numAttributes()]; for (int k = 0; k < instances.numAttributes(); k++) consequence.m_items[k] = -1; consequence.m_items[i] = j; consequences.addElement(consequence); } } } return consequences; } /** * Method that removes redundant rules out of the list of the best rules. * A rule is in that list if: * the expected predictive accuracy of this rule is among the best and it is * not subsumed by a rule with at least the same expected predictive accuracy * @param toInsert the rule that should be inserted into the list * @return true if the method has changed the list, false otherwise */ public boolean removeRedundant(RuleItem toInsert){ boolean redundant = false, fSubsumesT = false, tSubsumesF = false; RuleItem first; int subsumes = 0; Object [] best = m_best.toArray(); for(int i=0; i < best.length; i++){ first = (RuleItem)best[i]; fSubsumesT = aSubsumesB(first,toInsert); tSubsumesF = aSubsumesB(toInsert, first); if(fSubsumesT){ subsumes = 1; break; } else{ if(tSubsumesF){ boolean remove = m_best.remove(first); subsumes = 2; redundant =true; } } } if(subsumes == 0 || subsumes == 2) m_best.add(toInsert); return redundant; } /** * Gets the actual maximum value of the generation time * @return the actual maximum value of the generation time */ public int count(){ return m_count; } /** * Gets if the list fo the best rules has been changed * @return whether or not the list fo the best rules has been changed */ public boolean change(){ return m_change; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.4 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/RuleItem.java000066400000000000000000000143261214146114000260370ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * RuleItem.java * Copyright (C) 2004 University of Waikato, Hamilton, New Zealand * */ package weka.associations; import weka.core.Instances; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import java.io.Serializable; import java.util.Hashtable; /** * Class for storing an (class) association rule. * The premise and the consequence are stored each as separate item sets. * For every rule their expected predictive accuracy and the time of generation is stored. * These two measures allow to introduce a sort order for rules. * * @author Stefan Mutter * @version $Revision: 1.5 $ */ public class RuleItem implements Comparable, Serializable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = -3761299128347476534L; /** The premise of a rule. */ protected ItemSet m_premise; /** The consequence of a rule. */ protected ItemSet m_consequence; /** The expected predictive accuracy of a rule. */ protected double m_accuracy; /** The generation time of a rule. */ protected int m_genTime; /** * Constructor for an empty RuleItem */ public RuleItem(){ } /** * Constructor that generates a RuleItem out of a given one * @param toCopy RuleItem to copy */ public RuleItem(RuleItem toCopy){ m_premise = toCopy.m_premise; m_consequence = toCopy.m_consequence; m_accuracy = toCopy.m_accuracy; m_genTime = toCopy.m_genTime; } /** * Constructor * @param premise the premise of the future RuleItem * @param consequence the consequence of the future RuleItem * @param genTime the time of generation of the future RuleItem * @param ruleSupport support of the rule * @param m_midPoints the mid poitns of the intervals * @param m_priors Hashtable containing the estimated prior probablilities */ public RuleItem(ItemSet premise, ItemSet consequence, int genTime,int ruleSupport,double [] m_midPoints, Hashtable m_priors){ m_premise = premise; m_consequence = consequence; m_accuracy = RuleGeneration.expectation((double)ruleSupport,m_premise.m_counter,m_midPoints,m_priors); //overflow, underflow if(Double.isNaN(m_accuracy) || m_accuracy < 0){ m_accuracy = Double.MIN_VALUE; } m_consequence.m_counter = ruleSupport; m_genTime = genTime; } /** * Constructs a new RuleItem if the support of the given rule is above the support threshold. * @param premise the premise * @param consequence the consequence * @param instances the instances * @param genTime the time of generation of the current premise and consequence * @param minRuleCount the support threshold * @param m_midPoints the mid points of the intervals * @param m_priors the estimated priori probabilities (in a hashtable) * @return a RuleItem if its support is above the threshold, null otherwise */ public RuleItem generateRuleItem(ItemSet premise, ItemSet consequence, Instances instances,int genTime, int minRuleCount,double[] m_midPoints, Hashtable m_priors){ ItemSet rule = new ItemSet(instances.numInstances()); rule.m_items = new int[(consequence.m_items).length]; System.arraycopy(premise.m_items, 0, rule.m_items, 0, (premise.m_items).length); for(int k = 0;k < consequence.m_items.length; k++){ if(consequence.m_items[k] != -1) rule.m_items[k] = consequence.m_items[k]; } for (int i = 0; i < instances.numInstances(); i++) rule.upDateCounter(instances.instance(i)); int ruleSupport = rule.support(); if(ruleSupport > minRuleCount){ RuleItem newRule = new RuleItem(premise,consequence,genTime,ruleSupport,m_midPoints,m_priors); return newRule; } return null; } //Note: this class has a natural ordering that is inconsistent with equals /** * compares two RuleItems and allows an ordering concerning * expected predictive accuracy and time of generation * Note: this class has a natural ordering that is inconsistent with equals * @param o RuleItem to compare * @return integer indicating the sort oder of the two RuleItems */ public int compareTo(Object o) { if(this.m_accuracy == ((RuleItem)o).m_accuracy){ if((this.m_genTime == ((RuleItem)o).m_genTime)) return 0; if(this.m_genTime > ((RuleItem)o).m_genTime) return -1; if(this.m_genTime < ((RuleItem)o).m_genTime) return 1; } if(this.m_accuracy < ((RuleItem)o).m_accuracy) return -1; return 1; } /** * returns whether two RuleItems are equal * @param o RuleItem to compare * @return true if the rules are equal, false otherwise */ public boolean equals(Object o){ if(o == null) return false; if(m_premise.equals(((RuleItem)o).m_premise) && m_consequence.equals(((RuleItem)o).m_consequence)) return true; return false; } /** * Gets the expected predictive accuracy of a rule * @return the expected predictive accuracy of a rule stored as a RuleItem */ public double accuracy(){ return m_accuracy; } /** * Gets the premise of a rule * @return the premise of a rule stored as a RuleItem */ public ItemSet premise(){ return m_premise; } /** * Gets the consequence of a rule * @return the consequence of a rule stored as a RuleItem */ public ItemSet consequence(){ return m_consequence; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.5 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/SingleAssociatorEnhancer.java000066400000000000000000000132011214146114000312150ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * SingleAssociatorEnhancer.java * Copyright (C) 2007 University of Waikato, Hamilton, New Zealand * */ package weka.associations; import weka.core.Capabilities; import weka.core.Option; import weka.core.OptionHandler; import weka.core.Utils; import weka.core.Capabilities.Capability; import java.util.Enumeration; import java.util.Vector; /** * Abstract utility class for handling settings common to meta * associators that use a single base associator. * * @author Eibe Frank (eibe@cs.waikato.ac.nz) * @author FracPete (fracpete at waikato dot ac dot nz) * @version $Revision: 1.2 $ */ public abstract class SingleAssociatorEnhancer extends AbstractAssociator implements OptionHandler { /** for serialization */ private static final long serialVersionUID = -3665885256363525164L; /** The base associator to use */ protected Associator m_Associator = new Apriori(); /** * String describing default Associator. * * @return default classname */ protected String defaultAssociatorString() { return Apriori.class.getName(); } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector result = new Vector(); result.addElement(new Option( "\tFull name of base associator.\n" + "\t(default: " + defaultAssociatorString() +")", "W", 1, "-W")); if (m_Associator instanceof OptionHandler) { result.addElement(new Option( "", "", 0, "\nOptions specific to associator " + m_Associator.getClass().getName() + ":")); Enumeration enm = ((OptionHandler) m_Associator).listOptions(); while (enm.hasMoreElements()) result.addElement(enm.nextElement()); } return result.elements(); } /** * Parses a given list of options. Valid options are:

* * -W classname
* Specify the full class name of the base associator.

* * Options after -- are passed to the designated associator.

* * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { String tmpStr; tmpStr = Utils.getOption('W', options); if (tmpStr.length() > 0) { // This is just to set the associator in case the option // parsing fails. setAssociator(AbstractAssociator.forName(tmpStr, null)); setAssociator(AbstractAssociator.forName(tmpStr, Utils.partitionOptions(options))); } else { // This is just to set the associator in case the option // parsing fails. setAssociator(AbstractAssociator.forName(defaultAssociatorString(), null)); setAssociator(AbstractAssociator.forName(defaultAssociatorString(), Utils.partitionOptions(options))); } } /** * Gets the current settings of the associator. * * @return an array of strings suitable for passing to setOptions */ public String[] getOptions() { int i; Vector result; String[] options; result = new Vector(); result.add("-W"); result.add(getAssociator().getClass().getName()); if (getAssociator() instanceof OptionHandler) { options = ((OptionHandler) getAssociator()).getOptions(); result.add("--"); for (i = 0; i < options.length; i++) result.add(options[i]); } return result.toArray(new String[result.size()]); } /** * Returns the tip text for this property * * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String associatorTipText() { return "The base associator to be used."; } /** * Set the base associator. * * @param value the associator to use. */ public void setAssociator(Associator value) { m_Associator = value; } /** * Get the associator used as the base associator. * * @return the currently used associator */ public Associator getAssociator() { return m_Associator; } /** * Gets the associator specification string, which contains the class name of * the associator and any options to the associator * * @return the associator string */ protected String getAssociatorSpec() { Associator c = getAssociator(); return c.getClass().getName() + " " + Utils.joinOptions(((OptionHandler)c).getOptions()); } /** * Returns default capabilities of the base associator. * * @return the capabilities of the base associator */ public Capabilities getCapabilities() { Capabilities result; if (getAssociator() != null) result = getAssociator().getCapabilities(); else result = new Capabilities(this); // set dependencies for (Capability cap: Capability.values()) result.enableDependency(cap); result.setOwner(this); return result; } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/Tertius.java000066400000000000000000001525211214146114000257500ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * Tertius.java * Copyright (C) 2003 Peter A. Flach, Nicolas Lachiche * * Thanks to Amelie Deltour for porting the original C code to Java * and integrating it into Weka. */ package weka.associations; import weka.associations.tertius.AttributeValueLiteral; import weka.associations.tertius.IndividualInstances; import weka.associations.tertius.IndividualLiteral; import weka.associations.tertius.Literal; import weka.associations.tertius.Predicate; import weka.associations.tertius.Rule; import weka.associations.tertius.SimpleLinkedList; import weka.core.Attribute; import weka.core.Capabilities; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.SelectedTag; import weka.core.Tag; import weka.core.TechnicalInformation; import weka.core.TechnicalInformationHandler; import weka.core.Utils; import weka.core.Capabilities.Capability; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; import java.awt.BorderLayout; import java.awt.Button; import java.awt.Font; import java.awt.Frame; import java.awt.Label; import java.awt.TextField; import java.awt.event.ActionEvent; import java.awt.event.ActionListener; import java.io.BufferedReader; import java.io.File; import java.io.FileReader; import java.io.Reader; import java.util.ArrayList; import java.util.Date; import java.util.Enumeration; import java.util.Vector; /** * Finds rules according to confirmation measure (Tertius-type algorithm).
*
* For more information see:
*
* P. A. Flach, N. Lachiche (1999). Confirmation-Guided Discovery of first-order rules with Tertius. Machine Learning. 42:61-95. *

* * BibTeX: * 

 * @article{Flach1999,
 *    author = {P. A. Flach and N. Lachiche},
 *    journal = {Machine Learning},
 *    pages = {61-95},
 *    title = {Confirmation-Guided Discovery of first-order rules with Tertius},
 *    volume = {42},
 *    year = {1999}
 * }
 *
*

* * Valid options are:

* * 

 -K <number of values in result>
 *  Set maximum number of confirmation  values in the result. (default: 10)
* * 
 -F <frequency threshold>
 *  Set frequency threshold for pruning. (default: 0)
* * 
 -C <confirmation threshold>
 *  Set confirmation threshold. (default: 0)
* * 
 -N <noise threshold>
 *  Set noise threshold : maximum frequency of counter-examples.
 *  0 gives only satisfied rules. (default: 1)
* * 
 -R
 *  Allow attributes to be repeated in a same rule.
* * 
 -L <number of literals>
 *  Set maximum number of literals in a rule. (default: 4)
* * 
 -G <0=no negation | 1=body | 2=head | 3=body and head>
 *  Set the negations in the rule. (default: 0)
* * 
 -S
 *  Consider only classification rules.
* * 
 -c <class index>
 *  Set index of class attribute. (default: last).
* * 
 -H
 *  Consider only horn clauses.
* * 
 -E
 *  Keep equivalent rules.
* * 
 -M
 *  Keep same clauses.
* * 
 -T
 *  Keep subsumed rules.
* * 
 -I <0=always match | 1=never match | 2=significant>
 *  Set the way to handle missing values. (default: 0)
* * 
 -O
 *  Use ROC analysis.
* * 
 -p <name of file>
 *  Set the file containing the parts of the individual for individual-based learning.
* * 
 -P <0=no output | 1=on stdout | 2=in separate window>
 *  Set output of current values. (default: 0)
* * * @author Amelie Deltour * @version $Revision: 1.10 $ */ public class Tertius extends AbstractAssociator implements OptionHandler, Runnable, TechnicalInformationHandler { /** for serialization */ static final long serialVersionUID = 5556726848380738179L; /** The results. */ private SimpleLinkedList m_results; /** Number of hypotheses considered. */ private int m_hypotheses; /** Number of hypotheses explored. */ private int m_explored; /** Time needed for the search. */ private Date m_time; /** Field to output the current values. */ private TextField m_valuesText; /** Instances used for the search. */ private Instances m_instances; /** Predicates used in the rules. */ private ArrayList m_predicates; /** Status of the search. */ private int m_status; /** Status of the search: normal */ private static final int NORMAL = 0; /** Status of the search: memory problem */ private static final int MEMORY = 1; /** Status of the search: user interruption */ private static final int STOP = 2; /* Pruning options. */ /** Number of best confirmation values to search. */ private int m_best; /** Frequency threshold for the body and the negation of the head. */ private double m_frequencyThreshold; /** Confirmation threshold for the rules. */ private double m_confirmationThreshold; /** Maximal number of counter-instances. */ private double m_noiseThreshold; /* Search space & language bias options. */ /** Repeat attributes ? */ private boolean m_repeat; /** Number of literals in a rule. */ private int m_numLiterals; /** Type of negation: none */ private static final int NONE = 0; /** Type of negation: body */ private static final int BODY = 1; /** Type of negation: head */ private static final int HEAD = 2; /** Type of negation: all */ private static final int ALL = 3; /** Types of negation. */ private static final Tag [] TAGS_NEGATION = { new Tag(NONE, "None"), new Tag(BODY, "Body"), new Tag(HEAD, "Head"), new Tag(ALL, "Both") }; /** Type of negation used in the rules. */ private int m_negation; /** Classification bias. */ private boolean m_classification; /** Index of class attribute. */ private int m_classIndex; /** Horn clauses bias. */ private boolean m_horn; /* Subsumption tests options. */ /** Perform test on equivalent rules ? */ private boolean m_equivalent; /** Perform test on same clauses ? */ private boolean m_sameClause; /** Perform subsumption test ? */ private boolean m_subsumption; /** Way of handling missing values: min counterinstances */ public static final int EXPLICIT = 0; /** Way of handling missing values: max counterinstances */ public static final int IMPLICIT = 1; /** Way of handling missing values: missing as a particular value */ public static final int SIGNIFICANT = 2; /** Ways of handling missing values. */ private static final Tag [] TAGS_MISSING = { new Tag(EXPLICIT, "Matches all"), new Tag(IMPLICIT, "Matches none"), new Tag(SIGNIFICANT, "Significant") }; /** Way of handling missing values in the search. */ private int m_missing; /** Perform ROC analysis ? */ private boolean m_roc; /** Name of the file containing the parts for individual-based learning. */ private String m_partsString; /** Part instances for individual-based learning. */ private Instances m_parts; /** Type of values output: No */ private static final int NO = 0; /** Type of values output: stdout */ private static final int OUT = 1; /** Type of values output: Window */ private static final int WINDOW = 2; /** Types of values output. */ private static final Tag [] TAGS_VALUES = { new Tag(NO, "No"), new Tag(OUT, "stdout"), new Tag(WINDOW, "Window") }; /** Type of values output. */ private int m_printValues; /** * Constructor that sets the options to the default values. */ public Tertius() { resetOptions(); } /** * Returns a string describing this associator. * * @return A description of the evaluator suitable for * displaying in the explorer/experimenter gui. */ public String globalInfo() { return "Finds rules according to confirmation measure (Tertius-type " + "algorithm).\n\n" + "For more information see:\n\n" + getTechnicalInformation().toString(); } /** * Returns an instance of a TechnicalInformation object, containing * detailed information about the technical background of this class, * e.g., paper reference or book this class is based on. * * @return the technical information about this class */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.ARTICLE); result.setValue(Field.AUTHOR, "P. A. Flach and N. Lachiche"); result.setValue(Field.YEAR, "1999"); result.setValue(Field.TITLE, "Confirmation-Guided Discovery of first-order rules with Tertius"); result.setValue(Field.JOURNAL, "Machine Learning"); result.setValue(Field.VOLUME, "42"); result.setValue(Field.PAGES, "61-95"); return result; } /** * Resets the options to the default values. */ public void resetOptions() { /* Pruning options. */ m_best = 10; m_frequencyThreshold = 0; m_confirmationThreshold = 0; m_noiseThreshold = 1; /* Search space & language bias options. */ m_repeat = false; m_numLiterals = 4; m_negation = NONE; m_classification = false; m_classIndex = 0; m_horn = false; /* Subsumption tests options. */ m_equivalent = true; m_sameClause = true; m_subsumption = true; /* Missing values. */ m_missing = EXPLICIT; /* ROC analysis. */ m_roc = false; /* Individual-based learning. */ m_partsString = ""; m_parts = null; /* Values output. */ m_printValues = NO; } /** * Returns an enumeration describing the available options. * * @return An enumeration of all the available options. */ public Enumeration listOptions() { Vector newVector = new Vector(17); /* Pruning options. */ newVector.addElement(new Option("\tSet maximum number of confirmation " + "values in the result. (default: 10)", "K", 1, "-K ")); newVector.addElement(new Option("\tSet frequency threshold for pruning. " + "(default: 0)", "F", 1, "-F ")); newVector.addElement(new Option("\tSet confirmation threshold. " + "(default: 0)", "C", 1, "-C ")); newVector.addElement(new Option("\tSet noise threshold : maximum frequency " + "of counter-examples.\n\t0 gives only " + "satisfied rules. (default: 1)", "N", 1, "-N ")); /* Search space & language bias options. */ newVector.addElement(new Option("\tAllow attributes to be repeated in a " + "same rule.", "R", 0, "-R")); newVector.addElement(new Option("\tSet maximum number of literals in a " + "rule. (default: 4)", "L", 1, "-L ")); newVector.addElement(new Option("\tSet the negations in the rule. " + "(default: 0)", "G", 1, "-G <0=no negation | " + "1=body | " + "2=head | " + "3=body and head>")); newVector.addElement(new Option("\tConsider only classification rules.", "S", 0, "-S")); newVector.addElement(new Option("\tSet index of class attribute. " + "(default: last).", "c", 1, "-c ")); newVector.addElement(new Option("\tConsider only horn clauses.", "H", 0, "-H")); /* Subsumption tests options. */ newVector.addElement(new Option("\tKeep equivalent rules.", "E", 0, "-E")); newVector.addElement(new Option("\tKeep same clauses.", "M", 0, "-M")); newVector.addElement(new Option("\tKeep subsumed rules.", "T", 0, "-T")); /* Missing values options. */ newVector.addElement(new Option("\tSet the way to handle missing values. " + "(default: 0)", "I", 1, "-I <0=always match | " + "1=never match | " + "2=significant>")); /* ROC analysis. */ newVector.addElement(new Option("\tUse ROC analysis. ", "O", 0, "-O")); /* Individual-based learning. */ newVector.addElement(new Option("\tSet the file containing the parts of " + "the individual for individual-based " + "learning.", "p", 1, "-p ")); /* Values output. */ newVector.addElement(new Option("\tSet output of current values. " + "(default: 0)", "P", 1, "-P <0=no output | " + "1=on stdout | " + "2=in separate window>")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -K <number of values in result>
   *  Set maximum number of confirmation  values in the result. (default: 10)
* * 
 -F <frequency threshold>
   *  Set frequency threshold for pruning. (default: 0)
* * 
 -C <confirmation threshold>
   *  Set confirmation threshold. (default: 0)
* * 
 -N <noise threshold>
   *  Set noise threshold : maximum frequency of counter-examples.
   *  0 gives only satisfied rules. (default: 1)
* * 
 -R
   *  Allow attributes to be repeated in a same rule.
* * 
 -L <number of literals>
   *  Set maximum number of literals in a rule. (default: 4)
* * 
 -G <0=no negation | 1=body | 2=head | 3=body and head>
   *  Set the negations in the rule. (default: 0)
* * 
 -S
   *  Consider only classification rules.
* * 
 -c <class index>
   *  Set index of class attribute. (default: last).
* * 
 -H
   *  Consider only horn clauses.
* * 
 -E
   *  Keep equivalent rules.
* * 
 -M
   *  Keep same clauses.
* * 
 -T
   *  Keep subsumed rules.
* * 
 -I <0=always match | 1=never match | 2=significant>
   *  Set the way to handle missing values. (default: 0)
* * 
 -O
   *  Use ROC analysis.
* * 
 -p <name of file>
   *  Set the file containing the parts of the individual for individual-based learning.
* * 
 -P <0=no output | 1=on stdout | 2=in separate window>
   *  Set output of current values. (default: 0)
* * * @param options The list of options as an array of strings. * @throws Exception if an option is not supported. */ public void setOptions(String [] options) throws Exception { resetOptions(); /* Pruning options. */ String bestString = Utils.getOption('K', options); if (bestString.length() != 0) { try { m_best = Integer.parseInt(bestString); } catch (Exception e) { throw new Exception("Invalid value for -K option: " + e.getMessage() + "."); } if (m_best < 1) { throw new Exception("Number of confirmation values has to be " + "greater than one!"); } } String frequencyThresholdString = Utils.getOption('F', options); if (frequencyThresholdString.length() != 0) { try { m_frequencyThreshold = (new Double(frequencyThresholdString)).doubleValue(); } catch (Exception e) { throw new Exception("Invalid value for -F option: " + e.getMessage() + "."); } if (m_frequencyThreshold < 0 || m_frequencyThreshold > 1) { throw new Exception("Frequency threshold has to be between " + "zero and one!"); } } String confirmationThresholdString = Utils.getOption('C', options); if (confirmationThresholdString.length() != 0) { try { m_confirmationThreshold = (new Double(confirmationThresholdString)).doubleValue(); } catch (Exception e) { throw new Exception("Invalid value for -C option: " + e.getMessage() + "."); } if (m_confirmationThreshold < 0 || m_confirmationThreshold > 1) { throw new Exception("Confirmation threshold has to be between " + "zero and one!"); } if (bestString.length() != 0) { throw new Exception("Specifying both a number of confirmation " + "values and a confirmation threshold " + "doesn't make sense!"); } if (m_confirmationThreshold != 0) { m_best = 0; } } String noiseThresholdString = Utils.getOption('N', options); if (noiseThresholdString.length() != 0) { try { m_noiseThreshold = (new Double(noiseThresholdString)).doubleValue(); } catch (Exception e) { throw new Exception("Invalid value for -N option: " + e.getMessage() + "."); } if (m_noiseThreshold < 0 || m_noiseThreshold > 1) { throw new Exception("Noise threshold has to be between " + "zero and one!"); } } /* Search space and language bias options. */ m_repeat = Utils.getFlag('R', options); String numLiteralsString = Utils.getOption('L', options); if (numLiteralsString.length() != 0) { try { m_numLiterals = Integer.parseInt(numLiteralsString); } catch (Exception e) { throw new Exception("Invalid value for -L option: " + e.getMessage() + "."); } if (m_numLiterals < 1) { throw new Exception("Number of literals has to be " + "greater than one!"); } } String negationString = Utils.getOption('G', options); if (negationString.length() != 0) { SelectedTag selected; int tag; try { tag = Integer.parseInt(negationString); } catch (Exception e) { throw new Exception("Invalid value for -G option: " + e.getMessage() + "."); } try { selected = new SelectedTag(tag, TAGS_NEGATION); } catch (Exception e) { throw new Exception("Value for -G option has to be " + "between zero and three!"); } setNegation(selected); } m_classification = Utils.getFlag('S', options); String classIndexString = Utils.getOption('c', options); if (classIndexString.length() != 0) { try { m_classIndex = Integer.parseInt(classIndexString); } catch (Exception e) { throw new Exception("Invalid value for -c option: " + e.getMessage() + "."); } } m_horn = Utils.getFlag('H', options); if (m_horn && (m_negation != NONE)) { throw new Exception("Considering horn clauses doesn't make sense " + "if negation allowed!"); } /* Subsumption tests options. */ m_equivalent = !(Utils.getFlag('E', options)); m_sameClause = !(Utils.getFlag('M', options)); m_subsumption = !(Utils.getFlag('T', options)); /* Missing values options. */ String missingString = Utils.getOption('I', options); if (missingString.length() != 0) { SelectedTag selected; int tag; try { tag = Integer.parseInt(missingString); } catch (Exception e) { throw new Exception("Invalid value for -I option: " + e.getMessage() + "."); } try { selected = new SelectedTag(tag, TAGS_MISSING); } catch (Exception e) { throw new Exception("Value for -I option has to be " + "between zero and two!"); } setMissingValues(selected); } /* ROC analysis. */ m_roc = Utils.getFlag('O', options); /* Individual-based learning. */ m_partsString = Utils.getOption('p', options); if (m_partsString.length() != 0) { Reader reader; try { reader = new BufferedReader(new FileReader(m_partsString)); } catch (Exception e) { throw new Exception("Can't open file " + e.getMessage() + "."); } m_parts = new Instances(reader); } /* Values output. */ String printValuesString = Utils.getOption('P', options); if (printValuesString.length() != 0) { SelectedTag selected; int tag; try { tag = Integer.parseInt(printValuesString); } catch (Exception e) { throw new Exception("Invalid value for -P option: " + e.getMessage() + "."); } try { selected = new SelectedTag(tag, TAGS_VALUES); } catch (Exception e) { throw new Exception("Value for -P option has to be " + "between zero and two!"); } setValuesOutput(selected); } } /** * Gets the current settings of the Tertius object. * * @return An array of strings suitable for passing to setOptions. */ public String [] getOptions() { Vector result; result = new Vector(); /* Pruning options. */ if (m_best > 0) { result.add("-K"); result.add("" + m_best); } result.add("-F"); result.add("" + m_frequencyThreshold); if (m_confirmationThreshold > 0) { result.add("-C"); result.add("" + m_confirmationThreshold); } result.add("-N"); result.add("" + m_noiseThreshold); /* Search space and language bias options. */ if (m_repeat) result.add("-R"); result.add("-L"); result.add("" + m_numLiterals); result.add("-G"); result.add("" + m_negation); if (m_classification) result.add("-S"); result.add("-c"); result.add("" + m_classIndex); if (m_horn) result.add("-H"); /* Subsumption tests options. */ if (!m_equivalent) result.add("-E"); if (!m_sameClause) result.add("-M"); if (!m_subsumption) result.add("-T"); /* Missing values options. */ result.add("-I"); result.add("" + m_missing); /* ROC analysis. */ if (m_roc) result.add("-O"); /* Individual-based learning. */ result.add("-p"); result.add("" + m_partsString); /* Values output. */ result.add("-P"); result.add("" + m_printValues); return (String[]) result.toArray(new String[result.size()]); } /** * Returns the tip text for this property. * * @return Tip text for this property suitable for * displaying in the explorer/experimenter GUI. */ public String confirmationValuesTipText() { return "Number of best confirmation values to find."; } /** * Get the value of confirmationValues. * * @return Value of confirmationValues. */ public int getConfirmationValues() { return m_best; } /** * Set the value of confirmationValues. * * @param v Value to assign to confirmationValues. */ public void setConfirmationValues(int v) { m_best = v; } /** * Returns the tip text for this property. * * @return Tip text for this property suitable for * displaying in the explorer/experimenter GUI. */ public String frequencyThresholdTipText() { return "Minimum proportion of instances satisfying head and body of rules"; } /** * Get the value of frequencyThreshold. * * @return Value of frequencyThreshold. */ public double getFrequencyThreshold() { return m_frequencyThreshold; } /** * Set the value of frequencyThreshold. * * @param v Value to assign to frequencyThreshold. */ public void setFrequencyThreshold(double v) { m_frequencyThreshold = v; } /** * Returns the tip text for this property. * * @return Tip text for this property suitable for * displaying in the explorer/experimenter GUI. */ public String confirmationThresholdTipText() { return "Minimum confirmation of the rules."; } /** * Get the value of confirmationThreshold. * * @return Value of confirmationThreshold. */ public double getConfirmationThreshold() { return m_confirmationThreshold; } /** * Set the value of confirmationThreshold. * * @param v Value to assign to confirmationThreshold. */ public void setConfirmationThreshold(double v) { m_confirmationThreshold = v; if (v != 0) { m_best = 0; } } /** * Returns the tip text for this property. * * @return Tip text for this property suitable for * displaying in the explorer/experimenter GUI. */ public String noiseThresholdTipText() { return "Maximum proportion of counter-instances of rules. " + "If set to 0, only satisfied rules will be given."; } /** * Get the value of noiseThreshold. * * @return Value of noiseThreshold. */ public double getNoiseThreshold() { return m_noiseThreshold; } /** * Set the value of noiseThreshold. * * @param v Value to assign to noiseThreshold. */ public void setNoiseThreshold(double v) { m_noiseThreshold = v; } /** * Returns the tip text for this property. * * @return Tip text for this property suitable for * displaying in the explorer/experimenter GUI. */ public String repeatLiteralsTipText() { return "Repeated attributes allowed."; } /** * Get the value of repeatLiterals. * * @return Value of repeatLiterals. */ public boolean getRepeatLiterals() { return m_repeat; } /** * Set the value of repeatLiterals. * * @param v Value to assign to repeatLiterals. */ public void setRepeatLiterals(boolean v) { m_repeat = v; } /** * Returns the tip text for this property. * * @return Tip text for this property suitable for * displaying in the explorer/experimenter GUI. */ public String numberLiteralsTipText() { return "Maximum number of literals in a rule."; } /** * Get the value of numberLiterals. * * @return Value of numberLiterals. */ public int getNumberLiterals() { return m_numLiterals; } /** * Set the value of numberLiterals. * * @param v Value to assign to numberLiterals. */ public void setNumberLiterals(int v) { m_numLiterals = v; } /** * Returns the tip text for this property. * * @return Tip text for this property suitable for * displaying in the explorer/experimenter GUI. */ public String negationTipText() { return "Set the type of negation allowed in the rule. " + "Negation can be allowed in the body, in the head, in both " + "or in none."; } /** * Get the value of negation. * * @return Value of negation. */ public SelectedTag getNegation() { return new SelectedTag(m_negation, TAGS_NEGATION); } /** * Set the value of negation. * * @param v Value to assign to negation. */ public void setNegation(SelectedTag v) { if (v.getTags() == TAGS_NEGATION) { m_negation = v.getSelectedTag().getID(); } } /** * Returns the tip text for this property. * * @return Tip text for this property suitable for * displaying in the explorer/experimenter GUI. */ public String classificationTipText() { return "Find only rules with the class in the head."; } /** * Get the value of classification. * * @return Value of classification. */ public boolean getClassification() { return m_classification; } /** * Set the value of classification. * * @param v Value to assign to classification. */ public void setClassification(boolean v) { m_classification = v; } /** * Returns the tip text for this property. * * @return Tip text for this property suitable for * displaying in the explorer/experimenter GUI. */ public String classIndexTipText() { return "Index of the class attribute. If set to 0, the class will be the last attribute."; } /** * Get the value of classIndex. * * @return Value of classIndex. */ public int getClassIndex() { return m_classIndex; } /** * Set the value of classIndex. * * @param v Value to assign to classIndex. */ public void setClassIndex(int v) { m_classIndex = v; } /** * Returns the tip text for this property. * * @return Tip text for this property suitable for * displaying in the explorer/experimenter GUI. */ public String hornClausesTipText() { return "Find rules with a single conclusion literal only."; } /** * Get the value of hornClauses. * * @return Value of hornClauses. */ public boolean getHornClauses() { return m_horn; } /** * Set the value of hornClauses. * * @param v Value to assign to hornClauses. */ public void setHornClauses(boolean v) { m_horn = v; } /** * Returns the tip text for this property. * * @return Tip text for this property suitable for * displaying in the explorer/experimenter GUI. */ public String equivalentTipText() { return "Keep equivalent rules. " + "A rule r2 is equivalent to a rule r1 if the body of r2 is the " + "negation of the head of r1, and the head of r2 is the " + "negation of the body of r1."; } /** * Get the value of equivalent. * * @return Value of equivalent. */ public boolean disabled_getEquivalent() { return !m_equivalent; } /** * Set the value of equivalent. * * @param v Value to assign to equivalent. */ public void disabled_setEquivalent(boolean v) { m_equivalent = !v; } /** * Returns the tip text for this property. * * @return Tip text for this property suitable for * displaying in the explorer/experimenter GUI. */ public String sameClauseTipText() { return "Keep rules corresponding to the same clauses. " + "If set to false, only the rule with the best confirmation " + "value and rules with a lower number of counter-instances " + "will be kept."; } /** * Get the value of sameClause. * * @return Value of sameClause. */ public boolean disabled_getSameClause() { return !m_sameClause; } /** * Set the value of sameClause. * * @param v Value to assign to sameClause. */ public void disabled_setSameClause(boolean v) { m_sameClause = !v; } /** * Returns the tip text for this property. * * @return Tip text for this property suitable for * displaying in the explorer/experimenter GUI. */ public String subsumptionTipText() { return "Keep subsumed rules. " + "If set to false, subsumed rules will only be kept if they " + "have a better confirmation or a lower number of counter-instances."; } /** * Get the value of subsumption. * * @return Value of subsumption. */ public boolean disabled_getSubsumption() { return !m_subsumption; } /** * Set the value of subsumption. * * @param v Value to assign to subsumption. */ public void disabled_setSubsumption(boolean v) { m_subsumption = !v; } /** * Returns the tip text for this property. * * @return Tip text for this property suitable for * displaying in the explorer/experimenter GUI. */ public String missingValuesTipText() { return "Set the way to handle missing values. " + "Missing values can be set to match any value, or never match values " + "or to be significant and possibly appear in rules."; } /** * Get the value of missingValues. * * @return Value of missingValues. */ public SelectedTag getMissingValues() { return new SelectedTag(m_missing, TAGS_MISSING); } /** * Set the value of missingValues. * * @param v Value to assign to missingValues. */ public void setMissingValues(SelectedTag v) { if (v.getTags() == TAGS_MISSING) { m_missing = v.getSelectedTag().getID(); } } /** * Returns the tip text for this property. * * @return Tip text for this property suitable for * displaying in the explorer/experimenter GUI. */ public String rocAnalysisTipText() { return "Return TP-rate and FP-rate for each rule found."; } /** * Get the value of rocAnalysis. * * @return Value of rocAnalysis. */ public boolean getRocAnalysis() { return m_roc; } /** * Set the value of rocAnalysis. * * @param v Value to assign to rocAnalysis. */ public void setRocAnalysis(boolean v) { m_roc = v; } /** * Returns the tip text for this property. * * @return Tip text for this property suitable for * displaying in the explorer/experimenter GUI. */ public String partFileTipText() { return "Set file containing the parts of the individual " + "for individual-based learning."; } /** * Get the value of partFile. * * @return Value of partFile. */ public File disabled_getPartFile() { return new File(m_partsString); } /** * Set the value of partFile. * * @param v Value to assign to partFile. * @throws Exception if file cannot be opened */ public void disabled_setPartFile(File v) throws Exception { m_partsString = v.getAbsolutePath(); if (m_partsString.length() != 0) { Reader reader; try { reader = new BufferedReader(new FileReader(m_partsString)); } catch (Exception e) { throw new Exception("Can't open file " + e.getMessage() + "."); } m_parts = new Instances(reader); } } /** * Returns the tip text for this property. * * @return Tip text for this property suitable for * displaying in the explorer/experimenter GUI. */ public String valuesOutputTipText() { return "Give visual feedback during the search. " + "The current best and worst values can be output either to stdout or to a separate window."; } /** * Get the value of valuesOutput. * * @return Value of valuesOutput. */ public SelectedTag getValuesOutput() { return new SelectedTag(m_printValues, TAGS_VALUES); } /** * Set the value of valuesOutput. * * @param v Value to assign to valuesOutput. */ public void setValuesOutput(SelectedTag v) { if (v.getTags() == TAGS_VALUES) { m_printValues = v.getSelectedTag().getID(); } } /** * Build the predicate corresponding to an attribute. * * @param instances The instances this predicates comes from. * @param attr The attribute this predicate corresponds to. * @param isClass Saying if the attribute is the class attribute. * @return The corresponding Predicate. * @throws Exception if the predicate could not be build * (the attribute is numeric). */ private Predicate buildPredicate(Instances instances, Attribute attr, boolean isClass) throws Exception { Predicate predicate; /* The result. */ Literal lit; Literal negation; boolean missingValues; /* Missing values for this attribute ? */ boolean individual = (m_parts != null); /* Individual-based learning ? */ int type = (instances == m_parts) ? IndividualLiteral.PART_PROPERTY : IndividualLiteral.INDIVIDUAL_PROPERTY; /* Type of property. */ if (attr.isNumeric()) { throw new Exception("Can't handle numeric attributes!"); } missingValues = instances.attributeStats(attr.index()).missingCount > 0; /* Build predicate. */ if (individual) { predicate = new Predicate(instances.relationName() + "." + attr.name(), attr.index(), isClass); } else { predicate = new Predicate(attr.name(), attr.index(), isClass); } if (attr.numValues() == 2 && (!missingValues || m_missing == EXPLICIT)) { /* Case of two values. * If there are missing values, this case is treated like other cases. */ if (individual) { lit = new IndividualLiteral(predicate, attr.value(0), 0, Literal.POS, m_missing, type); negation = new IndividualLiteral(predicate, attr.value(1), 1, Literal.POS, m_missing, type); } else { lit = new AttributeValueLiteral(predicate, attr.value(0), 0, Literal.POS, m_missing); negation = new AttributeValueLiteral(predicate, attr.value(1), 1, Literal.POS, m_missing); } lit.setNegation(negation); negation.setNegation(lit); predicate.addLiteral(lit); } else { /* Case of several values. */ for (int i = 0; i < attr.numValues(); i++) { if (individual) { lit = new IndividualLiteral(predicate, attr.value(i), i, Literal.POS, m_missing, type); } else { lit = new AttributeValueLiteral(predicate, attr.value(i), i, Literal.POS, m_missing); } if (m_negation != NONE) { if (individual) { negation = new IndividualLiteral(predicate, attr.value(i), i, Literal.NEG, m_missing, type); } else { negation = new AttributeValueLiteral(predicate, attr.value(i), i, Literal.NEG, m_missing); } lit.setNegation(negation); negation.setNegation(lit); } predicate.addLiteral(lit); } /* One more value if missing is significant. */ if (missingValues && m_missing == SIGNIFICANT) { if (individual) { lit = new IndividualLiteral(predicate, "?", -1, Literal.POS, m_missing, type); } else { lit = new AttributeValueLiteral(predicate, "?", -1, Literal.POS, m_missing); } if (m_negation != NONE) { if (individual) { negation = new IndividualLiteral(predicate, "?", -1, Literal.NEG, m_missing, type); } else { negation = new AttributeValueLiteral(predicate, "?", -1, Literal.NEG, m_missing); } lit.setNegation(negation); negation.setNegation(lit); } predicate.addLiteral(lit); } } return predicate; } /** * Build the predicates to use in the rules. * * @return the predicates * @throws Exception If the predicates could not be built * (numeric attribute). */ private ArrayList buildPredicates() throws Exception { ArrayList predicates = new ArrayList(); /* The result. */ Predicate predicate; Attribute attr; Enumeration attributes = m_instances.enumerateAttributes(); boolean individual = (m_parts != null); /* Individual-based learning ? */ /* Attributes. */ while (attributes.hasMoreElements()) { attr = (Attribute) attributes.nextElement(); /* Identifiers do not appear in rules in individual-based learning. */ if (!(individual && attr.name().equals("id"))) { predicate = buildPredicate(m_instances, attr, false); predicates.add(predicate); } } /* Class attribute. */ attr = m_instances.classAttribute(); /* Identifiers do not appear in rules. */ if (!(individual && attr.name().equals("id"))) { predicate = buildPredicate(m_instances, attr, true); predicates.add(predicate); } /* Attributes of the parts in individual-based learning. */ if (individual) { attributes = m_parts.enumerateAttributes(); while (attributes.hasMoreElements()) { attr = (Attribute) attributes.nextElement(); /* Identifiers do not appear in rules. */ if (!attr.name().equals("id")) { predicate = buildPredicate(m_parts, attr, false); predicates.add(predicate); } } } return predicates; } /** * Count the number of distinct confirmation values in the results. * * @return Number of confirmation values in the results. */ private int numValuesInResult() { int result = 0; SimpleLinkedList.LinkedListIterator iter = m_results.iterator(); Rule current; Rule next; if (!iter.hasNext()) { return result; } else { current = (Rule) iter.next(); while (iter.hasNext()) { next = (Rule) iter.next(); if (current.getConfirmation() > next.getConfirmation()) { result++; } current = next; } return result + 1; } } /** * Test if it is worth refining a rule. * * @param rule The rule to consider. * @return True if the rule can be refined, false otherwise. */ private boolean canRefine(Rule rule) { if (rule.isEmpty()) { return true; } if (m_best != 0) { if (numValuesInResult() < m_best) { return true; } Rule worstResult = (Rule) m_results.getLast(); if (rule.getOptimistic() >= worstResult.getConfirmation()) { return true; } return false; } else { return true; } } /** * Test if it is worth calculating the optimistic estimate of a rule. * * @param rule The rule to consider. * @return True if the optimistic estimate can be calculated, false otherwise. */ private boolean canCalculateOptimistic(Rule rule) { if (rule.hasTrueBody() || rule.hasFalseHead()) { return false; } if (!rule.overFrequencyThreshold(m_frequencyThreshold)) { return false; } return true; } /** * Test if a rule can be explored (if it is interesting for the results * or for refining). * * @param rule The rule to consider. * @return True if the rule can be explored, false otherwise. */ private boolean canExplore(Rule rule) { if (rule.getOptimistic() < m_confirmationThreshold) { return false; } if (m_best != 0) { if (numValuesInResult() < m_best) { return true; } Rule worstResult = (Rule) m_results.getLast(); if (rule.getOptimistic() >= worstResult.getConfirmation()) { return true; } return false; } else { return true; } } /** * Test if a rule can be stored in the agenda. * * @param rule The rule to consider. * @return True if the rule can be stored, false otherwise. */ private boolean canStoreInNodes(Rule rule) { if (rule.getObservedNumber() == 0) { return false; } return true; } /** * Test if it is worth calculating the confirmation of a rule. * * @param rule The rule to consider. * @return True if the confirmation can be calculated, false otherwise. */ private boolean canCalculateConfirmation(Rule rule) { if (rule.getObservedFrequency() > m_noiseThreshold) { return false; } return true; } /** * Test if a rule can be added to the results. * * @param rule The rule to consider. * @return True if the rule can be stored, false otherwise. */ private boolean canStoreInResults(Rule rule) { if (rule.getConfirmation() < m_confirmationThreshold) { return false; } if (m_best != 0) { if (numValuesInResult() < m_best) { return true; } Rule worstResult = (Rule) m_results.getLast(); if (rule.getConfirmation() >= worstResult.getConfirmation()) { return true; } return false; } else { return true; } } /** * Add a rule in the appropriate place in the list of the results, * according to the confirmation and * number of counter-instances of the rule.

* Subsumption tests are performed and the rule may not be added.

* Previous results may also be removed because of sumption. */ private void addResult(Rule rule) { Rule current; boolean added = false; /* Iterate the list until we find the right place. */ SimpleLinkedList.LinkedListIterator iter = m_results.iterator(); while (iter.hasNext()) { current = (Rule) iter.next(); if (Rule.confirmationThenObservedComparator.compare(current, rule) > 0) { iter.addBefore(rule); added = true; break; } /* Subsumption tests to see if the rule can be added. */ if ((m_subsumption || m_sameClause || m_equivalent) && current.subsumes(rule)) { if (current.numLiterals() == rule.numLiterals()) { if (current.equivalentTo(rule)) { /* Equivalent rules. */ if (m_equivalent) { return; } } else { /* Same clauses. */ if (m_sameClause && Rule.confirmationComparator.compare(current, rule) < 0) { return; } } } else { /* Subsumption. */ if (m_subsumption && Rule.observedComparator.compare(current, rule) <= 0) { return; } } } } if (added == false) { /* The rule must be added in the end of the results. */ m_results.add(rule); } /* Iterate the results with a lower confirmation * to see if some of them must be removed. */ SimpleLinkedList.LinkedListInverseIterator inverse = m_results.inverseIterator(); while (inverse.hasPrevious()) { current = (Rule) inverse.previous(); if (Rule.confirmationThenObservedComparator.compare(current, rule) < 0) { break; } if (current != rule && rule.subsumes(current)) { if (current.numLiterals() == rule.numLiterals()) { if (!current.equivalentTo(rule)) { /* Same clauses. */ if (m_sameClause && Rule.confirmationComparator.compare(current, rule) > 0) { inverse.remove(); } } } else { /* Subsumption. */ if (m_subsumption && Rule.observedComparator.compare(rule, current) <= 0) { inverse.remove(); } } } } /* Remove the rules with the worst confirmation value * if there are too many results. */ if (m_best != 0 && numValuesInResult() > m_best) { Rule worstRule = (Rule) m_results.getLast(); inverse = m_results.inverseIterator(); while (inverse.hasPrevious()) { current = (Rule) inverse.previous(); if (Rule.confirmationComparator.compare(current, worstRule) < 0) { break; } inverse.remove(); } } /* Print the new current values. */ printValues(); } /** * Returns default capabilities of the classifier. * * @return the capabilities of this classifier */ public Capabilities getCapabilities() { Capabilities result = super.getCapabilities(); // attributes result.enable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.MISSING_VALUES); // class result.enable(Capability.NOMINAL_CLASS); result.enable(Capability.MISSING_CLASS_VALUES); return result; } /** * Method that launches the search to find the rules with the highest * confirmation. * * @param instances The instances to be used for generating the rules. * @throws Exception if rules can't be built successfully. */ public void buildAssociations(Instances instances) throws Exception { Frame valuesFrame = null; /* Frame to display the current values. */ /* Initialization of the search. */ if (m_parts == null) { m_instances = new Instances(instances); } else { m_instances = new IndividualInstances(new Instances(instances), m_parts); } m_results = new SimpleLinkedList(); m_hypotheses = 0; m_explored = 0; m_status = NORMAL; if (m_classIndex == -1) m_instances.setClassIndex(m_instances.numAttributes()-1); else if (m_classIndex < m_instances.numAttributes() && m_classIndex >= 0) m_instances.setClassIndex(m_classIndex); else throw new Exception("Invalid class index."); // can associator handle the data? getCapabilities().testWithFail(m_instances); /* Initialization of the window for current values. */ if (m_printValues == WINDOW) { m_valuesText = new TextField(37); m_valuesText.setEditable(false); m_valuesText.setFont(new Font("Monospaced", Font.PLAIN, 12)); Label valuesLabel = new Label("Best and worst current values:"); Button stop = new Button("Stop search"); stop.addActionListener(new ActionListener() { public void actionPerformed(ActionEvent e) { /* Signal the interruption to the search. */ m_status = STOP; } }); valuesFrame = new Frame("Tertius status"); valuesFrame.setResizable(false); valuesFrame.add(m_valuesText, BorderLayout.CENTER); valuesFrame.add(stop, BorderLayout.SOUTH); valuesFrame.add(valuesLabel, BorderLayout.NORTH); valuesFrame.pack(); valuesFrame.setVisible(true); } else if (m_printValues == OUT) { System.out.println("Best and worst current values:"); } Date start = new Date(); /* Build the predicates and launch the search. */ m_predicates = buildPredicates(); beginSearch(); Date end = new Date(); if (m_printValues == WINDOW) { valuesFrame.dispose(); } m_time = new Date(end.getTime() - start.getTime()); } /** * Run the search. */ public void run() { try { search(); } catch (OutOfMemoryError e) { /* Garbage collect what can be collected to be able to continue. */ System.gc(); m_status = MEMORY; } endSearch(); } /** * Begin the search by starting a new thread. */ private synchronized void beginSearch() throws Exception { /* This method must be synchronized to be able to * call the wait() method. */ Thread search = new Thread(this); search.start(); try { /* Wait for the end of the thread. */ wait(); } catch (InterruptedException e) { /* Signal the interruption to the search. */ m_status = STOP; } } /** * End the search by notifying to the waiting thread that it is finished. */ private synchronized void endSearch() { /* This method must be synchronized to be able to * call the notify() method. */ notify(); } /** * Search in the space of hypotheses the rules that have the highest * confirmation. * The search starts with the empty rule, other rules are generated by * refinement. */ public void search() { SimpleLinkedList nodes = new SimpleLinkedList(); /* The agenda. */ Rule currentNode; SimpleLinkedList children; SimpleLinkedList.LinkedListIterator iter; Rule child; boolean negBody = (m_negation == BODY || m_negation == ALL); boolean negHead = (m_negation == HEAD || m_negation == ALL); /* Start with the empty rule. */ nodes.add(new Rule(m_repeat, m_numLiterals, negBody, negHead, m_classification, m_horn)); /* Print the current values. */ printValues(); /* Explore the rules in the agenda. */ while (m_status != STOP && !nodes.isEmpty()) { currentNode = (Rule) nodes.removeFirst(); if (canRefine(currentNode)) { children = currentNode.refine(m_predicates); iter = children.iterator(); /* Calculate the optimistic estimate of the children and * consider them for adding to the agenda and to the results. */ while (iter.hasNext()) { m_hypotheses++; child = (Rule) iter.next(); child.upDate(m_instances); if (canCalculateOptimistic(child)) { child.calculateOptimistic(); if (canExplore(child)) { m_explored++; if (canStoreInNodes(child)) { } else { iter.remove(); } if (canCalculateConfirmation(child)) { child.calculateConfirmation(); if (canStoreInResults(child)) { addResult(child); } } } else { iter.remove(); } } else { iter.remove(); } } /* Since the agenda is already sorted it is more efficient * to sort the children only and then merge. */ children.sort(Rule.optimisticThenObservedComparator); nodes.merge(children, Rule.optimisticThenObservedComparator); } else { /* The agenda being sorted, it is not worth considering the following * nodes. */ break; } } } /** * returns the results * * @return the results */ public SimpleLinkedList getResults() { return m_results; } /** * Print the current best and worst values. */ private void printValues() { if (m_printValues == NO) { return; } else { if (m_results.isEmpty()) { if (m_printValues == OUT) { System.out.print("0.000000 0.000000 - 0.000000 0.000000"); } else { //m_printValues == WINDOW m_valuesText.setText("0.000000 0.000000 - 0.000000 0.000000"); } } else { Rule best = (Rule) m_results.getFirst(); Rule worst = (Rule) m_results.getLast(); String values = best.valuesToString() + " - " + worst.valuesToString(); if (m_printValues == OUT) { System.out.print("\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b"); System.out.print(values); } else { //m_printValues == WINDOW m_valuesText.setText(values); } } } } /** * Outputs the best rules found with their confirmation value and number * of counter-instances. * Also gives the number of hypotheses considered and explored, and the * time needed. */ public String toString() { StringBuffer text = new StringBuffer(); SimpleLinkedList.LinkedListIterator iter = m_results.iterator(); int size = m_results.size(); int i = 0; text.append("\nTertius\n=======\n\n"); while (iter.hasNext()) { Rule current = (Rule) iter.next(); text.append(Utils.doubleToString((double) i + 1, (int) (Math.log(size) / Math.log(10) + 1), 0) + ". "); text.append("/* "); if (m_roc) { text.append(current.rocToString()); } else { text.append(current.valuesToString()); } text.append(" */ "); text.append(current.toString()); text.append("\n"); i++; } text.append("\nNumber of hypotheses considered: " + m_hypotheses); text.append("\nNumber of hypotheses explored: " + m_explored); if (m_status == MEMORY) { text.append("\n\nNot enough memory to continue the search"); } else if (m_status == STOP) { text.append("\n\nSearch interrupted"); } return text.toString(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.10 $"); } /** * Main method. * * @param args the commandline parameters */ public static void main(String [] args) { runAssociator(new Tertius(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/gsp/000077500000000000000000000000001214146114000242315ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/gsp/Element.java000066400000000000000000000162611214146114000264730ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * Element.java * Copyright (C) 2007 Sebastian Beer * */ package weka.associations.gsp; import weka.core.FastVector; import weka.core.Instance; import weka.core.Instances; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import java.io.Serializable; /** * Class representing an Element, i.e., a set of events/items. * * @author Sebastian Beer * @version $Revision: 1.2 $ */ public class Element implements Cloneable, Serializable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = -7900701276019516371L; /** events/items stored as an array of ints */ protected int[] m_Events; /** * Constructor */ public Element() { } /** * Constructor accepting an initial size of the events Array as parameter. * * @param size the size */ public Element(int size) { m_Events = new int[size]; } /** * Returns all events of the given data set as Elements containing a single * event. The order of events is determined by the header information of * the corresponding ARFF file. * * @param instances the data set * @return the set of 1-Elements */ public static FastVector getOneElements (Instances instances) { FastVector setOfOneElements = new FastVector(); Element curElement; for (int i = 0; i < instances.numAttributes(); i++) { for (int j = 0; j < instances.attribute(i).numValues(); j++) { curElement = new Element(); curElement.setEvents(new int [instances.numAttributes()]); for (int k = 0; k < instances.numAttributes(); k++) { curElement.getEvents()[k] = -1; } curElement.getEvents()[i] = j; setOfOneElements.addElement(curElement); } } return setOfOneElements; } /** * Merges two Elements into one. * * @param element1 first Element * @param element2 second Element * @return the merged Element */ public static Element merge(Element element1, Element element2) { int[] element1Events = element1.getEvents(); int[] element2Events = element2.getEvents(); Element resultElement = new Element(element1Events.length); int[] resultEvents = resultElement.getEvents(); for (int i = 0; i < element1Events.length; i++) { if (element2Events[i] > -1) { resultEvents[i] = element2Events[i]; } else { resultEvents[i] = element1Events[i]; } } resultElement.setEvents(resultEvents); return resultElement; } /** * Returns a deep clone of an Element. * * @return the cloned Element */ public Element clone() { try { Element clone = (Element) super.clone(); int[] cloneEvents = new int[m_Events.length]; for (int i = 0; i < m_Events.length; i++) { cloneEvents[i] = m_Events[i]; } clone.setEvents(cloneEvents); return clone; } catch (CloneNotSupportedException exc) { exc.printStackTrace(); } return null; } /** * Checks if an Element contains over one event. * * @return true, if the Element contains over one event, else false */ public boolean containsOverOneEvent() { int numEvents = 0; for (int i = 0; i < m_Events.length; i++) { if (m_Events[i] > -1) { numEvents++; } if (numEvents == 2) { return true; } } return false; } /** * Deletes the first or last event of an Element. * * @param position the position of the event to be deleted (first or last) */ public void deleteEvent(String position) { if (position.equals("first")) { //delete first event for (int i = 0; i < m_Events.length; i++) { if (m_Events[i] > -1) { m_Events[i] = -1; break; } } } if (position.equals("last")) { //delete last event for (int i = m_Events.length-1; i >= 0; i--) { if (m_Events[i] > -1) { m_Events[i] = -1; break; } } } } /** * Checks if two Elements are equal. * * @return true, if the two Elements are equal, else false */ public boolean equals(Object obj) { Element element2 = (Element) obj; for (int i=0; i < m_Events.length; i++) { if (!(m_Events[i] == element2.getEvents()[i])) { return false; } } return true; } /** * Returns the events Array of an Element. * * @return the events Array */ public int[] getEvents() { return m_Events; } /** * Checks if an Element is contained by a given Instance. * * @param instance the given Instance * @return true, if the Instance contains the Element, else false */ public boolean isContainedBy(Instance instance) { for (int i=0; i < instance.numAttributes(); i++) { if (m_Events[i] > -1) { if (instance.isMissing(i)) { return false; } if (m_Events[i] != (int) instance.value(i)) { return false; } } } return true; } /** * Checks if the Element contains any events. * * @return true, if the Element contains no event, else false */ public boolean isEmpty() { for (int i=0; i < m_Events.length; i++) { if (m_Events[i] > -1) { return false; } } return true; } /** * Sets the events Array of an Element. * * @param events the events Array to set */ protected void setEvents(int[] events) { m_Events = events; } /** * Returns a String representation of an Element where the numeric value * of each event/item is represented by its respective nominal value. * * @param dataSet the corresponding data set containing the header information * @return the String representation */ public String toNominalString(Instances dataSet) { StringBuffer result = new StringBuffer(); int addedValues = 0; result.append("{"); for (int i=0; i < m_Events.length; i++) { if (m_Events[i] > -1) { result.append(dataSet.attribute(i).value(m_Events[i]) + ","); addedValues++; } } result.deleteCharAt(result.length()-1); result.append("}"); return result.toString(); } /** * Returns a String representation of an Element. * * @return the String representation */ public String toString() { String result = ""; result += "{"; for (int i=0; i < m_Events.length; i++) { result += m_Events[i]; if (i+1 < m_Events.length) { result += ","; } } result += "}"; return result; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.2 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/gsp/Sequence.java000066400000000000000000000425231214146114000266520ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * Sequence.java * Copyright (C) 2007 Sebastian Beer * */ package weka.associations.gsp; import weka.core.FastVector; import weka.core.Instances; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import java.io.Serializable; import java.util.Enumeration; /** * Class representing a sequence of elements/itemsets. * * @author Sebastian Beer * @version $Revision: 1.2 $ */ public class Sequence implements Cloneable, Serializable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = -5001018056339156390L; /** the support count of the Sequence */ protected int m_SupportCount; /** ordered list of the comprised elements/itemsets */ protected FastVector m_Elements; /** * Constructor. */ public Sequence() { m_SupportCount = 0; m_Elements = new FastVector(); } /** * Constructor accepting a set of elements as parameter. * * @param elements the Elements of the Sequence */ public Sequence(FastVector elements) { m_SupportCount = 0; m_Elements = elements; } /** * Constructor accepting an int value as parameter to set the support count. * * @param supportCount the support count to set */ public Sequence(int supportCount) { m_SupportCount = supportCount; m_Elements = new FastVector(); } /** * Generates all possible candidate k-Sequences and prunes the ones that * contain an infrequent (k-1)-Sequence. * * @param kMinusOneSequences the set of (k-1)-Sequences, used for verification * @return the generated set of k-candidates * @throws CloneNotSupportedException */ public static FastVector aprioriGen(FastVector kMinusOneSequences) throws CloneNotSupportedException { FastVector allCandidates = generateKCandidates(kMinusOneSequences); FastVector prunedCandidates = pruneCadidates(allCandidates, kMinusOneSequences); return prunedCandidates; } /** * Deletes Sequences of a given set which don't meet the minimum support * count threshold. * * @param sequences the set Sequences to be checked * @param minSupportCount the minimum support count * @return the set of Sequences after deleting */ public static FastVector deleteInfrequentSequences(FastVector sequences, long minSupportCount) { FastVector deletedSequences = new FastVector(); Enumeration seqEnum = sequences.elements(); while (seqEnum.hasMoreElements()) { Sequence currentSeq = (Sequence) seqEnum.nextElement(); long curSupportCount = currentSeq.getSupportCount(); if (curSupportCount >= minSupportCount) { deletedSequences.addElement(currentSeq); } } return deletedSequences; } /** * Generates candidate k-Sequences on the basis of a given (k-1)-Sequence set. * * @param kMinusOneSequences the set of (k-1)-Sequences * @return the set of candidate k-Sequences * @throws CloneNotSupportedException */ protected static FastVector generateKCandidates(FastVector kMinusOneSequences) throws CloneNotSupportedException { FastVector candidates = new FastVector(); FastVector mergeResult = new FastVector(); for (int i = 0; i < kMinusOneSequences.size(); i++) { for (int j = 0; j < kMinusOneSequences.size(); j++) { Sequence originalSeq1 = (Sequence) kMinusOneSequences.elementAt(i); Sequence seq1 = originalSeq1.clone(); Sequence originalSeq2 = (Sequence) kMinusOneSequences.elementAt(j); Sequence seq2 = originalSeq2.clone(); Sequence subseq1 = seq1.deleteEvent("first"); Sequence subseq2 = seq2.deleteEvent("last"); if (subseq1.equals(subseq2)) { //seq1 and seq2 are 1-sequences if ((subseq1.getElements().size() == 0) && (subseq2.getElements().size() == 0)) { if (i >= j) { mergeResult = merge(seq1, seq2, true, true); } else { mergeResult = merge(seq1, seq2, true, false); } //seq1 and seq2 are k-sequences } else { mergeResult = merge(seq1, seq2, false, false); } candidates.appendElements(mergeResult); } } } return candidates; } /** * Merges two Sequences in the course of candidate generation. Differentiates * between merging 1-Sequences and k-Sequences, k > 1. * * @param seq1 Sequence at first position * @param seq2 Sequence at second position * @param oneElements true, if 1-Elements should be merged, else false * @param mergeElements true, if two 1-Elements were not already merged * (regardless of their position), else false * @return set of resulting Sequences */ protected static FastVector merge(Sequence seq1, Sequence seq2, boolean oneElements, boolean mergeElements) { FastVector mergeResult = new FastVector(); //merge 1-sequences if (oneElements) { Element element1 = (Element) seq1.getElements().firstElement(); Element element2 = (Element) seq2.getElements().firstElement(); Element element3 = null; if (mergeElements) { for (int i = 0; i < element1.getEvents().length; i++) { if (element1.getEvents()[i] > -1) { if (element2.getEvents()[i] > -1) { break; } else { element3 = Element.merge(element1, element2); } } } } FastVector newElements1 = new FastVector(); //generate <{x}{y}> newElements1.addElement(element1); newElements1.addElement(element2); mergeResult.addElement(new Sequence(newElements1)); //generate <{x,y}> if (element3 != null) { FastVector newElements2 = new FastVector(); newElements2.addElement(element3); mergeResult.addElement(new Sequence(newElements2)); } return mergeResult; //merge k-sequences, k > 1 } else { Element lastElementSeq1 = (Element) seq1.getElements().lastElement(); Element lastElementSeq2 = (Element) seq2.getElements().lastElement(); Sequence resultSeq = new Sequence(); FastVector resultSeqElements = resultSeq.getElements(); //if last two events/items belong to the same element/itemset if (lastElementSeq2.containsOverOneEvent()) { for (int i = 0; i < (seq1.getElements().size()-1); i++) { resultSeqElements.addElement(seq1.getElements().elementAt(i)); } resultSeqElements.addElement(Element.merge(lastElementSeq1, lastElementSeq2)); mergeResult.addElement(resultSeq); return mergeResult; //if last two events/items belong to different elements/itemsets } else { for (int i = 0; i < (seq1.getElements().size()); i++) { resultSeqElements.addElement(seq1.getElements().elementAt(i)); } resultSeqElements.addElement(lastElementSeq2); mergeResult.addElement(resultSeq); return mergeResult; } } } /** * Converts a set of 1-Elements into a set of 1-Sequences. * * @param elements the set of 1-Elements * @return the set of 1-Sequences */ public static FastVector oneElementsToSequences(FastVector elements) { FastVector sequences = new FastVector(); Enumeration elementEnum = elements.elements(); while (elementEnum.hasMoreElements()) { Sequence seq = new Sequence(); FastVector seqElements = seq.getElements(); seqElements.addElement(elementEnum.nextElement()); sequences.addElement(seq); } return sequences; } /** * Prints a set of Sequences as String output. * * @param setOfSequences the set of sequences */ public static void printSetOfSequences(FastVector setOfSequences) { Enumeration seqEnum = setOfSequences.elements(); int i = 1; while(seqEnum.hasMoreElements()) { Sequence seq = (Sequence) seqEnum.nextElement(); System.out.print("[" + i++ + "]" + " " + seq.toString()); } } /** * Prunes a k-Sequence of a given candidate set if one of its (k-1)-Sequences * is infrequent. * * @param allCandidates the set of all potential k-Sequences * @param kMinusOneSequences the set of (k-1)-Sequences for verification * @return the set of the pruned candidates */ protected static FastVector pruneCadidates(FastVector allCandidates, FastVector kMinusOneSequences) { FastVector prunedCandidates = new FastVector(); boolean isFrequent; //for each candidate for (int i = 0; i < allCandidates.size(); i++) { Sequence candidate = (Sequence) allCandidates.elementAt(i); isFrequent = true; FastVector canElements = candidate.getElements(); //generate each possible (k-1)-sequence and verify if it's frequent for (int j = 0; j < canElements.size(); j++) { if(isFrequent) { Element origElement = (Element) canElements.elementAt(j); int[] origEvents = origElement.getEvents(); for (int k = 0; k < origEvents.length; k++) { if (origEvents[k] > -1) { int helpEvent = origEvents[k]; origEvents[k] = -1; if (origElement.isEmpty()) { canElements.removeElementAt(j); //check if the (k-1)-sequence is contained in the set of kMinusOneSequences int containedAt = kMinusOneSequences.indexOf(candidate); if (containedAt != -1) { origEvents[k] = helpEvent; canElements.insertElementAt(origElement, j); break; } else { isFrequent = false; break; } } else { //check if the (k-1)-sequence is contained in the set of kMinusOneSequences int containedAt = kMinusOneSequences.indexOf(candidate); if (containedAt != -1) { origEvents[k] = helpEvent; continue; } else { isFrequent = false; break; } } } } } else { break; } } if (isFrequent) { prunedCandidates.addElement(candidate); } } return prunedCandidates; } /** * Returns a String representation of a set of Sequences where the numeric * value of each event/item is represented by its respective nominal value. * * @param setOfSequences the set of Sequences * @param dataSet the corresponding data set containing the header * information * @param filterAttributes the attributes to filter out * @return the String representation */ public static String setOfSequencesToString(FastVector setOfSequences, Instances dataSet, FastVector filterAttributes) { StringBuffer resString = new StringBuffer(); Enumeration SequencesEnum = setOfSequences.elements(); int i = 1; boolean printSeq; while(SequencesEnum.hasMoreElements()) { Sequence seq = (Sequence) SequencesEnum.nextElement(); Integer filterAttr = (Integer) filterAttributes.elementAt(0); printSeq = true; if (filterAttr.intValue() != -1) { for (int j=0; j < filterAttributes.size(); j++) { filterAttr = (Integer) filterAttributes.elementAt(j); FastVector seqElements = seq.getElements(); if (printSeq) { for (int k=0; k < seqElements.size(); k++) { Element currentElement = (Element) seqElements.elementAt(k); int[] currentEvents = currentElement.getEvents(); if (currentEvents[filterAttr.intValue()] != -1) { continue; } else { printSeq = false; break; } } } } } if (printSeq) { resString.append("[" + i++ + "]" + " " + seq.toNominalString(dataSet)); } } return resString.toString(); } /** * Updates the support count of a set of Sequence candidates according to a * given set of data sequences. * * @param candidates the set of candidates * @param dataSequences the set of data sequences */ public static void updateSupportCount(FastVector candidates, FastVector dataSequences) { Enumeration canEnumeration = candidates.elements(); while(canEnumeration.hasMoreElements()){ Enumeration dataSeqEnumeration = dataSequences.elements(); Sequence candidate = (Sequence) canEnumeration.nextElement(); while(dataSeqEnumeration.hasMoreElements()) { Instances dataSequence = (Instances) dataSeqEnumeration.nextElement(); if (candidate.isSubsequenceOf(dataSequence)) { candidate.setSupportCount(candidate.getSupportCount() + 1); } } } } /** * Returns a deep clone of a Sequence. * * @return the cloned Sequence */ public Sequence clone() { try { Sequence clone = (Sequence) super.clone(); clone.setSupportCount(m_SupportCount); FastVector cloneElements = new FastVector(m_Elements.size()); for (int i = 0; i < m_Elements.size(); i++) { Element helpElement = (Element) m_Elements.elementAt(i); cloneElements.addElement(helpElement.clone()); } clone.setElements(cloneElements); return clone; } catch (CloneNotSupportedException exc) { exc.printStackTrace(); } return null; } /** * Deletes either the first or the last event/item of a Sequence. If the * deleted event/item is the only value in the Element, it is removed, as well. * * @param position the position of the event/item (first or last) * @return the Sequence with either the first or the last * event/item deleted */ protected Sequence deleteEvent(String position) { Sequence cloneSeq = clone(); if (position.equals("first")) { Element element = (Element) cloneSeq.getElements().firstElement(); element.deleteEvent("first"); if (element.isEmpty()) { cloneSeq.getElements().removeElementAt(0); } return cloneSeq; } if (position.equals("last")) { Element element = (Element) cloneSeq.getElements().lastElement(); element.deleteEvent("last"); if (element.isEmpty()) { cloneSeq.getElements().removeElementAt(m_Elements.size()-1); } return cloneSeq; } return null; } /** * Checks if two Sequences are equal. * * @return true, if the two Sequences are equal, else false */ public boolean equals(Object obj) { Sequence seq2 = (Sequence) obj; FastVector seq2Elements = seq2.getElements(); for (int i = 0; i < m_Elements.size(); i++) { Element thisElement = (Element) m_Elements.elementAt(i); Element seq2Element = (Element) seq2Elements.elementAt(i); if (!thisElement.equals(seq2Element)) { return false; } } return true; } /** * Returns the Elements of the Sequence. * * @return the Elements */ protected FastVector getElements() { return m_Elements; } /** * Returns the support count of the Sequence. * * @return the support count */ protected int getSupportCount() { return m_SupportCount; } /** * Checks if the Sequence is subsequence of a given data sequence. * * @param dataSequence the data sequence to verify against * @return true, if the Sequnce is subsequence of the data * sequence, else false */ protected boolean isSubsequenceOf(Instances dataSequence) { FastVector elements = getElements(); Enumeration elementEnum = elements.elements(); Element curElement = (Element) elementEnum.nextElement(); for (int i = 0; i < dataSequence.numInstances(); i++) { if (curElement.isContainedBy(dataSequence.instance(i))) { if (!elementEnum.hasMoreElements()) { return true; } else { curElement = (Element) elementEnum.nextElement(); continue; } } } return false; } /** * Sets the Elements of the Sequence. * * @param elements the Elements to set */ protected void setElements(FastVector elements) { m_Elements = elements; } /** * Sets the support count of the Sequence. * * @param supportCount the support count to set */ protected void setSupportCount(int supportCount) { m_SupportCount = supportCount; } /** * Returns a String representation of a Sequences where the numeric value * of each event/item is represented by its respective nominal value. * * @param dataSet the corresponding data set containing the header * information * @return the String representation */ public String toNominalString(Instances dataSet) { String result = ""; result += "<"; for (int i = 0; i < m_Elements.size(); i++) { Element element = (Element) m_Elements.elementAt(i); result += element.toNominalString(dataSet); } result += "> (" + getSupportCount() + ")\n"; return result; } /** * Returns a String representation of a Sequence. * * @return the String representation */ public String toString() { String result = ""; result += "Sequence Output\n"; result += "------------------------------\n"; result += "Support Count: " + getSupportCount() + "\n"; result += "contained elements/itemsets:\n"; for (int i = 0; i < m_Elements.size(); i++) { Element element = (Element) m_Elements.elementAt(i); result += element.toString(); } result += "\n\n"; return result; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.2 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/tertius/000077500000000000000000000000001214146114000251375ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/tertius/AttributeValueLiteral.java000066400000000000000000000061431214146114000322630ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * AttributeValueLiteral.java * Copyright (C) 2003 Peter A. Flach, Nicolas Lachiche * * Thanks to Amelie Deltour for porting the original C code to Java * and integrating it into Weka. */ package weka.associations.tertius; import weka.core.Instance; import weka.core.RevisionUtils; import weka.associations.Tertius; /** * @author Peter A. Flach * @author Nicolas Lachiche * @version $Revision: 1.6 $ */ public class AttributeValueLiteral extends Literal { /** for serialization */ private static final long serialVersionUID = 4077436297281456239L; private String m_value; private int m_index; public AttributeValueLiteral(Predicate predicate, String value, int index, int sign, int missing) { super(predicate, sign, missing); m_value = value; m_index = index; } public boolean satisfies(Instance instance) { if (m_index == -1) { if (positive()) { return instance.isMissing(getPredicate().getIndex()); } else { return !instance.isMissing(getPredicate().getIndex()); } } else if (instance.isMissing(getPredicate().getIndex())) { if (positive()) { return false; } else { return m_missing != Tertius.EXPLICIT; } } else { if (positive()) { return (instance.value(getPredicate().getIndex()) == m_index); } else { return (instance.value(getPredicate().getIndex()) != m_index); } } } public boolean negationSatisfies(Instance instance) { if (m_index == -1) { if (positive()) { return !instance.isMissing(getPredicate().getIndex()); } else { return instance.isMissing(getPredicate().getIndex()); } } else if (instance.isMissing(getPredicate().getIndex())) { if (positive()) { return m_missing != Tertius.EXPLICIT; } else { return false; } } else { if (positive()) { return (instance.value(getPredicate().getIndex()) != m_index); } else { return (instance.value(getPredicate().getIndex()) == m_index); } } } public String toString() { StringBuffer text = new StringBuffer(); if (negative()) { text.append("not "); } text.append(getPredicate().toString() + " = " + m_value); return text.toString(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.6 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/tertius/Body.java000066400000000000000000000062061214146114000267030ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * Body.java * Copyright (C) 2003 Peter A. Flach, Nicolas Lachiche * * Thanks to Amelie Deltour for porting the original C code to Java * and integrating it into Weka. */ package weka.associations.tertius; import weka.core.Instance; import weka.core.Instances; import weka.core.RevisionUtils; import java.util.Iterator; /** * Class representing the body of a rule. * * @author Amelie Deltour * @version $Revision: 1.6 $ */ public class Body extends LiteralSet { /** for serialization */ private static final long serialVersionUID = 4870689270432218016L; /** * Constructor without storing the counter-instances. */ public Body() { super(); } /** * Constructor storing the counter-instances. * * @param instances The dataset. */ public Body(Instances instances) { super(instances); } /** * Test if an instance can be kept as a counter-instance, * if a new literal is added to this body. * * @param instance The instance to test. * @param newLit The new literal. * @return True if the instance is still a counter-instance * (if the new literal satisfies the instance). */ public boolean canKeep(Instance instance, Literal newLit) { return newLit.satisfies(instance); } /** * Test if this Body is included in a rule. * It is the literals of this Body are contained in the body of the other rule, * or if their negation is included in the head of the other rule. */ public boolean isIncludedIn(Rule otherRule) { Iterator iter = this.enumerateLiterals(); while (iter.hasNext()) { Literal current = (Literal) iter.next(); if (!otherRule.bodyContains(current) && !otherRule.headContains(current.getNegation())) { return false; } } return true; } /** * Gives a String representation of this set of literals as a conjunction. */ public String toString() { Iterator iter = this.enumerateLiterals(); if (!iter.hasNext()) { return "TRUE"; } StringBuffer text = new StringBuffer(); while (iter.hasNext()) { text.append(iter.next().toString()); if (iter.hasNext()) { text.append(" and "); } } return text.toString(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.6 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/tertius/Head.java000066400000000000000000000062341214146114000266500ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * Head.java * Copyright (C) 2003 Peter A. Flach, Nicolas Lachiche * * Thanks to Amelie Deltour for porting the original C code to Java * and integrating it into Weka. */ package weka.associations.tertius; import weka.core.Instance; import weka.core.Instances; import weka.core.RevisionUtils; import java.util.Iterator; /** * Class representing the head of a rule. * * @author Amelie Deltour * @version $Revision: 1.6 $ */ public class Head extends LiteralSet { /** for serialization */ private static final long serialVersionUID = 5068076274253706199L; /** * Constructor without storing the counter-instances. */ public Head() { super(); } /** * Constructor storing the counter-instances. * * @param instances The dataset. */ public Head(Instances instances) { super(instances); } /** * Test if an instance can be kept as a counter-instance, * if a new literal is added to this head. * * @param instance The instance to test. * @param newLit The new literal. * @return True if the instance is still a counter-instance * (if the negation of the new literal satisfies the instance). */ public boolean canKeep(Instance instance, Literal newLit) { return newLit.negationSatisfies(instance); } /** * Test if this Head is included in a rule. * It is the literals of this Head are contained in the head of the other rule, * or if their negation is included in the body of the other rule. */ public boolean isIncludedIn(Rule otherRule) { Iterator iter = this.enumerateLiterals(); while (iter.hasNext()) { Literal current = (Literal) iter.next(); if (!otherRule.headContains(current) && !otherRule.bodyContains(current.getNegation())) { return false; } } return true; } /** * Gives a String representation of this set of literals as a disjunction. */ public String toString() { Iterator iter = this.enumerateLiterals(); if (!iter.hasNext()) { return "FALSE"; } StringBuffer text = new StringBuffer(); while (iter.hasNext()) { text.append(iter.next().toString()); if (iter.hasNext()) { text.append(" or "); } } return text.toString(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.6 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/tertius/IndividualInstance.java000066400000000000000000000036461214146114000315700ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * IndividualInstance.java * Copyright (C) 2003 Peter A. Flach, Nicolas Lachiche * * Thanks to Amelie Deltour for porting the original C code to Java * and integrating it into Weka. */ package weka.associations.tertius; import weka.core.Instance; import weka.core.Instances; import weka.core.RevisionUtils; /** * @author Peter A. Flach * @author Nicolas Lachiche * @version $Revision: 1.5 $ */ public class IndividualInstance extends Instance { /** for serialization */ private static final long serialVersionUID = -7903938733476585114L; private Instances m_parts; public IndividualInstance(Instance individual, Instances parts) { super(individual); m_parts = parts; } public IndividualInstance(IndividualInstance instance) { super(instance); m_parts = instance.m_parts; } public Object copy() { IndividualInstance result = new IndividualInstance(this); result.m_Dataset = m_Dataset; return result; } public Instances getParts() { return m_parts; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.5 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/tertius/IndividualInstances.java000066400000000000000000000051231214146114000317430ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * IndividualInstances.java * Copyright (C) 2003 Peter A. Flach, Nicolas Lachiche * * Thanks to Amelie Deltour for porting the original C code to Java * and integrating it into Weka. * */ package weka.associations.tertius; import weka.core.Attribute; import weka.core.Instance; import weka.core.Instances; import weka.core.RevisionUtils; import java.util.Enumeration; /** * @author Peter A. Flach * @author Nicolas Lachiche * @version $Revision: 1.6 $ */ public class IndividualInstances extends Instances { /** for serialization */ private static final long serialVersionUID = -7355054814895636733L; public IndividualInstances(Instances individuals, Instances parts) throws Exception { super(individuals, individuals.numInstances()); Attribute individualIdentifier = attribute("id"); if (individualIdentifier == null) { throw new Exception("No identifier found in individuals dataset."); } Attribute partIdentifier = parts.attribute("id"); if (partIdentifier == null) { throw new Exception("No identifier found in parts dataset."); } Enumeration enumIndividuals = individuals.enumerateInstances(); while (enumIndividuals.hasMoreElements()) { Instance individual = (Instance) enumIndividuals.nextElement(); Instances partsOfIndividual = new Instances(parts, 0); Enumeration enumParts = parts.enumerateInstances(); while (enumParts.hasMoreElements()) { Instance part = (Instance) enumParts.nextElement(); if (individual.value(individualIdentifier) == part.value(partIdentifier)) { partsOfIndividual.add(part); } } add(new IndividualInstance(individual, partsOfIndividual)); } } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.6 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/tertius/IndividualLiteral.java000066400000000000000000000034011214146114000314050ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * IndividualLiteral.java * Copyright (C) 2003 Peter A. Flach, Nicolas Lachiche * * Thanks to Amelie Deltour for porting the original C code to Java * and integrating it into Weka. * */ package weka.associations.tertius; import weka.core.RevisionUtils; /** * @author Peter A. Flach * @author Nicolas Lachiche * @version $Revision: 1.5 $ */ public class IndividualLiteral extends AttributeValueLiteral { /** for serialization */ private static final long serialVersionUID = 4712404824517887435L; private int m_type; public static int INDIVIDUAL_PROPERTY = 0; public static int PART_PROPERTY = 1; public IndividualLiteral(Predicate predicate, String value, int index, int sign, int missing, int type) { super(predicate, value, index, sign, missing); m_type = type; } public int getType() { return m_type; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.5 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/tertius/Literal.java000066400000000000000000000042111214146114000273740ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * Literal.java * Copyright (C) 2003 Peter A. Flach, Nicolas Lachiche * * Thanks to Amelie Deltour for porting the original C code to Java * and integrating it into Weka. * */ package weka.associations.tertius; import weka.core.Instance; import weka.core.RevisionHandler; import java.io.Serializable; /** * @author Peter A. Flach * @author Nicolas Lachiche * @version $Revision: 1.5 $ */ public abstract class Literal implements Serializable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = 2675363669503575771L; private Predicate m_predicate; public static final int NEG = 0; public static final int POS = 1; private int m_sign; private Literal m_negation; protected int m_missing; public Literal(Predicate predicate, int sign, int missing) { m_predicate = predicate; m_sign = sign; m_negation = null; m_missing = missing; } public Predicate getPredicate() { return m_predicate; } public Literal getNegation() { return m_negation; } public void setNegation(Literal negation) { m_negation = negation; } public boolean positive() { return m_sign == POS; } public boolean negative() { return m_sign == NEG; } public abstract boolean satisfies(Instance instance); public abstract boolean negationSatisfies(Instance instance); public abstract String toString(); } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/tertius/LiteralSet.java000066400000000000000000000241641214146114000300610ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * LiteralSet.java * Copyright (C) 2003 Peter A. Flach, Nicolas Lachiche * * Thanks to Amelie Deltour for porting the original C code to Java * and integrating it into Weka. */ package weka.associations.tertius; import weka.core.Instance; import weka.core.Instances; import weka.core.RevisionHandler; import java.io.Serializable; import java.util.ArrayList; import java.util.Enumeration; import java.util.Iterator; /** * Class representing a set of literals, being either the body or the head * of a rule. * * @author Amelie Deltour * @version $Revision: 1.7 $ */ public abstract class LiteralSet implements Serializable, Cloneable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = 6094536488654503152L; /** Literals contained in this set. */ private ArrayList m_literals; /** Last literal added to this set. */ private Literal m_lastLiteral; /** Number of instances in the data the set deals with. */ private int m_numInstances; /** Set of counter-instances of this part of the rule. */ private ArrayList m_counterInstances; /* For a body, counter-instances are the instances satisfying the body. * For a head, conter-instances are the instances satisfying the negation. */ /** Counter for the number of counter-instances. */ private int m_counter; /** * Type of properties expressed in this set * (individual or parts properties). */ private int m_type; /** * Constructor for a set that does not store its counter-instances. */ public LiteralSet() { m_literals = new ArrayList(); m_lastLiteral = null; m_counterInstances = null; m_type = -1; } /** * Constructor initializing the set of counter-instances to all the instances. * * @param instances The dataset. */ public LiteralSet(Instances instances) { this(); m_numInstances = instances.numInstances(); m_counterInstances = new ArrayList(m_numInstances); Enumeration enu = instances.enumerateInstances(); while (enu.hasMoreElements()) { m_counterInstances.add(enu.nextElement()); } } /** * Returns a shallow copy of this set. * The structured is copied but the literals themselves are not copied. * * @return A copy of this LiteralSet. */ public Object clone() { Object result = null; try { result = super.clone(); /* Clone the set of literals, but not the literals themselves. */ ((LiteralSet) result).m_literals = (ArrayList) m_literals.clone(); if (m_counterInstances != null) { /* Clone the set of instances, but not the instances themselves. */ ((LiteralSet) result).m_counterInstances = (ArrayList) m_counterInstances.clone(); } } catch(Exception e) { /* An exception is not supposed to happen here. */ e.printStackTrace(); System.exit(0); } return result; } /** * Update the number of counter-instances of this set in the dataset. * This method should be used is the set does not store its counter-instances. * * @param instances The dataset. */ public void upDate(Instances instances) { Enumeration enu = instances.enumerateInstances(); m_numInstances = instances.numInstances(); m_counter = 0; while (enu.hasMoreElements()) { if (this.counterInstance((Instance) enu.nextElement())) { m_counter++; } } } /** * Get the number of counter-instances of this LiteralSet. * * @return The number of counter-instances. */ public int getCounterInstancesNumber() { if (m_counterInstances != null) { return m_counterInstances.size(); } else { return m_counter; } } /** * Get the frequency of counter-instances of this LiteralSet in the data. * * @return The frequency of counter-instances. */ public double getCounterInstancesFrequency() { return (double) getCounterInstancesNumber() / (double) m_numInstances; } /** * Test if this LiteralSet has more counter-instances than the threshold. * * @param minFrequency The frequency threshold. * @return True if there are more counter-instances than the threshold. */ public boolean overFrequencyThreshold(double minFrequency) { return getCounterInstancesFrequency() >= minFrequency; } /** * Test if all the intances are counter-instances. * * @return True if all the instances are counter-instances. */ public boolean hasMaxCounterInstances() { return getCounterInstancesNumber() == m_numInstances; } /** * Add a Literal to this set. * * @param element The element to add. */ public void addElement(Literal element) { m_literals.add(element); /* Update the last literal. */ m_lastLiteral = element; /* Update the type in the case of individual-based learning. */ if (element instanceof IndividualLiteral) { int type = ((IndividualLiteral) element).getType(); if (type > m_type) { m_type = type; } } /* Update the set of counter-instances. */ if (m_counterInstances != null) { for (int i = m_counterInstances.size() - 1; i >= 0; i--) { Instance current = (Instance) m_counterInstances.get(i); if (!canKeep(current, element)) { m_counterInstances.remove(i); } } } } /** * Test if this set is empty. * * @return True if the set is empty. */ public final boolean isEmpty() { return m_literals.size() == 0; } /** * Give the number of literals in this set. * * @return The number of literals. */ public final int numLiterals() { return m_literals.size(); } /** * Enumerate the literals contained in this set. * * @return An Iterator for the literals. */ public final Iterator enumerateLiterals() { return m_literals.iterator(); } /** * Give the last literal added to this set. * * @return The last literal added. */ public Literal getLastLiteral() { return m_lastLiteral; } /** * Test if the negation of this LiteralSet is included in another LiteralSet. * * @param otherSet The other LiteralSet. * @return True if the negation of this LiteralSet is included in * the other LiteralSet. */ public boolean negationIncludedIn(LiteralSet otherSet) { Iterator iter = this.enumerateLiterals(); while (iter.hasNext()) { Literal current = (Literal) iter.next(); if (!otherSet.contains(current.getNegation())) { return false; } } return true; } /** * Test if this LiteralSet contains a given Literal. * * @param lit The literal that is looked for. * @return True if this literal is contained in this LiteralSet. */ public boolean contains(Literal lit) { return m_literals.contains(lit); } /** * Give the type of properties in this set (individual or part properties). */ public int getType() { return m_type; } /** * Test if an individual instance, given a part instance of this individual, * is a counter-instance of this LiteralSet. * * @param individual The individual instance. * @param part The part instance. * @return True if the individual is a counter-instance. */ public boolean counterInstance(Instance individual, Instance part) { Iterator iter = this.enumerateLiterals(); while (iter.hasNext()) { IndividualLiteral current = (IndividualLiteral) iter.next(); if (current.getType() == IndividualLiteral.INDIVIDUAL_PROPERTY && !canKeep(individual, current)) { return false; } else if (current.getType() == IndividualLiteral.PART_PROPERTY && !canKeep(part, current)) { return false; } } return true; } /** * Test if an instance is a counter-instance of this LiteralSet. * * @param instance The instance to test. * @return True if the instance is a counter-instance. */ public boolean counterInstance(Instance instance) { if ((instance instanceof IndividualInstance) && (m_type == IndividualLiteral.PART_PROPERTY)) { /* In the case of an individual instance, all the parts of the individual * have to be considered. * Part properties can be found in the body only, so here we test for * an instance satisfying the set. * It satisfies the set if there exists a part satisfying the set. */ Enumeration enu = ((IndividualInstance) instance).getParts().enumerateInstances(); while (enu.hasMoreElements()) { if (counterInstance(instance, (Instance) enu.nextElement())) { return true; } } return false; } else { /* Usual case. */ Iterator iter = this.enumerateLiterals(); while (iter.hasNext()) { Literal current = (Literal) iter.next(); if (!canKeep(instance, current)) { return false; } } return true; } } /** * Test if an instance can be kept as a counter-instance, * given a new literal. * * @param instance The instance to test. * @param newLit The new literal. * @return True if the instance is still a counter-instance. */ public abstract boolean canKeep(Instance instance, Literal newLit); /** * Test if this LiteralSet is included in a rule. * * @param otherRule The rule to test. * @return True if this set of literals is included in the rule. */ public abstract boolean isIncludedIn(Rule otherRule); /** * Gives a String representation for this set of literals. */ public abstract String toString(); } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/tertius/Predicate.java000066400000000000000000000051761214146114000277130ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * Predicate.java * Copyright (C) 2003 Peter A. Flach, Nicolas Lachiche * * Thanks to Amelie Deltour for porting the original C code to Java * and integrating it into Weka. */ package weka.associations.tertius; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import java.io.Serializable; import java.util.ArrayList; /** * @author Peter A. Flach * @author Nicolas Lachiche * @version $Revision: 1.5 $ */ public class Predicate implements Serializable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = -8374702481965026640L; private ArrayList m_literals; private String m_name; private int m_index; private boolean m_isClass; public Predicate(String name, int index, boolean isClass) { m_literals = new ArrayList(); m_name = name; m_index = index; m_isClass = isClass; } public void addLiteral(Literal lit) { m_literals.add(lit); } public Literal getLiteral(int index) { return (Literal) m_literals.get(index); } public int getIndex() { return m_index; } public int indexOf(Literal lit) { int index = m_literals.indexOf(lit); return ((index != -1) ? index : m_literals.indexOf(lit.getNegation())); } public int numLiterals() { return m_literals.size(); } public boolean isClass() { return m_isClass; } public String toString() { return m_name; } public String description() { StringBuffer text = new StringBuffer(); text.append(this.toString() + "\n"); for (int i = 0; i < numLiterals(); i++) { Literal lit = getLiteral(i); Literal neg = lit.getNegation(); text.append("\t" + lit + "\t" + neg + "\n"); } return text.toString(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.5 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/tertius/Rule.java000066400000000000000000000611151214146114000267150ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * Rule.java * Copyright (C) 2003 Peter A. Flach, Nicolas Lachiche * * Thanks to Amelie Deltour for porting the original C code to Java * and integrating it into Weka. */ package weka.associations.tertius; import weka.core.Instance; import weka.core.Instances; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import java.io.Serializable; import java.text.DecimalFormat; import java.util.ArrayList; import java.util.Comparator; import java.util.Enumeration; /** * Class representing a rule with a body and a head. * * @author Amelie Deltour * @version $Revision: 1.7 $ */ public class Rule implements Serializable, Cloneable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = -7763378359090435505L; /** The body of the rule. */ private Body m_body; /** The head of the rule. */ private Head m_head; /** Can repeat predicates in the rule ? */ private boolean m_repeatPredicate; /** Maximal number of literals in the rule. */ private int m_maxLiterals; /** Can there be negations in the body ? */ private boolean m_negBody; /** Can there be negations in the head ? */ private boolean m_negHead; /** Is this rule a classification rule ? */ private boolean m_classRule; /** Can there be only one literal in the head ? */ private boolean m_singleHead; /** Number of instances in the data this rule deals with. */ private int m_numInstances; /** Set of counter-instances of this rule. */ private ArrayList m_counterInstances; /** Counter for the counter-instances of this rule. */ private int m_counter; /** Confirmation of this rule. */ private double m_confirmation; /** Optimistic estimate of this rule. */ private double m_optimistic; /** * Constructor for a rule when the counter-instances are not stored, * giving all the constraints applied to this rule. * * @param repeatPredicate True if predicates can be repeated. * @param maxLiterals Maximum number of literals. * @param negBody True if negation is allowed in the body. * @param negHead True if negation is allowed in the head. * @param classRule True if the rule is a classification rule. * @param horn True if the rule is a horn clause. */ public Rule(boolean repeatPredicate, int maxLiterals, boolean negBody, boolean negHead, boolean classRule, boolean horn) { m_body = new Body(); m_head = new Head(); m_repeatPredicate = repeatPredicate; m_maxLiterals = maxLiterals; m_negBody = negBody && !horn; m_negHead = negHead && !horn; m_classRule = classRule; m_singleHead = classRule || horn; } /** * Constructor for a rule when the counter-instances are stored, * giving all the constraints applied to this rule. * The counter-instances are initialized to all the instances in the dataset. * * @param instances The dataset. * @param repeatPredicate True if predicates can be repeated. * @param maxLiterals Maximum number of literals. * @param negBody True if negation is allowed in the body. * @param negHead True if negation is allowed in the head. * @param classRule True if the rule is a classification rule. * @param horn True if the rule is a horn clause. */ public Rule(Instances instances, boolean repeatPredicate, int maxLiterals, boolean negBody, boolean negHead, boolean classRule, boolean horn) { m_body = new Body(instances); m_head = new Head(instances); m_repeatPredicate = repeatPredicate; m_maxLiterals = maxLiterals; m_negBody = negBody && !horn; m_negHead = negHead && !horn; m_classRule = classRule; m_singleHead = classRule || horn; m_numInstances = instances.numInstances(); m_counterInstances = new ArrayList(m_numInstances); Enumeration enu = instances.enumerateInstances(); while (enu.hasMoreElements()) { m_counterInstances.add(enu.nextElement()); } } /** * Returns a shallow copy of this rule. * The structured is copied but the literals themselves are not copied. * * @return A copy of this Rule. */ public Object clone() { Object result = null; try { result = super.clone(); /* Clone the body and the head. */ ((Rule) result).m_body = (Body) m_body.clone(); ((Rule) result).m_head = (Head) m_head.clone(); /* Clone the set of counter-instances. */ if (m_counterInstances != null) { ((Rule) result).m_counterInstances = (ArrayList) m_counterInstances.clone(); } } catch (Exception e) { /* An exception is not supposed to happen here. */ e.printStackTrace(); System.exit(0); } return result; } /** * Test if an instance is a counter-instance of this rule. * * @param instance The instance to test. * @return True if the instance is a counter-instance. */ public boolean counterInstance(Instance instance) { return ((m_body.counterInstance(instance) && m_head.counterInstance(instance))); } /** * Update the number of counter-instances of this rule in the dataset. * This method should be used is the rule does not store its counter-instances. * * @param instances The dataset. */ public void upDate(Instances instances) { Enumeration enu = instances.enumerateInstances(); m_numInstances = instances.numInstances(); m_counter = 0; while (enu.hasMoreElements()) { if (this.counterInstance((Instance) enu.nextElement())) { m_counter++; } } m_head.upDate(instances); m_body.upDate(instances); } /** * Get the confirmation value of this rule. * * @return The confirmation. */ public double getConfirmation() { return m_confirmation; } /** * Get the optimistic estimate of the confirmation obtained by refining * this rule. * * @return The optimistic estimate. */ public double getOptimistic() { return m_optimistic; } /* * Get the expected number of counter-instances of this rule, * calculated from the number of instances satisfying the body and * the number of instances satisfying the negation of the head. * * @return The expected number of counter-instances. */ public double getExpectedNumber() { return (double) m_body.getCounterInstancesNumber() * (double) m_head.getCounterInstancesNumber() / (double) m_numInstances; } /** * Get the expected frequency of counter-instances of this rule. * * @return The expected frequency of counter-instances. */ public double getExpectedFrequency() { return getExpectedNumber() / (double) m_numInstances; } /** * Get the observed number of counter-instances of this rule in the dataset. * * @return The observed number of counter-instances. */ public int getObservedNumber() { if (m_counterInstances != null) { return m_counterInstances.size(); } else { return m_counter; } } /** * Get the observed frequency of counter-instances of this rule in the dataset. * * @return The expected frequency of counter-instances. */ public double getObservedFrequency() { return (double) getObservedNumber() / (double) m_numInstances; } /** * Get the rate of True Positive instances of this rule. * * @return The TP-rate. */ public double getTPRate() { int tp = m_body.getCounterInstancesNumber() - getObservedNumber(); int fn = m_numInstances - m_head.getCounterInstancesNumber() - tp; return ((double) tp / (double) (tp + fn)); } /** * Get the rate of False Positive instances of this rule. * * @return The FP-rate. */ public double getFPRate() { int fp = getObservedNumber(); int tn = m_head.getCounterInstancesNumber() - fp; return ((double) fp / (double) (fp + tn)); } /** * Calculate the confirmation of this rule. */ public void calculateConfirmation() { double expected = getExpectedFrequency(); double observed = getObservedFrequency(); if ((expected == 0) || (expected == 1)) { m_confirmation = 0; } else { m_confirmation = (expected - observed) / (Math.sqrt(expected) - expected); } } /** * Calculate the optimistic estimate of this rule. */ public void calculateOptimistic() { int counterInstances = this.getObservedNumber(); int body = m_body.getCounterInstancesNumber(); int notHead = m_head.getCounterInstancesNumber(); int n = m_numInstances; double expectedOptimistic; /* optimistic expected number of counter-instances */ if (counterInstances <= body - notHead) { expectedOptimistic = (double) (notHead * (body - counterInstances)) / (double) (n * n); } else if (counterInstances <= notHead - body) { expectedOptimistic = (double) (body * (notHead - counterInstances)) / (double) (n * n); } else { expectedOptimistic = (double) ((notHead + body - counterInstances) * (notHead + body - counterInstances)) / (double) (4 * n * n); } if ((expectedOptimistic == 0) || (expectedOptimistic == 1)) { m_optimistic = 0; } else { m_optimistic = expectedOptimistic / (Math.sqrt(expectedOptimistic) - expectedOptimistic); } } /** * Test if this rule is empty. * * @return True if it is the empty rule. */ public boolean isEmpty() { return (m_head.isEmpty() && m_body.isEmpty()); } /** * Give the number of literals in this rule. * * @return The number of literals. */ public int numLiterals() { return m_body.numLiterals() + m_head.numLiterals(); } /** * Add a literal to the body of the rule. * * @param newLit The literal to add. * @return The rule obtained by adding the literal, null if the literal can * not be added because of the constraints on the rule. */ private Rule addTermToBody(Literal newLit) { if (!m_negBody && newLit.negative() || (m_classRule && newLit.getPredicate().isClass()) || (newLit instanceof IndividualLiteral && (((IndividualLiteral) newLit).getType() - m_body.getType()) > 1 && (((IndividualLiteral) newLit).getType() - m_head.getType()) > 1)) { return null; } else { Rule result = (Rule) this.clone(); result.m_body.addElement(newLit); /* Update the counter-instances. */ if (m_counterInstances != null) { for (int i = result.m_counterInstances.size() - 1; i >= 0; i--) { Instance current = (Instance) result.m_counterInstances.get(i); if (!result.m_body.canKeep(current, newLit)) { result.m_counterInstances.remove(i); } } } return result; } } /** * Add a literal to the head of the rule. * * @param newLit The literal to add. * @return The rule obtained by adding the literal, null if the literal can * not be added because of the constraints on the rule. */ private Rule addTermToHead(Literal newLit) { if ((!m_negHead && newLit.negative()) || (m_classRule && !newLit.getPredicate().isClass()) || (m_singleHead && !m_head.isEmpty()) || (newLit instanceof IndividualLiteral && ((IndividualLiteral) newLit).getType() != IndividualLiteral.INDIVIDUAL_PROPERTY)) { return null; } else { Rule result = (Rule) this.clone(); result.m_head.addElement(newLit); /* Update counter-instances. */ if (m_counterInstances != null) { for (int i = result.m_counterInstances.size() - 1; i >= 0; i--) { Instance current = (Instance) result.m_counterInstances.get(i); if (!result.m_head.canKeep(current, newLit)) { result.m_counterInstances.remove(i); } } } return result; } } /** * Refine a rule by adding a range of literals of a predicate, either to * the head or to the body of the rule. * * @param pred The predicate to consider. * @param firstIndex The index of the first literal of the predicate to add. * @param lastIndex The index of the last literal of the predicate to add. * @param addTobody True if the literals should be added to the body. * @param addToHead True if the literals should be added to the head. * @return A list of rules obtained by refinement. */ private SimpleLinkedList refine(Predicate pred, int firstIndex, int lastIndex, boolean addToBody, boolean addToHead) { SimpleLinkedList result = new SimpleLinkedList(); Literal currentLit; Literal negation; Rule refinement; for (int i = firstIndex; i < lastIndex; i++) { currentLit = pred.getLiteral(i); if (addToBody) { refinement = addTermToBody(currentLit); if (refinement != null) { result.add(refinement); } } if (addToHead) { refinement = addTermToHead(currentLit); if (refinement != null) { result.add(refinement); } } negation = currentLit.getNegation(); if (negation != null) { if (addToBody) { refinement = addTermToBody(negation); if (refinement != null) { result.add(refinement); } } if (addToHead) { refinement = addTermToHead(negation); if (refinement != null) { result.add(refinement); } } } } return result; } /** * Refine a rule by adding literal from a set of predictes. * * @param predicates The predicates available. * @return The list of the children obtained by refining the rule. */ public SimpleLinkedList refine(ArrayList predicates) { SimpleLinkedList result = new SimpleLinkedList(); Predicate currentPred; boolean addToBody; boolean addToHead; if (this.numLiterals() == m_maxLiterals) { return result; } if (this.isEmpty()) { /* Literals can be added on both sides of the rule. */ for (int i = 0; i < predicates.size(); i++) { currentPred = (Predicate) predicates.get(i); result.addAll(refine(currentPred, 0, currentPred.numLiterals(), true, true)); } } else if (m_body.isEmpty() || m_head.isEmpty()) { /* Literals can be added to the empty side only. */ LiteralSet side; Literal last; if (m_body.isEmpty()) { side = m_head; addToBody = true; addToHead = false; } else { // m_head.isEmpty() side = m_body; addToBody = false; addToHead = true; } last = side.getLastLiteral(); currentPred = last.getPredicate(); if (m_repeatPredicate) { result.addAll(refine(currentPred, currentPred.indexOf(last) + 1, currentPred.numLiterals(), addToBody, addToHead)); } for (int i = predicates.indexOf(currentPred) + 1; i < predicates.size(); i++) { currentPred = (Predicate) predicates.get(i); result.addAll(refine(currentPred, 0, currentPred.numLiterals(), addToBody, addToHead)); } } else { Literal lastLitBody = m_body.getLastLiteral(); Literal lastLitHead = m_head.getLastLiteral(); Predicate lastPredBody = lastLitBody.getPredicate(); Predicate lastPredHead = lastLitHead.getPredicate(); int lastLitBodyIndex = lastPredBody.indexOf(lastLitBody); int lastLitHeadIndex = lastPredHead.indexOf(lastLitHead); int lastPredBodyIndex = predicates.indexOf(lastPredBody); int lastPredHeadIndex = predicates.indexOf(lastPredHead); Predicate inferiorPred; Predicate superiorPred; int inferiorLit; int superiorLit; addToBody = (m_head.numLiterals() == 1 && (lastPredBodyIndex < lastPredHeadIndex || (lastPredBodyIndex == lastPredHeadIndex && lastLitBodyIndex < lastLitHeadIndex))); addToHead = (m_body.numLiterals() == 1 && (lastPredHeadIndex < lastPredBodyIndex || (lastPredHeadIndex == lastPredBodyIndex && lastLitHeadIndex < lastLitBodyIndex))); if (addToBody || addToHead) { /* Add literals in the gap between the body and the head. */ if (addToBody) { inferiorPred = lastPredBody; inferiorLit = lastLitBodyIndex; superiorPred = lastPredHead; superiorLit = lastLitHeadIndex; } else { // addToHead inferiorPred = lastPredHead; inferiorLit = lastLitHeadIndex; superiorPred = lastPredBody; superiorLit = lastLitBodyIndex; } if (predicates.indexOf(inferiorPred) < predicates.indexOf(superiorPred)) { if (m_repeatPredicate) { result.addAll(refine(inferiorPred, inferiorLit + 1, inferiorPred.numLiterals(), addToBody, addToHead)); } for (int j = predicates.indexOf(inferiorPred) + 1; j < predicates.indexOf(superiorPred); j++) { currentPred = (Predicate) predicates.get(j); result.addAll(refine(currentPred, 0, currentPred.numLiterals(), addToBody, addToHead)); } if (m_repeatPredicate) { result.addAll(refine(superiorPred, 0, superiorLit, addToBody, addToHead)); } } else { //((inferiorPred.getIndex() == superiorPred.getIndex()) //&& (inferiorLit < superiorLit)) if (m_repeatPredicate) { result.addAll(refine(inferiorPred, inferiorLit + 1, superiorLit, addToBody, addToHead)); } } } /* Add other literals. */ if (predicates.indexOf(lastPredBody) > predicates.indexOf(lastPredHead)) { superiorPred = lastPredBody; superiorLit = lastPredBody.indexOf(lastLitBody); } else if (predicates.indexOf(lastPredBody) < predicates.indexOf(lastPredHead)) { superiorPred = lastPredHead; superiorLit = lastPredHead.indexOf(lastLitHead); } else { superiorPred = lastPredBody; if (lastLitBodyIndex > lastLitHeadIndex) { superiorLit = lastPredBody.indexOf(lastLitBody); } else { superiorLit = lastPredHead.indexOf(lastLitHead); } } if (m_repeatPredicate) { result.addAll(refine(superiorPred, superiorLit + 1, superiorPred.numLiterals(), true, true)); } for (int j = predicates.indexOf(superiorPred) + 1; j < predicates.size(); j++) { currentPred = (Predicate) predicates.get(j); result.addAll(refine(currentPred, 0, currentPred.numLiterals(), true, true)); } } return result; } /** * Test if this rule subsumes another rule. * * @param otherRule The other rule. * @return True if the other rule is subsumed. */ public boolean subsumes(Rule otherRule) { if (this.numLiterals() > otherRule.numLiterals()) { return false; } return (m_body.isIncludedIn(otherRule) && m_head.isIncludedIn(otherRule)); } /** * Test if this rule and another rule correspond to the same clause. * * @param otherRule The other rule. * @return True if both rules correspond to the same clause. */ public boolean sameClauseAs(Rule otherRule) { return (this.numLiterals() == otherRule.numLiterals() && this.subsumes(otherRule)); } /** * Test if this rule is equivalent to another rule. * * @param otherRule The other rule. * @return True if both rules are equivalent. */ public boolean equivalentTo(Rule otherRule) { return (this.numLiterals() == otherRule.numLiterals() && m_head.negationIncludedIn(otherRule.m_body) && m_body.negationIncludedIn(otherRule.m_head)); } /** * Test if the body of the rule contains a literal. * * @param lit The literal to look for. * @return True if the literal is contained in the body of the rule. */ public boolean bodyContains(Literal lit) { return m_body.contains(lit); } /** * Test if the head of the rule contains a literal. * * @param lit The literal to look for. * @return True if the literal is contained in the head of the rule. */ public boolean headContains(Literal lit) { return m_head.contains(lit); } /** * Test if this rule is over the frequency threshold. * * @param minFrequency The frequency threshold. * @return True if the rule is over the threshold. */ public boolean overFrequencyThreshold(double minFrequency) { return (m_body.overFrequencyThreshold(minFrequency) && m_head.overFrequencyThreshold(minFrequency)); } /** * Test if the body of the rule is true. * * @return True if the body is always satisfied. */ public boolean hasTrueBody() { return (!m_body.isEmpty() && m_body.hasMaxCounterInstances()); } /** * Test if the head of the rule is false. * * @return True if the body is never satisfied. */ public boolean hasFalseHead() { return (!m_head.isEmpty() && m_head.hasMaxCounterInstances()); } /** * Return a String giving the confirmation and optimistic estimate of * this rule. * * @return A String with the values of the rule. */ public String valuesToString() { StringBuffer text = new StringBuffer(); DecimalFormat decimalFormat = new DecimalFormat("0.000000"); text.append(decimalFormat.format(getConfirmation())); text.append(" "); text.append(decimalFormat.format(getObservedFrequency())); return text.toString(); } /** * Return a String giving the TP-rate and FP-rate of * this rule. * * @return A String with the values of the rule. */ public String rocToString() { StringBuffer text = new StringBuffer(); DecimalFormat decimalFormat = new DecimalFormat("0.000000"); text.append(decimalFormat.format(getConfirmation())); text.append(" "); text.append(decimalFormat.format(getTPRate())); text.append(" "); text.append(decimalFormat.format(getFPRate())); return text.toString(); } /** * Retrun a String for this rule. * * @return The String describing this rule. */ public String toString() { StringBuffer text = new StringBuffer(); text.append(m_body.toString()); text.append(" ==> "); text.append(m_head.toString()); return text.toString(); } /** * Comparator used to compare two rules according to their confirmation value. */ public static Comparator confirmationComparator = new Comparator() { public int compare(Object o1, Object o2) { Rule r1 = (Rule) o1; Rule r2 = (Rule) o2; double conf1 = r1.getConfirmation(); double conf2 = r2.getConfirmation(); if (conf1 > conf2) { return -1; } else if (conf1 < conf2) { return 1; } else { return 0; } } }; /** * Comparator used to compare two rules according to their observed number * of counter-instances. */ public static Comparator observedComparator = new Comparator() { public int compare(Object o1, Object o2) { Rule r1 = (Rule) o1; Rule r2 = (Rule) o2; double obs1 = r1.getObservedFrequency(); double obs2 = r2.getObservedFrequency(); if (obs1 < obs2) { return -1; } else if (obs1 > obs2) { return 1; } else { return 0; } } }; /** * Comparator used to compare two rules according to their optimistic estimate. */ public static Comparator optimisticComparator = new Comparator() { public int compare(Object o1, Object o2) { Rule r1 = (Rule) o1; Rule r2 = (Rule) o2; double opt1 = r1.getOptimistic(); double opt2 = r2.getOptimistic(); if (opt1 > opt2) { return -1; } else if (opt1 < opt2) { return 1; } else { return 0; } } }; /** * Comparator used to compare two rules according to their confirmation and * then their observed number of counter-instances. */ public static Comparator confirmationThenObservedComparator = new Comparator() { public int compare(Object o1, Object o2) { int confirmationComparison = confirmationComparator.compare(o1, o2); if (confirmationComparison != 0) { return confirmationComparison; } else { return observedComparator.compare(o1, o2); } } }; /** * Comparator used to compare two rules according to their optimistic estimate * and then their observed number of counter-instances. */ public static Comparator optimisticThenObservedComparator = new Comparator() { public int compare(Object o1, Object o2) { int optimisticComparison = optimisticComparator.compare(o1, o2); if (optimisticComparison != 0) { return optimisticComparison; } else { return observedComparator.compare(o1, o2); } } }; /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.7 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/associations/tertius/SimpleLinkedList.java000066400000000000000000000214011214146114000312140ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * SimpleLinkedList.java * Copyright (C) 2003 Peter A. Flach, Nicolas Lachiche * * Thanks to Amelie Deltour for porting the original C code to Java * and integrating it into Weka. */ package weka.associations.tertius; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import java.io.Serializable; import java.util.Comparator; import java.util.NoSuchElementException; /** * @author Peter A. Flach * @author Nicolas Lachiche * @version $Revision: 1.6 $ */ public class SimpleLinkedList implements Serializable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = -1491148276509976299L; public class LinkedListIterator implements Serializable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = -2448555236100426759L; Entry current = first; Entry lastReturned = null; public boolean hasNext() { return current.next != last; } public Object next() { if (current == last) { throw new NoSuchElementException(); } current = current.next; lastReturned = current; return current.element; } public void remove() { if (lastReturned == last || lastReturned == null) { throw new IllegalStateException(); } lastReturned.previous.next = lastReturned.next; lastReturned.next.previous = lastReturned.previous; current = lastReturned.previous; lastReturned = null; } public void addBefore(Object o) { if (lastReturned == null) { throw new IllegalStateException(); } Entry newEntry = new Entry(o, lastReturned, lastReturned.previous); lastReturned.previous.next = newEntry; lastReturned.previous = newEntry; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.6 $"); } } public class LinkedListInverseIterator implements Serializable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = 6290379064027832108L; Entry current = last; Entry lastReturned = null; public boolean hasPrevious() { return current.previous != first; } public Object previous() { if (current == first) { throw new NoSuchElementException(); } current = current.previous; lastReturned = current; return current.element; } public void remove() { if (lastReturned == first || lastReturned == null) { throw new IllegalStateException(); } lastReturned.previous.next = lastReturned.next; lastReturned.next.previous = lastReturned.previous; current = lastReturned.next; lastReturned = null; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.6 $"); } } private static class Entry implements Serializable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = 7888492479685339831L; Object element; Entry next; Entry previous; Entry(Object element, Entry next, Entry previous) { this.element = element; this.next = next; this.previous = previous; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.6 $"); } } private Entry first; private Entry last; public SimpleLinkedList() { first = new Entry(null, null, null); last = new Entry(null, null, null); first.next = last; last.previous = first; } public Object removeFirst() { if (first.next == last) { throw new NoSuchElementException(); } Object result = first.next.element; first.next.next.previous = first; first.next = first.next.next; return result; } public Object getFirst() { if (first.next == last) { throw new NoSuchElementException(); } return first.next.element; } public Object getLast() { if (last.previous == first) { throw new NoSuchElementException(); } return last.previous.element; } public void addFirst(Object o) { Entry newEntry = new Entry(o, first.next, first); first.next.previous = newEntry; first.next = newEntry; } public void add(Object o) { Entry newEntry = new Entry(o, last, last.previous); last.previous.next = newEntry; last.previous = newEntry; } public void addAll(SimpleLinkedList list) { last.previous.next = list.first.next; list.first.next.previous = last.previous; last = list.last; } public void clear() { first.next = last; last.previous = first; } public boolean isEmpty() { return first.next == last; } public LinkedListIterator iterator() { return new LinkedListIterator(); } public LinkedListInverseIterator inverseIterator() { return new LinkedListInverseIterator(); } public int size() { int result = 0; LinkedListIterator iter = new LinkedListIterator(); while (iter.hasNext()) { result++; iter.next(); } return result; } public void merge(SimpleLinkedList list, Comparator comp) { LinkedListIterator iter1 = this.iterator(); LinkedListIterator iter2 = list.iterator(); Object elem1 = iter1.next(); Object elem2 = iter2.next(); while (elem2 != null) { if ((elem1 == null) || (comp.compare(elem2, elem1) < 0)) { iter1.addBefore(elem2); elem2 = iter2.next(); } else { elem1 = iter1.next(); } } } public void sort(Comparator comp) { LinkedListIterator iter = this.iterator(); if (iter.hasNext()) { SimpleLinkedList lower = new SimpleLinkedList(); SimpleLinkedList upper = new SimpleLinkedList(); Object ref = iter.next(); Object elem; while (iter.hasNext()) { elem = iter.next(); if (comp.compare(elem, ref) < 0) { lower.add(elem); } else { upper.add(elem); } } lower.sort(comp); upper.sort(comp); clear(); addAll(lower); add(ref); addAll(upper); } } public String toString() { StringBuffer text = new StringBuffer(); LinkedListIterator iter = iterator(); text.append("["); while (iter.hasNext()) { text.append(String.valueOf(iter.next())); if (iter.hasNext()) { text.append(", "); } } text.append("]"); return text.toString(); } /** * Save the state of this LinkedList instance to a stream (that * is, serialize it). * * @serialData The size of the list (the number of elements it * contains) is emitted (int), followed by all of its * elements (each an Object) in the proper order. */ private synchronized void writeObject(java.io.ObjectOutputStream s) throws java.io.IOException { // Write out any hidden serialization magic s.defaultWriteObject(); // Write out size s.writeInt(size()); // Write out all elements in the proper order. for (Entry e = first.next; e != last; e = e.next) s.writeObject(e.element); } /** * Reconstitute this LinkedList instance from a stream (that is * deserialize it). */ private synchronized void readObject(java.io.ObjectInputStream s) throws java.io.IOException, ClassNotFoundException { // Read in any hidden serialization magic s.defaultReadObject(); // Read in size int size = s.readInt(); // Initialize header first = new Entry(null, null, null); last = new Entry(null, null, null); first.next = last; last.previous = first; // Read in all elements in the proper order. for (int i=0; i * * Valid options are:

* * -h
* Display help.

* * -i <name of input file>
* Specify the training data file.

* * -c <class index>
* The index of the attribute to use as the class.

* * -s <search method>
* The full class name of the search method followed by search method options * (if any).
* Eg. -s "weka.attributeSelection.BestFirst -N 10"

* * -x <number of folds>
* Perform a cross validation.

* * -n <random number seed>
* Specify a random number seed. Use in conjuction with -x. (Default = 1).

* * ------------------------------------------------------------------------

* * Example usage as the main of an attribute evaluator (called FunkyEvaluator): * 

 * public static void main(String [] args) {
 *   runEvaluator(new FunkyEvaluator(), args);
 * }
 *
*

* * ------------------------------------------------------------------------

* * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.47 $ */ public class AttributeSelection implements Serializable, RevisionHandler { /** for serialization */ static final long serialVersionUID = 4170171824147584330L; /** the instances to select attributes from */ private Instances m_trainInstances; /** the attribute/subset evaluator */ private ASEvaluation m_ASEvaluator; /** the search method */ private ASSearch m_searchMethod; /** the number of folds to use for cross validation */ private int m_numFolds; /** holds a string describing the results of the attribute selection */ private StringBuffer m_selectionResults; /** rank features (if allowed by the search method) */ private boolean m_doRank; /** do cross validation */ private boolean m_doXval; /** seed used to randomly shuffle instances for cross validation */ private int m_seed; /** number of attributes requested from ranked results */ private int m_numToSelect; /** the selected attributes */ private int [] m_selectedAttributeSet; /** the attribute indexes and associated merits if a ranking is produced */ private double [][] m_attributeRanking; /** if a feature selection run involves an attribute transformer */ private AttributeTransformer m_transformer = null; /** the attribute filter for processing instances with respect to the most recent feature selection run */ private Remove m_attributeFilter = null; /** hold statistics for repeated feature selection, such as under cross validation */ private double [][] m_rankResults = null; private double [] m_subsetResults = null; private int m_trials = 0; /** * Return the number of attributes selected from the most recent * run of attribute selection * @return the number of attributes selected */ public int numberAttributesSelected() throws Exception { int [] att = selectedAttributes(); return att.length-1; } /** * get the final selected set of attributes. * @return an array of attribute indexes * @exception Exception if attribute selection has not been performed yet */ public int [] selectedAttributes () throws Exception { if (m_selectedAttributeSet == null) { throw new Exception("Attribute selection has not been performed yet!"); } return m_selectedAttributeSet; } /** * get the final ranking of the attributes. * @return a two dimensional array of ranked attribute indexes and their * associated merit scores as doubles. * @exception Exception if a ranking has not been produced */ public double [][] rankedAttributes () throws Exception { if (m_attributeRanking == null) { throw new Exception("Ranking has not been performed"); } return m_attributeRanking; } /** * set the attribute/subset evaluator * @param evaluator the evaluator to use */ public void setEvaluator (ASEvaluation evaluator) { m_ASEvaluator = evaluator; } /** * set the search method * @param search the search method to use */ public void setSearch (ASSearch search) { m_searchMethod = search; if (m_searchMethod instanceof RankedOutputSearch) { setRanking(((RankedOutputSearch)m_searchMethod).getGenerateRanking()); } } /** * set the number of folds for cross validation * @param folds the number of folds */ public void setFolds (int folds) { m_numFolds = folds; } /** * produce a ranking (if possible with the set search and evaluator) * @param r true if a ranking is to be produced */ public void setRanking (boolean r) { m_doRank = r; } /** * do a cross validation * @param x true if a cross validation is to be performed */ public void setXval (boolean x) { m_doXval = x; } /** * set the seed for use in cross validation * @param s the seed */ public void setSeed (int s) { m_seed = s; } /** * get a description of the attribute selection * @return a String describing the results of attribute selection */ public String toResultsString() { return m_selectionResults.toString(); } /** * reduce the dimensionality of a set of instances to include only those * attributes chosen by the last run of attribute selection. * @param in the instances to be reduced * @return a dimensionality reduced set of instances * @exception Exception if the instances can't be reduced */ public Instances reduceDimensionality(Instances in) throws Exception { if (m_attributeFilter == null) { throw new Exception("No feature selection has been performed yet!"); } if (m_transformer != null) { Instances transformed = new Instances(m_transformer.transformedHeader(), in.numInstances()); for (int i=0;i 0) { CvString.append(Utils.doubleToString(Math. abs(m_rankResults[0][s[i]]), 6, 3) + " +-" + Utils.doubleToString(m_rankResults[2][s[i]], 6, 3) + " " + Utils.doubleToString(m_rankResults[1][s[i]], fieldWidth+2, 1) + " +-" + Utils.doubleToString(m_rankResults[3][s[i]], 5, 2) +" " + Utils.doubleToString(((double)(s[i] + 1)), fieldWidth, 0) + " " + m_trainInstances.attribute(s[i]).name() + "\n"); } } } else { CvString.append("number of folds (%) attribute\n"); for (int i = 0; i < m_subsetResults.length; i++) { if ((m_ASEvaluator instanceof UnsupervisedSubsetEvaluator) || (i != m_trainInstances.classIndex())) { CvString.append(Utils.doubleToString(m_subsetResults[i], 12, 0) + "(" + Utils.doubleToString((m_subsetResults[i] / m_numFolds * 100.0) , 3, 0) + " %) " + Utils.doubleToString(((double)(i + 1)), fieldWidth, 0) + " " + m_trainInstances.attribute(i).name() + "\n"); } } } return CvString.toString(); } /** * Select attributes for a split of the data. Calling this function * updates the statistics on attribute selection. CVResultsString() * returns a string summarizing the results of repeated calls to * this function. Assumes that splits are from the same dataset--- * ie. have the same number and types of attributes as previous * splits. * * @param split the instances to select attributes from * @exception Exception if an error occurs */ public void selectAttributesCVSplit(Instances split) throws Exception { double[][] attributeRanking = null; // if the train instances are null then set equal to this split. // If this is the case then this function is more than likely being // called from outside this class in order to obtain CV statistics // and all we need m_trainIstances for is to get at attribute names // and types etc. if (m_trainInstances == null) { m_trainInstances = split; } // create space to hold statistics if (m_rankResults == null && m_subsetResults == null) { m_subsetResults = new double[split.numAttributes()]; m_rankResults = new double[4][split.numAttributes()]; } m_ASEvaluator.buildEvaluator(split); // Do the search int[] attributeSet = m_searchMethod.search(m_ASEvaluator, split); // Do any postprocessing that a attribute selection method might // require attributeSet = m_ASEvaluator.postProcess(attributeSet); if ((m_searchMethod instanceof RankedOutputSearch) && (m_doRank == true)) { attributeRanking = ((RankedOutputSearch)m_searchMethod). rankedAttributes(); // System.out.println(attributeRanking[0][1]); for (int j = 0; j < attributeRanking.length; j++) { // merit m_rankResults[0][(int)attributeRanking[j][0]] += attributeRanking[j][1]; // squared merit m_rankResults[2][(int)attributeRanking[j][0]] += (attributeRanking[j][1]*attributeRanking[j][1]); // rank m_rankResults[1][(int)attributeRanking[j][0]] += (j + 1); // squared rank m_rankResults[3][(int)attributeRanking[j][0]] += (j + 1)*(j + 1); // += (attributeRanking[j][0] * attributeRanking[j][0]); } } else { for (int j = 0; j < attributeSet.length; j++) { m_subsetResults[attributeSet[j]]++; } } m_trials++; } /** * Perform a cross validation for attribute selection. With subset * evaluators the number of times each attribute is selected over * the cross validation is reported. For attribute evaluators, the * average merit and average ranking + std deviation is reported for * each attribute. * * @return the results of cross validation as a String * @exception Exception if an error occurs during cross validation */ public String CrossValidateAttributes () throws Exception { Instances cvData = new Instances(m_trainInstances); Instances train; Random random = new Random(m_seed); cvData.randomize(random); if (!(m_ASEvaluator instanceof UnsupervisedSubsetEvaluator) && !(m_ASEvaluator instanceof UnsupervisedAttributeEvaluator)) { if (cvData.classAttribute().isNominal()) { cvData.stratify(m_numFolds); } } for (int i = 0; i < m_numFolds; i++) { // Perform attribute selection train = cvData.trainCV(m_numFolds, i, random); selectAttributesCVSplit(train); } return CVResultsString(); } /** * Perform attribute selection on the supplied training instances. * * @param data the instances to select attributes from * @exception Exception if there is a problem during selection */ public void SelectAttributes (Instances data) throws Exception { int [] attributeSet; m_transformer = null; m_attributeFilter = null; m_trainInstances = data; if (m_doXval == true && (m_ASEvaluator instanceof AttributeTransformer)) { throw new Exception("Can't cross validate an attribute transformer."); } if (m_ASEvaluator instanceof SubsetEvaluator && m_searchMethod instanceof Ranker) { throw new Exception(m_ASEvaluator.getClass().getName() +" must use a search method other than Ranker"); } if (m_ASEvaluator instanceof AttributeEvaluator && !(m_searchMethod instanceof Ranker)) { // System.err.println("AttributeEvaluators must use a Ranker search " // +"method. Switching to Ranker..."); // m_searchMethod = new Ranker(); throw new Exception("AttributeEvaluators must use the Ranker search " + "method"); } if (m_searchMethod instanceof RankedOutputSearch) { m_doRank = ((RankedOutputSearch)m_searchMethod).getGenerateRanking(); } if (m_ASEvaluator instanceof UnsupervisedAttributeEvaluator || m_ASEvaluator instanceof UnsupervisedSubsetEvaluator) { // unset the class index // m_trainInstances.setClassIndex(-1); } else { // check that a class index has been set if (m_trainInstances.classIndex() < 0) { m_trainInstances.setClassIndex(m_trainInstances.numAttributes()-1); } } // Initialize the attribute evaluator m_ASEvaluator.buildEvaluator(m_trainInstances); if (m_ASEvaluator instanceof AttributeTransformer) { m_trainInstances = ((AttributeTransformer)m_ASEvaluator).transformedHeader(); m_transformer = (AttributeTransformer)m_ASEvaluator; } int fieldWidth = (int)(Math.log(m_trainInstances.numAttributes()) +1.0); // Do the search attributeSet = m_searchMethod.search(m_ASEvaluator, m_trainInstances); // try and determine if the search method uses an attribute transformer--- // this is a bit of a hack to make things work properly with RankSearch // using PrincipalComponents as its attribute ranker try { BeanInfo bi = Introspector.getBeanInfo(m_searchMethod.getClass()); PropertyDescriptor properties[]; MethodDescriptor methods[]; // methods = bi.getMethodDescriptors(); properties = bi.getPropertyDescriptors(); for (int i=0;i 0) { precision = Math.abs((Math.log(Math.abs(precision)) / Math.log(10)))+3; } if (precision > f_p) { f_p = (int)precision; } if (intPart == 0) { if (w_p < 2) { w_p = 2; } } else if ((Math.abs((Math.log(Math.abs(m_attributeRanking[i][1])) / Math.log(10)))+1) > w_p) { if (m_attributeRanking[i][1] > 0) { w_p = (int)Math.abs((Math.log(Math.abs(m_attributeRanking[i][1])) / Math.log(10)))+1; } } } for (int i = 0; i < m_numToSelect; i++) { m_selectionResults. append(Utils.doubleToString(m_attributeRanking[i][1], f_p+w_p+1,f_p) + Utils.doubleToString((m_attributeRanking[i][0] + 1), fieldWidth+1,0) + " " + m_trainInstances. attribute((int)m_attributeRanking[i][0]).name() + "\n"); } // set up the selected attributes array - usable by a filter or // whatever if (m_trainInstances.classIndex() >= 0) { if ((!(m_ASEvaluator instanceof UnsupervisedSubsetEvaluator) && !(m_ASEvaluator instanceof UnsupervisedAttributeEvaluator)) || m_ASEvaluator instanceof AttributeTransformer) { // one more for the class m_selectedAttributeSet = new int[m_numToSelect + 1]; m_selectedAttributeSet[m_numToSelect] = m_trainInstances.classIndex(); } else { m_selectedAttributeSet = new int[m_numToSelect]; } } else { m_selectedAttributeSet = new int[m_numToSelect]; } m_selectionResults.append("\nSelected attributes: "); for (int i = 0; i < m_numToSelect; i++) { m_selectedAttributeSet[i] = (int)m_attributeRanking[i][0]; if (i == m_numToSelect - 1) { m_selectionResults.append(((int)m_attributeRanking[i][0] + 1) + " : " + (i + 1) + "\n"); } else { m_selectionResults.append(((int)m_attributeRanking[i][0] + 1)); m_selectionResults.append(","); } } } else { // set up the selected attributes array - usable by a filter or // whatever if ((!(m_ASEvaluator instanceof UnsupervisedSubsetEvaluator) && !(m_ASEvaluator instanceof UnsupervisedAttributeEvaluator)) || m_trainInstances.classIndex() >= 0) // one more for the class { m_selectedAttributeSet = new int[attributeSet.length + 1]; m_selectedAttributeSet[attributeSet.length] = m_trainInstances.classIndex(); } else { m_selectedAttributeSet = new int[attributeSet.length]; } for (int i = 0; i < attributeSet.length; i++) { m_selectedAttributeSet[i] = attributeSet[i]; } m_selectionResults.append("Selected attributes: "); for (int i = 0; i < attributeSet.length; i++) { if (i == (attributeSet.length - 1)) { m_selectionResults.append((attributeSet[i] + 1) + " : " + attributeSet.length + "\n"); } else { m_selectionResults.append((attributeSet[i] + 1) + ","); } } for (int i=0;i train.numAttributes()))) { throw new Exception("Class index out of range."); } if (classIndex != -1) { train.setClassIndex(classIndex - 1); } else { // classIndex = train.numAttributes(); // train.setClassIndex(classIndex - 1); } foldsString = Utils.getOption('x', options); if (foldsString.length() != 0) { folds = Integer.parseInt(foldsString); doCrossVal = true; } trainSelector.setFolds(folds); trainSelector.setXval(doCrossVal); seedString = Utils.getOption('n', options); if (seedString.length() != 0) { seed = Integer.parseInt(seedString); } trainSelector.setSeed(seed); searchName = Utils.getOption('s', options); if ((searchName.length() == 0) && (!(ASEvaluator instanceof AttributeEvaluator))) { throw new Exception("No search method given."); } if (searchName.length() != 0) { searchName = searchName.trim(); // split off any search options int breakLoc = searchName.indexOf(' '); searchClassName = searchName; String searchOptionsString = ""; if (breakLoc != -1) { searchClassName = searchName.substring(0, breakLoc); searchOptionsString = searchName.substring(breakLoc).trim(); searchOptions = Utils.splitOptions(searchOptionsString); } } else { try { searchClassName = new String("weka.attributeSelection.Ranker"); searchMethod = (ASSearch)Class. forName(searchClassName).newInstance(); } catch (Exception e) { throw new Exception("Can't create Ranker object"); } } // if evaluator is a subset evaluator // create search method and set its options (if any) if (searchMethod == null) { searchMethod = ASSearch.forName(searchClassName, searchOptions); } // set the search method trainSelector.setSearch(searchMethod); } catch (Exception e) { throw new Exception('\n' + e.getMessage() + makeOptionString(ASEvaluator, searchMethod)); } try { // Set options for ASEvaluator if (ASEvaluator instanceof OptionHandler) { ((OptionHandler)ASEvaluator).setOptions(options); } /* // Set options for Search method if (searchMethod instanceof OptionHandler) { if (searchOptions != null) { ((OptionHandler)searchMethod).setOptions(searchOptions); } } Utils.checkForRemainingOptions(searchOptions); */ } catch (Exception e) { throw new Exception("\n" + e.getMessage() + makeOptionString(ASEvaluator, searchMethod)); } try { Utils.checkForRemainingOptions(options); } catch (Exception e) { throw new Exception('\n' + e.getMessage() + makeOptionString(ASEvaluator, searchMethod)); } if (helpRequested) { System.out.println(makeOptionString(ASEvaluator, searchMethod)); System.exit(0); } // set the attribute evaluator trainSelector.setEvaluator(ASEvaluator); // do the attribute selection trainSelector.SelectAttributes(train); // return the results string return trainSelector.toResultsString(); } /** * Assembles a text description of the attribute selection results. * * @return a string describing the results of attribute selection. */ private String printSelectionResults () { StringBuffer text = new StringBuffer(); text.append("\n\n=== Attribute Selection on all input data ===\n\n" + "Search Method:\n"); text.append(m_searchMethod.toString()); text.append("\nAttribute "); if (m_ASEvaluator instanceof SubsetEvaluator) { text.append("Subset Evaluator ("); } else { text.append("Evaluator ("); } if (!(m_ASEvaluator instanceof UnsupervisedSubsetEvaluator) && !(m_ASEvaluator instanceof UnsupervisedAttributeEvaluator)) { text.append("supervised, "); text.append("Class ("); if (m_trainInstances.attribute(m_trainInstances.classIndex()) .isNumeric()) { text.append("numeric): "); } else { text.append("nominal): "); } text.append((m_trainInstances.classIndex() + 1) + " " + m_trainInstances.attribute(m_trainInstances .classIndex()).name() + "):\n"); } else { text.append("unsupervised):\n"); } text.append(m_ASEvaluator.toString() + "\n"); return text.toString(); } /** * Make up the help string giving all the command line options * * @param ASEvaluator the attribute evaluator to include options for * @param searchMethod the search method to include options for * @return a string detailing the valid command line options * @throws Exception if something goes wrong */ private static String makeOptionString (ASEvaluation ASEvaluator, ASSearch searchMethod) throws Exception { StringBuffer optionsText = new StringBuffer(""); // General options optionsText.append("\n\nGeneral options:\n\n"); optionsText.append("-h\n\tdisplay this help\n"); optionsText.append("-i \n"); optionsText.append("\tSets training file.\n"); optionsText.append("-c \n"); optionsText.append("\tSets the class index for supervised attribute\n"); optionsText.append("\tselection. Default=last column.\n"); optionsText.append("-s \n"); optionsText.append("\tSets search method for subset evaluators.\n"); optionsText.append("-x \n"); optionsText.append("\tPerform a cross validation.\n"); optionsText.append("-n \n"); optionsText.append("\tUse in conjunction with -x.\n"); // Get attribute evaluator-specific options if (ASEvaluator instanceof OptionHandler) { optionsText.append("\nOptions specific to " + ASEvaluator.getClass().getName() + ":\n\n"); Enumeration enu = ((OptionHandler)ASEvaluator).listOptions(); while (enu.hasMoreElements()) { Option option = (Option)enu.nextElement(); optionsText.append(option.synopsis() + '\n'); optionsText.append(option.description() + "\n"); } } if (searchMethod != null) { if (searchMethod instanceof OptionHandler) { optionsText.append("\nOptions specific to " + searchMethod.getClass().getName() + ":\n\n"); Enumeration enu = ((OptionHandler)searchMethod).listOptions(); while (enu.hasMoreElements()) { Option option = (Option)enu.nextElement(); optionsText.append(option.synopsis() + '\n'); optionsText.append(option.description() + "\n"); } } } else { if (ASEvaluator instanceof SubsetEvaluator) { System.out.println("No search method given."); } } return optionsText.toString(); } /** * Main method for testing this class. * * @param args the options */ public static void main (String[] args) { try { if (args.length == 0) { throw new Exception("The first argument must be the name of an " + "attribute/subset evaluator"); } String EvaluatorName = args[0]; args[0] = ""; ASEvaluation newEval = ASEvaluation.forName(EvaluatorName, null); System.out.println(SelectAttributes(newEval, args)); } catch (Exception e) { System.out.println(e.getMessage()); } } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.47 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/AttributeSetEvaluator.java000066400000000000000000000060241214146114000317610ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * RELEASE INFORMATION (December 27, 2004) * * FCBF algorithm: * Template obtained from Weka * Developped for Weka by Zheng Alan Zhao * December 27, 2004 * * FCBF algorithm is a feature selection method based on Symmetrical Uncertainty * Measurement for relevance redundancy analysis. The details of FCBF algorithm are * in L. Yu and H. Liu. Feature selection for high-dimensional data: a fast * correlation-based filter solution. In Proceedings of the twentieth International * Conference on Machine Learning, pages 856--863, 2003. * * * CONTACT INFORMATION * * For algorithm implementation: * Zheng Zhao: zhaozheng at asu.edu * * For the algorithm: * Lei Yu: leiyu at asu.edu * Huan Liu: hliu at asu.edu * * Data Mining and Machine Learning Lab * Computer Science and Engineering Department * Fulton School of Engineering * Arizona State University * Tempe, AZ 85287 * * AttributeSetEvaluator.java * * Copyright (C) 2004 Data Mining and Machine Learning Lab, * Computer Science and Engineering Department, * Fulton School of Engineering, * Arizona State University * */ package weka.attributeSelection; /** * Abstract attribute set evaluator. * * @author Zheng Zhao: zhaozheng at asu.edu * @version $Revision: 1.3 $ */ public abstract class AttributeSetEvaluator extends ASEvaluation { /** for serialization */ private static final long serialVersionUID = -5744881009422257389L; // =============== // Public methods. // =============== /** * evaluates an individual attribute * * @param attribute the index of the attribute to be evaluated * @return the "merit" of the attribute * @exception Exception if the attribute could not be evaluated */ public abstract double evaluateAttribute(int attribute) throws Exception; /** * Evaluates a set of attributes * * @param attributes an int[] value * @param classAttributes an int[] value * @return a double value * @exception Exception if an error occurs */ public abstract double evaluateAttribute(int[] attributes, int[] classAttributes) throws Exception; } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/AttributeTransformer.java000066400000000000000000000041761214146114000316530ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * AttributeTransformer.java * Copyright (C) 2000 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.core.Instance; import weka.core.Instances; /** * Abstract attribute transformer. Transforms the dataset. * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.9 $ */ public interface AttributeTransformer { // =============== // Public methods. // =============== /** * Returns just the header for the transformed data (ie. an empty * set of instances. This is so that AttributeSelection can * determine the structure of the transformed data without actually * having to get all the transformed data through getTransformedData(). * @return the header of the transformed data. * @exception Exception if the header of the transformed data can't * be determined. */ Instances transformedHeader() throws Exception; /** * Returns the transformed data * @return A set of instances representing the transformed data * @exception Exception if the attribute could not be evaluated */ Instances transformedData() throws Exception; /** * Transforms an instance in the format of the original data to the * transformed space * @return a transformed instance * @exception Exception if the instance could not be transformed */ Instance convertInstance(Instance instance) throws Exception; } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/BestFirst.java000066400000000000000000000602141214146114000273650ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * BestFirst.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.core.FastVector; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.Range; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import weka.core.SelectedTag; import weka.core.Tag; import weka.core.Utils; import java.io.Serializable; import java.util.BitSet; import java.util.Enumeration; import java.util.Hashtable; import java.util.Vector; /** * BestFirst:
*
* Searches the space of attribute subsets by greedy hillclimbing augmented with a backtracking facility. Setting the number of consecutive non-improving nodes allowed controls the level of backtracking done. Best first may start with the empty set of attributes and search forward, or start with the full set of attributes and search backward, or start at any point and search in both directions (by considering all possible single attribute additions and deletions at a given point).
*

* * Valid options are:

* * 

 -P <start set>
 *  Specify a starting set of attributes.
 *  Eg. 1,3,5-7.
* * 
 -D <0 = backward | 1 = forward | 2 = bi-directional>
 *  Direction of search. (default = 1).
* * 
 -N <num>
 *  Number of non-improving nodes to
 *  consider before terminating search.
* * 
 -S <num>
 *  Size of lookup cache for evaluated subsets.
 *  Expressed as a multiple of the number of
 *  attributes in the data set. (default = 1)
* * * @author Mark Hall (mhall@cs.waikato.ac.nz) * Martin Guetlein (cashing merit of expanded nodes) * @version $Revision: 1.29 $ */ public class BestFirst extends ASSearch implements OptionHandler, StartSetHandler { /** for serialization */ static final long serialVersionUID = 7841338689536821867L; // Inner classes /** * Class for a node in a linked list. Used in best first search. * @author Mark Hall (mhall@cs.waikato.ac.nz) **/ public class Link2 implements Serializable, RevisionHandler { /** for serialization */ static final long serialVersionUID = -8236598311516351420L; /* BitSet group; */ Object [] m_data; double m_merit; /** * Constructor */ public Link2 (Object [] data, double mer) { // group = (BitSet)gr.clone(); m_data = data; m_merit = mer; } /** Get a group */ public Object [] getData () { return m_data; } public String toString () { return ("Node: " + m_data.toString() + " " + m_merit); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.29 $"); } } /** * Class for handling a linked list. Used in best first search. * Extends the Vector class. * @author Mark Hall (mhall@cs.waikato.ac.nz) **/ public class LinkedList2 extends FastVector { /** for serialization */ static final long serialVersionUID = 3250538292330398929L; /** Max number of elements in the list */ int m_MaxSize; // ================ // Public methods // ================ public LinkedList2 (int sz) { super(); m_MaxSize = sz; } /** * removes an element (Link) at a specific index from the list. * @param index the index of the element to be removed. **/ public void removeLinkAt (int index) throws Exception { if ((index >= 0) && (index < size())) { removeElementAt(index); } else { throw new Exception("index out of range (removeLinkAt)"); } } /** * returns the element (Link) at a specific index from the list. * @param index the index of the element to be returned. **/ public Link2 getLinkAt (int index) throws Exception { if (size() == 0) { throw new Exception("List is empty (getLinkAt)"); } else {if ((index >= 0) && (index < size())) { return ((Link2)(elementAt(index))); } else { throw new Exception("index out of range (getLinkAt)"); } } } /** * adds an element (Link) to the list. * @param data the attribute set specification * @param mer the "merit" of this attribute set **/ public void addToList (Object [] data, double mer) throws Exception { Link2 newL = new Link2(data, mer); if (size() == 0) { addElement(newL); } else {if (mer > ((Link2)(firstElement())).m_merit) { if (size() == m_MaxSize) { removeLinkAt(m_MaxSize - 1); } //---------- insertElementAt(newL, 0); } else { int i = 0; int size = size(); boolean done = false; //------------ // don't insert if list contains max elements an this // is worst than the last if ((size == m_MaxSize) && (mer <= ((Link2)(lastElement())).m_merit)) { } //--------------- else { while ((!done) && (i < size)) { if (mer > ((Link2)(elementAt(i))).m_merit) { if (size == m_MaxSize) { removeLinkAt(m_MaxSize - 1); } // --------------------- insertElementAt(newL, i); done = true; } else {if (i == size - 1) { addElement(newL); done = true; } else { i++; } } } } } } } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.29 $"); } } // member variables /** maximum number of stale nodes before terminating search */ protected int m_maxStale; /** 0 == backward search, 1 == forward search, 2 == bidirectional */ protected int m_searchDirection; /** search direction: backward */ protected static final int SELECTION_BACKWARD = 0; /** search direction: forward */ protected static final int SELECTION_FORWARD = 1; /** search direction: bidirectional */ protected static final int SELECTION_BIDIRECTIONAL = 2; /** search directions */ public static final Tag [] TAGS_SELECTION = { new Tag(SELECTION_BACKWARD, "Backward"), new Tag(SELECTION_FORWARD, "Forward"), new Tag(SELECTION_BIDIRECTIONAL, "Bi-directional"), }; /** holds an array of starting attributes */ protected int[] m_starting; /** holds the start set for the search as a Range */ protected Range m_startRange; /** does the data have a class */ protected boolean m_hasClass; /** holds the class index */ protected int m_classIndex; /** number of attributes in the data */ protected int m_numAttribs; /** total number of subsets evaluated during a search */ protected int m_totalEvals; /** for debugging */ protected boolean m_debug; /** holds the merit of the best subset found */ protected double m_bestMerit; /** holds the maximum size of the lookup cache for evaluated subsets */ protected int m_cacheSize; /** * Returns a string describing this search method * @return a description of the search method suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "BestFirst:\n\n" +"Searches the space of attribute subsets by greedy hillclimbing " +"augmented with a backtracking facility. Setting the number of " +"consecutive non-improving nodes allowed controls the level of " +"backtracking done. Best first may start with the empty set of " +"attributes and search forward, or start with the full set of " +"attributes and search backward, or start at any point and search " +"in both directions (by considering all possible single attribute " +"additions and deletions at a given point).\n"; } /** *Constructor */ public BestFirst () { resetOptions(); } /** * Returns an enumeration describing the available options. * @return an enumeration of all the available options. * **/ public Enumeration listOptions () { Vector newVector = new Vector(4); newVector.addElement(new Option("\tSpecify a starting set of attributes." + "\n\tEg. 1,3,5-7." ,"P",1 , "-P ")); newVector.addElement(new Option("\tDirection of search. (default = 1)." , "D", 1 , "-D <0 = backward | 1 = forward " + "| 2 = bi-directional>")); newVector.addElement(new Option("\tNumber of non-improving nodes to" + "\n\tconsider before terminating search." , "N", 1, "-N ")); newVector.addElement(new Option("\tSize of lookup cache for evaluated subsets." +"\n\tExpressed as a multiple of the number of" +"\n\tattributes in the data set. (default = 1)", "S", 1, "-S ")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -P <start set>
   *  Specify a starting set of attributes.
   *  Eg. 1,3,5-7.
* * 
 -D <0 = backward | 1 = forward | 2 = bi-directional>
   *  Direction of search. (default = 1).
* * 
 -N <num>
   *  Number of non-improving nodes to
   *  consider before terminating search.
* * 
 -S <num>
   *  Size of lookup cache for evaluated subsets.
   *  Expressed as a multiple of the number of
   *  attributes in the data set. (default = 1)
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported * **/ public void setOptions (String[] options) throws Exception { String optionString; resetOptions(); optionString = Utils.getOption('P', options); if (optionString.length() != 0) { setStartSet(optionString); } optionString = Utils.getOption('D', options); if (optionString.length() != 0) { setDirection(new SelectedTag(Integer.parseInt(optionString), TAGS_SELECTION)); } else { setDirection(new SelectedTag(SELECTION_FORWARD, TAGS_SELECTION)); } optionString = Utils.getOption('N', options); if (optionString.length() != 0) { setSearchTermination(Integer.parseInt(optionString)); } optionString = Utils.getOption('S', options); if (optionString.length() != 0) { setLookupCacheSize(Integer.parseInt(optionString)); } m_debug = Utils.getFlag('Z', options); } /** * Set the maximum size of the evaluated subset cache (hashtable). This is * expressed as a multiplier for the number of attributes in the data set. * (default = 1). * * @param size the maximum size of the hashtable */ public void setLookupCacheSize(int size) { if (size >= 0) { m_cacheSize = size; } } /** * Return the maximum size of the evaluated subset cache (expressed as a multiplier * for the number of attributes in a data set. * * @return the maximum size of the hashtable. */ public int getLookupCacheSize() { return m_cacheSize; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String lookupCacheSizeTipText() { return "Set the maximum size of the lookup cache of evaluated subsets. This is " +"expressed as a multiplier of the number of attributes in the data set. " +"(default = 1)."; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String startSetTipText() { return "Set the start point for the search. This is specified as a comma " +"seperated list off attribute indexes starting at 1. It can include " +"ranges. Eg. 1,2,5-9,17."; } /** * Sets a starting set of attributes for the search. It is the * search method's responsibility to report this start set (if any) * in its toString() method. * @param startSet a string containing a list of attributes (and or ranges), * eg. 1,2,6,10-15. * @throws Exception if start set can't be set. */ public void setStartSet (String startSet) throws Exception { m_startRange.setRanges(startSet); } /** * Returns a list of attributes (and or attribute ranges) as a String * @return a list of attributes (and or attribute ranges) */ public String getStartSet () { return m_startRange.getRanges(); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String searchTerminationTipText() { return "Set the amount of backtracking. Specify the number of "; } /** * Set the numnber of non-improving nodes to consider before terminating * search. * * @param t the number of non-improving nodes * @throws Exception if t is less than 1 */ public void setSearchTermination (int t) throws Exception { if (t < 1) { throw new Exception("Value of -N must be > 0."); } m_maxStale = t; } /** * Get the termination criterion (number of non-improving nodes). * * @return the number of non-improving nodes */ public int getSearchTermination () { return m_maxStale; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String directionTipText() { return "Set the direction of the search."; } /** * Set the search direction * * @param d the direction of the search */ public void setDirection (SelectedTag d) { if (d.getTags() == TAGS_SELECTION) { m_searchDirection = d.getSelectedTag().getID(); } } /** * Get the search direction * * @return the direction of the search */ public SelectedTag getDirection () { return new SelectedTag(m_searchDirection, TAGS_SELECTION); } /** * Gets the current settings of BestFirst. * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { String[] options = new String[6]; int current = 0; if (!(getStartSet().equals(""))) { options[current++] = "-P"; options[current++] = ""+startSetToString(); } options[current++] = "-D"; options[current++] = "" + m_searchDirection; options[current++] = "-N"; options[current++] = "" + m_maxStale; while (current < options.length) { options[current++] = ""; } return options; } /** * converts the array of starting attributes to a string. This is * used by getOptions to return the actual attributes specified * as the starting set. This is better than using m_startRanges.getRanges() * as the same start set can be specified in different ways from the * command line---eg 1,2,3 == 1-3. This is to ensure that stuff that * is stored in a database is comparable. * @return a comma seperated list of individual attribute numbers as a String */ private String startSetToString() { StringBuffer FString = new StringBuffer(); boolean didPrint; if (m_starting == null) { return getStartSet(); } for (int i = 0; i < m_starting.length; i++) { didPrint = false; if ((m_hasClass == false) || (m_hasClass == true && i != m_classIndex)) { FString.append((m_starting[i] + 1)); didPrint = true; } if (i == (m_starting.length - 1)) { FString.append(""); } else { if (didPrint) { FString.append(","); } } } return FString.toString(); } /** * returns a description of the search as a String * @return a description of the search */ public String toString () { StringBuffer BfString = new StringBuffer(); BfString.append("\tBest first.\n\tStart set: "); if (m_starting == null) { BfString.append("no attributes\n"); } else { BfString.append(startSetToString()+"\n"); } BfString.append("\tSearch direction: "); if (m_searchDirection == SELECTION_BACKWARD) { BfString.append("backward\n"); } else {if (m_searchDirection == SELECTION_FORWARD) { BfString.append("forward\n"); } else { BfString.append("bi-directional\n"); } } BfString.append("\tStale search after " + m_maxStale + " node expansions\n"); BfString.append("\tTotal number of subsets evaluated: " + m_totalEvals + "\n"); BfString.append("\tMerit of best subset found: " +Utils.doubleToString(Math.abs(m_bestMerit),8,3)+"\n"); return BfString.toString(); } protected void printGroup (BitSet tt, int numAttribs) { int i; for (i = 0; i < numAttribs; i++) { if (tt.get(i) == true) { System.out.print((i + 1) + " "); } } System.out.println(); } /** * Searches the attribute subset space by best first search * * @param ASEval the attribute evaluator to guide the search * @param data the training instances. * @return an array (not necessarily ordered) of selected attribute indexes * @throws Exception if the search can't be completed */ public int[] search (ASEvaluation ASEval, Instances data) throws Exception { m_totalEvals = 0; if (!(ASEval instanceof SubsetEvaluator)) { throw new Exception(ASEval.getClass().getName() + " is not a " + "Subset evaluator!"); } if (ASEval instanceof UnsupervisedSubsetEvaluator) { m_hasClass = false; } else { m_hasClass = true; m_classIndex = data.classIndex(); } SubsetEvaluator ASEvaluator = (SubsetEvaluator)ASEval; m_numAttribs = data.numAttributes(); int i, j; int best_size = 0; int size = 0; int done; int sd = m_searchDirection; BitSet best_group, temp_group; int stale; double best_merit; double merit; boolean z; boolean added; Link2 tl; Hashtable lookup = new Hashtable(m_cacheSize * m_numAttribs); int insertCount = 0; int cacheHits = 0; LinkedList2 bfList = new LinkedList2(m_maxStale); best_merit = -Double.MAX_VALUE; stale = 0; best_group = new BitSet(m_numAttribs); m_startRange.setUpper(m_numAttribs-1); if (!(getStartSet().equals(""))) { m_starting = m_startRange.getSelection(); } // If a starting subset has been supplied, then initialise the bitset if (m_starting != null) { for (i = 0; i < m_starting.length; i++) { if ((m_starting[i]) != m_classIndex) { best_group.set(m_starting[i]); } } best_size = m_starting.length; m_totalEvals++; } else { if (m_searchDirection == SELECTION_BACKWARD) { setStartSet("1-last"); m_starting = new int[m_numAttribs]; // init initial subset to all attributes for (i = 0, j = 0; i < m_numAttribs; i++) { if (i != m_classIndex) { best_group.set(i); m_starting[j++] = i; } } best_size = m_numAttribs - 1; m_totalEvals++; } } // evaluate the initial subset best_merit = ASEvaluator.evaluateSubset(best_group); // add the initial group to the list and the hash table Object [] best = new Object[1]; best[0] = best_group.clone(); bfList.addToList(best, best_merit); BitSet tt = (BitSet)best_group.clone(); String hashC = tt.toString(); lookup.put(hashC, new Double(best_merit)); while (stale < m_maxStale) { added = false; if (m_searchDirection == SELECTION_BIDIRECTIONAL) { // bi-directional search done = 2; sd = SELECTION_FORWARD; } else { done = 1; } // finished search? if (bfList.size() == 0) { stale = m_maxStale; break; } // copy the attribute set at the head of the list tl = bfList.getLinkAt(0); temp_group = (BitSet)(tl.getData()[0]); temp_group = (BitSet)temp_group.clone(); // remove the head of the list bfList.removeLinkAt(0); // count the number of bits set (attributes) int kk; for (kk = 0, size = 0; kk < m_numAttribs; kk++) { if (temp_group.get(kk)) { size++; } } do { for (i = 0; i < m_numAttribs; i++) { if (sd == SELECTION_FORWARD) { z = ((i != m_classIndex) && (!temp_group.get(i))); } else { z = ((i != m_classIndex) && (temp_group.get(i))); } if (z) { // set the bit (attribute to add/delete) if (sd == SELECTION_FORWARD) { temp_group.set(i); size++; } else { temp_group.clear(i); size--; } /* if this subset has been seen before, then it is already in the list (or has been fully expanded) */ tt = (BitSet)temp_group.clone(); hashC = tt.toString(); if (lookup.containsKey(hashC) == false) { merit = ASEvaluator.evaluateSubset(temp_group); m_totalEvals++; // insert this one in the hashtable if (insertCount > m_cacheSize * m_numAttribs) { lookup = new Hashtable(m_cacheSize * m_numAttribs); insertCount = 0; } hashC = tt.toString(); lookup.put(hashC, new Double(merit)); insertCount++; } else { merit = ((Double)lookup.get(hashC)).doubleValue(); cacheHits++; } // insert this one in the list Object[] add = new Object[1]; add[0] = tt.clone(); bfList.addToList(add, merit); if (m_debug) { System.out.print("Group: "); printGroup(tt, m_numAttribs); System.out.println("Merit: " + merit); } // is this better than the best? if (sd == SELECTION_FORWARD) { z = ((merit - best_merit) > 0.00001); } else { if (merit == best_merit) { z = (size < best_size); } else { z = (merit > best_merit); } } if (z) { added = true; stale = 0; best_merit = merit; // best_size = (size + best_size); best_size = size; best_group = (BitSet)(temp_group.clone()); } // unset this addition(deletion) if (sd == SELECTION_FORWARD) { temp_group.clear(i); size--; } else { temp_group.set(i); size++; } } } if (done == 2) { sd = SELECTION_BACKWARD; } done--; } while (done > 0); /* if we haven't added a new attribute subset then full expansion of this node hasen't resulted in anything better */ if (!added) { stale++; } } m_bestMerit = best_merit; return attributeList(best_group); } /** * Reset options to default values */ protected void resetOptions () { m_maxStale = 5; m_searchDirection = SELECTION_FORWARD; m_starting = null; m_startRange = new Range(); m_classIndex = -1; m_totalEvals = 0; m_cacheSize = 1; m_debug = false; } /** * converts a BitSet into a list of attribute indexes * @param group the BitSet to convert * @return an array of attribute indexes **/ protected int[] attributeList (BitSet group) { int count = 0; // count how many were selected for (int i = 0; i < m_numAttribs; i++) { if (group.get(i)) { count++; } } int[] list = new int[count]; count = 0; for (int i = 0; i < m_numAttribs; i++) { if (group.get(i)) { list[count++] = i; } } return list; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.29 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/CfsSubsetEval.java000066400000000000000000000740001214146114000301670ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * CfsSubsetEval.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.core.Capabilities; import weka.core.ContingencyTables; import weka.core.Instance; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.TechnicalInformation; import weka.core.TechnicalInformationHandler; import weka.core.Utils; import weka.core.Capabilities.Capability; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; import weka.filters.Filter; import weka.filters.supervised.attribute.Discretize; import java.util.BitSet; import java.util.Enumeration; import java.util.Vector; /** * CfsSubsetEval :
*
* Evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them.
*
* Subsets of features that are highly correlated with the class while having low intercorrelation are preferred.
*
* For more information see:
*
* M. A. Hall (1998). Correlation-based Feature Subset Selection for Machine Learning. Hamilton, New Zealand. *

* * BibTeX: * 

 * @phdthesis{Hall1998,
 *    address = {Hamilton, New Zealand},
 *    author = {M. A. Hall},
 *    school = {University of Waikato},
 *    title = {Correlation-based Feature Subset Selection for Machine Learning},
 *    year = {1998}
 * }
 *
*

* * Valid options are:

* * 

 -M
 *  Treat missing values as a seperate value.
* * 
 -L
 *  Don't include locally predictive attributes.
* * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.28 $ * @see Discretize */ public class CfsSubsetEval extends ASEvaluation implements SubsetEvaluator, OptionHandler, TechnicalInformationHandler { /** for serialization */ static final long serialVersionUID = 747878400813276317L; /** The training instances */ private Instances m_trainInstances; /** Discretise attributes when class in nominal */ private Discretize m_disTransform; /** The class index */ private int m_classIndex; /** Is the class numeric */ private boolean m_isNumeric; /** Number of attributes in the training data */ private int m_numAttribs; /** Number of instances in the training data */ private int m_numInstances; /** Treat missing values as seperate values */ private boolean m_missingSeperate; /** Include locally predicitive attributes */ private boolean m_locallyPredictive; /** Holds the matrix of attribute correlations */ // private Matrix m_corr_matrix; private float [][] m_corr_matrix; /** Standard deviations of attributes (when using pearsons correlation) */ private double[] m_std_devs; /** Threshold for admitting locally predictive features */ private double m_c_Threshold; /** * Returns a string describing this attribute evaluator * @return a description of the evaluator suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "CfsSubsetEval :\n\nEvaluates the worth of a subset of attributes " +"by considering the individual predictive ability of each feature " +"along with the degree of redundancy between them.\n\n" +"Subsets of features that are highly correlated with the class " +"while having low intercorrelation are preferred.\n\n" + "For more information see:\n\n" + getTechnicalInformation().toString(); } /** * Returns an instance of a TechnicalInformation object, containing * detailed information about the technical background of this class, * e.g., paper reference or book this class is based on. * * @return the technical information about this class */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.PHDTHESIS); result.setValue(Field.AUTHOR, "M. A. Hall"); result.setValue(Field.YEAR, "1998"); result.setValue(Field.TITLE, "Correlation-based Feature Subset Selection for Machine Learning"); result.setValue(Field.SCHOOL, "University of Waikato"); result.setValue(Field.ADDRESS, "Hamilton, New Zealand"); return result; } /** * Constructor */ public CfsSubsetEval () { resetOptions(); } /** * Returns an enumeration describing the available options. * @return an enumeration of all the available options. * **/ public Enumeration listOptions () { Vector newVector = new Vector(3); newVector.addElement(new Option("\tTreat missing values as a seperate " + "value.", "M", 0, "-M")); newVector.addElement(new Option("\tDon't include locally predictive attributes" + ".", "L", 0, "-L")); return newVector.elements(); } /** * Parses and sets a given list of options.

* * Valid options are:

* * 

 -M
   *  Treat missing values as a seperate value.
* * 
 -L
   *  Don't include locally predictive attributes.
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported * **/ public void setOptions (String[] options) throws Exception { resetOptions(); setMissingSeperate(Utils.getFlag('M', options)); setLocallyPredictive(!Utils.getFlag('L', options)); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String locallyPredictiveTipText() { return "Identify locally predictive attributes. Iteratively adds " +"attributes with the highest correlation with the class as long " +"as there is not already an attribute in the subset that has a " +"higher correlation with the attribute in question"; } /** * Include locally predictive attributes * * @param b true or false */ public void setLocallyPredictive (boolean b) { m_locallyPredictive = b; } /** * Return true if including locally predictive attributes * * @return true if locally predictive attributes are to be used */ public boolean getLocallyPredictive () { return m_locallyPredictive; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String missingSeperateTipText() { return "Treat missing as a separate value. Otherwise, counts for missing " +"values are distributed across other values in proportion to their " +"frequency."; } /** * Treat missing as a seperate value * * @param b true or false */ public void setMissingSeperate (boolean b) { m_missingSeperate = b; } /** * Return true is missing is treated as a seperate value * * @return true if missing is to be treated as a seperate value */ public boolean getMissingSeperate () { return m_missingSeperate; } /** * Gets the current settings of CfsSubsetEval * * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { String[] options = new String[2]; int current = 0; if (getMissingSeperate()) { options[current++] = "-M"; } if (!getLocallyPredictive()) { options[current++] = "-L"; } while (current < options.length) { options[current++] = ""; } return options; } /** * Returns the capabilities of this evaluator. * * @return the capabilities of this evaluator * @see Capabilities */ public Capabilities getCapabilities() { Capabilities result = super.getCapabilities(); // attributes result.enable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.NUMERIC_ATTRIBUTES); result.enable(Capability.DATE_ATTRIBUTES); result.enable(Capability.MISSING_VALUES); // class result.enable(Capability.NOMINAL_CLASS); result.enable(Capability.NUMERIC_CLASS); result.enable(Capability.DATE_CLASS); result.enable(Capability.MISSING_CLASS_VALUES); return result; } /** * Generates a attribute evaluator. Has to initialize all fields of the * evaluator that are not being set via options. * * CFS also discretises attributes (if necessary) and initializes * the correlation matrix. * * @param data set of instances serving as training data * @throws Exception if the evaluator has not been * generated successfully */ public void buildEvaluator (Instances data) throws Exception { // can evaluator handle data? getCapabilities().testWithFail(data); m_trainInstances = new Instances(data); m_trainInstances.deleteWithMissingClass(); m_classIndex = m_trainInstances.classIndex(); m_numAttribs = m_trainInstances.numAttributes(); m_numInstances = m_trainInstances.numInstances(); m_isNumeric = m_trainInstances.attribute(m_classIndex).isNumeric(); if (!m_isNumeric) { m_disTransform = new Discretize(); m_disTransform.setUseBetterEncoding(true); m_disTransform.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, m_disTransform); } m_std_devs = new double[m_numAttribs]; m_corr_matrix = new float [m_numAttribs][]; for (int i = 0; i < m_numAttribs; i++) { m_corr_matrix[i] = new float [i+1]; } for (int i = 0; i < m_corr_matrix.length; i++) { m_corr_matrix[i][i] = 1.0f; m_std_devs[i] = 1.0; } for (int i = 0; i < m_numAttribs; i++) { for (int j = 0; j < m_corr_matrix[i].length - 1; j++) { m_corr_matrix[i][j] = -999; } } } /** * evaluates a subset of attributes * * @param subset a bitset representing the attribute subset to be * evaluated * @return the merit * @throws Exception if the subset could not be evaluated */ public double evaluateSubset (BitSet subset) throws Exception { double num = 0.0; double denom = 0.0; float corr; int larger, smaller; // do numerator for (int i = 0; i < m_numAttribs; i++) { if (i != m_classIndex) { if (subset.get(i)) { if (i > m_classIndex) { larger = i; smaller = m_classIndex; } else { smaller = i; larger = m_classIndex; } /* int larger = (i > m_classIndex ? i : m_classIndex); int smaller = (i > m_classIndex ? m_classIndex : i); */ if (m_corr_matrix[larger][smaller] == -999) { corr = correlate(i, m_classIndex); m_corr_matrix[larger][smaller] = corr; num += (m_std_devs[i] * corr); } else { num += (m_std_devs[i] * m_corr_matrix[larger][smaller]); } } } } // do denominator for (int i = 0; i < m_numAttribs; i++) { if (i != m_classIndex) { if (subset.get(i)) { denom += (1.0 * m_std_devs[i] * m_std_devs[i]); for (int j = 0; j < m_corr_matrix[i].length - 1; j++) { if (subset.get(j)) { if (m_corr_matrix[i][j] == -999) { corr = correlate(i, j); m_corr_matrix[i][j] = corr; denom += (2.0 * m_std_devs[i] * m_std_devs[j] * corr); } else { denom += (2.0 * m_std_devs[i] * m_std_devs[j] * m_corr_matrix[i][j]); } } } } } } if (denom < 0.0) { denom *= -1.0; } if (denom == 0.0) { return (0.0); } double merit = (num/Math.sqrt(denom)); if (merit < 0.0) { merit *= -1.0; } return merit; } private float correlate (int att1, int att2) { if (!m_isNumeric) { return (float) symmUncertCorr(att1, att2); } boolean att1_is_num = (m_trainInstances.attribute(att1).isNumeric()); boolean att2_is_num = (m_trainInstances.attribute(att2).isNumeric()); if (att1_is_num && att2_is_num) { return (float) num_num(att1, att2); } else {if (att2_is_num) { return (float) num_nom2(att1, att2); } else {if (att1_is_num) { return (float) num_nom2(att2, att1); } } } return (float) nom_nom(att1, att2); } private double symmUncertCorr (int att1, int att2) { int i, j, k, ii, jj; int ni, nj; double sum = 0.0; double sumi[], sumj[]; double counts[][]; Instance inst; double corr_measure; boolean flag = false; double temp = 0.0; if (att1 == m_classIndex || att2 == m_classIndex) { flag = true; } ni = m_trainInstances.attribute(att1).numValues() + 1; nj = m_trainInstances.attribute(att2).numValues() + 1; counts = new double[ni][nj]; sumi = new double[ni]; sumj = new double[nj]; for (i = 0; i < ni; i++) { sumi[i] = 0.0; for (j = 0; j < nj; j++) { sumj[j] = 0.0; counts[i][j] = 0.0; } } // Fill the contingency table for (i = 0; i < m_numInstances; i++) { inst = m_trainInstances.instance(i); if (inst.isMissing(att1)) { ii = ni - 1; } else { ii = (int)inst.value(att1); } if (inst.isMissing(att2)) { jj = nj - 1; } else { jj = (int)inst.value(att2); } counts[ii][jj]++; } // get the row totals for (i = 0; i < ni; i++) { sumi[i] = 0.0; for (j = 0; j < nj; j++) { sumi[i] += counts[i][j]; sum += counts[i][j]; } } // get the column totals for (j = 0; j < nj; j++) { sumj[j] = 0.0; for (i = 0; i < ni; i++) { sumj[j] += counts[i][j]; } } // distribute missing counts if (!m_missingSeperate && (sumi[ni-1] < m_numInstances) && (sumj[nj-1] < m_numInstances)) { double[] i_copy = new double[sumi.length]; double[] j_copy = new double[sumj.length]; double[][] counts_copy = new double[sumi.length][sumj.length]; for (i = 0; i < ni; i++) { System.arraycopy(counts[i], 0, counts_copy[i], 0, sumj.length); } System.arraycopy(sumi, 0, i_copy, 0, sumi.length); System.arraycopy(sumj, 0, j_copy, 0, sumj.length); double total_missing = (sumi[ni - 1] + sumj[nj - 1] - counts[ni - 1][nj - 1]); // do the missing i's if (sumi[ni - 1] > 0.0) { for (j = 0; j < nj - 1; j++) { if (counts[ni - 1][j] > 0.0) { for (i = 0; i < ni - 1; i++) { temp = ((i_copy[i]/(sum - i_copy[ni - 1]))*counts[ni - 1][j]); counts[i][j] += temp; sumi[i] += temp; } counts[ni - 1][j] = 0.0; } } } sumi[ni - 1] = 0.0; // do the missing j's if (sumj[nj - 1] > 0.0) { for (i = 0; i < ni - 1; i++) { if (counts[i][nj - 1] > 0.0) { for (j = 0; j < nj - 1; j++) { temp = ((j_copy[j]/(sum - j_copy[nj - 1]))*counts[i][nj - 1]); counts[i][j] += temp; sumj[j] += temp; } counts[i][nj - 1] = 0.0; } } } sumj[nj - 1] = 0.0; // do the both missing if (counts[ni - 1][nj - 1] > 0.0 && total_missing != sum) { for (i = 0; i < ni - 1; i++) { for (j = 0; j < nj - 1; j++) { temp = (counts_copy[i][j]/(sum - total_missing)) * counts_copy[ni - 1][nj - 1]; counts[i][j] += temp; sumi[i] += temp; sumj[j] += temp; } } counts[ni - 1][nj - 1] = 0.0; } } corr_measure = ContingencyTables.symmetricalUncertainty(counts); if (Utils.eq(corr_measure, 0.0)) { if (flag == true) { return (0.0); } else { return (1.0); } } else { return (corr_measure); } } private double num_num (int att1, int att2) { int i; Instance inst; double r, diff1, diff2, num = 0.0, sx = 0.0, sy = 0.0; double mx = m_trainInstances.meanOrMode(m_trainInstances.attribute(att1)); double my = m_trainInstances.meanOrMode(m_trainInstances.attribute(att2)); for (i = 0; i < m_numInstances; i++) { inst = m_trainInstances.instance(i); diff1 = (inst.isMissing(att1))? 0.0 : (inst.value(att1) - mx); diff2 = (inst.isMissing(att2))? 0.0 : (inst.value(att2) - my); num += (diff1*diff2); sx += (diff1*diff1); sy += (diff2*diff2); } if (sx != 0.0) { if (m_std_devs[att1] == 1.0) { m_std_devs[att1] = Math.sqrt((sx/m_numInstances)); } } if (sy != 0.0) { if (m_std_devs[att2] == 1.0) { m_std_devs[att2] = Math.sqrt((sy/m_numInstances)); } } if ((sx*sy) > 0.0) { r = (num/(Math.sqrt(sx*sy))); return ((r < 0.0)? -r : r); } else { if (att1 != m_classIndex && att2 != m_classIndex) { return 1.0; } else { return 0.0; } } } private double num_nom2 (int att1, int att2) { int i, ii, k; double temp; Instance inst; int mx = (int)m_trainInstances. meanOrMode(m_trainInstances.attribute(att1)); double my = m_trainInstances. meanOrMode(m_trainInstances.attribute(att2)); double stdv_num = 0.0; double diff1, diff2; double r = 0.0, rr; int nx = (!m_missingSeperate) ? m_trainInstances.attribute(att1).numValues() : m_trainInstances.attribute(att1).numValues() + 1; double[] prior_nom = new double[nx]; double[] stdvs_nom = new double[nx]; double[] covs = new double[nx]; for (i = 0; i < nx; i++) { stdvs_nom[i] = covs[i] = prior_nom[i] = 0.0; } // calculate frequencies (and means) of the values of the nominal // attribute for (i = 0; i < m_numInstances; i++) { inst = m_trainInstances.instance(i); if (inst.isMissing(att1)) { if (!m_missingSeperate) { ii = mx; } else { ii = nx - 1; } } else { ii = (int)inst.value(att1); } // increment freq for nominal prior_nom[ii]++; } for (k = 0; k < m_numInstances; k++) { inst = m_trainInstances.instance(k); // std dev of numeric attribute diff2 = (inst.isMissing(att2))? 0.0 : (inst.value(att2) - my); stdv_num += (diff2*diff2); // for (i = 0; i < nx; i++) { if (inst.isMissing(att1)) { if (!m_missingSeperate) { temp = (i == mx)? 1.0 : 0.0; } else { temp = (i == (nx - 1))? 1.0 : 0.0; } } else { temp = (i == inst.value(att1))? 1.0 : 0.0; } diff1 = (temp - (prior_nom[i]/m_numInstances)); stdvs_nom[i] += (diff1*diff1); covs[i] += (diff1*diff2); } } // calculate weighted correlation for (i = 0, temp = 0.0; i < nx; i++) { // calculate the weighted variance of the nominal temp += ((prior_nom[i]/m_numInstances)*(stdvs_nom[i]/m_numInstances)); if ((stdvs_nom[i]*stdv_num) > 0.0) { //System.out.println("Stdv :"+stdvs_nom[i]); rr = (covs[i]/(Math.sqrt(stdvs_nom[i]*stdv_num))); if (rr < 0.0) { rr = -rr; } r += ((prior_nom[i]/m_numInstances)*rr); } /* if there is zero variance for the numeric att at a specific level of the catergorical att then if neither is the class then make this correlation at this level maximally bad i.e. 1.0. If either is the class then maximally bad correlation is 0.0 */ else {if (att1 != m_classIndex && att2 != m_classIndex) { r += ((prior_nom[i]/m_numInstances)*1.0); } } } // set the standard deviations for these attributes if necessary // if ((att1 != classIndex) && (att2 != classIndex)) // ============= if (temp != 0.0) { if (m_std_devs[att1] == 1.0) { m_std_devs[att1] = Math.sqrt(temp); } } if (stdv_num != 0.0) { if (m_std_devs[att2] == 1.0) { m_std_devs[att2] = Math.sqrt((stdv_num/m_numInstances)); } } if (r == 0.0) { if (att1 != m_classIndex && att2 != m_classIndex) { r = 1.0; } } return r; } private double nom_nom (int att1, int att2) { int i, j, ii, jj, z; double temp1, temp2; Instance inst; int mx = (int)m_trainInstances. meanOrMode(m_trainInstances.attribute(att1)); int my = (int)m_trainInstances. meanOrMode(m_trainInstances.attribute(att2)); double diff1, diff2; double r = 0.0, rr; int nx = (!m_missingSeperate) ? m_trainInstances.attribute(att1).numValues() : m_trainInstances.attribute(att1).numValues() + 1; int ny = (!m_missingSeperate) ? m_trainInstances.attribute(att2).numValues() : m_trainInstances.attribute(att2).numValues() + 1; double[][] prior_nom = new double[nx][ny]; double[] sumx = new double[nx]; double[] sumy = new double[ny]; double[] stdvsx = new double[nx]; double[] stdvsy = new double[ny]; double[][] covs = new double[nx][ny]; for (i = 0; i < nx; i++) { sumx[i] = stdvsx[i] = 0.0; } for (j = 0; j < ny; j++) { sumy[j] = stdvsy[j] = 0.0; } for (i = 0; i < nx; i++) { for (j = 0; j < ny; j++) { covs[i][j] = prior_nom[i][j] = 0.0; } } // calculate frequencies (and means) of the values of the nominal // attribute for (i = 0; i < m_numInstances; i++) { inst = m_trainInstances.instance(i); if (inst.isMissing(att1)) { if (!m_missingSeperate) { ii = mx; } else { ii = nx - 1; } } else { ii = (int)inst.value(att1); } if (inst.isMissing(att2)) { if (!m_missingSeperate) { jj = my; } else { jj = ny - 1; } } else { jj = (int)inst.value(att2); } // increment freq for nominal prior_nom[ii][jj]++; sumx[ii]++; sumy[jj]++; } for (z = 0; z < m_numInstances; z++) { inst = m_trainInstances.instance(z); for (j = 0; j < ny; j++) { if (inst.isMissing(att2)) { if (!m_missingSeperate) { temp2 = (j == my)? 1.0 : 0.0; } else { temp2 = (j == (ny - 1))? 1.0 : 0.0; } } else { temp2 = (j == inst.value(att2))? 1.0 : 0.0; } diff2 = (temp2 - (sumy[j]/m_numInstances)); stdvsy[j] += (diff2*diff2); } // for (i = 0; i < nx; i++) { if (inst.isMissing(att1)) { if (!m_missingSeperate) { temp1 = (i == mx)? 1.0 : 0.0; } else { temp1 = (i == (nx - 1))? 1.0 : 0.0; } } else { temp1 = (i == inst.value(att1))? 1.0 : 0.0; } diff1 = (temp1 - (sumx[i]/m_numInstances)); stdvsx[i] += (diff1*diff1); for (j = 0; j < ny; j++) { if (inst.isMissing(att2)) { if (!m_missingSeperate) { temp2 = (j == my)? 1.0 : 0.0; } else { temp2 = (j == (ny - 1))? 1.0 : 0.0; } } else { temp2 = (j == inst.value(att2))? 1.0 : 0.0; } diff2 = (temp2 - (sumy[j]/m_numInstances)); covs[i][j] += (diff1*diff2); } } } // calculate weighted correlation for (i = 0; i < nx; i++) { for (j = 0; j < ny; j++) { if ((stdvsx[i]*stdvsy[j]) > 0.0) { //System.out.println("Stdv :"+stdvs_nom[i]); rr = (covs[i][j]/(Math.sqrt(stdvsx[i]*stdvsy[j]))); if (rr < 0.0) { rr = -rr; } r += ((prior_nom[i][j]/m_numInstances)*rr); } // if there is zero variance for either of the categorical atts then if // neither is the class then make this // correlation at this level maximally bad i.e. 1.0. If either is // the class then maximally bad correlation is 0.0 else {if (att1 != m_classIndex && att2 != m_classIndex) { r += ((prior_nom[i][j]/m_numInstances)*1.0); } } } } // calculate weighted standard deviations for these attributes // (if necessary) for (i = 0, temp1 = 0.0; i < nx; i++) { temp1 += ((sumx[i]/m_numInstances)*(stdvsx[i]/m_numInstances)); } if (temp1 != 0.0) { if (m_std_devs[att1] == 1.0) { m_std_devs[att1] = Math.sqrt(temp1); } } for (j = 0, temp2 = 0.0; j < ny; j++) { temp2 += ((sumy[j]/m_numInstances)*(stdvsy[j]/m_numInstances)); } if (temp2 != 0.0) { if (m_std_devs[att2] == 1.0) { m_std_devs[att2] = Math.sqrt(temp2); } } if (r == 0.0) { if (att1 != m_classIndex && att2 != m_classIndex) { r = 1.0; } } return r; } /** * returns a string describing CFS * * @return the description as a string */ public String toString () { StringBuffer text = new StringBuffer(); if (m_trainInstances == null) { text.append("CFS subset evaluator has not been built yet\n"); } else { text.append("\tCFS Subset Evaluator\n"); if (m_missingSeperate) { text.append("\tTreating missing values as a seperate value\n"); } if (m_locallyPredictive) { text.append("\tIncluding locally predictive attributes\n"); } } return text.toString(); } private void addLocallyPredictive (BitSet best_group) { int i, j; boolean done = false; boolean ok = true; double temp_best = -1.0; float corr; j = 0; BitSet temp_group = (BitSet)best_group.clone(); int larger, smaller; while (!done) { temp_best = -1.0; // find best not already in group for (i = 0; i < m_numAttribs; i++) { if (i > m_classIndex) { larger = i; smaller = m_classIndex; } else { smaller = i; larger = m_classIndex; } /* int larger = (i > m_classIndex ? i : m_classIndex); int smaller = (i > m_classIndex ? m_classIndex : i); */ if ((!temp_group.get(i)) && (i != m_classIndex)) { if (m_corr_matrix[larger][smaller] == -999) { corr = correlate(i, m_classIndex); m_corr_matrix[larger][smaller] = corr; } if (m_corr_matrix[larger][smaller] > temp_best) { temp_best = m_corr_matrix[larger][smaller]; j = i; } } } if (temp_best == -1.0) { done = true; } else { ok = true; temp_group.set(j); // check the best against correlations with others already // in group for (i = 0; i < m_numAttribs; i++) { if (i > j) { larger = i; smaller = j; } else { larger = j; smaller = i; } /* int larger = (i > j ? i : j); int smaller = (i > j ? j : i); */ if (best_group.get(i)) { if (m_corr_matrix[larger][smaller] == -999) { corr = correlate(i, j); m_corr_matrix[larger][smaller] = corr; } if (m_corr_matrix[larger][smaller] > temp_best - m_c_Threshold) { ok = false; break; } } } // if ok then add to best_group if (ok) { best_group.set(j); } } } } /** * Calls locallyPredictive in order to include locally predictive * attributes (if requested). * * @param attributeSet the set of attributes found by the search * @return a possibly ranked list of postprocessed attributes * @throws Exception if postprocessing fails for some reason */ public int[] postProcess (int[] attributeSet) throws Exception { int j = 0; if (!m_locallyPredictive) { // m_trainInstances = new Instances(m_trainInstances,0); return attributeSet; } BitSet bestGroup = new BitSet(m_numAttribs); for (int i = 0; i < attributeSet.length; i++) { bestGroup.set(attributeSet[i]); } addLocallyPredictive(bestGroup); // count how many are set for (int i = 0; i < m_numAttribs; i++) { if (bestGroup.get(i)) { j++; } } int[] newSet = new int[j]; j = 0; for (int i = 0; i < m_numAttribs; i++) { if (bestGroup.get(i)) { newSet[j++] = i; } } // m_trainInstances = new Instances(m_trainInstances,0); return newSet; } protected void resetOptions () { m_trainInstances = null; m_missingSeperate = false; m_locallyPredictive = true; m_c_Threshold = 0.0; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.28 $"); } /** * Main method for testing this class. * * @param args the options */ public static void main (String[] args) { runEvaluator(new CfsSubsetEval(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/CheckAttributeSelection.java000066400000000000000000001575731214146114000322460ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * CheckAttributeSelection.java * Copyright (C) 2006 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.core.Attribute; import weka.core.CheckScheme; import weka.core.FastVector; import weka.core.Instances; import weka.core.MultiInstanceCapabilitiesHandler; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.SerializationHelper; import weka.core.SerializedObject; import weka.core.TestInstances; import weka.core.Utils; import weka.core.WeightedInstancesHandler; import java.util.Enumeration; import java.util.Random; import java.util.Vector; /** * Class for examining the capabilities and finding problems with * attribute selection schemes. If you implement an attribute selection using * the WEKA.libraries, you should run the checks on it to ensure robustness * and correct operation. Passing all the tests of this object does not mean * bugs in the attribute selection don't exist, but this will help find some * common ones.

* * Typical usage:

* java weka.attributeSelection.CheckAttributeSelection -W ASscheme_name * -- ASscheme_options

* * CheckAttributeSelection reports on the following: *

    *
  • Scheme abilities *
      *
    • Possible command line options to the scheme
      • *
    • Whether the scheme can predict nominal, numeric, string, * date or relational class attributes.
      • *
    • Whether the scheme can handle numeric predictor attributes
      • *
    • Whether the scheme can handle nominal predictor attributes
      • *
    • Whether the scheme can handle string predictor attributes
      • *
    • Whether the scheme can handle date predictor attributes
      • *
    • Whether the scheme can handle relational predictor attributes
      • *
    • Whether the scheme can handle multi-instance data
      • *
    • Whether the scheme can handle missing predictor values
      • *
    • Whether the scheme can handle missing class values
      • *
    • Whether a nominal scheme only handles 2 class problems
      • *
    • Whether the scheme can handle instance weights
      • *
    *
    • *
  • Correct functioning *
      *
    • Correct initialisation during search (i.e. no result * changes when search is performed repeatedly)
      • *
    • Whether the scheme alters the data pased to it * (number of instances, instance order, instance weights, etc)
      • *
    *
    • *
  • Degenerate cases *
      *
    • building scheme with zero instances
      • *
    • all but one predictor attribute values missing
      • *
    • all predictor attribute values missing
      • *
    • all but one class values missing
      • *
    • all class values missing
      • *
    *
    • *
* Running CheckAttributeSelection with the debug option set will output the * training dataset for any failed tests.

* * The weka.attributeSelection.AbstractAttributeSelectionTest * uses this class to test all the schemes. Any changes here, have to be * checked in that abstract test class, too.

* * Valid options are:

* * 

 -D
 *  Turn on debugging output.
* * 
 -S
 *  Silent mode - prints nothing to stdout.
* * 
 -N <num>
 *  The number of instances in the datasets (default 20).
* * 
 -nominal <num>
 *  The number of nominal attributes (default 2).
* * 
 -nominal-values <num>
 *  The number of values for nominal attributes (default 1).
* * 
 -numeric <num>
 *  The number of numeric attributes (default 1).
* * 
 -string <num>
 *  The number of string attributes (default 1).
* * 
 -date <num>
 *  The number of date attributes (default 1).
* * 
 -relational <num>
 *  The number of relational attributes (default 1).
* * 
 -num-instances-relational <num>
 *  The number of instances in relational/bag attributes (default 10).
* * 
 -words <comma-separated-list>
 *  The words to use in string attributes.
* * 
 -word-separators <chars>
 *  The word separators to use in string attributes.
* * 
 -eval name [options]
 *  Full name and options of the evaluator analyzed.
 *  eg: weka.attributeSelection.CfsSubsetEval
* * 
 -search name [options]
 *  Full name and options of the search method analyzed.
 *  eg: weka.attributeSelection.Ranker
* * 
 -test <eval|search>
 *  The scheme to test, either the evaluator or the search method.
 *  (Default: eval)
* * 
 
 * Options specific to evaluator weka.attributeSelection.CfsSubsetEval:
 *
* * 
 -M
 *  Treat missing values as a seperate value.
* * 
 -L
 *  Don't include locally predictive attributes.
* * 
 
 * Options specific to search method weka.attributeSelection.Ranker:
 *
* * 
 -P <start set>
 *  Specify a starting set of attributes.
 *  Eg. 1,3,5-7.
 *  Any starting attributes specified are
 *  ignored during the ranking.
* * 
 -T <threshold>
 *  Specify a theshold by which attributes
 *  may be discarded from the ranking.
* * 
 -N <num to select>
 *  Specify number of attributes to select
* * * @author Len Trigg (trigg@cs.waikato.ac.nz) * @author FracPete (fracpete at waikato dot ac dot nz) * @version $Revision: 1.7 $ * @see TestInstances */ public class CheckAttributeSelection extends CheckScheme { /* * Note about test methods: * - methods return array of booleans * - first index: success or not * - second index: acceptable or not (e.g., Exception is OK) * * FracPete (fracpete at waikato dot ac dot nz) */ /*** The evaluator to be examined */ protected ASEvaluation m_Evaluator = new CfsSubsetEval(); /*** The search method to be used */ protected ASSearch m_Search = new Ranker(); /** whether to test the evaluator (default) or the search method */ protected boolean m_TestEvaluator = true; /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector result = new Vector(); Enumeration en = super.listOptions(); while (en.hasMoreElements()) result.addElement(en.nextElement()); result.addElement(new Option( "\tFull name and options of the evaluator analyzed.\n" +"\teg: weka.attributeSelection.CfsSubsetEval", "eval", 1, "-eval name [options]")); result.addElement(new Option( "\tFull name and options of the search method analyzed.\n" +"\teg: weka.attributeSelection.Ranker", "search", 1, "-search name [options]")); result.addElement(new Option( "\tThe scheme to test, either the evaluator or the search method.\n" +"\t(Default: eval)", "test", 1, "-test ")); if ((m_Evaluator != null) && (m_Evaluator instanceof OptionHandler)) { result.addElement(new Option("", "", 0, "\nOptions specific to evaluator " + m_Evaluator.getClass().getName() + ":")); Enumeration enm = ((OptionHandler) m_Evaluator).listOptions(); while (enm.hasMoreElements()) result.addElement(enm.nextElement()); } if ((m_Search != null) && (m_Search instanceof OptionHandler)) { result.addElement(new Option("", "", 0, "\nOptions specific to search method " + m_Search.getClass().getName() + ":")); Enumeration enm = ((OptionHandler) m_Search).listOptions(); while (enm.hasMoreElements()) result.addElement(enm.nextElement()); } return result.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -D
   *  Turn on debugging output.
* * 
 -S
   *  Silent mode - prints nothing to stdout.
* * 
 -N <num>
   *  The number of instances in the datasets (default 20).
* * 
 -nominal <num>
   *  The number of nominal attributes (default 2).
* * 
 -nominal-values <num>
   *  The number of values for nominal attributes (default 1).
* * 
 -numeric <num>
   *  The number of numeric attributes (default 1).
* * 
 -string <num>
   *  The number of string attributes (default 1).
* * 
 -date <num>
   *  The number of date attributes (default 1).
* * 
 -relational <num>
   *  The number of relational attributes (default 1).
* * 
 -num-instances-relational <num>
   *  The number of instances in relational/bag attributes (default 10).
* * 
 -words <comma-separated-list>
   *  The words to use in string attributes.
* * 
 -word-separators <chars>
   *  The word separators to use in string attributes.
* * 
 -eval name [options]
   *  Full name and options of the evaluator analyzed.
   *  eg: weka.attributeSelection.CfsSubsetEval
* * 
 -search name [options]
   *  Full name and options of the search method analyzed.
   *  eg: weka.attributeSelection.Ranker
* * 
 -test <eval|search>
   *  The scheme to test, either the evaluator or the search method.
   *  (Default: eval)
* * 
 
   * Options specific to evaluator weka.attributeSelection.CfsSubsetEval:
   *
* * 
 -M
   *  Treat missing values as a seperate value.
* * 
 -L
   *  Don't include locally predictive attributes.
* * 
 
   * Options specific to search method weka.attributeSelection.Ranker:
   *
* * 
 -P <start set>
   *  Specify a starting set of attributes.
   *  Eg. 1,3,5-7.
   *  Any starting attributes specified are
   *  ignored during the ranking.
* * 
 -T <threshold>
   *  Specify a theshold by which attributes
   *  may be discarded from the ranking.
* * 
 -N <num to select>
   *  Specify number of attributes to select
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { String tmpStr; String[] tmpOptions; super.setOptions(options); tmpStr = Utils.getOption("eval", options); tmpOptions = Utils.splitOptions(tmpStr); if (tmpOptions.length != 0) { tmpStr = tmpOptions[0]; tmpOptions[0] = ""; setEvaluator( (ASEvaluation) forName( "weka.attributeSelection", ASEvaluation.class, tmpStr, tmpOptions)); } tmpStr = Utils.getOption("search", options); tmpOptions = Utils.splitOptions(tmpStr); if (tmpOptions.length != 0) { tmpStr = tmpOptions[0]; tmpOptions[0] = ""; setSearch( (ASSearch) forName( "weka.attributeSelection", ASSearch.class, tmpStr, tmpOptions)); } tmpStr = Utils.getOption("test", options); setTestEvaluator(!tmpStr.equalsIgnoreCase("search")); } /** * Gets the current settings of the CheckAttributeSelection. * * @return an array of strings suitable for passing to setOptions */ public String[] getOptions() { Vector result; String[] options; int i; result = new Vector(); options = super.getOptions(); for (i = 0; i < options.length; i++) result.add(options[i]); result.add("-eval"); if (getEvaluator() instanceof OptionHandler) result.add( getEvaluator().getClass().getName() + " " + Utils.joinOptions(((OptionHandler) getEvaluator()).getOptions())); else result.add( getEvaluator().getClass().getName()); result.add("-search"); if (getSearch() instanceof OptionHandler) result.add( getSearch().getClass().getName() + " " + Utils.joinOptions(((OptionHandler) getSearch()).getOptions())); else result.add( getSearch().getClass().getName()); result.add("-test"); if (getTestEvaluator()) result.add("eval"); else result.add("search"); return (String[]) result.toArray(new String[result.size()]); } /** * Begin the tests, reporting results to System.out */ public void doTests() { if (getTestObject() == null) { println("\n=== No scheme set ==="); return; } println("\n=== Check on scheme: " + getTestObject().getClass().getName() + " ===\n"); // Start tests m_ClasspathProblems = false; println("--> Checking for interfaces"); canTakeOptions(); boolean weightedInstancesHandler = weightedInstancesHandler()[0]; boolean multiInstanceHandler = multiInstanceHandler()[0]; println("--> Scheme tests"); declaresSerialVersionUID(); testsPerClassType(Attribute.NOMINAL, weightedInstancesHandler, multiInstanceHandler); testsPerClassType(Attribute.NUMERIC, weightedInstancesHandler, multiInstanceHandler); testsPerClassType(Attribute.DATE, weightedInstancesHandler, multiInstanceHandler); testsPerClassType(Attribute.STRING, weightedInstancesHandler, multiInstanceHandler); testsPerClassType(Attribute.RELATIONAL, weightedInstancesHandler, multiInstanceHandler); } /** * Set the evaluator to test. * * @param value the evaluator to use. */ public void setEvaluator(ASEvaluation value) { m_Evaluator = value; } /** * Get the current evaluator * * @return the current evaluator */ public ASEvaluation getEvaluator() { return m_Evaluator; } /** * Set the search method to test. * * @param value the search method to use. */ public void setSearch(ASSearch value) { m_Search = value; } /** * Get the current search method * * @return the current search method */ public ASSearch getSearch() { return m_Search; } /** * Sets whether the evaluator or the search method is being tested. * * @param value if true then the evaluator will be tested */ public void setTestEvaluator(boolean value) { m_TestEvaluator = value; } /** * Gets whether the evaluator is being tested or the search method. * * @return true if the evaluator is being tested */ public boolean getTestEvaluator() { return m_TestEvaluator; } /** * returns either the evaluator or the search method. * * @return the object to be tested * @see #m_TestEvaluator */ protected Object getTestObject() { if (getTestEvaluator()) return getEvaluator(); else return getSearch(); } /** * returns deep copies of the given object * * @param obj the object to copy * @param num the number of copies * @return the deep copies * @throws Exception if copying fails */ protected Object[] makeCopies(Object obj, int num) throws Exception { if (obj == null) throw new Exception("No object set"); Object[] objs = new Object[num]; SerializedObject so = new SerializedObject(obj); for(int i = 0; i < objs.length; i++) { objs[i] = so.getObject(); } return objs; } /** * Performs a attribute selection with the given search and evaluation scheme * on the provided data. The generated AttributeSelection object is returned. * * @param search the search scheme to use * @param eval the evaluator to use * @param data the data to work on * @return the used attribute selection object * @throws Exception if the attribute selection fails */ protected AttributeSelection search(ASSearch search, ASEvaluation eval, Instances data) throws Exception { AttributeSelection result; result = new AttributeSelection(); result.setSeed(42); result.setSearch(search); result.setEvaluator(eval); result.SelectAttributes(data); return result; } /** * Run a battery of tests for a given class attribute type * * @param classType true if the class attribute should be numeric * @param weighted true if the scheme says it handles weights * @param multiInstance true if the scheme handles multi-instance data */ protected void testsPerClassType(int classType, boolean weighted, boolean multiInstance) { boolean PNom = canPredict(true, false, false, false, false, multiInstance, classType)[0]; boolean PNum = canPredict(false, true, false, false, false, multiInstance, classType)[0]; boolean PStr = canPredict(false, false, true, false, false, multiInstance, classType)[0]; boolean PDat = canPredict(false, false, false, true, false, multiInstance, classType)[0]; boolean PRel; if (!multiInstance) PRel = canPredict(false, false, false, false, true, multiInstance, classType)[0]; else PRel = false; if (PNom || PNum || PStr || PDat || PRel) { if (weighted) instanceWeights(PNom, PNum, PStr, PDat, PRel, multiInstance, classType); if (classType == Attribute.NOMINAL) canHandleNClasses(PNom, PNum, PStr, PDat, PRel, multiInstance, 4); if (!multiInstance) { canHandleClassAsNthAttribute(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, 0); canHandleClassAsNthAttribute(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, 1); } canHandleZeroTraining(PNom, PNum, PStr, PDat, PRel, multiInstance, classType); boolean handleMissingPredictors = canHandleMissing(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, true, false, 20)[0]; if (handleMissingPredictors) canHandleMissing(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, true, false, 100); boolean handleMissingClass = canHandleMissing(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, false, true, 20)[0]; if (handleMissingClass) canHandleMissing(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, false, true, 100); correctSearchInitialisation(PNom, PNum, PStr, PDat, PRel, multiInstance, classType); datasetIntegrity(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, handleMissingPredictors, handleMissingClass); } } /** * Checks whether the scheme can take command line options. * * @return index 0 is true if the scheme can take options */ protected boolean[] canTakeOptions() { boolean[] result = new boolean[2]; print("options..."); if (getTestObject() instanceof OptionHandler) { println("yes"); if (m_Debug) { println("\n=== Full report ==="); Enumeration enu = ((OptionHandler) getTestObject()).listOptions(); while (enu.hasMoreElements()) { Option option = (Option) enu.nextElement(); print(option.synopsis() + "\n" + option.description() + "\n"); } println("\n"); } result[0] = true; } else { println("no"); result[0] = false; } return result; } /** * Checks whether the scheme says it can handle instance weights. * * @return true if the scheme handles instance weights */ protected boolean[] weightedInstancesHandler() { boolean[] result = new boolean[2]; print("weighted instances scheme..."); if (getTestObject() instanceof WeightedInstancesHandler) { println("yes"); result[0] = true; } else { println("no"); result[0] = false; } return result; } /** * Checks whether the scheme handles multi-instance data. * * @return true if the scheme handles multi-instance data */ protected boolean[] multiInstanceHandler() { boolean[] result = new boolean[2]; print("multi-instance scheme..."); if (getTestObject() instanceof MultiInstanceCapabilitiesHandler) { println("yes"); result[0] = true; } else { println("no"); result[0] = false; } return result; } /** * tests for a serialVersionUID. Fails in case the schemes don't declare * a UID (both must!). * * @return index 0 is true if the scheme declares a UID */ protected boolean[] declaresSerialVersionUID() { boolean[] result = new boolean[2]; boolean eval; boolean search; print("serialVersionUID..."); eval = !SerializationHelper.needsUID(m_Evaluator.getClass()); search = !SerializationHelper.needsUID(m_Search.getClass()); result[0] = eval && search; if (result[0]) println("yes"); else println("no"); return result; } /** * Checks basic prediction of the scheme, for simple non-troublesome * datasets. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NOMINAL, NUMERIC, etc.) * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable */ protected boolean[] canPredict( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType) { print("basic predict"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); FastVector accepts = new FastVector(); accepts.addElement("unary"); accepts.addElement("binary"); accepts.addElement("nominal"); accepts.addElement("numeric"); accepts.addElement("string"); accepts.addElement("date"); accepts.addElement("relational"); accepts.addElement("multi-instance"); accepts.addElement("not in classpath"); int numTrain = getNumInstances(), numClasses = 2, missingLevel = 0; boolean predictorMissing = false, classMissing = false; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, missingLevel, predictorMissing, classMissing, numTrain, numClasses, accepts); } /** * Checks whether nominal schemes can handle more than two classes. * If a scheme is only designed for two-class problems it should * throw an appropriate exception for multi-class problems. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param numClasses the number of classes to test * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable */ protected boolean[] canHandleNClasses( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int numClasses) { print("more than two class problems"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, Attribute.NOMINAL); print("..."); FastVector accepts = new FastVector(); accepts.addElement("number"); accepts.addElement("class"); int numTrain = getNumInstances(), missingLevel = 0; boolean predictorMissing = false, classMissing = false; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, Attribute.NOMINAL, missingLevel, predictorMissing, classMissing, numTrain, numClasses, accepts); } /** * Checks whether the scheme can handle class attributes as Nth attribute. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param classIndex the index of the class attribute (0-based, -1 means last attribute) * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable * @see TestInstances#CLASS_IS_LAST */ protected boolean[] canHandleClassAsNthAttribute( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType, int classIndex) { if (classIndex == TestInstances.CLASS_IS_LAST) print("class attribute as last attribute"); else print("class attribute as " + (classIndex + 1) + ". attribute"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); FastVector accepts = new FastVector(); int numTrain = getNumInstances(), numClasses = 2, missingLevel = 0; boolean predictorMissing = false, classMissing = false; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, classIndex, missingLevel, predictorMissing, classMissing, numTrain, numClasses, accepts); } /** * Checks whether the scheme can handle zero training instances. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable */ protected boolean[] canHandleZeroTraining( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType) { print("handle zero training instances"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); FastVector accepts = new FastVector(); accepts.addElement("train"); accepts.addElement("value"); int numTrain = 0, numClasses = 2, missingLevel = 0; boolean predictorMissing = false, classMissing = false; return runBasicTest( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, missingLevel, predictorMissing, classMissing, numTrain, numClasses, accepts); } /** * Checks whether the scheme correctly initialises models when * ASSearch.search is called. This test calls search with * one training dataset. ASSearch is then called on a training set with * different structure, and then again with the original training set. * If the equals method of the ASEvaluation class returns false, this is * noted as incorrect search initialisation. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @return index 0 is true if the test was passed, index 1 is always false */ protected boolean[] correctSearchInitialisation( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType) { boolean[] result = new boolean[2]; print("correct initialisation during search"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); int numTrain = getNumInstances(), numClasses = 2, missingLevel = 0; boolean predictorMissing = false, classMissing = false; Instances train1 = null; Instances train2 = null; ASSearch search = null; ASEvaluation evaluation1A = null; ASEvaluation evaluation1B = null; ASEvaluation evaluation2 = null; AttributeSelection attsel1A = null; AttributeSelection attsel1B = null; int stage = 0; try { // Make two train sets with different numbers of attributes train1 = makeTestDataset(42, numTrain, nominalPredictor ? getNumNominal() : 0, numericPredictor ? getNumNumeric() : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, multiInstance); train2 = makeTestDataset(84, numTrain, nominalPredictor ? getNumNominal() + 1 : 0, numericPredictor ? getNumNumeric() + 1 : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, multiInstance); if (missingLevel > 0) { addMissing(train1, missingLevel, predictorMissing, classMissing); addMissing(train2, missingLevel, predictorMissing, classMissing); } search = ASSearch.makeCopies(getSearch(), 1)[0]; evaluation1A = ASEvaluation.makeCopies(getEvaluator(), 1)[0]; evaluation1B = ASEvaluation.makeCopies(getEvaluator(), 1)[0]; evaluation2 = ASEvaluation.makeCopies(getEvaluator(), 1)[0]; } catch (Exception ex) { throw new Error("Error setting up for tests: " + ex.getMessage()); } try { stage = 0; attsel1A = search(search, evaluation1A, train1); stage = 1; search(search, evaluation2, train2); stage = 2; attsel1B = search(search, evaluation1B, train1); stage = 3; if (!attsel1A.toResultsString().equals(attsel1B.toResultsString())) { if (m_Debug) { println( "\n=== Full report ===\n" + "\nFirst search\n" + evaluation1A.toString() + "\n\n"); println( "\nSecond search\n" + evaluation1B.toString() + "\n\n"); } throw new Exception("Results differ between search calls"); } println("yes"); result[0] = true; if (false && m_Debug) { println( "\n=== Full report ===\n" + "\nFirst search\n" + evaluation1A.toString() + "\n\n"); println( "\nSecond search\n" + evaluation1B.toString() + "\n\n"); } } catch (Exception ex) { println("no"); result[0] = false; if (m_Debug) { println("\n=== Full Report ==="); print("Problem during training"); switch (stage) { case 0: print(" of dataset 1"); break; case 1: print(" of dataset 2"); break; case 2: print(" of dataset 1 (2nd build)"); break; case 3: print(", comparing results from builds of dataset 1"); break; } println(": " + ex.getMessage() + "\n"); println("here are the datasets:\n"); println("=== Train1 Dataset ===\n" + train1.toString() + "\n"); println("=== Train2 Dataset ===\n" + train2.toString() + "\n"); } } return result; } /** * Checks basic missing value handling of the scheme. If the missing * values cause an exception to be thrown by the scheme, this will be * recorded. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param predictorMissing true if the missing values may be in * the predictors * @param classMissing true if the missing values may be in the class * @param missingLevel the percentage of missing values * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable */ protected boolean[] canHandleMissing( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType, boolean predictorMissing, boolean classMissing, int missingLevel) { if (missingLevel == 100) print("100% "); print("missing"); if (predictorMissing) { print(" predictor"); if (classMissing) print(" and"); } if (classMissing) print(" class"); print(" values"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); FastVector accepts = new FastVector(); accepts.addElement("missing"); accepts.addElement("value"); accepts.addElement("train"); accepts.addElement("no attributes"); int numTrain = getNumInstances(), numClasses = 2; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, missingLevel, predictorMissing, classMissing, numTrain, numClasses, accepts); } /** * Checks whether the scheme can handle instance weights. * This test compares the scheme performance on two datasets * that are identical except for the training weights. If the * results change, then the scheme must be using the weights. It * may be possible to get a false positive from this test if the * weight changes aren't significant enough to induce a change * in scheme performance (but the weights are chosen to minimize * the likelihood of this). * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @return index 0 true if the test was passed */ protected boolean[] instanceWeights( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType) { print("scheme uses instance weights"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); int numTrain = 2*getNumInstances(), numClasses = 2, missingLevel = 0; boolean predictorMissing = false, classMissing = false; boolean[] result = new boolean[2]; Instances train = null; ASSearch[] search = null; ASEvaluation evaluationB = null; ASEvaluation evaluationI = null; AttributeSelection attselB = null; AttributeSelection attselI = null; boolean evalFail = false; try { train = makeTestDataset(42, numTrain, nominalPredictor ? getNumNominal() + 1 : 0, numericPredictor ? getNumNumeric() + 1 : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, multiInstance); if (missingLevel > 0) addMissing(train, missingLevel, predictorMissing, classMissing); search = ASSearch.makeCopies(getSearch(), 2); evaluationB = ASEvaluation.makeCopies(getEvaluator(), 1)[0]; evaluationI = ASEvaluation.makeCopies(getEvaluator(), 1)[0]; attselB = search(search[0], evaluationB, train); } catch (Exception ex) { throw new Error("Error setting up for tests: " + ex.getMessage()); } try { // Now modify instance weights and re-built/test for (int i = 0; i < train.numInstances(); i++) { train.instance(i).setWeight(0); } Random random = new Random(1); for (int i = 0; i < train.numInstances() / 2; i++) { int inst = Math.abs(random.nextInt()) % train.numInstances(); int weight = Math.abs(random.nextInt()) % 10 + 1; train.instance(inst).setWeight(weight); } attselI = search(search[1], evaluationI, train); if (attselB.toResultsString().equals(attselI.toResultsString())) { // println("no"); evalFail = true; throw new Exception("evalFail"); } println("yes"); result[0] = true; } catch (Exception ex) { println("no"); result[0] = false; if (m_Debug) { println("\n=== Full Report ==="); if (evalFail) { println("Results don't differ between non-weighted and " + "weighted instance models."); println("Here are the results:\n"); println("\nboth methods\n"); println(evaluationB.toString()); } else { print("Problem during training"); println(": " + ex.getMessage() + "\n"); } println("Here is the dataset:\n"); println("=== Train Dataset ===\n" + train.toString() + "\n"); println("=== Train Weights ===\n"); for (int i = 0; i < train.numInstances(); i++) { println(" " + (i + 1) + " " + train.instance(i).weight()); } } } return result; } /** * Checks whether the scheme alters the training dataset during * training. If the scheme needs to modify the training * data it should take a copy of the training data. Currently checks * for changes to header structure, number of instances, order of * instances, instance weights. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param predictorMissing true if we know the scheme can handle * (at least) moderate missing predictor values * @param classMissing true if we know the scheme can handle * (at least) moderate missing class values * @return index 0 is true if the test was passed */ protected boolean[] datasetIntegrity( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType, boolean predictorMissing, boolean classMissing) { print("scheme doesn't alter original datasets"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); int numTrain = getNumInstances(), numClasses = 2, missingLevel = 20; boolean[] result = new boolean[2]; Instances train = null; Instances trainCopy = null; ASSearch search = null; ASEvaluation evaluation = null; try { train = makeTestDataset(42, numTrain, nominalPredictor ? getNumNominal() : 0, numericPredictor ? getNumNumeric() : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, multiInstance); if (missingLevel > 0) addMissing(train, missingLevel, predictorMissing, classMissing); search = ASSearch.makeCopies(getSearch(), 1)[0]; evaluation = ASEvaluation.makeCopies(getEvaluator(), 1)[0]; trainCopy = new Instances(train); } catch (Exception ex) { throw new Error("Error setting up for tests: " + ex.getMessage()); } try { search(search, evaluation, trainCopy); compareDatasets(train, trainCopy); println("yes"); result[0] = true; } catch (Exception ex) { println("no"); result[0] = false; if (m_Debug) { println("\n=== Full Report ==="); print("Problem during training"); println(": " + ex.getMessage() + "\n"); println("Here are the datasets:\n"); println("=== Train Dataset (original) ===\n" + trainCopy.toString() + "\n"); println("=== Train Dataset ===\n" + train.toString() + "\n"); } } return result; } /** * Runs a text on the datasets with the given characteristics. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param missingLevel the percentage of missing values * @param predictorMissing true if the missing values may be in * the predictors * @param classMissing true if the missing values may be in the class * @param numTrain the number of instances in the training set * @param numClasses the number of classes * @param accepts the acceptable string in an exception * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable */ protected boolean[] runBasicTest(boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType, int missingLevel, boolean predictorMissing, boolean classMissing, int numTrain, int numClasses, FastVector accepts) { return runBasicTest( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, TestInstances.CLASS_IS_LAST, missingLevel, predictorMissing, classMissing, numTrain, numClasses, accepts); } /** * Runs a text on the datasets with the given characteristics. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param classIndex the attribute index of the class * @param missingLevel the percentage of missing values * @param predictorMissing true if the missing values may be in * the predictors * @param classMissing true if the missing values may be in the class * @param numTrain the number of instances in the training set * @param numClasses the number of classes * @param accepts the acceptable string in an exception * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable */ protected boolean[] runBasicTest(boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType, int classIndex, int missingLevel, boolean predictorMissing, boolean classMissing, int numTrain, int numClasses, FastVector accepts) { boolean[] result = new boolean[2]; Instances train = null; ASSearch search = null; ASEvaluation evaluation = null; try { train = makeTestDataset(42, numTrain, nominalPredictor ? getNumNominal() : 0, numericPredictor ? getNumNumeric() : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, classIndex, multiInstance); if (missingLevel > 0) addMissing(train, missingLevel, predictorMissing, classMissing); search = ASSearch.makeCopies(getSearch(), 1)[0]; evaluation = ASEvaluation.makeCopies(getEvaluator(), 1)[0]; } catch (Exception ex) { ex.printStackTrace(); throw new Error("Error setting up for tests: " + ex.getMessage()); } try { search(search, evaluation, train); println("yes"); result[0] = true; } catch (Exception ex) { boolean acceptable = false; String msg; if (ex.getMessage() == null) msg = ""; else msg = ex.getMessage().toLowerCase(); if (msg.indexOf("not in classpath") > -1) m_ClasspathProblems = true; for (int i = 0; i < accepts.size(); i++) { if (msg.indexOf((String)accepts.elementAt(i)) >= 0) { acceptable = true; } } println("no" + (acceptable ? " (OK error message)" : "")); result[1] = acceptable; if (m_Debug) { println("\n=== Full Report ==="); print("Problem during training"); println(": " + ex.getMessage() + "\n"); if (!acceptable) { if (accepts.size() > 0) { print("Error message doesn't mention "); for (int i = 0; i < accepts.size(); i++) { if (i != 0) { print(" or "); } print('"' + (String)accepts.elementAt(i) + '"'); } } println("here is the dataset:\n"); println("=== Train Dataset ===\n" + train.toString() + "\n"); } } } return result; } /** * Make a simple set of instances, which can later be modified * for use in specific tests. * * @param seed the random number seed * @param numInstances the number of instances to generate * @param numNominal the number of nominal attributes * @param numNumeric the number of numeric attributes * @param numString the number of string attributes * @param numDate the number of date attributes * @param numRelational the number of relational attributes * @param numClasses the number of classes (if nominal class) * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param multiInstance whether the dataset should a multi-instance dataset * @return the test dataset * @throws Exception if the dataset couldn't be generated * @see #process(Instances) */ protected Instances makeTestDataset(int seed, int numInstances, int numNominal, int numNumeric, int numString, int numDate, int numRelational, int numClasses, int classType, boolean multiInstance) throws Exception { return makeTestDataset( seed, numInstances, numNominal, numNumeric, numString, numDate, numRelational, numClasses, classType, TestInstances.CLASS_IS_LAST, multiInstance); } /** * Make a simple set of instances with variable position of the class * attribute, which can later be modified for use in specific tests. * * @param seed the random number seed * @param numInstances the number of instances to generate * @param numNominal the number of nominal attributes * @param numNumeric the number of numeric attributes * @param numString the number of string attributes * @param numDate the number of date attributes * @param numRelational the number of relational attributes * @param numClasses the number of classes (if nominal class) * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param classIndex the index of the class (0-based, -1 as last) * @param multiInstance whether the dataset should a multi-instance dataset * @return the test dataset * @throws Exception if the dataset couldn't be generated * @see TestInstances#CLASS_IS_LAST * @see #process(Instances) */ protected Instances makeTestDataset(int seed, int numInstances, int numNominal, int numNumeric, int numString, int numDate, int numRelational, int numClasses, int classType, int classIndex, boolean multiInstance) throws Exception { TestInstances dataset = new TestInstances(); dataset.setSeed(seed); dataset.setNumInstances(numInstances); dataset.setNumNominal(numNominal); dataset.setNumNumeric(numNumeric); dataset.setNumString(numString); dataset.setNumDate(numDate); dataset.setNumRelational(numRelational); dataset.setNumClasses(numClasses); dataset.setClassType(classType); dataset.setClassIndex(classIndex); dataset.setNumClasses(numClasses); dataset.setMultiInstance(multiInstance); dataset.setWords(getWords()); dataset.setWordSeparators(getWordSeparators()); return process(dataset.generate()); } /** * Print out a short summary string for the dataset characteristics * * @param nominalPredictor true if nominal predictor attributes are present * @param numericPredictor true if numeric predictor attributes are present * @param stringPredictor true if string predictor attributes are present * @param datePredictor true if date predictor attributes are present * @param relationalPredictor true if relational predictor attributes are present * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) */ protected void printAttributeSummary(boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType) { String str = ""; if (numericPredictor) str += " numeric"; if (nominalPredictor) { if (str.length() > 0) str += " &"; str += " nominal"; } if (stringPredictor) { if (str.length() > 0) str += " &"; str += " string"; } if (datePredictor) { if (str.length() > 0) str += " &"; str += " date"; } if (relationalPredictor) { if (str.length() > 0) str += " &"; str += " relational"; } str += " predictors)"; switch (classType) { case Attribute.NUMERIC: str = " (numeric class," + str; break; case Attribute.NOMINAL: str = " (nominal class," + str; break; case Attribute.STRING: str = " (string class," + str; break; case Attribute.DATE: str = " (date class," + str; break; case Attribute.RELATIONAL: str = " (relational class," + str; break; } print(str); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.7 $"); } /** * Test method for this class * * @param args the commandline parameters */ public static void main(String [] args) { runCheck(new CheckAttributeSelection(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/ChiSquaredAttributeEval.java000066400000000000000000000325331214146114000322070ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * ChiSquaredAttributeEval.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.core.Capabilities; import weka.core.ContingencyTables; import weka.core.Instance; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.Utils; import weka.core.Capabilities.Capability; import weka.filters.Filter; import weka.filters.supervised.attribute.Discretize; import weka.filters.unsupervised.attribute.NumericToBinary; import java.util.Enumeration; import java.util.Vector; /** * ChiSquaredAttributeEval :
*
* Evaluates the worth of an attribute by computing the value of the chi-squared statistic with respect to the class.
*

* * Valid options are:

* * 

 -M
 *  treat missing values as a seperate value.
* * 
 -B
 *  just binarize numeric attributes instead 
 *  of properly discretizing them.
* * * @author Eibe Frank (eibe@cs.waikato.ac.nz) * @version $Revision: 1.16 $ * @see Discretize * @see NumericToBinary */ public class ChiSquaredAttributeEval extends ASEvaluation implements AttributeEvaluator, OptionHandler { /** for serialization */ static final long serialVersionUID = -8316857822521717692L; /** Treat missing values as a seperate value */ private boolean m_missing_merge; /** Just binarize numeric attributes */ private boolean m_Binarize; /** The chi-squared value for each attribute */ private double[] m_ChiSquareds; /** * Returns a string describing this attribute evaluator * @return a description of the evaluator suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "ChiSquaredAttributeEval :\n\nEvaluates the worth of an attribute " +"by computing the value of the chi-squared statistic with respect to the class.\n"; } /** * Constructor */ public ChiSquaredAttributeEval () { resetOptions(); } /** * Returns an enumeration describing the available options * @return an enumeration of all the available options **/ public Enumeration listOptions () { Vector newVector = new Vector(2); newVector.addElement(new Option("\ttreat missing values as a seperate " + "value.", "M", 0, "-M")); newVector.addElement(new Option("\tjust binarize numeric attributes instead \n" +"\tof properly discretizing them.", "B", 0, "-B")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -M
   *  treat missing values as a seperate value.
* * 
 -B
   *  just binarize numeric attributes instead 
   *  of properly discretizing them.
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions (String[] options) throws Exception { resetOptions(); setMissingMerge(!(Utils.getFlag('M', options))); setBinarizeNumericAttributes(Utils.getFlag('B', options)); } /** * Gets the current settings. * * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { String[] options = new String[2]; int current = 0; if (!getMissingMerge()) { options[current++] = "-M"; } if (getBinarizeNumericAttributes()) { options[current++] = "-B"; } while (current < options.length) { options[current++] = ""; } return options; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String binarizeNumericAttributesTipText() { return "Just binarize numeric attributes instead of properly discretizing them."; } /** * Binarize numeric attributes. * * @param b true=binarize numeric attributes */ public void setBinarizeNumericAttributes (boolean b) { m_Binarize = b; } /** * get whether numeric attributes are just being binarized. * * @return true if missing values are being distributed. */ public boolean getBinarizeNumericAttributes () { return m_Binarize; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String missingMergeTipText() { return "Distribute counts for missing values. Counts are distributed " +"across other values in proportion to their frequency. Otherwise, " +"missing is treated as a separate value."; } /** * distribute the counts for missing values across observed values * * @param b true=distribute missing values. */ public void setMissingMerge (boolean b) { m_missing_merge = b; } /** * get whether missing values are being distributed or not * * @return true if missing values are being distributed. */ public boolean getMissingMerge () { return m_missing_merge; } /** * Returns the capabilities of this evaluator. * * @return the capabilities of this evaluator * @see Capabilities */ public Capabilities getCapabilities() { Capabilities result = super.getCapabilities(); // attributes result.enable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.NUMERIC_ATTRIBUTES); result.enable(Capability.DATE_ATTRIBUTES); result.enable(Capability.MISSING_VALUES); // class result.enable(Capability.NOMINAL_CLASS); result.enable(Capability.MISSING_CLASS_VALUES); return result; } /** * Initializes a chi-squared attribute evaluator. * Discretizes all attributes that are numeric. * * @param data set of instances serving as training data * @throws Exception if the evaluator has not been * generated successfully */ public void buildEvaluator (Instances data) throws Exception { // can evaluator handle data? getCapabilities().testWithFail(data); int classIndex = data.classIndex(); int numInstances = data.numInstances(); if (!m_Binarize) { Discretize disTransform = new Discretize(); disTransform.setUseBetterEncoding(true); disTransform.setInputFormat(data); data = Filter.useFilter(data, disTransform); } else { NumericToBinary binTransform = new NumericToBinary(); binTransform.setInputFormat(data); data = Filter.useFilter(data, binTransform); } int numClasses = data.attribute(classIndex).numValues(); // Reserve space and initialize counters double[][][] counts = new double[data.numAttributes()][][]; for (int k = 0; k < data.numAttributes(); k++) { if (k != classIndex) { int numValues = data.attribute(k).numValues(); counts[k] = new double[numValues + 1][numClasses + 1]; } } // Initialize counters double[] temp = new double[numClasses + 1]; for (int k = 0; k < numInstances; k++) { Instance inst = data.instance(k); if (inst.classIsMissing()) { temp[numClasses] += inst.weight(); } else { temp[(int)inst.classValue()] += inst.weight(); } } for (int k = 0; k < counts.length; k++) { if (k != classIndex) { for (int i = 0; i < temp.length; i++) { counts[k][0][i] = temp[i]; } } } // Get counts for (int k = 0; k < numInstances; k++) { Instance inst = data.instance(k); for (int i = 0; i < inst.numValues(); i++) { if (inst.index(i) != classIndex) { if (inst.isMissingSparse(i) || inst.classIsMissing()) { if (!inst.isMissingSparse(i)) { counts[inst.index(i)][(int)inst.valueSparse(i)][numClasses] += inst.weight(); counts[inst.index(i)][0][numClasses] -= inst.weight(); } else if (!inst.classIsMissing()) { counts[inst.index(i)][data.attribute(inst.index(i)).numValues()] [(int)inst.classValue()] += inst.weight(); counts[inst.index(i)][0][(int)inst.classValue()] -= inst.weight(); } else { counts[inst.index(i)][data.attribute(inst.index(i)).numValues()] [numClasses] += inst.weight(); counts[inst.index(i)][0][numClasses] -= inst.weight(); } } else { counts[inst.index(i)][(int)inst.valueSparse(i)] [(int)inst.classValue()] += inst.weight(); counts[inst.index(i)][0][(int)inst.classValue()] -= inst.weight(); } } } } // distribute missing counts if required if (m_missing_merge) { for (int k = 0; k < data.numAttributes(); k++) { if (k != classIndex) { int numValues = data.attribute(k).numValues(); // Compute marginals double[] rowSums = new double[numValues]; double[] columnSums = new double[numClasses]; double sum = 0; for (int i = 0; i < numValues; i++) { for (int j = 0; j < numClasses; j++) { rowSums[i] += counts[k][i][j]; columnSums[j] += counts[k][i][j]; } sum += rowSums[i]; } if (Utils.gr(sum, 0)) { double[][] additions = new double[numValues][numClasses]; // Compute what needs to be added to each row for (int i = 0; i < numValues; i++) { for (int j = 0; j < numClasses; j++) { additions[i][j] = (rowSums[i] / sum) * counts[k][numValues][j]; } } // Compute what needs to be added to each column for (int i = 0; i < numClasses; i++) { for (int j = 0; j < numValues; j++) { additions[j][i] += (columnSums[i] / sum) * counts[k][j][numClasses]; } } // Compute what needs to be added to each cell for (int i = 0; i < numClasses; i++) { for (int j = 0; j < numValues; j++) { additions[j][i] += (counts[k][j][i] / sum) * counts[k][numValues][numClasses]; } } // Make new contingency table double[][] newTable = new double[numValues][numClasses]; for (int i = 0; i < numValues; i++) { for (int j = 0; j < numClasses; j++) { newTable[i][j] = counts[k][i][j] + additions[i][j]; } } counts[k] = newTable; } } } } // Compute chi-squared values m_ChiSquareds = new double[data.numAttributes()]; for (int i = 0; i < data.numAttributes(); i++) { if (i != classIndex) { m_ChiSquareds[i] = ContingencyTables. chiVal(ContingencyTables.reduceMatrix(counts[i]), false); } } } /** * Reset options to their default values */ protected void resetOptions () { m_ChiSquareds = null; m_missing_merge = true; m_Binarize = false; } /** * evaluates an individual attribute by measuring its * chi-squared value. * * @param attribute the index of the attribute to be evaluated * @return the chi-squared value * @throws Exception if the attribute could not be evaluated */ public double evaluateAttribute (int attribute) throws Exception { return m_ChiSquareds[attribute]; } /** * Describe the attribute evaluator * @return a description of the attribute evaluator as a string */ public String toString () { StringBuffer text = new StringBuffer(); if (m_ChiSquareds == null) { text.append("Chi-squared attribute evaluator has not been built"); } else { text.append("\tChi-squared Ranking Filter"); if (!m_missing_merge) { text.append("\n\tMissing values treated as seperate"); } if (m_Binarize) { text.append("\n\tNumeric attributes are just binarized"); } } text.append("\n"); return text.toString(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.16 $"); } /** * Main method. * * @param args the options */ public static void main (String[] args) { runEvaluator(new ChiSquaredAttributeEval(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/ClassifierSubsetEval.java000066400000000000000000000500371214146114000315440ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * ClassifierSubsetEval.java * Copyright (C) 2000 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.classifiers.Classifier; import weka.classifiers.Evaluation; import weka.classifiers.rules.ZeroR; import weka.core.Capabilities; import weka.core.Instance; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.Utils; import weka.core.Capabilities.Capability; import weka.filters.Filter; import weka.filters.unsupervised.attribute.Remove; import java.io.File; import java.util.BitSet; import java.util.Enumeration; import java.util.Vector; /** * Classifier subset evaluator:
*
* Evaluates attribute subsets on training data or a seperate hold out testing set. Uses a classifier to estimate the 'merit' of a set of attributes. *

* * Valid options are:

* * 

 -B <classifier>
 *  class name of the classifier to use for accuracy estimation.
 *  Place any classifier options LAST on the command line
 *  following a "--". eg.:
 *   -B weka.classifiers.bayes.NaiveBayes ... -- -K
 *  (default: weka.classifiers.rules.ZeroR)
* * 
 -T
 *  Use the training data to estimate accuracy.
* * 
 -H <filename>
 *  Name of the hold out/test set to 
 *  estimate accuracy on.
* * 
 
 * Options specific to scheme weka.classifiers.rules.ZeroR:
 *
* * 
 -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
* * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.18 $ */ public class ClassifierSubsetEval extends HoldOutSubsetEvaluator implements OptionHandler, ErrorBasedMeritEvaluator { /** for serialization */ static final long serialVersionUID = 7532217899385278710L; /** training instances */ private Instances m_trainingInstances; /** class index */ private int m_classIndex; /** number of attributes in the training data */ private int m_numAttribs; /** number of training instances */ private int m_numInstances; /** holds the classifier to use for error estimates */ private Classifier m_Classifier = new ZeroR(); /** holds the evaluation object to use for evaluating the classifier */ private Evaluation m_Evaluation; /** the file that containts hold out/test instances */ private File m_holdOutFile = new File("Click to set hold out or " +"test instances"); /** the instances to test on */ private Instances m_holdOutInstances = null; /** evaluate on training data rather than seperate hold out/test set */ private boolean m_useTraining = true; /** * Returns a string describing this attribute evaluator * @return a description of the evaluator suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Classifier subset evaluator:\n\nEvaluates attribute subsets on training data or a seperate " + "hold out testing set. Uses a classifier to estimate the 'merit' of a set of attributes."; } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. **/ public Enumeration listOptions () { Vector newVector = new Vector(3); newVector.addElement(new Option( "\tclass name of the classifier to use for accuracy estimation.\n" + "\tPlace any classifier options LAST on the command line\n" + "\tfollowing a \"--\". eg.:\n" + "\t\t-B weka.classifiers.bayes.NaiveBayes ... -- -K\n" + "\t(default: weka.classifiers.rules.ZeroR)", "B", 1, "-B ")); newVector.addElement(new Option( "\tUse the training data to estimate" +" accuracy.", "T",0,"-T")); newVector.addElement(new Option( "\tName of the hold out/test set to " +"\n\testimate accuracy on.", "H", 1,"-H ")); if ((m_Classifier != null) && (m_Classifier instanceof OptionHandler)) { newVector.addElement(new Option("", "", 0, "\nOptions specific to " + "scheme " + m_Classifier.getClass().getName() + ":")); Enumeration enu = ((OptionHandler)m_Classifier).listOptions(); while (enu.hasMoreElements()) { newVector.addElement(enu.nextElement()); } } return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -B <classifier>
   *  class name of the classifier to use for accuracy estimation.
   *  Place any classifier options LAST on the command line
   *  following a "--". eg.:
   *   -B weka.classifiers.bayes.NaiveBayes ... -- -K
   *  (default: weka.classifiers.rules.ZeroR)
* * 
 -T
   *  Use the training data to estimate accuracy.
* * 
 -H <filename>
   *  Name of the hold out/test set to 
   *  estimate accuracy on.
* * 
 
   * Options specific to scheme weka.classifiers.rules.ZeroR:
   *
* * 
 -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions (String[] options) throws Exception { String optionString; resetOptions(); optionString = Utils.getOption('B', options); if (optionString.length() == 0) optionString = ZeroR.class.getName(); setClassifier(Classifier.forName(optionString, Utils.partitionOptions(options))); optionString = Utils.getOption('H',options); if (optionString.length() != 0) { setHoldOutFile(new File(optionString)); } setUseTraining(Utils.getFlag('T',options)); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String classifierTipText() { return "Classifier to use for estimating the accuracy of subsets"; } /** * Set the classifier to use for accuracy estimation * * @param newClassifier the Classifier to use. */ public void setClassifier (Classifier newClassifier) { m_Classifier = newClassifier; } /** * Get the classifier used as the base learner. * * @return the classifier used as the classifier */ public Classifier getClassifier () { return m_Classifier; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String holdOutFileTipText() { return "File containing hold out/test instances."; } /** * Gets the file that holds hold out/test instances. * @return File that contains hold out instances */ public File getHoldOutFile() { return m_holdOutFile; } /** * Set the file that contains hold out/test instances * @param h the hold out file */ public void setHoldOutFile(File h) { m_holdOutFile = h; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String useTrainingTipText() { return "Use training data instead of hold out/test instances."; } /** * Get if training data is to be used instead of hold out/test data * @return true if training data is to be used instead of hold out data */ public boolean getUseTraining() { return m_useTraining; } /** * Set if training data is to be used instead of hold out/test data * @param t true if training data is to be used instead of hold out data */ public void setUseTraining(boolean t) { m_useTraining = t; } /** * Gets the current settings of ClassifierSubsetEval * * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { String[] classifierOptions = new String[0]; if ((m_Classifier != null) && (m_Classifier instanceof OptionHandler)) { classifierOptions = ((OptionHandler)m_Classifier).getOptions(); } String[] options = new String[6 + classifierOptions.length]; int current = 0; if (getClassifier() != null) { options[current++] = "-B"; options[current++] = getClassifier().getClass().getName(); } if (getUseTraining()) { options[current++] = "-T"; } options[current++] = "-H"; options[current++] = getHoldOutFile().getPath(); if (classifierOptions.length > 0) { options[current++] = "--"; System.arraycopy(classifierOptions, 0, options, current, classifierOptions.length); current += classifierOptions.length; } while (current < options.length) { options[current++] = ""; } return options; } /** * Returns the capabilities of this evaluator. * * @return the capabilities of this evaluator * @see Capabilities */ public Capabilities getCapabilities() { Capabilities result; if (getClassifier() == null) result = super.getCapabilities(); else result = getClassifier().getCapabilities(); // set dependencies for (Capability cap: Capability.values()) result.enableDependency(cap); return result; } /** * Generates a attribute evaluator. Has to initialize all fields of the * evaluator that are not being set via options. * * @param data set of instances serving as training data * @throws Exception if the evaluator has not been * generated successfully */ public void buildEvaluator (Instances data) throws Exception { // can evaluator handle data? getCapabilities().testWithFail(data); m_trainingInstances = data; m_classIndex = m_trainingInstances.classIndex(); m_numAttribs = m_trainingInstances.numAttributes(); m_numInstances = m_trainingInstances.numInstances(); // load the testing data if (!m_useTraining && (!getHoldOutFile().getPath().startsWith("Click to set"))) { java.io.Reader r = new java.io.BufferedReader( new java.io.FileReader(getHoldOutFile().getPath())); m_holdOutInstances = new Instances(r); m_holdOutInstances.setClassIndex(m_trainingInstances.classIndex()); if (m_trainingInstances.equalHeaders(m_holdOutInstances) == false) { throw new Exception("Hold out/test set is not compatable with " +"training data."); } } } /** * Evaluates a subset of attributes * * @param subset a bitset representing the attribute subset to be * evaluated * @return the error rate * @throws Exception if the subset could not be evaluated */ public double evaluateSubset (BitSet subset) throws Exception { int i,j; double errorRate = 0; int numAttributes = 0; Instances trainCopy=null; Instances testCopy=null; Remove delTransform = new Remove(); delTransform.setInvertSelection(true); // copy the training instances trainCopy = new Instances(m_trainingInstances); if (!m_useTraining) { if (m_holdOutInstances == null) { throw new Exception("Must specify a set of hold out/test instances " +"with -H"); } // copy the test instances testCopy = new Instances(m_holdOutInstances); } // count attributes set in the BitSet for (i = 0; i < m_numAttribs; i++) { if (subset.get(i)) { numAttributes++; } } // set up an array of attribute indexes for the filter (+1 for the class) int[] featArray = new int[numAttributes + 1]; for (i = 0, j = 0; i < m_numAttribs; i++) { if (subset.get(i)) { featArray[j++] = i; } } featArray[j] = m_classIndex; delTransform.setAttributeIndicesArray(featArray); delTransform.setInputFormat(trainCopy); trainCopy = Filter.useFilter(trainCopy, delTransform); if (!m_useTraining) { testCopy = Filter.useFilter(testCopy, delTransform); } // build the classifier m_Classifier.buildClassifier(trainCopy); m_Evaluation = new Evaluation(trainCopy); if (!m_useTraining) { m_Evaluation.evaluateModel(m_Classifier, testCopy); } else { m_Evaluation.evaluateModel(m_Classifier, trainCopy); } if (m_trainingInstances.classAttribute().isNominal()) { errorRate = m_Evaluation.errorRate(); } else { errorRate = m_Evaluation.meanAbsoluteError(); } m_Evaluation = null; // return the negative of the error rate as search methods need to // maximize something return -errorRate; } /** * Evaluates a subset of attributes with respect to a set of instances. * Calling this function overides any test/hold out instancs set from * setHoldOutFile. * @param subset a bitset representing the attribute subset to be * evaluated * @param holdOut a set of instances (possibly seperate and distinct * from those use to build/train the evaluator) with which to * evaluate the merit of the subset * @return the "merit" of the subset on the holdOut data * @throws Exception if the subset cannot be evaluated */ public double evaluateSubset(BitSet subset, Instances holdOut) throws Exception { int i,j; double errorRate; int numAttributes = 0; Instances trainCopy=null; Instances testCopy=null; if (m_trainingInstances.equalHeaders(holdOut) == false) { throw new Exception("evaluateSubset : Incompatable instance types."); } Remove delTransform = new Remove(); delTransform.setInvertSelection(true); // copy the training instances trainCopy = new Instances(m_trainingInstances); testCopy = new Instances(holdOut); // count attributes set in the BitSet for (i = 0; i < m_numAttribs; i++) { if (subset.get(i)) { numAttributes++; } } // set up an array of attribute indexes for the filter (+1 for the class) int[] featArray = new int[numAttributes + 1]; for (i = 0, j = 0; i < m_numAttribs; i++) { if (subset.get(i)) { featArray[j++] = i; } } featArray[j] = m_classIndex; delTransform.setAttributeIndicesArray(featArray); delTransform.setInputFormat(trainCopy); trainCopy = Filter.useFilter(trainCopy, delTransform); testCopy = Filter.useFilter(testCopy, delTransform); // build the classifier m_Classifier.buildClassifier(trainCopy); m_Evaluation = new Evaluation(trainCopy); m_Evaluation.evaluateModel(m_Classifier, testCopy); if (m_trainingInstances.classAttribute().isNominal()) { errorRate = m_Evaluation.errorRate(); } else { errorRate = m_Evaluation.meanAbsoluteError(); } m_Evaluation = null; // return the negative of the error as search methods need to // maximize something return -errorRate; } /** * Evaluates a subset of attributes with respect to a single instance. * Calling this function overides any hold out/test instances set * through setHoldOutFile. * @param subset a bitset representing the attribute subset to be * evaluated * @param holdOut a single instance (possibly not one of those used to * build/train the evaluator) with which to evaluate the merit of the subset * @param retrain true if the classifier should be retrained with respect * to the new subset before testing on the holdOut instance. * @return the "merit" of the subset on the holdOut instance * @throws Exception if the subset cannot be evaluated */ public double evaluateSubset(BitSet subset, Instance holdOut, boolean retrain) throws Exception { int i,j; double error; int numAttributes = 0; Instances trainCopy=null; Instance testCopy=null; if (m_trainingInstances.equalHeaders(holdOut.dataset()) == false) { throw new Exception("evaluateSubset : Incompatable instance types."); } Remove delTransform = new Remove(); delTransform.setInvertSelection(true); // copy the training instances trainCopy = new Instances(m_trainingInstances); testCopy = (Instance)holdOut.copy(); // count attributes set in the BitSet for (i = 0; i < m_numAttribs; i++) { if (subset.get(i)) { numAttributes++; } } // set up an array of attribute indexes for the filter (+1 for the class) int[] featArray = new int[numAttributes + 1]; for (i = 0, j = 0; i < m_numAttribs; i++) { if (subset.get(i)) { featArray[j++] = i; } } featArray[j] = m_classIndex; delTransform.setAttributeIndicesArray(featArray); delTransform.setInputFormat(trainCopy); if (retrain) { trainCopy = Filter.useFilter(trainCopy, delTransform); // build the classifier m_Classifier.buildClassifier(trainCopy); } delTransform.input(testCopy); testCopy = delTransform.output(); double pred; double [] distrib; distrib = m_Classifier.distributionForInstance(testCopy); if (m_trainingInstances.classAttribute().isNominal()) { pred = distrib[(int)testCopy.classValue()]; } else { pred = distrib[0]; } if (m_trainingInstances.classAttribute().isNominal()) { error = 1.0 - pred; } else { error = testCopy.classValue() - pred; } // return the negative of the error as search methods need to // maximize something return -error; } /** * Returns a string describing classifierSubsetEval * * @return the description as a string */ public String toString() { StringBuffer text = new StringBuffer(); if (m_trainingInstances == null) { text.append("\tClassifier subset evaluator has not been built yet\n"); } else { text.append("\tClassifier Subset Evaluator\n"); text.append("\tLearning scheme: " + getClassifier().getClass().getName() + "\n"); text.append("\tScheme options: "); String[] classifierOptions = new String[0]; if (m_Classifier instanceof OptionHandler) { classifierOptions = ((OptionHandler)m_Classifier).getOptions(); for (int i = 0; i < classifierOptions.length; i++) { text.append(classifierOptions[i] + " "); } } text.append("\n"); text.append("\tHold out/test set: "); if (!m_useTraining) { if (getHoldOutFile().getPath().startsWith("Click to set")) { text.append("none\n"); } else { text.append(getHoldOutFile().getPath()+'\n'); } } else { text.append("Training data\n"); } if (m_trainingInstances.attribute(m_classIndex).isNumeric()) { text.append("\tAccuracy estimation: MAE\n"); } else { text.append("\tAccuracy estimation: classification error\n"); } } return text.toString(); } /** * reset to defaults */ protected void resetOptions () { m_trainingInstances = null; m_Evaluation = null; m_Classifier = new ZeroR(); m_holdOutFile = new File("Click to set hold out or test instances"); m_holdOutInstances = null; m_useTraining = false; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.18 $"); } /** * Main method for testing this class. * * @param args the options */ public static void main (String[] args) { runEvaluator(new ClassifierSubsetEval(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/ConsistencySubsetEval.java000066400000000000000000000341311214146114000317560ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * ConsistencySubsetEval.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.core.Capabilities; import weka.core.Instance; import weka.core.Instances; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import weka.core.TechnicalInformation; import weka.core.TechnicalInformationHandler; import weka.core.Utils; import weka.core.Capabilities.Capability; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; import weka.filters.Filter; import weka.filters.supervised.attribute.Discretize; import java.io.Serializable; import java.util.BitSet; import java.util.Enumeration; import java.util.Hashtable; /** * ConsistencySubsetEval :
*
* Evaluates the worth of a subset of attributes by the level of consistency in the class values when the training instances are projected onto the subset of attributes.
*
* Consistency of any subset can never be lower than that of the full set of attributes, hence the usual practice is to use this subset evaluator in conjunction with a Random or Exhaustive search which looks for the smallest subset with consistency equal to that of the full set of attributes.
*
* For more information see:
*
* H. Liu, R. Setiono: A probabilistic approach to feature selection - A filter solution. In: 13th International Conference on Machine Learning, 319-327, 1996. *

* * BibTeX: * 

 * @inproceedings{Liu1996,
 *    author = {H. Liu and R. Setiono},
 *    booktitle = {13th International Conference on Machine Learning},
 *    pages = {319-327},
 *    title = {A probabilistic approach to feature selection - A filter solution},
 *    year = {1996}
 * }
 *
*

* * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.17 $ * @see Discretize */ public class ConsistencySubsetEval extends ASEvaluation implements SubsetEvaluator, TechnicalInformationHandler { /** for serialization */ static final long serialVersionUID = -2880323763295270402L; /** training instances */ private Instances m_trainInstances; /** class index */ private int m_classIndex; /** number of attributes in the training data */ private int m_numAttribs; /** number of instances in the training data */ private int m_numInstances; /** Discretise numeric attributes */ private Discretize m_disTransform; /** Hash table for evaluating feature subsets */ private Hashtable m_table; /** * Class providing keys to the hash table. */ public class hashKey implements Serializable, RevisionHandler { /** for serialization */ static final long serialVersionUID = 6144138512017017408L; /** Array of attribute values for an instance */ private double [] attributes; /** True for an index if the corresponding attribute value is missing. */ private boolean [] missing; /** The key */ private int key; /** * Constructor for a hashKey * * @param t an instance from which to generate a key * @param numAtts the number of attributes * @throws Exception if something goes wrong */ public hashKey(Instance t, int numAtts) throws Exception { int i; int cindex = t.classIndex(); key = -999; attributes = new double [numAtts]; missing = new boolean [numAtts]; for (i=0;i")); newVector.addElement(new Option( "\tName of a directory to search for cost files when loading\n" +"\tcosts on demand (default current directory).", "N", 1, "-N ")); newVector.addElement(new Option( "\tThe cost matrix in Matlab single line format.", "cost-matrix", 1, "-cost-matrix ")); newVector.addElement(new Option( "\tThe seed to use for random number generation.", "S", 1, "-S ")); newVector.addElement(new Option( "\tFull name of base evaluator. Options after -- are " +"passed to the evaluator.\n" + "\t(default: " + defaultEvaluatorString() +")", "W", 1, "-W")); if (m_evaluator instanceof OptionHandler) { newVector.addElement(new Option( "", "", 0, "\nOptions specific to evaluator " + m_evaluator.getClass().getName() + ":")); Enumeration enu = ((OptionHandler)m_evaluator).listOptions(); while (enu.hasMoreElements()) { newVector.addElement(enu.nextElement()); } } return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -C <cost file name>
   *  File name of a cost matrix to use. If this is not supplied,
   *  a cost matrix will be loaded on demand. The name of the
   *  on-demand file is the relation name of the training data
   *  plus ".cost", and the path to the on-demand file is
   *  specified with the -N option.
* * 
 -N <directory>
   *  Name of a directory to search for cost files when loading
   *  costs on demand (default current directory).
* * 
 -cost-matrix <matrix>
   *  The cost matrix in Matlab single line format.
* * 
 -S <integer>
   *  The seed to use for random number generation.
* * 
 -W
   *  Full name of base evaluator.
   *  (default: weka.attributeSelection.CfsSubsetEval)
* * Options after -- are passed to the designated evaluator.

* * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { String costFile = Utils.getOption('C', options); if (costFile.length() != 0) { try { setCostMatrix(new CostMatrix(new BufferedReader( new FileReader(costFile)))); } catch (Exception ex) { // now flag as possible old format cost matrix. Delay cost matrix // loading until buildClassifer is called setCostMatrix(null); } setCostMatrixSource(new SelectedTag(MATRIX_SUPPLIED, TAGS_MATRIX_SOURCE)); m_CostFile = costFile; } else { setCostMatrixSource(new SelectedTag(MATRIX_ON_DEMAND, TAGS_MATRIX_SOURCE)); } String demandDir = Utils.getOption('N', options); if (demandDir.length() != 0) { setOnDemandDirectory(new File(demandDir)); } String cost_matrix = Utils.getOption("cost-matrix", options); if (cost_matrix.length() != 0) { StringWriter writer = new StringWriter(); CostMatrix.parseMatlab(cost_matrix).write(writer); setCostMatrix(new CostMatrix(new StringReader(writer.toString()))); setCostMatrixSource(new SelectedTag(MATRIX_SUPPLIED, TAGS_MATRIX_SOURCE)); } String seed = Utils.getOption('S', options); if (seed.length() != 0) { setSeed(Integer.parseInt(seed)); } else { setSeed(1); } String evaluatorName = Utils.getOption('W', options); if (evaluatorName.length() > 0) { // This is just to set the evaluator in case the option // parsing fails. setEvaluator(ASEvaluation.forName(evaluatorName, null)); setEvaluator(ASEvaluation.forName(evaluatorName, Utils.partitionOptions(options))); } else { // This is just to set the classifier in case the option // parsing fails. setEvaluator(ASEvaluation.forName(defaultEvaluatorString(), null)); setEvaluator(ASEvaluation.forName(defaultEvaluatorString(), Utils.partitionOptions(options))); } } /** * Gets the current settings of the subset evaluator. * * @return an array of strings suitable for passing to setOptions */ public String[] getOptions() { ArrayList options = new ArrayList(); if (m_MatrixSource == MATRIX_SUPPLIED) { if (m_CostFile != null) { options.add("-C"); options.add("" + m_CostFile); } else { options.add("-cost-matrix"); options.add(getCostMatrix().toMatlab()); } } else { options.add("-N"); options.add("" + getOnDemandDirectory()); } options.add("-S"); options.add("" + getSeed()); options.add("-W"); options.add(m_evaluator.getClass().getName()); if (m_evaluator instanceof OptionHandler) { String[] evaluatorOptions = ((OptionHandler)m_evaluator).getOptions(); if (evaluatorOptions.length > 0) { options.add("--"); for (int i = 0; i < evaluatorOptions.length; i++) { options.add(evaluatorOptions[i]); } } } return options.toArray(new String[0]); } /** * @return a description of the classifier suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "A meta subset evaluator that makes its base subset evaluator cost-sensitive. "; } /** * Return the name of the default evaluator. * * @return the name of the default evaluator */ public String defaultEvaluatorString() { return "weka.attributeSelection.CfsSubsetEval"; } /** * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String costMatrixSourceTipText() { return "Sets where to get the cost matrix. The two options are" + "to use the supplied explicit cost matrix (the setting of the " + "costMatrix property), or to load a cost matrix from a file when " + "required (this file will be loaded from the directory set by the " + "onDemandDirectory property and will be named relation_name" + CostMatrix.FILE_EXTENSION + ")."; } /** * Gets the source location method of the cost matrix. Will be one of * MATRIX_ON_DEMAND or MATRIX_SUPPLIED. * * @return the cost matrix source. */ public SelectedTag getCostMatrixSource() { return new SelectedTag(m_MatrixSource, TAGS_MATRIX_SOURCE); } /** * Sets the source location of the cost matrix. Values other than * MATRIX_ON_DEMAND or MATRIX_SUPPLIED will be ignored. * * @param newMethod the cost matrix location method. */ public void setCostMatrixSource(SelectedTag newMethod) { if (newMethod.getTags() == TAGS_MATRIX_SOURCE) { m_MatrixSource = newMethod.getSelectedTag().getID(); } } /** * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String onDemandDirectoryTipText() { return "Sets the directory where cost files are loaded from. This option " + "is used when the costMatrixSource is set to \"On Demand\"."; } /** * Returns the directory that will be searched for cost files when * loading on demand. * * @return The cost file search directory. */ public File getOnDemandDirectory() { return m_OnDemandDirectory; } /** * Sets the directory that will be searched for cost files when * loading on demand. * * @param newDir The cost file search directory. */ public void setOnDemandDirectory(File newDir) { if (newDir.isDirectory()) { m_OnDemandDirectory = newDir; } else { m_OnDemandDirectory = new File(newDir.getParent()); } m_MatrixSource = MATRIX_ON_DEMAND; } /** * Gets the evaluator specification string, which contains the class name of * the evaluator and any options to the evaluator * * @return the evaluator string. */ protected String getEvaluatorSpec() { ASEvaluation ase = getEvaluator(); if (ase instanceof OptionHandler) { return ase.getClass().getName() + " " + Utils.joinOptions(((OptionHandler)ase).getOptions()); } return ase.getClass().getName(); } /** * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String costMatrixTipText() { return "Sets the cost matrix explicitly. This matrix is used if the " + "costMatrixSource property is set to \"Supplied\"."; } /** * Gets the misclassification cost matrix. * * @return the cost matrix */ public CostMatrix getCostMatrix() { return m_CostMatrix; } /** * Sets the misclassification cost matrix. * * @param newCostMatrix the cost matrix */ public void setCostMatrix(CostMatrix newCostMatrix) { m_CostMatrix = newCostMatrix; m_MatrixSource = MATRIX_SUPPLIED; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String seedTipText() { return "The random number seed to be used."; } /** * Set the seed for random number generation. * * @param seed the seed */ public void setSeed(int seed) { m_seed = seed; } /** * Gets the seed for the random number generations. * * @return the seed for the random number generation */ public int getSeed() { return m_seed; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String evaluatorTipText() { return "The base evaluator to be used."; } /** * Set the base evaluator. * * @param newEvaluator the evaluator to use. * @throws IllegalArgumentException if the evaluator is of the wrong type */ public void setEvaluator(ASEvaluation newEvaluator) throws IllegalArgumentException { m_evaluator = newEvaluator; } /** * Get the evaluator used as the base evaluator. * * @return the evaluator used as the base evaluator */ public ASEvaluation getEvaluator() { return m_evaluator; } /** * Returns default capabilities of the classifier. * * @return the capabilities of this classifier */ public Capabilities getCapabilities() { Capabilities result; if (getEvaluator() != null) { result = getEvaluator().getCapabilities(); } else { result = new Capabilities(this); } // class result.disableAllClasses(); result.disableAllClassDependencies(); result.enable(Capability.NOMINAL_CLASS); return result; } /** * Generates a attribute evaluator. Has to initialize all fields of the * evaluator that are not being set via options. * * @param data set of instances serving as training data * @exception Exception if the evaluator has not been * generated successfully */ public void buildEvaluator(Instances data) throws Exception { // can evaluator handle the data? getCapabilities().testWithFail(data); // remove instances with missing class data = new Instances(data); data.deleteWithMissingClass(); if (m_evaluator == null) { throw new Exception("No base evaluator has been set!"); } if (m_MatrixSource == MATRIX_ON_DEMAND) { String costName = data.relationName() + CostMatrix.FILE_EXTENSION; File costFile = new File(getOnDemandDirectory(), costName); if (!costFile.exists()) { throw new Exception("On-demand cost file doesn't exist: " + costFile); } setCostMatrix(new CostMatrix(new BufferedReader( new FileReader(costFile)))); } else if (m_CostMatrix == null) { // try loading an old format cost file m_CostMatrix = new CostMatrix(data.numClasses()); m_CostMatrix.readOldFormat(new BufferedReader( new FileReader(m_CostFile))); } Random random = null; if (!(m_evaluator instanceof WeightedInstancesHandler)) { random = new Random(m_seed); } data = m_CostMatrix.applyCostMatrix(data, random); m_evaluator.buildEvaluator(data); } /** * Provides a chance for a attribute evaluator to do any special * post processing of the selected attribute set. * * @param attributeSet the set of attributes found by the search * @return a possibly ranked list of postprocessed attributes * @exception Exception if postprocessing fails for some reason */ public int [] postProcess(int [] attributeSet) throws Exception { return m_evaluator.postProcess(attributeSet); } /** * Output a representation of this evaluator * * @return a string representation of the classifier */ public String toString() { if (m_evaluator == null) { return "CostSensitiveASEvaluation: No model built yet."; } String result = (m_evaluator instanceof AttributeEvaluator) ? "CostSensitiveAttributeEval using " : "CostSensitiveSubsetEval using "; result += "\n\n" + getEvaluatorSpec() + "\n\nEvaluator\n" + m_evaluator.toString() + "\n\nCost Matrix\n" + m_CostMatrix.toString(); return result; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.3 $"); } }rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/CostSensitiveAttributeEval.java000066400000000000000000000112771214146114000327630ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * CostSensitiveAttributeEval.java * Copyright (C) 2008 Pentaho Corporation * */ package weka.attributeSelection; import weka.core.OptionHandler; import weka.core.RevisionUtils; import java.util.BitSet; import java.io.Serializable; /** * A meta subset evaluator that makes its base subset evaluator cost-sensitive. *

* * Valid options are:

* * 

 -C <cost file name>
 *  File name of a cost matrix to use. If this is not supplied,
 *  a cost matrix will be loaded on demand. The name of the
 *  on-demand file is the relation name of the training data
 *  plus ".cost", and the path to the on-demand file is
 *  specified with the -N option.
* * 
 -N <directory>
 *  Name of a directory to search for cost files when loading
 *  costs on demand (default current directory).
* * 
 -cost-matrix <matrix>
 *  The cost matrix in Matlab single line format.
* * 
 -S <integer>
 *  The seed to use for random number generation.
* * 
 -W
 *  Full name of base evaluator.
 *  (default: weka.attributeSelection.ReliefFAttributeEval)
* * 
 
 * Options specific to evaluator weka.attributeSelection.ReliefFAttributeEval:
 *
* * 
 -M <num instances>
 *  Specify the number of instances to
 *  sample when estimating attributes.
 *  If not specified, then all instances
 *  will be used.
* * 
 -D <seed>
 *  Seed for randomly sampling instances.
 *  (Default = 1)
* * 
 -K <number of neighbours>
 *  Number of nearest neighbours (k) used
 *  to estimate attribute relevances
 *  (Default = 10).
* * 
 -W
 *  Weight nearest neighbours by distance
* * 
 -A <num>
 *  Specify sigma value (used in an exp
 *  function to control how quickly
 *  weights for more distant instances
 *  decrease. Use in conjunction with -W.
 *  Sensible value=1/5 to 1/10 of the
 *  number of nearest neighbours.
 *  (Default = 2)
* * * @author Mark Hall (mhall{[at]}pentaho{[dot]}com) * @version $Revision: 1.2 $ */ public class CostSensitiveAttributeEval extends CostSensitiveASEvaluation implements Serializable, AttributeEvaluator, OptionHandler { /** For serialization */ static final long serialVersionUID = 4484876541145458447L; /** * Default constructor. */ public CostSensitiveAttributeEval() { setEvaluator(new ReliefFAttributeEval()); } /** * Return the name of the default evaluator. * * @return the name of the default evaluator */ public String defaultEvaluatorString() { return "weka.attributeSelection.ReliefFAttributeEval"; } /** * Set the base evaluator. * * @param newEvaluator the evaluator to use. * @throws IllegalArgumentException if the evaluator is not an instance of AttributeEvaluator */ public void setEvaluator(ASEvaluation newEvaluator) throws IllegalArgumentException { if (!(newEvaluator instanceof AttributeEvaluator)) { throw new IllegalArgumentException("Evaluator must be an AttributeEvaluator!"); } m_evaluator = newEvaluator; } /** * Evaluates an individual attribute. Delegates the actual evaluation to the * base attribute evaluator. * * @param attribute the index of the attribute to be evaluated * @return the "merit" of the attribute * @exception Exception if the attribute could not be evaluated */ public double evaluateAttribute(int attribute) throws Exception { return ((AttributeEvaluator)m_evaluator).evaluateAttribute(attribute); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.2 $"); } /** * Main method for testing this class. * * @param args the options */ public static void main (String[] args) { runEvaluator(new CostSensitiveAttributeEval(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/CostSensitiveSubsetEval.java000066400000000000000000000074751214146114000322720ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * CostSensitiveSubsetEval.java * Copyright (C) 2008 Pentaho Corporation * */ package weka.attributeSelection; import weka.core.OptionHandler; import weka.core.RevisionUtils; import java.util.BitSet; import java.io.Serializable; /** * A meta subset evaluator that makes its base subset evaluator cost-sensitive. *

* * Valid options are:

* * 

 -C <cost file name>
 *  File name of a cost matrix to use. If this is not supplied,
 *  a cost matrix will be loaded on demand. The name of the
 *  on-demand file is the relation name of the training data
 *  plus ".cost", and the path to the on-demand file is
 *  specified with the -N option.
* * 
 -N <directory>
 *  Name of a directory to search for cost files when loading
 *  costs on demand (default current directory).
* * 
 -cost-matrix <matrix>
 *  The cost matrix in Matlab single line format.
* * 
 -S <integer>
 *  The seed to use for random number generation.
* * 
 -W
 *  Full name of base evaluator.
 *  (default: weka.attributeSelection.CfsSubsetEval)
* * 
 
 * Options specific to evaluator weka.attributeSelection.CfsSubsetEval:
 *
* * 
 -M
 *  Treat missing values as a seperate value.
* * 
 -L
 *  Don't include locally predictive attributes.
* * * @author Mark Hall (mhall{[at]}pentaho{[dot]}com) * @version $Revision: 1.2 $ */ public class CostSensitiveSubsetEval extends CostSensitiveASEvaluation implements Serializable, SubsetEvaluator, OptionHandler { /** For serialization */ static final long serialVersionUID = 2924546096103426700L; /** * Default constructor. */ public CostSensitiveSubsetEval() { setEvaluator(new CfsSubsetEval()); } /** * Set the base evaluator. * * @param newEvaluator the evaluator to use. * @throws IllegalArgumentException if the evaluator is not an instance of SubsetEvaluator */ public void setEvaluator(ASEvaluation newEvaluator) throws IllegalArgumentException { if (!(newEvaluator instanceof SubsetEvaluator)) { throw new IllegalArgumentException("Evaluator must be an SubsetEvaluator!"); } m_evaluator = newEvaluator; } /** * Evaluates a subset of attributes. Delegates the actual evaluation to * the base subset evaluator. * * @param subset a bitset representing the attribute subset to be * evaluated * @return the "merit" of the subset * @exception Exception if the subset could not be evaluated */ public double evaluateSubset(BitSet subset) throws Exception { return ((SubsetEvaluator)m_evaluator).evaluateSubset(subset); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.2 $"); } /** * Main method for testing this class. * * @param args the options */ public static void main (String[] args) { runEvaluator(new CostSensitiveSubsetEval(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/ErrorBasedMeritEvaluator.java000066400000000000000000000021621214146114000323720ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * ErrorBasedMeritEvaluator.java * Copyright (C) 2000 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; /** * Interface for evaluators that calculate the "merit" of attributes/subsets * as the error of a learning scheme * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.6 $ */ public interface ErrorBasedMeritEvaluator { } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/ExhaustiveSearch.java000066400000000000000000000266071214146114000307430ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * ExhaustiveSearch.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.Utils; import java.math.BigInteger; import java.util.BitSet; import java.util.Enumeration; import java.util.Vector; /** * ExhaustiveSearch :
*
* Performs an exhaustive search through the space of attribute subsets starting from the empty set of attrubutes. Reports the best subset found. *

* * Valid options are:

* * 

 -V
 *  Output subsets as the search progresses.
 *  (default = false).
* * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.15 $ */ public class ExhaustiveSearch extends ASSearch implements OptionHandler { /** for serialization */ static final long serialVersionUID = 5741842861142379712L; /** the best feature set found during the search */ private BitSet m_bestGroup; /** the merit of the best subset found */ private double m_bestMerit; /** does the data have a class */ private boolean m_hasClass; /** holds the class index */ private int m_classIndex; /** number of attributes in the data */ private int m_numAttribs; /** if true, then ouput new best subsets as the search progresses */ private boolean m_verbose; /** the number of subsets evaluated during the search */ private int m_evaluations; /** * Returns a string describing this search method * @return a description of the search suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "ExhaustiveSearch : \n\nPerforms an exhaustive search through " +"the space of attribute subsets starting from the empty set of " +"attrubutes. Reports the best subset found."; } /** * Constructor */ public ExhaustiveSearch () { resetOptions(); } /** * Returns an enumeration describing the available options. * @return an enumeration of all the available options. **/ public Enumeration listOptions () { Vector newVector = new Vector(2); newVector.addElement(new Option("\tOutput subsets as the search progresses." +"\n\t(default = false)." , "V", 0 , "-V")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -V
   *  Output subsets as the search progresses.
   *  (default = false).
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported * **/ public void setOptions (String[] options) throws Exception { resetOptions(); setVerbose(Utils.getFlag('V',options)); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String verboseTipText() { return "Print progress information. Sends progress info to the terminal " +"as the search progresses."; } /** * set whether or not to output new best subsets as the search proceeds * @param v true if output is to be verbose */ public void setVerbose(boolean v) { m_verbose = v; } /** * get whether or not output is verbose * @return true if output is set to verbose */ public boolean getVerbose() { return m_verbose; } /** * Gets the current settings of RandomSearch. * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { String[] options = new String[1]; int current = 0; if (m_verbose) { options[current++] = "-V"; } while (current < options.length) { options[current++] = ""; } return options; } /** * prints a description of the search * @return a description of the search as a string */ public String toString() { StringBuffer text = new StringBuffer(); text.append("\tExhaustive Search.\n\tStart set: "); text.append("no attributes\n"); text.append("\tNumber of evaluations: "+m_evaluations+"\n"); text.append("\tMerit of best subset found: " +Utils.doubleToString(Math.abs(m_bestMerit),8,3)+"\n"); return text.toString(); } /** * Searches the attribute subset space using an exhaustive search. * * @param ASEval the attribute evaluator to guide the search * @param data the training instances. * @return an array (not necessarily ordered) of selected attribute indexes * @throws Exception if the search can't be completed */ public int[] search (ASEvaluation ASEval, Instances data) throws Exception { double best_merit; double tempMerit; boolean done = false; int sizeOfBest; int tempSize; BigInteger space = BigInteger.ZERO; m_evaluations = 0; m_numAttribs = data.numAttributes(); m_bestGroup = new BitSet(m_numAttribs); if (!(ASEval instanceof SubsetEvaluator)) { throw new Exception(ASEval.getClass().getName() + " is not a " + "Subset evaluator!"); } if (ASEval instanceof UnsupervisedSubsetEvaluator) { m_hasClass = false; } else { m_hasClass = true; m_classIndex = data.classIndex(); } SubsetEvaluator ASEvaluator = (SubsetEvaluator)ASEval; m_numAttribs = data.numAttributes(); best_merit = ASEvaluator.evaluateSubset(m_bestGroup); m_evaluations++; sizeOfBest = countFeatures(m_bestGroup); BitSet tempGroup = new BitSet(m_numAttribs); tempMerit = ASEvaluator.evaluateSubset(tempGroup); if (m_verbose) { System.out.println("Zero feature subset (" +Utils.doubleToString(Math. abs(tempMerit),8,5) +")"); } if (tempMerit >= best_merit) { tempSize = countFeatures(tempGroup); if (tempMerit > best_merit || (tempSize < sizeOfBest)) { best_merit = tempMerit; m_bestGroup = (BitSet)(tempGroup.clone()); sizeOfBest = tempSize; } } int numatts = (m_hasClass) ? m_numAttribs - 1 : m_numAttribs; BigInteger searchSpaceEnd = BigInteger.ONE.add(BigInteger.ONE).pow(numatts).subtract(BigInteger.ONE); while (!done) { // the next subset space = space.add(BigInteger.ONE); if (space.equals(searchSpaceEnd)) { done = true; } tempGroup.clear(); for (int i = 0; i < numatts; i++) { if (space.testBit(i)) { if (!m_hasClass) { tempGroup.set(i); } else { int j = (i >= m_classIndex) ? i + 1 : i; tempGroup.set(j); } } } tempMerit = ASEvaluator.evaluateSubset(tempGroup); m_evaluations++; if (tempMerit >= best_merit) { tempSize = countFeatures(tempGroup); if (tempMerit > best_merit || (tempSize < sizeOfBest)) { best_merit = tempMerit; m_bestGroup = (BitSet)(tempGroup.clone()); sizeOfBest = tempSize; if (m_verbose) { System.out.println("New best subset (" +Utils.doubleToString(Math. abs(best_merit),8,5) +"): "+printSubset(m_bestGroup)); } } } } m_bestMerit = best_merit; return attributeList(m_bestGroup); } /** * counts the number of features in a subset * @param featureSet the feature set for which to count the features * @return the number of features in the subset */ private int countFeatures(BitSet featureSet) { int count = 0; for (int i=0;i=0;i--) { if (temp.get(i)) { temp.clear(i); int newP; if (i != (m_numAttribs-1-counter)) { newP = i+1; if (newP == m_classIndex) { newP++; } if (newP < m_numAttribs) { temp.set(newP); for (j=0;j * Lei Yu, Huan Liu: Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution. In: Proceedings of the Twentieth International Conference on Machine Learning, 856-863, 2003. * * * CONTACT INFORMATION * * For algorithm implementation: * Zheng Zhao: zhaozheng at asu.edu * * For the algorithm: * Lei Yu: leiyu at asu.edu * Huan Liu: hliu at asu.edu * * Data Mining and Machine Learning Lab * Computer Science and Engineering Department * Fulton School of Engineering * Arizona State University * Tempe, AZ 85287 * * FCBFSearch.java * * Copyright (C) 2004 Data Mining and Machine Learning Lab, * Computer Science and Engineering Department, * Fulton School of Engineering, * Arizona State University * */ package weka.attributeSelection; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.Range; import weka.core.RevisionUtils; import weka.core.TechnicalInformation; import weka.core.TechnicalInformation.Type; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformationHandler; import weka.core.Utils; import java.util.Enumeration; import java.util.Vector; /** * FCBF :
*
* Feature selection method based on correlation measureand relevance&redundancy analysis. Use in conjunction with an attribute set evaluator (SymmetricalUncertAttributeEval).
*
* For more information see:
*
* Lei Yu, Huan Liu: Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution. In: Proceedings of the Twentieth International Conference on Machine Learning, 856-863, 2003. *

* * BibTeX: * 

 * @inproceedings{Yu2003,
 *    author = {Lei Yu and Huan Liu},
 *    booktitle = {Proceedings of the Twentieth International Conference on Machine Learning},
 *    pages = {856-863},
 *    publisher = {AAAI Press},
 *    title = {Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution},
 *    year = {2003}
 * }
 *
*

* * Valid options are:

* * 

 -D <create dataset>
 *  Specify Whether the selector generates a new dataset.
* * 
 -P <start set>
 *  Specify a starting set of attributes.
 *   Eg. 1,3,5-7.
 *  Any starting attributes specified are
 *  ignored during the ranking.
* * 
 -T <threshold>
 *  Specify a theshold by which attributes
 *  may be discarded from the ranking.
* * 
 -N <num to select>
 *  Specify number of attributes to select
* * * @author Zheng Zhao: zhaozheng at asu.edu * @version $Revision: 1.7 $ */ public class FCBFSearch extends ASSearch implements RankedOutputSearch, StartSetHandler, OptionHandler, TechnicalInformationHandler { /** for serialization */ static final long serialVersionUID = 8209699587428369942L; /** Holds the starting set as an array of attributes */ private int[] m_starting; /** Holds the start set for the search as a range */ private Range m_startRange; /** Holds the ordered list of attributes */ private int[] m_attributeList; /** Holds the list of attribute merit scores */ private double[] m_attributeMerit; /** Data has class attribute---if unsupervised evaluator then no class */ private boolean m_hasClass; /** Class index of the data if supervised evaluator */ private int m_classIndex; /** The number of attribtes */ private int m_numAttribs; /** * A threshold by which to discard attributes---used by the * AttributeSelection module */ private double m_threshold; /** The number of attributes to select. -1 indicates that all attributes are to be retained. Has precedence over m_threshold */ private int m_numToSelect = -1; /** Used to compute the number to select */ private int m_calculatedNumToSelect = -1; /*-----------------add begin 2004-11-15 by alan-----------------*/ /** Used to determine whether we create a new dataset according to the selected features */ private boolean m_generateOutput = false; /** Used to store the ref of the Evaluator we use*/ private ASEvaluation m_asEval; /** Holds the list of attribute merit scores generated by FCBF */ private double[][] m_rankedFCBF; /** Hold the list of selected features*/ private double[][] m_selectedFeatures; /*-----------------add end 2004-11-15 by alan-----------------*/ /** * Returns a string describing this search method * @return a description of the search suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "FCBF : \n\nFeature selection method based on correlation measure" + "and relevance&redundancy analysis. " + "Use in conjunction with an attribute set evaluator (SymmetricalUncertAttributeEval).\n\n" + "For more information see:\n\n" + getTechnicalInformation().toString(); } /** * Returns an instance of a TechnicalInformation object, containing * detailed information about the technical background of this class, * e.g., paper reference or book this class is based on. * * @return the technical information about this class */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.INPROCEEDINGS); result.setValue(Field.AUTHOR, "Lei Yu and Huan Liu"); result.setValue(Field.TITLE, "Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution"); result.setValue(Field.BOOKTITLE, "Proceedings of the Twentieth International Conference on Machine Learning"); result.setValue(Field.YEAR, "2003"); result.setValue(Field.PAGES, "856-863"); result.setValue(Field.PUBLISHER, "AAAI Press"); return result; } /** * Constructor */ public FCBFSearch () { resetOptions(); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String numToSelectTipText() { return "Specify the number of attributes to retain. The default value " +"(-1) indicates that all attributes are to be retained. Use either " +"this option or a threshold to reduce the attribute set."; } /** * Specify the number of attributes to select from the ranked list. -1 * indicates that all attributes are to be retained. * @param n the number of attributes to retain */ public void setNumToSelect(int n) { m_numToSelect = n; } /** * Gets the number of attributes to be retained. * @return the number of attributes to retain */ public int getNumToSelect() { return m_numToSelect; } /** * Gets the calculated number to select. This might be computed * from a threshold, or if < 0 is set as the number to select then * it is set to the number of attributes in the (transformed) data. * @return the calculated number of attributes to select */ public int getCalculatedNumToSelect() { if (m_numToSelect >= 0) { m_calculatedNumToSelect = m_numToSelect; } if (m_selectedFeatures.length>0 && m_selectedFeatures.length")); newVector.addElement(new Option( "\tSpecify a starting set of attributes.\n" + "\t\tEg. 1,3,5-7.\n" + "\tAny starting attributes specified are\n" + "\tignored during the ranking.", "P", 1 , "-P ")); newVector.addElement(new Option( "\tSpecify a theshold by which attributes\n" + "\tmay be discarded from the ranking.", "T", 1, "-T ")); newVector.addElement(new Option( "\tSpecify number of attributes to select", "N", 1, "-N ")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -D <create dataset>
   *  Specify Whether the selector generates a new dataset.
* * 
 -P <start set>
   *  Specify a starting set of attributes.
   *   Eg. 1,3,5-7.
   *  Any starting attributes specified are
   *  ignored during the ranking.
* * 
 -T <threshold>
   *  Specify a theshold by which attributes
   *  may be discarded from the ranking.
* * 
 -N <num to select>
   *  Specify number of attributes to select
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported * **/ public void setOptions (String[] options) throws Exception { String optionString; resetOptions(); optionString = Utils.getOption('D', options); if (optionString.length() != 0) { setGenerateDataOutput(Boolean.getBoolean(optionString)); } optionString = Utils.getOption('P', options); if (optionString.length() != 0) { setStartSet(optionString); } optionString = Utils.getOption('T', options); if (optionString.length() != 0) { Double temp; temp = Double.valueOf(optionString); setThreshold(temp.doubleValue()); } optionString = Utils.getOption('N', options); if (optionString.length() != 0) { setNumToSelect(Integer.parseInt(optionString)); } } /** * Gets the current settings of ReliefFAttributeEval. * * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { String[] options = new String[8]; int current = 0; options[current++] = "-D"; options[current++] = ""+getGenerateDataOutput(); if (!(getStartSet().equals(""))) { options[current++] = "-P"; options[current++] = ""+startSetToString(); } options[current++] = "-T"; options[current++] = "" + getThreshold(); options[current++] = "-N"; options[current++] = ""+getNumToSelect(); while (current < options.length) { options[current++] = ""; } return options; } /** * converts the array of starting attributes to a string. This is * used by getOptions to return the actual attributes specified * as the starting set. This is better than using m_startRanges.getRanges() * as the same start set can be specified in different ways from the * command line---eg 1,2,3 == 1-3. This is to ensure that stuff that * is stored in a database is comparable. * @return a comma seperated list of individual attribute numbers as a String */ private String startSetToString() { StringBuffer FString = new StringBuffer(); boolean didPrint; if (m_starting == null) { return getStartSet(); } for (int i = 0; i < m_starting.length; i++) { didPrint = false; if ((m_hasClass == false) || (m_hasClass == true && i != m_classIndex)) { FString.append((m_starting[i] + 1)); didPrint = true; } if (i == (m_starting.length - 1)) { FString.append(""); } else { if (didPrint) { FString.append(","); } } } return FString.toString(); } /** * Kind of a dummy search algorithm. Calls a Attribute evaluator to * evaluate each attribute not included in the startSet and then sorts * them to produce a ranked list of attributes. * * @param ASEval the attribute evaluator to guide the search * @param data the training instances. * @return an array (not necessarily ordered) of selected attribute indexes * @throws Exception if the search can't be completed */ public int[] search (ASEvaluation ASEval, Instances data) throws Exception { int i, j; if (!(ASEval instanceof AttributeSetEvaluator)) { throw new Exception(ASEval.getClass().getName() + " is not an " + "Attribute Set evaluator!"); } m_numAttribs = data.numAttributes(); if (ASEval instanceof UnsupervisedAttributeEvaluator) { m_hasClass = false; } else { m_classIndex = data.classIndex(); if (m_classIndex >= 0) { m_hasClass = true; } else { m_hasClass = false; } } // get the transformed data and check to see if the transformer // preserves a class index if (ASEval instanceof AttributeTransformer) { data = ((AttributeTransformer)ASEval).transformedHeader(); if (m_classIndex >= 0 && data.classIndex() >= 0) { m_classIndex = data.classIndex(); m_hasClass = true; } } m_startRange.setUpper(m_numAttribs - 1); if (!(getStartSet().equals(""))) { m_starting = m_startRange.getSelection(); } int sl=0; if (m_starting != null) { sl = m_starting.length; } if ((m_starting != null) && (m_hasClass == true)) { // see if the supplied list contains the class index boolean ok = false; for (i = 0; i < sl; i++) { if (m_starting[i] == m_classIndex) { ok = true; break; } } if (ok == false) { sl++; } } else { if (m_hasClass == true) { sl++; } } m_attributeList = new int[m_numAttribs - sl]; m_attributeMerit = new double[m_numAttribs - sl]; // add in those attributes not in the starting (omit list) for (i = 0, j = 0; i < m_numAttribs; i++) { if (!inStarting(i)) { m_attributeList[j++] = i; } } this.m_asEval = ASEval; AttributeSetEvaluator ASEvaluator = (AttributeSetEvaluator)ASEval; for (i = 0; i < m_attributeList.length; i++) { m_attributeMerit[i] = ASEvaluator.evaluateAttribute(m_attributeList[i]); } double[][] tempRanked = rankedAttributes(); int[] rankedAttributes = new int[m_selectedFeatures.length]; for (i = 0; i < m_selectedFeatures.length; i++) { rankedAttributes[i] = (int)tempRanked[i][0]; } return rankedAttributes; } /** * Sorts the evaluated attribute list * * @return an array of sorted (highest eval to lowest) attribute indexes * @throws Exception of sorting can't be done. */ public double[][] rankedAttributes () throws Exception { int i, j; if (m_attributeList == null || m_attributeMerit == null) { throw new Exception("Search must be performed before a ranked " + "attribute list can be obtained"); } int[] ranked = Utils.sort(m_attributeMerit); // reverse the order of the ranked indexes double[][] bestToWorst = new double[ranked.length][2]; for (i = ranked.length - 1, j = 0; i >= 0; i--) { bestToWorst[j++][0] = ranked[i]; //alan: means in the arrary ranked, varialbe is from ranked as from small to large } // convert the indexes to attribute indexes for (i = 0; i < bestToWorst.length; i++) { int temp = ((int)bestToWorst[i][0]); bestToWorst[i][0] = m_attributeList[temp]; //for the index bestToWorst[i][1] = m_attributeMerit[temp]; //for the value of the index } if (m_numToSelect > bestToWorst.length) { throw new Exception("More attributes requested than exist in the data"); } this.FCBFElimination(bestToWorst); if (m_numToSelect <= 0) { if (m_threshold == -Double.MAX_VALUE) { m_calculatedNumToSelect = m_selectedFeatures.length; } else { determineNumToSelectFromThreshold(m_selectedFeatures); } } /* if (m_numToSelect > 0) { determineThreshFromNumToSelect(bestToWorst); } */ return m_selectedFeatures; } private void determineNumToSelectFromThreshold(double [][] ranking) { int count = 0; for (int i = 0; i < ranking.length; i++) { if (ranking[i][1] > m_threshold) { count++; } } m_calculatedNumToSelect = count; } private void determineThreshFromNumToSelect(double [][] ranking) throws Exception { if (m_numToSelect > ranking.length) { throw new Exception("More attributes requested than exist in the data"); } if (m_numToSelect == ranking.length) { return; } m_threshold = (ranking[m_numToSelect-1][1] + ranking[m_numToSelect][1]) / 2.0; } /** * returns a description of the search as a String * @return a description of the search */ public String toString () { StringBuffer BfString = new StringBuffer(); BfString.append("\tAttribute ranking.\n"); if (m_starting != null) { BfString.append("\tIgnored attributes: "); BfString.append(startSetToString()); BfString.append("\n"); } if (m_threshold != -Double.MAX_VALUE) { BfString.append("\tThreshold for discarding attributes: " + Utils.doubleToString(m_threshold,8,4)+"\n"); } BfString.append("\n\n"); BfString.append(" J || SU(j,Class) || I || SU(i,j). \n"); for (int i=0; i * Class for running an arbitrary attribute evaluator on data that has been passed through an * arbitrary filter (note: filters that alter the order or number of attributes are not allowed). * Like the evaluator, the structure of the filter is based exclusively on the training data. *

* * Valid options are:

* * 

 -W <evaluator specification>
 *  Full name of base evaluator to use, followed by evaluator options.
 *  eg: "weka.attributeSelection.InfoGainAttributeEval -M"
* * 
 -F <filter specification>
 *  Full class name of filter to use, followed
 *  by filter options.
 *  eg: "weka.filters.supervised.instance.SpreadSubsample -M 1"
* * * @author Mark Hall (mhall{[at]}pentaho{[dot]}com) * @version $Revision: 1.2 $ */ public class FilteredAttributeEval extends ASEvaluation implements Serializable, AttributeEvaluator, OptionHandler { /** For serialization */ static final long serialVersionUID = 2111121880778327334L; /** Base evaluator */ protected AttributeEvaluator m_evaluator = new InfoGainAttributeEval(); /** Filter */ protected Filter m_filter = new weka.filters.supervised.instance.SpreadSubsample(); /** Filtered instances structure */ protected Instances m_filteredInstances; public FilteredAttributeEval() { m_filteredInstances = null; } /** * Returns default capabilities of the evaluator. * * @return the capabilities of this evaluator. */ public Capabilities getCapabilities() { Capabilities result; if (getFilter() == null) { result = super.getCapabilities(); } else { result = getFilter().getCapabilities(); } // set dependencies for (Capability cap: Capability.values()) { result.enableDependency(cap); } return result; } /** * @return a description of the evaluator suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Class for running an arbitrary attribute evaluator on data that has been passed " + "through an arbitrary filter (note: filters that alter the order or number of " + "attributes are not allowed). Like the evaluator, the structure of the filter " + "is based exclusively on the training data."; } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector newVector = new Vector(2); newVector.addElement(new Option( "\tFull name of base evaluator to use, followed by " +"evaluator options.\n" + "\teg: \"weka.attributeSelection.InfoGainAttributeEval -M\"", "W", 1, "-W ")); newVector.addElement(new Option( "\tFull class name of filter to use, followed\n" + "\tby filter options.\n" + "\teg: \"weka.filters.supervised.instance.SpreadSubsample -M 1\"", "F", 1, "-F ")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -W <evaluator specification>
   *  Full name of base evaluator to use, followed by evaluator options.
   *  eg: "weka.attributeSelection.InfoGainAttributeEval -M"
* * 
 -F <filter specification>
   *  Full class name of filter to use, followed
   *  by filter options.
   *  eg: "weka.filters.supervised.instance.SpreadSubsample -M 1"
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { String evaluator = Utils.getOption('W', options); if (evaluator.length() > 0) { String[] evaluatorSpec = Utils.splitOptions(evaluator); if (evaluatorSpec.length == 0) { throw new IllegalArgumentException("Invalid evaluator specification string"); } String evaluatorName = evaluatorSpec[0]; evaluatorSpec[0] = ""; setAttributeEvaluator((ASEvaluation)Utils.forName(AttributeEvaluator.class, evaluatorName, evaluatorSpec)); } else { setAttributeEvaluator(new InfoGainAttributeEval()); } // Same for filter String filterString = Utils.getOption('F', options); if (filterString.length() > 0) { String [] filterSpec = Utils.splitOptions(filterString); if (filterSpec.length == 0) { throw new IllegalArgumentException("Invalid filter specification string"); } String filterName = filterSpec[0]; filterSpec[0] = ""; setFilter((Filter) Utils.forName(Filter.class, filterName, filterSpec)); } else { setFilter(new weka.filters.supervised.instance.SpreadSubsample()); } } /** * Gets the current settings of the subset evaluator. * * @return an array of strings suitable for passing to setOptions */ public String[] getOptions() { ArrayList options = new ArrayList(); options.add("-W"); options.add(getEvaluatorSpec()); options.add("-F"); options.add(getFilterSpec()); return options.toArray(new String[0]); } /** * Get the evaluator + options as a string * * @return a String containing the name of the evalautor + any options */ protected String getEvaluatorSpec() { AttributeEvaluator a = m_evaluator; if (a instanceof OptionHandler) { return a.getClass().getName() + " " + Utils.joinOptions(((OptionHandler)a).getOptions()); } return a.getClass().getName(); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String attributeEvaluatorTipText() { return "The attribute evaluator to be used."; } /** * Set the attribute evaluator to use * * @param newEvaluator the attribute evaluator to use */ public void setAttributeEvaluator(ASEvaluation newEvaluator) { if (!(newEvaluator instanceof AttributeEvaluator)) { throw new IllegalArgumentException("Evaluator must be an AttributeEvaluator!"); } m_evaluator = (AttributeEvaluator)newEvaluator; } /** * Get the attribute evaluator to use * * @return the attribute evaluator to use */ public ASEvaluation getAttributeEvaluator() { return (ASEvaluation)m_evaluator; } /** * Get the filter + options as a string * * @return a String containing the name of the filter + any options */ protected String getFilterSpec() { Filter c = getFilter(); if (c instanceof OptionHandler) { return c.getClass().getName() + " " + Utils.joinOptions(((OptionHandler)c).getOptions()); } return c.getClass().getName(); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String filterTipText() { return "The filter to be used."; } /** * Set the filter to use * * @param newFilter the filter to use */ public void setFilter(Filter newFilter) { m_filter = newFilter; } /** * Get the filter to use * * @return the filter to use */ public Filter getFilter() { return m_filter; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.2 $"); } /** * Initializes a filtered attribute evaluator. * * @param data set of instances serving as training data * @throws Exception if the evaluator has not been * generated successfully */ public void buildEvaluator(Instances data) throws Exception { // can evaluator handle data? getCapabilities().testWithFail(data); // Structure of original Instances original = new Instances(data, 0); m_filter.setInputFormat(data); data = Filter.useFilter(data, m_filter); // Can only proceed if filter has not altered the order or // number of attributes in the data if (data.numAttributes() != original.numAttributes()) { throw new Exception("Filter must not alter the number of " +"attributes in the data!"); } // Check the class index (if set) if (original.classIndex() >= 0) { if (data.classIndex() != original.classIndex()) { throw new Exception("Filter must not change the class attribute!"); } } // check the order for (int i = 0; i < original.numAttributes(); i++) { if (!data.attribute(i).name().equals(original.attribute(i).name())) { throw new Exception("Filter must not alter the order of the attributes!"); } } // can the evaluator handle this data? ((ASEvaluation)getAttributeEvaluator()).getCapabilities().testWithFail(data); m_filteredInstances = data.stringFreeStructure(); ((ASEvaluation)m_evaluator).buildEvaluator(data); } /** * Evaluates an individual attribute by delegating to the base * evaluator. * * @param attribute the index of the attribute to be evaluated * @return the merit of the attribute according to the base evaluator * @throws Exception if the attribute could not be evaluated */ public double evaluateAttribute(int attribute) throws Exception { return m_evaluator.evaluateAttribute(attribute); } /** * Describe the attribute evaluator * @return a description of the attribute evaluator as a string */ public String toString() { StringBuffer text = new StringBuffer(); if (m_filteredInstances == null) { text.append("Filtered attribute evaluator has not been built"); } else { text.append("Filtered Attribute Evaluator"); text.append("\nFilter: " + getFilterSpec()); text.append("\nAttribute evaluator: " + getEvaluatorSpec()); text.append("\n\nFiltered header:\n"); text.append(m_filteredInstances); } text.append("\n"); return text.toString(); } // ============ // Test method. // ============ /** * Main method for testing this class. * * @param args the options */ public static void main (String[] args) { runEvaluator(new FilteredAttributeEval(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/FilteredSubsetEval.java000066400000000000000000000270611214146114000312170ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * FilteredSubsetEval.java * Copyright (C) 2008 Pentaho Corporation * */ package weka.attributeSelection; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.filters.Filter; import weka.core.Instances; import weka.core.Capabilities; import weka.core.Capabilities.Capability; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.Utils; import java.util.Enumeration; import java.util.Vector; import java.util.ArrayList; import java.util.BitSet; import java.io.Serializable; /** * Class for running an arbitrary subset evaluator on data that has been passed through an arbitrary * filter (note: filters that alter the order or number of attributes are not allowed). * Like the evaluator, the structure of the filter is based exclusively on the training data. *

* * Valid options are:

* * 

 -W <evaluator specification>
 *  Full name of base evaluator to use, followed by evaluator options.
 *  eg: "weka.attributeSelection.CfsSubsetEval -L"
* * 
 -F <filter specification>
 *  Full class name of filter to use, followed
 *  by filter options.
 *  eg: "weka.filters.supervised.instance.SpreadSubsample -M 1"
* * * @author Mark Hall (mhall{[at]}pentaho{[dot]}com) * @version $Revision: 1.1 $ */ public class FilteredSubsetEval extends ASEvaluation implements Serializable, SubsetEvaluator, OptionHandler { /** For serialization */ static final long serialVersionUID = 2111121880778327334L; /** Base evaluator */ protected SubsetEvaluator m_evaluator = new CfsSubsetEval(); /** Filter */ protected Filter m_filter = new weka.filters.supervised.instance.SpreadSubsample(); /** Filtered instances structure */ protected Instances m_filteredInstances; public FilteredSubsetEval() { m_filteredInstances = null; } /** * Returns default capabilities of the evaluator. * * @return the capabilities of this evaluator. */ public Capabilities getCapabilities() { Capabilities result; if (getFilter() == null) { result = super.getCapabilities(); } else { result = getFilter().getCapabilities(); } // set dependencies for (Capability cap: Capability.values()) { result.enableDependency(cap); } return result; } /** * @return a description of the evaluator suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Class for running an arbitrary subset evaluator on data that has been passed " + "through an arbitrary filter (note: filters that alter the order or number of " + "attributes are not allowed). Like the evaluator, the structure of the filter " + "is based exclusively on the training data."; } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector newVector = new Vector(2); newVector.addElement(new Option( "\tFull name of base evaluator to use, followed by " +"evaluator options.\n" + "\teg: \"weka.attributeSelection.CfsSubsetEval -L\"", "W", 1, "-W ")); newVector.addElement(new Option( "\tFull class name of filter to use, followed\n" + "\tby filter options.\n" + "\teg: \"weka.filters.supervised.instance.SpreadSubsample -M 1\"", "F", 1, "-F ")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -W <evaluator specification>
   *  Full name of base evaluator to use, followed by evaluator options.
   *  eg: "weka.attributeSelection.CfsSubsetEval -L"
* * 
 -F <filter specification>
   *  Full class name of filter to use, followed
   *  by filter options.
   *  eg: "weka.filters.supervised.instance.SpreadSubsample -M 1"
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { String evaluator = Utils.getOption('W', options); if (evaluator.length() > 0) { String[] evaluatorSpec = Utils.splitOptions(evaluator); if (evaluatorSpec.length == 0) { throw new IllegalArgumentException("Invalid evaluator specification string"); } String evaluatorName = evaluatorSpec[0]; evaluatorSpec[0] = ""; setSubsetEvaluator((ASEvaluation)Utils.forName(SubsetEvaluator.class, evaluatorName, evaluatorSpec)); } else { setSubsetEvaluator(new CfsSubsetEval()); } // Same for filter String filterString = Utils.getOption('F', options); if (filterString.length() > 0) { String [] filterSpec = Utils.splitOptions(filterString); if (filterSpec.length == 0) { throw new IllegalArgumentException("Invalid filter specification string"); } String filterName = filterSpec[0]; filterSpec[0] = ""; setFilter((Filter) Utils.forName(Filter.class, filterName, filterSpec)); } else { setFilter(new weka.filters.supervised.instance.SpreadSubsample()); } } /** * Gets the current settings of the subset evaluator. * * @return an array of strings suitable for passing to setOptions */ public String[] getOptions() { ArrayList options = new ArrayList(); options.add("-W"); options.add(getEvaluatorSpec()); options.add("-F"); options.add(getFilterSpec()); return options.toArray(new String[0]); } /** * Get the evaluator + options as a string * * @return a String containing the name of the evalautor + any options */ protected String getEvaluatorSpec() { SubsetEvaluator a = m_evaluator; if (a instanceof OptionHandler) { return a.getClass().getName() + " " + Utils.joinOptions(((OptionHandler)a).getOptions()); } return a.getClass().getName(); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String subsetEvaluatorTipText() { return "The subset evaluator to be used."; } /** * Set the subset evaluator to use * * @param newEvaluator the subset evaluator to use */ public void setSubsetEvaluator(ASEvaluation newEvaluator) { if (!(newEvaluator instanceof SubsetEvaluator)) { throw new IllegalArgumentException("Evaluator must be a SubsetEvaluator!"); } m_evaluator = (SubsetEvaluator)newEvaluator; } /** * Get the subset evaluator to use * * @return the subset evaluator to use */ public ASEvaluation getSubsetEvaluator() { return (ASEvaluation)m_evaluator; } /** * Get the filter + options as a string * * @return a String containing the name of the filter + any options */ protected String getFilterSpec() { Filter c = getFilter(); if (c instanceof OptionHandler) { return c.getClass().getName() + " " + Utils.joinOptions(((OptionHandler)c).getOptions()); } return c.getClass().getName(); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String filterTipText() { return "The filter to be used."; } /** * Set the filter to use * * @param newFilter the filter to use */ public void setFilter(Filter newFilter) { m_filter = newFilter; } /** * Get the filter to use * * @return the filter to use */ public Filter getFilter() { return m_filter; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.1 $"); } /** * Initializes a filtered attribute evaluator. * * @param data set of instances serving as training data * @throws Exception if the evaluator has not been * generated successfully */ public void buildEvaluator(Instances data) throws Exception { // can evaluator handle data? getCapabilities().testWithFail(data); // Structure of original Instances original = new Instances(data, 0); m_filter.setInputFormat(data); data = Filter.useFilter(data, m_filter); // Can only proceed if filter has not altered the order or // number of attributes in the data if (data.numAttributes() != original.numAttributes()) { throw new Exception("Filter must not alter the number of " +"attributes in the data!"); } // Check the class index (if set) if (original.classIndex() >= 0) { if (data.classIndex() != original.classIndex()) { throw new Exception("Filter must not change the class attribute!"); } } // check the order for (int i = 0; i < original.numAttributes(); i++) { if (!data.attribute(i).name().equals(original.attribute(i).name())) { throw new Exception("Filter must not alter the order of the attributes!"); } } // can the evaluator handle this data? ((ASEvaluation)getSubsetEvaluator()).getCapabilities().testWithFail(data); m_filteredInstances = data.stringFreeStructure(); ((ASEvaluation)m_evaluator).buildEvaluator(data); } /** * evaluates a subset of attributes * * @param subset a bitset representing the attribute subset to be * evaluated * @return the "merit" of the subset * @exception Exception if the subset could not be evaluated */ public double evaluateSubset(BitSet subset) throws Exception { return m_evaluator.evaluateSubset(subset); } /** * Describe the attribute evaluator * @return a description of the attribute evaluator as a string */ public String toString() { StringBuffer text = new StringBuffer(); if (m_filteredInstances == null) { text.append("Filtered attribute evaluator has not been built"); } else { text.append("Filtered Attribute Evaluator"); text.append("\nFilter: " + getFilterSpec()); text.append("\nAttribute evaluator: " + getEvaluatorSpec()); text.append("\n\nFiltered header:\n"); text.append(m_filteredInstances); } text.append("\n"); return text.toString(); } // ============ // Test method. // ============ /** * Main method for testing this class. * * @param args the options */ public static void main (String[] args) { runEvaluator(new FilteredSubsetEval(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/GainRatioAttributeEval.java000066400000000000000000000257141214146114000320370ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * GainRatioAttributeEval.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.core.Capabilities; import weka.core.ContingencyTables; import weka.core.Instance; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.Utils; import weka.core.Capabilities.Capability; import weka.filters.Filter; import weka.filters.supervised.attribute.Discretize; import java.util.Enumeration; import java.util.Vector; /** * GainRatioAttributeEval :
*
* Evaluates the worth of an attribute by measuring the gain ratio with respect to the class.
*
* GainR(Class, Attribute) = (H(Class) - H(Class | Attribute)) / H(Attribute).
*

* * Valid options are:

* * 

 -M
 *  treat missing values as a seperate value.
* * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.23 $ * @see Discretize */ public class GainRatioAttributeEval extends ASEvaluation implements AttributeEvaluator, OptionHandler { /** for serialization */ static final long serialVersionUID = -8504656625598579926L; /** The training instances */ private Instances m_trainInstances; /** The class index */ private int m_classIndex; /** The number of attributes */ private int m_numAttribs; /** The number of instances */ private int m_numInstances; /** The number of classes */ private int m_numClasses; /** Merge missing values */ private boolean m_missing_merge; /** * Returns a string describing this attribute evaluator * @return a description of the evaluator suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "GainRatioAttributeEval :\n\nEvaluates the worth of an attribute " +"by measuring the gain ratio with respect to the class.\n\n" +"GainR(Class, Attribute) = (H(Class) - H(Class | Attribute)) / " +"H(Attribute).\n"; } /** * Constructor */ public GainRatioAttributeEval () { resetOptions(); } /** * Returns an enumeration describing the available options. * @return an enumeration of all the available options. **/ public Enumeration listOptions () { Vector newVector = new Vector(1); newVector.addElement(new Option("\ttreat missing values as a seperate " + "value.", "M", 0, "-M")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -M
   *  treat missing values as a seperate value.
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported **/ public void setOptions (String[] options) throws Exception { resetOptions(); setMissingMerge(!(Utils.getFlag('M', options))); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String missingMergeTipText() { return "Distribute counts for missing values. Counts are distributed " +"across other values in proportion to their frequency. Otherwise, " +"missing is treated as a separate value."; } /** * distribute the counts for missing values across observed values * * @param b true=distribute missing values. */ public void setMissingMerge (boolean b) { m_missing_merge = b; } /** * get whether missing values are being distributed or not * * @return true if missing values are being distributed. */ public boolean getMissingMerge () { return m_missing_merge; } /** * Gets the current settings of WrapperSubsetEval. * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { String[] options = new String[1]; int current = 0; if (!getMissingMerge()) { options[current++] = "-M"; } while (current < options.length) { options[current++] = ""; } return options; } /** * Returns the capabilities of this evaluator. * * @return the capabilities of this evaluator * @see Capabilities */ public Capabilities getCapabilities() { Capabilities result = super.getCapabilities(); // attributes result.enable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.NUMERIC_ATTRIBUTES); result.enable(Capability.DATE_ATTRIBUTES); result.enable(Capability.MISSING_VALUES); // class result.enable(Capability.NOMINAL_CLASS); result.enable(Capability.MISSING_CLASS_VALUES); return result; } /** * Initializes a gain ratio attribute evaluator. * Discretizes all attributes that are numeric. * * @param data set of instances serving as training data * @throws Exception if the evaluator has not been * generated successfully */ public void buildEvaluator (Instances data) throws Exception { // can evaluator handle data? getCapabilities().testWithFail(data); m_trainInstances = data; m_classIndex = m_trainInstances.classIndex(); m_numAttribs = m_trainInstances.numAttributes(); m_numInstances = m_trainInstances.numInstances(); Discretize disTransform = new Discretize(); disTransform.setUseBetterEncoding(true); disTransform.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, disTransform); m_numClasses = m_trainInstances.attribute(m_classIndex).numValues(); } /** * reset options to default values */ protected void resetOptions () { m_trainInstances = null; m_missing_merge = true; } /** * evaluates an individual attribute by measuring the gain ratio * of the class given the attribute. * * @param attribute the index of the attribute to be evaluated * @return the gain ratio * @throws Exception if the attribute could not be evaluated */ public double evaluateAttribute (int attribute) throws Exception { int i, j, ii, jj; int ni, nj; double sum = 0.0; ni = m_trainInstances.attribute(attribute).numValues() + 1; nj = m_numClasses + 1; double[] sumi, sumj; Instance inst; double temp = 0.0; sumi = new double[ni]; sumj = new double[nj]; double[][] counts = new double[ni][nj]; sumi = new double[ni]; sumj = new double[nj]; for (i = 0; i < ni; i++) { sumi[i] = 0.0; for (j = 0; j < nj; j++) { sumj[j] = 0.0; counts[i][j] = 0.0; } } // Fill the contingency table for (i = 0; i < m_numInstances; i++) { inst = m_trainInstances.instance(i); if (inst.isMissing(attribute)) { ii = ni - 1; } else { ii = (int)inst.value(attribute); } if (inst.isMissing(m_classIndex)) { jj = nj - 1; } else { jj = (int)inst.value(m_classIndex); } counts[ii][jj]++; } // get the row totals for (i = 0; i < ni; i++) { sumi[i] = 0.0; for (j = 0; j < nj; j++) { sumi[i] += counts[i][j]; sum += counts[i][j]; } } // get the column totals for (j = 0; j < nj; j++) { sumj[j] = 0.0; for (i = 0; i < ni; i++) { sumj[j] += counts[i][j]; } } // distribute missing counts if (m_missing_merge && (sumi[ni-1] < m_numInstances) && (sumj[nj-1] < m_numInstances)) { double[] i_copy = new double[sumi.length]; double[] j_copy = new double[sumj.length]; double[][] counts_copy = new double[sumi.length][sumj.length]; for (i = 0; i < ni; i++) { System.arraycopy(counts[i], 0, counts_copy[i], 0, sumj.length); } System.arraycopy(sumi, 0, i_copy, 0, sumi.length); System.arraycopy(sumj, 0, j_copy, 0, sumj.length); double total_missing = (sumi[ni - 1] + sumj[nj - 1] - counts[ni - 1][nj - 1]); // do the missing i's if (sumi[ni - 1] > 0.0) { for (j = 0; j < nj - 1; j++) { if (counts[ni - 1][j] > 0.0) { for (i = 0; i < ni - 1; i++) { temp = ((i_copy[i]/(sum - i_copy[ni - 1]))*counts[ni - 1][j]); counts[i][j] += temp; sumi[i] += temp; } counts[ni - 1][j] = 0.0; } } } sumi[ni - 1] = 0.0; // do the missing j's if (sumj[nj - 1] > 0.0) { for (i = 0; i < ni - 1; i++) { if (counts[i][nj - 1] > 0.0) { for (j = 0; j < nj - 1; j++) { temp = ((j_copy[j]/(sum - j_copy[nj - 1]))*counts[i][nj - 1]); counts[i][j] += temp; sumj[j] += temp; } counts[i][nj - 1] = 0.0; } } } sumj[nj - 1] = 0.0; // do the both missing if (counts[ni - 1][nj - 1] > 0.0 && total_missing != sum) { for (i = 0; i < ni - 1; i++) { for (j = 0; j < nj - 1; j++) { temp = (counts_copy[i][j]/(sum - total_missing)) * counts_copy[ni - 1][nj - 1]; counts[i][j] += temp; sumi[i] += temp; sumj[j] += temp; } } counts[ni - 1][nj - 1] = 0.0; } } return ContingencyTables.gainRatio(counts); } /** * Return a description of the evaluator * @return description as a string */ public String toString () { StringBuffer text = new StringBuffer(); if (m_trainInstances == null) { text.append("\tGain Ratio evaluator has not been built"); } else { text.append("\tGain Ratio feature evaluator"); if (!m_missing_merge) { text.append("\n\tMissing values treated as seperate"); } } text.append("\n"); return text.toString(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.23 $"); } /** * Main method. * * @param args the options * -t training file */ public static void main (String[] args) { runEvaluator(new GainRatioAttributeEval(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/GeneticSearch.java000066400000000000000000001105721214146114000301670ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * GeneticSearch.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.Range; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import weka.core.TechnicalInformation; import weka.core.TechnicalInformationHandler; import weka.core.Utils; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; import java.io.Serializable; import java.util.BitSet; import java.util.Enumeration; import java.util.Hashtable; import java.util.Random; import java.util.Vector; /** * GeneticSearch:
*
* Performs a search using the simple genetic algorithm described in Goldberg (1989).
*
* For more information see:
*
* David E. Goldberg (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley. *

* * BibTeX: * 

 * @book{Goldberg1989,
 *    author = {David E. Goldberg},
 *    publisher = {Addison-Wesley},
 *    title = {Genetic algorithms in search, optimization and machine learning},
 *    year = {1989},
 *    ISBN = {0201157675}
 * }
 *
*

* * Valid options are:

* * 

 -P <start set>
 *  Specify a starting set of attributes.
 *  Eg. 1,3,5-7.If supplied, the starting set becomes
 *  one member of the initial random
 *  population.
* * 
 -Z <population size>
 *  Set the size of the population.
 *  (default = 10).
* * 
 -G <number of generations>
 *  Set the number of generations.
 *  (default = 20)
* * 
 -C <probability of crossover>
 *  Set the probability of crossover.
 *  (default = 0.6)
* * 
 -M <probability of mutation>
 *  Set the probability of mutation.
 *  (default = 0.033)
* * 
 -R <report frequency>
 *  Set frequency of generation reports.
 *  e.g, setting the value to 5 will 
 *  report every 5th generation
 *  (default = number of generations)
* * 
 -S <seed>
 *  Set the random number seed.
 *  (default = 1)
* * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.19 $ */ public class GeneticSearch extends ASSearch implements StartSetHandler, OptionHandler, TechnicalInformationHandler { /** for serialization */ static final long serialVersionUID = -1618264232838472679L; /** * holds a starting set as an array of attributes. Becomes one member of the * initial random population */ private int[] m_starting; /** holds the start set for the search as a Range */ private Range m_startRange; /** does the data have a class */ private boolean m_hasClass; /** holds the class index */ private int m_classIndex; /** number of attributes in the data */ private int m_numAttribs; /** the current population */ private GABitSet [] m_population; /** the number of individual solutions */ private int m_popSize; /** the best population member found during the search */ private GABitSet m_best; /** the number of features in the best population member */ private int m_bestFeatureCount; /** the number of entries to cache for lookup */ private int m_lookupTableSize; /** the lookup table */ private Hashtable m_lookupTable; /** random number generation */ private Random m_random; /** seed for random number generation */ private int m_seed; /** the probability of crossover occuring */ private double m_pCrossover; /** the probability of mutation occuring */ private double m_pMutation; /** sum of the current population fitness */ private double m_sumFitness; private double m_maxFitness; private double m_minFitness; private double m_avgFitness; /** the maximum number of generations to evaluate */ private int m_maxGenerations; /** how often reports are generated */ private int m_reportFrequency; /** holds the generation reports */ private StringBuffer m_generationReports; // Inner class /** * A bitset for the genetic algorithm */ protected class GABitSet implements Cloneable, Serializable, RevisionHandler { /** for serialization */ static final long serialVersionUID = -2930607837482622224L; /** the bitset */ private BitSet m_chromosome; /** holds raw merit */ private double m_objective = -Double.MAX_VALUE; /** the fitness */ private double m_fitness; /** * Constructor */ public GABitSet () { m_chromosome = new BitSet(); } /** * makes a copy of this GABitSet * @return a copy of the object * @throws CloneNotSupportedException if something goes wrong */ public Object clone() throws CloneNotSupportedException { GABitSet temp = new GABitSet(); temp.setObjective(this.getObjective()); temp.setFitness(this.getFitness()); temp.setChromosome((BitSet)(this.m_chromosome.clone())); return temp; //return super.clone(); } /** * sets the objective merit value * @param objective the objective value of this population member */ public void setObjective(double objective) { m_objective = objective; } /** * gets the objective merit * @return the objective merit of this population member */ public double getObjective() { return m_objective; } /** * sets the scaled fitness * @param fitness the scaled fitness of this population member */ public void setFitness(double fitness) { m_fitness = fitness; } /** * gets the scaled fitness * @return the scaled fitness of this population member */ public double getFitness() { return m_fitness; } /** * get the chromosome * @return the chromosome of this population member */ public BitSet getChromosome() { return m_chromosome; } /** * set the chromosome * @param c the chromosome to be set for this population member */ public void setChromosome(BitSet c) { m_chromosome = c; } /** * unset a bit in the chromosome * @param bit the bit to be cleared */ public void clear(int bit) { m_chromosome.clear(bit); } /** * set a bit in the chromosome * @param bit the bit to be set */ public void set(int bit) { m_chromosome.set(bit); } /** * get the value of a bit in the chromosome * @param bit the bit to query * @return the value of the bit */ public boolean get(int bit) { return m_chromosome.get(bit); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.19 $"); } } /** * Returns an enumeration describing the available options. * @return an enumeration of all the available options. **/ public Enumeration listOptions () { Vector newVector = new Vector(6); newVector.addElement(new Option("\tSpecify a starting set of attributes." + "\n\tEg. 1,3,5-7." +"If supplied, the starting set becomes" +"\n\tone member of the initial random" +"\n\tpopulation." ,"P",1 , "-P ")); newVector.addElement(new Option("\tSet the size of the population." +"\n\t(default = 10)." , "Z", 1 , "-Z ")); newVector.addElement(new Option("\tSet the number of generations." +"\n\t(default = 20)" , "G", 1, "-G ")); newVector.addElement(new Option("\tSet the probability of crossover." +"\n\t(default = 0.6)" , "C", 1, "-C ")); newVector.addElement(new Option("\tSet the probability of mutation." +"\n\t(default = 0.033)" , "M", 1, "-M ")); newVector.addElement(new Option("\tSet frequency of generation reports." +"\n\te.g, setting the value to 5 will " +"\n\treport every 5th generation" +"\n\t(default = number of generations)" , "R", 1, "-R ")); newVector.addElement(new Option("\tSet the random number seed." +"\n\t(default = 1)" , "S", 1, "-S ")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -P <start set>
   *  Specify a starting set of attributes.
   *  Eg. 1,3,5-7.If supplied, the starting set becomes
   *  one member of the initial random
   *  population.
* * 
 -Z <population size>
   *  Set the size of the population.
   *  (default = 10).
* * 
 -G <number of generations>
   *  Set the number of generations.
   *  (default = 20)
* * 
 -C <probability of crossover>
   *  Set the probability of crossover.
   *  (default = 0.6)
* * 
 -M <probability of mutation>
   *  Set the probability of mutation.
   *  (default = 0.033)
* * 
 -R <report frequency>
   *  Set frequency of generation reports.
   *  e.g, setting the value to 5 will 
   *  report every 5th generation
   *  (default = number of generations)
* * 
 -S <seed>
   *  Set the random number seed.
   *  (default = 1)
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported * **/ public void setOptions (String[] options) throws Exception { String optionString; resetOptions(); optionString = Utils.getOption('P', options); if (optionString.length() != 0) { setStartSet(optionString); } optionString = Utils.getOption('Z', options); if (optionString.length() != 0) { setPopulationSize(Integer.parseInt(optionString)); } optionString = Utils.getOption('G', options); if (optionString.length() != 0) { setMaxGenerations(Integer.parseInt(optionString)); setReportFrequency(Integer.parseInt(optionString)); } optionString = Utils.getOption('C', options); if (optionString.length() != 0) { setCrossoverProb((new Double(optionString)).doubleValue()); } optionString = Utils.getOption('M', options); if (optionString.length() != 0) { setMutationProb((new Double(optionString)).doubleValue()); } optionString = Utils.getOption('R', options); if (optionString.length() != 0) { setReportFrequency(Integer.parseInt(optionString)); } optionString = Utils.getOption('S', options); if (optionString.length() != 0) { setSeed(Integer.parseInt(optionString)); } } /** * Gets the current settings of ReliefFAttributeEval. * * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { String[] options = new String[14]; int current = 0; if (!(getStartSet().equals(""))) { options[current++] = "-P"; options[current++] = ""+startSetToString(); } options[current++] = "-Z"; options[current++] = "" + getPopulationSize(); options[current++] = "-G"; options[current++] = "" + getMaxGenerations(); options[current++] = "-C"; options[current++] = "" + getCrossoverProb(); options[current++] = "-M"; options[current++] = "" + getMutationProb(); options[current++] = "-R"; options[current++] = "" + getReportFrequency(); options[current++] = "-S"; options[current++] = "" + getSeed(); while (current < options.length) { options[current++] = ""; } return options; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String startSetTipText() { return "Set a start point for the search. This is specified as a comma " +"seperated list off attribute indexes starting at 1. It can include " +"ranges. Eg. 1,2,5-9,17. The start set becomes one of the population " +"members of the initial population."; } /** * Sets a starting set of attributes for the search. It is the * search method's responsibility to report this start set (if any) * in its toString() method. * @param startSet a string containing a list of attributes (and or ranges), * eg. 1,2,6,10-15. * @throws Exception if start set can't be set. */ public void setStartSet (String startSet) throws Exception { m_startRange.setRanges(startSet); } /** * Returns a list of attributes (and or attribute ranges) as a String * @return a list of attributes (and or attribute ranges) */ public String getStartSet () { return m_startRange.getRanges(); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String seedTipText() { return "Set the random seed."; } /** * set the seed for random number generation * @param s seed value */ public void setSeed(int s) { m_seed = s; } /** * get the value of the random number generator's seed * @return the seed for random number generation */ public int getSeed() { return m_seed; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String reportFrequencyTipText() { return "Set how frequently reports are generated. Default is equal to " +"the number of generations meaning that a report will be printed for " +"initial and final generations. Setting the value to 5 will result in " +"a report being printed every 5 generations."; } /** * set how often reports are generated * @param f generate reports every f generations */ public void setReportFrequency(int f) { m_reportFrequency = f; } /** * get how often repports are generated * @return how often reports are generated */ public int getReportFrequency() { return m_reportFrequency; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String mutationProbTipText() { return "Set the probability of mutation occuring."; } /** * set the probability of mutation * @param m the probability for mutation occuring */ public void setMutationProb(double m) { m_pMutation = m; } /** * get the probability of mutation * @return the probability of mutation occuring */ public double getMutationProb() { return m_pMutation; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String crossoverProbTipText() { return "Set the probability of crossover. This is the probability that " +"two population members will exchange genetic material."; } /** * set the probability of crossover * @param c the probability that two population members will exchange * genetic material */ public void setCrossoverProb(double c) { m_pCrossover = c; } /** * get the probability of crossover * @return the probability of crossover */ public double getCrossoverProb() { return m_pCrossover; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String maxGenerationsTipText() { return "Set the number of generations to evaluate."; } /** * set the number of generations to evaluate * @param m the number of generations */ public void setMaxGenerations(int m) { m_maxGenerations = m; } /** * get the number of generations * @return the maximum number of generations */ public int getMaxGenerations() { return m_maxGenerations; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String populationSizeTipText() { return "Set the population size. This is the number of individuals " +"(attribute sets) in the population."; } /** * set the population size * @param p the size of the population */ public void setPopulationSize(int p) { m_popSize = p; } /** * get the size of the population * @return the population size */ public int getPopulationSize() { return m_popSize; } /** * Returns a string describing this search method * @return a description of the search suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "GeneticSearch:\n\nPerforms a search using the simple genetic " + "algorithm described in Goldberg (1989).\n\n" + "For more information see:\n\n" + getTechnicalInformation().toString(); } /** * Returns an instance of a TechnicalInformation object, containing * detailed information about the technical background of this class, * e.g., paper reference or book this class is based on. * * @return the technical information about this class */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.BOOK); result.setValue(Field.AUTHOR, "David E. Goldberg"); result.setValue(Field.YEAR, "1989"); result.setValue(Field.TITLE, "Genetic algorithms in search, optimization and machine learning"); result.setValue(Field.ISBN, "0201157675"); result.setValue(Field.PUBLISHER, "Addison-Wesley"); return result; } /** * Constructor. Make a new GeneticSearch object */ public GeneticSearch() { resetOptions(); } /** * converts the array of starting attributes to a string. This is * used by getOptions to return the actual attributes specified * as the starting set. This is better than using m_startRanges.getRanges() * as the same start set can be specified in different ways from the * command line---eg 1,2,3 == 1-3. This is to ensure that stuff that * is stored in a database is comparable. * @return a comma seperated list of individual attribute numbers as a String */ private String startSetToString() { StringBuffer FString = new StringBuffer(); boolean didPrint; if (m_starting == null) { return getStartSet(); } for (int i = 0; i < m_starting.length; i++) { didPrint = false; if ((m_hasClass == false) || (m_hasClass == true && i != m_classIndex)) { FString.append((m_starting[i] + 1)); didPrint = true; } if (i == (m_starting.length - 1)) { FString.append(""); } else { if (didPrint) { FString.append(","); } } } return FString.toString(); } /** * returns a description of the search * @return a description of the search as a String */ public String toString() { StringBuffer GAString = new StringBuffer(); GAString.append("\tGenetic search.\n\tStart set: "); if (m_starting == null) { GAString.append("no attributes\n"); } else { GAString.append(startSetToString()+"\n"); } GAString.append("\tPopulation size: "+m_popSize); GAString.append("\n\tNumber of generations: "+m_maxGenerations); GAString.append("\n\tProbability of crossover: " +Utils.doubleToString(m_pCrossover,6,3)); GAString.append("\n\tProbability of mutation: " +Utils.doubleToString(m_pMutation,6,3)); GAString.append("\n\tReport frequency: "+m_reportFrequency); GAString.append("\n\tRandom number seed: "+m_seed+"\n"); GAString.append(m_generationReports.toString()); return GAString.toString(); } /** * Searches the attribute subset space using a genetic algorithm. * * @param ASEval the attribute evaluator to guide the search * @param data the training instances. * @return an array (not necessarily ordered) of selected attribute indexes * @throws Exception if the search can't be completed */ public int[] search (ASEvaluation ASEval, Instances data) throws Exception { m_best = null; m_generationReports = new StringBuffer(); if (!(ASEval instanceof SubsetEvaluator)) { throw new Exception(ASEval.getClass().getName() + " is not a " + "Subset evaluator!"); } if (ASEval instanceof UnsupervisedSubsetEvaluator) { m_hasClass = false; } else { m_hasClass = true; m_classIndex = data.classIndex(); } SubsetEvaluator ASEvaluator = (SubsetEvaluator)ASEval; m_numAttribs = data.numAttributes(); m_startRange.setUpper(m_numAttribs-1); if (!(getStartSet().equals(""))) { m_starting = m_startRange.getSelection(); } // initial random population m_lookupTable = new Hashtable(m_lookupTableSize); m_random = new Random(m_seed); m_population = new GABitSet [m_popSize]; // set up random initial population initPopulation(); evaluatePopulation(ASEvaluator); populationStatistics(); scalePopulation(); checkBest(); m_generationReports.append(populationReport(0)); boolean converged; for (int i=1;i<=m_maxGenerations;i++) { generation(); evaluatePopulation(ASEvaluator); populationStatistics(); scalePopulation(); // find the best pop member and check for convergence converged = checkBest(); if ((i == m_maxGenerations) || ((i % m_reportFrequency) == 0) || (converged == true)) { m_generationReports.append(populationReport(i)); if (converged == true) { break; } } } return attributeList(m_best.getChromosome()); } /** * converts a BitSet into a list of attribute indexes * @param group the BitSet to convert * @return an array of attribute indexes **/ private int[] attributeList (BitSet group) { int count = 0; // count how many were selected for (int i = 0; i < m_numAttribs; i++) { if (group.get(i)) { count++; } } int[] list = new int[count]; count = 0; for (int i = 0; i < m_numAttribs; i++) { if (group.get(i)) { list[count++] = i; } } return list; } /** * checks to see if any population members in the current * population are better than the best found so far. Also checks * to see if the search has converged---that is there is no difference * in fitness between the best and worse population member * @return true is the search has converged * @throws Exception if something goes wrong */ private boolean checkBest() throws Exception { int i,count,lowestCount = m_numAttribs; double b = -Double.MAX_VALUE; GABitSet localbest = null; BitSet temp; boolean converged = false; int oldcount = Integer.MAX_VALUE; if (m_maxFitness - m_minFitness > 0) { // find the best in this population for (i=0;i b) { b = m_population[i].getObjective(); localbest = m_population[i]; oldcount = countFeatures(localbest.getChromosome()); } else if (Utils.eq(m_population[i].getObjective(), b)) { // see if it contains fewer features count = countFeatures(m_population[i].getChromosome()); if (count < oldcount) { b = m_population[i].getObjective(); localbest = m_population[i]; oldcount = count; } } } } else { // look for the smallest subset for (i=0;i m_best.getObjective()) { m_best = (GABitSet)localbest.clone(); m_bestFeatureCount = count; } else if (Utils.eq(m_best.getObjective(), b)) { // see if the localbest has fewer features than the best so far if (count < m_bestFeatureCount) { m_best = (GABitSet)localbest.clone(); m_bestFeatureCount = count; } } return converged; } /** * counts the number of features in a subset * @param featureSet the feature set for which to count the features * @return the number of features in the subset */ private int countFeatures(BitSet featureSet) { int count = 0; for (int i=0;i best_fit) { j = i; best_fit = m_population[i].getFitness(); old_count = countFeatures(m_population[i].getChromosome()); } else if (Utils.eq(m_population[i].getFitness(), best_fit)) { count = countFeatures(m_population[i].getChromosome()); if (count < old_count) { j = i; best_fit = m_population[i].getFitness(); old_count = count; } } } newPop[0] = (GABitSet)(m_population[j].clone()); newPop[1] = newPop[0]; for (j=2;j= 3) { if (r < m_pCrossover) { // cross point int cp = Math.abs(m_random.nextInt()); cp %= (m_numAttribs-2); cp ++; for (i=0;i= r) { break; } } // if none was found, take first if (i == m_popSize) i = 0; return i; } /** * evaluates an entire population. Population members are looked up in * a hash table and if they are not found then they are evaluated using * ASEvaluator. * @param ASEvaluator the subset evaluator to use for evaluating population * members * @throws Exception if something goes wrong during evaluation */ private void evaluatePopulation (SubsetEvaluator ASEvaluator) throws Exception { int i; double merit; for (i=0;i m_numAttribs) { throw new Exception("Problem in population init"); } m_population[i].set(bit); } } } /** * calculates summary statistics for the current population */ private void populationStatistics() { int i; m_sumFitness = m_minFitness = m_maxFitness = m_population[0].getObjective(); for (i=1;i m_maxFitness) { m_maxFitness = m_population[i].getObjective(); } else if (m_population[i].getObjective() < m_minFitness) { m_minFitness = m_population[i].getObjective(); } } m_avgFitness = (m_sumFitness / m_popSize); } /** * scales the raw (objective) merit of the population members */ private void scalePopulation() { int j; double a = 0; double b = 0; double fmultiple = 2.0; double delta; // prescale if (m_minFitness > ((fmultiple * m_avgFitness - m_maxFitness) / (fmultiple - 1.0))) { delta = m_maxFitness - m_avgFitness; a = ((fmultiple - 1.0) * m_avgFitness / delta); b = m_avgFitness * (m_maxFitness - fmultiple * m_avgFitness) / delta; } else { delta = m_avgFitness - m_minFitness; a = m_avgFitness / delta; b = -m_minFitness * m_avgFitness / delta; } // scalepop m_sumFitness = 0; for (j=0;j * GreedyStepwise :
*
* Performs a greedy forward or backward search through the space of attribute subsets. May start with no/all attributes or from an arbitrary point in the space. Stops when the addition/deletion of any remaining attributes results in a decrease in evaluation. Can also produce a ranked list of attributes by traversing the space from one side to the other and recording the order that attributes are selected.
*

* * Valid options are:

* * 

 -C
 *  Use conservative forward search
* * 
 -B
 *  Use a backward search instead of a
 *  forward one.
* * 
 -P <start set>
 *  Specify a starting set of attributes.
 *  Eg. 1,3,5-7.
* * 
 -R
 *  Produce a ranked list of attributes.
* * 
 -T <threshold>
 *  Specify a theshold by which attributes
 *  may be discarded from the ranking.
 *  Use in conjuction with -R
* * 
 -N <num to select>
 *  Specify number of attributes to select
* * * @author Mark Hall * @version $Revision: 1.10 $ */ public class GreedyStepwise extends ASSearch implements RankedOutputSearch, StartSetHandler, OptionHandler { /** for serialization */ static final long serialVersionUID = -6312951970168325471L; /** does the data have a class */ protected boolean m_hasClass; /** holds the class index */ protected int m_classIndex; /** number of attributes in the data */ protected int m_numAttribs; /** true if the user has requested a ranked list of attributes */ protected boolean m_rankingRequested; /** * go from one side of the search space to the other in order to generate * a ranking */ protected boolean m_doRank; /** used to indicate whether or not ranking has been performed */ protected boolean m_doneRanking; /** * A threshold by which to discard attributes---used by the * AttributeSelection module */ protected double m_threshold; /** The number of attributes to select. -1 indicates that all attributes are to be retained. Has precedence over m_threshold */ protected int m_numToSelect = -1; protected int m_calculatedNumToSelect; /** the merit of the best subset found */ protected double m_bestMerit; /** a ranked list of attribute indexes */ protected double [][] m_rankedAtts; protected int m_rankedSoFar; /** the best subset found */ protected BitSet m_best_group; protected ASEvaluation m_ASEval; protected Instances m_Instances; /** holds the start set for the search as a Range */ protected Range m_startRange; /** holds an array of starting attributes */ protected int [] m_starting; /** Use a backwards search instead of a forwards one */ protected boolean m_backward = false; /** If set then attributes will continue to be added during a forward search as long as the merit does not degrade */ protected boolean m_conservativeSelection = false; /** * Constructor */ public GreedyStepwise () { m_threshold = -Double.MAX_VALUE; m_doneRanking = false; m_startRange = new Range(); m_starting = null; resetOptions(); } /** * Returns a string describing this search method * @return a description of the search suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "GreedyStepwise :\n\nPerforms a greedy forward or backward search " +"through " +"the space of attribute subsets. May start with no/all attributes or from " +"an arbitrary point in the space. Stops when the addition/deletion of any " +"remaining attributes results in a decrease in evaluation. " +"Can also produce a ranked list of " +"attributes by traversing the space from one side to the other and " +"recording the order that attributes are selected.\n"; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String searchBackwardsTipText() { return "Search backwards rather than forwards."; } /** * Set whether to search backwards instead of forwards * * @param back true to search backwards */ public void setSearchBackwards(boolean back) { m_backward = back; if (m_backward) { setGenerateRanking(false); } } /** * Get whether to search backwards * * @return true if the search will proceed backwards */ public boolean getSearchBackwards() { return m_backward; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String thresholdTipText() { return "Set threshold by which attributes can be discarded. Default value " + "results in no attributes being discarded. Use in conjunction with " + "generateRanking"; } /** * Set the threshold by which the AttributeSelection module can discard * attributes. * @param threshold the threshold. */ public void setThreshold(double threshold) { m_threshold = threshold; } /** * Returns the threshold so that the AttributeSelection module can * discard attributes from the ranking. */ public double getThreshold() { return m_threshold; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String numToSelectTipText() { return "Specify the number of attributes to retain. The default value " +"(-1) indicates that all attributes are to be retained. Use either " +"this option or a threshold to reduce the attribute set."; } /** * Specify the number of attributes to select from the ranked list * (if generating a ranking). -1 * indicates that all attributes are to be retained. * @param n the number of attributes to retain */ public void setNumToSelect(int n) { m_numToSelect = n; } /** * Gets the number of attributes to be retained. * @return the number of attributes to retain */ public int getNumToSelect() { return m_numToSelect; } /** * Gets the calculated number of attributes to retain. This is the * actual number of attributes to retain. This is the same as * getNumToSelect if the user specifies a number which is not less * than zero. Otherwise it should be the number of attributes in the * (potentially transformed) data. */ public int getCalculatedNumToSelect() { if (m_numToSelect >= 0) { m_calculatedNumToSelect = m_numToSelect; } return m_calculatedNumToSelect; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String generateRankingTipText() { return "Set to true if a ranked list is required."; } /** * Records whether the user has requested a ranked list of attributes. * @param doRank true if ranking is requested */ public void setGenerateRanking(boolean doRank) { m_rankingRequested = doRank; } /** * Gets whether ranking has been requested. This is used by the * AttributeSelection module to determine if rankedAttributes() * should be called. * @return true if ranking has been requested. */ public boolean getGenerateRanking() { return m_rankingRequested; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String startSetTipText() { return "Set the start point for the search. This is specified as a comma " +"seperated list off attribute indexes starting at 1. It can include " +"ranges. Eg. 1,2,5-9,17."; } /** * Sets a starting set of attributes for the search. It is the * search method's responsibility to report this start set (if any) * in its toString() method. * @param startSet a string containing a list of attributes (and or ranges), * eg. 1,2,6,10-15. * @throws Exception if start set can't be set. */ public void setStartSet (String startSet) throws Exception { m_startRange.setRanges(startSet); } /** * Returns a list of attributes (and or attribute ranges) as a String * @return a list of attributes (and or attribute ranges) */ public String getStartSet () { return m_startRange.getRanges(); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String conservativeForwardSelectionTipText() { return "If true (and forward search is selected) then attributes " +"will continue to be added to the best subset as long as merit does " +"not degrade."; } /** * Set whether attributes should continue to be added during * a forward search as long as merit does not decrease * @param c true if atts should continue to be atted */ public void setConservativeForwardSelection(boolean c) { m_conservativeSelection = c; } /** * Gets whether conservative selection has been enabled * @return true if conservative forward selection is enabled */ public boolean getConservativeForwardSelection() { return m_conservativeSelection; } /** * Returns an enumeration describing the available options. * @return an enumeration of all the available options. **/ public Enumeration listOptions () { Vector newVector = new Vector(5); newVector.addElement(new Option("\tUse conservative forward search" ,"-C", 0, "-C")); newVector.addElement(new Option("\tUse a backward search instead of a" +"\n\tforward one." ,"-B", 0, "-B")); newVector .addElement(new Option("\tSpecify a starting set of attributes." + "\n\tEg. 1,3,5-7." ,"P",1 , "-P ")); newVector.addElement(new Option("\tProduce a ranked list of attributes." ,"R",0,"-R")); newVector .addElement(new Option("\tSpecify a theshold by which attributes" + "\n\tmay be discarded from the ranking." +"\n\tUse in conjuction with -R","T",1 , "-T ")); newVector .addElement(new Option("\tSpecify number of attributes to select" ,"N",1 , "-N ")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -C
   *  Use conservative forward search
* * 
 -B
   *  Use a backward search instead of a
   *  forward one.
* * 
 -P <start set>
   *  Specify a starting set of attributes.
   *  Eg. 1,3,5-7.
* * 
 -R
   *  Produce a ranked list of attributes.
* * 
 -T <threshold>
   *  Specify a theshold by which attributes
   *  may be discarded from the ranking.
   *  Use in conjuction with -R
* * 
 -N <num to select>
   *  Specify number of attributes to select
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions (String[] options) throws Exception { String optionString; resetOptions(); setSearchBackwards(Utils.getFlag('B', options)); setConservativeForwardSelection(Utils.getFlag('C', options)); optionString = Utils.getOption('P', options); if (optionString.length() != 0) { setStartSet(optionString); } setGenerateRanking(Utils.getFlag('R', options)); optionString = Utils.getOption('T', options); if (optionString.length() != 0) { Double temp; temp = Double.valueOf(optionString); setThreshold(temp.doubleValue()); } optionString = Utils.getOption('N', options); if (optionString.length() != 0) { setNumToSelect(Integer.parseInt(optionString)); } } /** * Gets the current settings of ReliefFAttributeEval. * * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { String[] options = new String[9]; int current = 0; if (getSearchBackwards()) { options[current++] = "-B"; } if (getConservativeForwardSelection()) { options[current++] = "-C"; } if (!(getStartSet().equals(""))) { options[current++] = "-P"; options[current++] = ""+startSetToString(); } if (getGenerateRanking()) { options[current++] = "-R"; } options[current++] = "-T"; options[current++] = "" + getThreshold(); options[current++] = "-N"; options[current++] = ""+getNumToSelect(); while (current < options.length) { options[current++] = ""; } return options; } /** * converts the array of starting attributes to a string. This is * used by getOptions to return the actual attributes specified * as the starting set. This is better than using m_startRanges.getRanges() * as the same start set can be specified in different ways from the * command line---eg 1,2,3 == 1-3. This is to ensure that stuff that * is stored in a database is comparable. * @return a comma seperated list of individual attribute numbers as a String */ protected String startSetToString() { StringBuffer FString = new StringBuffer(); boolean didPrint; if (m_starting == null) { return getStartSet(); } for (int i = 0; i < m_starting.length; i++) { didPrint = false; if ((m_hasClass == false) || (m_hasClass == true && i != m_classIndex)) { FString.append((m_starting[i] + 1)); didPrint = true; } if (i == (m_starting.length - 1)) { FString.append(""); } else { if (didPrint) { FString.append(","); } } } return FString.toString(); } /** * returns a description of the search. * @return a description of the search as a String. */ public String toString() { StringBuffer FString = new StringBuffer(); FString.append("\tGreedy Stepwise (" + ((m_backward) ? "backwards)" : "forwards)")+".\n\tStart set: "); if (m_starting == null) { if (m_backward) { FString.append("all attributes\n"); } else { FString.append("no attributes\n"); } } else { FString.append(startSetToString()+"\n"); } if (!m_doneRanking) { FString.append("\tMerit of best subset found: " +Utils.doubleToString(Math.abs(m_bestMerit),8,3)+"\n"); } if ((m_threshold != -Double.MAX_VALUE) && (m_doneRanking)) { FString.append("\tThreshold for discarding attributes: " + Utils.doubleToString(m_threshold,8,4)+"\n"); } return FString.toString(); } /** * Searches the attribute subset space by forward selection. * * @param ASEval the attribute evaluator to guide the search * @param data the training instances. * @return an array (not necessarily ordered) of selected attribute indexes * @throws Exception if the search can't be completed */ public int[] search (ASEvaluation ASEval, Instances data) throws Exception { int i; double best_merit = -Double.MAX_VALUE; double temp_best,temp_merit; int temp_index=0; BitSet temp_group; if (data != null) { // this is a fresh run so reset resetOptions(); m_Instances = data; } m_ASEval = ASEval; m_numAttribs = m_Instances.numAttributes(); if (m_best_group == null) { m_best_group = new BitSet(m_numAttribs); } if (!(m_ASEval instanceof SubsetEvaluator)) { throw new Exception(m_ASEval.getClass().getName() + " is not a " + "Subset evaluator!"); } m_startRange.setUpper(m_numAttribs-1); if (!(getStartSet().equals(""))) { m_starting = m_startRange.getSelection(); } if (m_ASEval instanceof UnsupervisedSubsetEvaluator) { m_hasClass = false; m_classIndex = -1; } else { m_hasClass = true; m_classIndex = m_Instances.classIndex(); } SubsetEvaluator ASEvaluator = (SubsetEvaluator)m_ASEval; if (m_rankedAtts == null) { m_rankedAtts = new double[m_numAttribs][2]; m_rankedSoFar = 0; } // If a starting subset has been supplied, then initialise the bitset if (m_starting != null && m_rankedSoFar <= 0) { for (i = 0; i < m_starting.length; i++) { if ((m_starting[i]) != m_classIndex) { m_best_group.set(m_starting[i]); } } } else { if (m_backward && m_rankedSoFar <= 0) { for (i = 0; i < m_numAttribs; i++) { if (i != m_classIndex) { m_best_group.set(i); } } } } // Evaluate the initial subset best_merit = ASEvaluator.evaluateSubset(m_best_group); // main search loop boolean done = false; boolean addone = false; boolean z; while (!done) { temp_group = (BitSet)m_best_group.clone(); temp_best = best_merit; if (m_doRank) { temp_best = -Double.MAX_VALUE; } done = true; addone = false; for (i=0;i= temp_best); } else { if (m_conservativeSelection) { z = (temp_merit >= temp_best); } else { z = (temp_merit > temp_best); } } if (z) { temp_best = temp_merit; temp_index = i; addone = true; done = false; } // unset this addition/deletion if (m_backward) { temp_group.set(i); } else { temp_group.clear(i); } if (m_doRank) { done = false; } } } if (addone) { if (m_backward) { m_best_group.clear(temp_index); } else { m_best_group.set(temp_index); } best_merit = temp_best; m_rankedAtts[m_rankedSoFar][0] = temp_index; m_rankedAtts[m_rankedSoFar][1] = best_merit; m_rankedSoFar++; } } m_bestMerit = best_merit; return attributeList(m_best_group); } /** * Produces a ranked list of attributes. Search must have been performed * prior to calling this function. Search is called by this function to * complete the traversal of the the search space. A list of * attributes and merits are returned. The attributes a ranked by the * order they are added to the subset during a forward selection search. * Individual merit values reflect the merit associated with adding the * corresponding attribute to the subset; because of this, merit values * may initially increase but then decrease as the best subset is * "passed by" on the way to the far side of the search space. * * @return an array of attribute indexes and associated merit values * @throws Exception if something goes wrong. */ public double [][] rankedAttributes() throws Exception { if (m_rankedAtts == null || m_rankedSoFar == -1) { throw new Exception("Search must be performed before attributes " +"can be ranked."); } m_doRank = true; search (m_ASEval, null); double [][] final_rank = new double [m_rankedSoFar][2]; for (int i=0;i final_rank.length) { throw new Exception("More attributes requested than exist in the data"); } if (m_numToSelect <= 0) { if (m_threshold == -Double.MAX_VALUE) { m_calculatedNumToSelect = final_rank.length; } else { determineNumToSelectFromThreshold(final_rank); } } return final_rank; } private void determineNumToSelectFromThreshold(double [][] ranking) { int count = 0; for (int i = 0; i < ranking.length; i++) { if (ranking[i][1] > m_threshold) { count++; } } m_calculatedNumToSelect = count; } /** * converts a BitSet into a list of attribute indexes * @param group the BitSet to convert * @return an array of attribute indexes **/ protected int[] attributeList (BitSet group) { int count = 0; // count how many were selected for (int i = 0; i < m_numAttribs; i++) { if (group.get(i)) { count++; } } int[] list = new int[count]; count = 0; for (int i = 0; i < m_numAttribs; i++) { if (group.get(i)) { list[count++] = i; } } return list; } /** * Resets options */ protected void resetOptions() { m_doRank = false; m_best_group = null; m_ASEval = null; m_Instances = null; m_rankedSoFar = -1; m_rankedAtts = null; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.10 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/HoldOutSubsetEvaluator.java000066400000000000000000000052121214146114000321040ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * HoldOutSubsetEvaluator.java * Copyright (C) 2000 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.core.Instance; import weka.core.Instances; import java.util.BitSet; /** * Abstract attribute subset evaluator capable of evaluating subsets with * respect to a data set that is distinct from that used to initialize/ * train the subset evaluator. * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.7 $ */ public abstract class HoldOutSubsetEvaluator extends ASEvaluation implements SubsetEvaluator { /** for serialization */ private static final long serialVersionUID = 8280529785412054174L; /** * Evaluates a subset of attributes with respect to a set of instances. * @param subset a bitset representing the attribute subset to be * evaluated * @param holdOut a set of instances (possibly seperate and distinct * from those use to build/train the evaluator) with which to * evaluate the merit of the subset * @return the "merit" of the subset on the holdOut data * @exception Exception if the subset cannot be evaluated */ public abstract double evaluateSubset(BitSet subset, Instances holdOut) throws Exception; /** * Evaluates a subset of attributes with respect to a single instance. * @param subset a bitset representing the attribute subset to be * evaluated * @param holdOut a single instance (possibly not one of those used to * build/train the evaluator) with which to evaluate the merit of the subset * @param retrain true if the classifier should be retrained with respect * to the new subset before testing on the holdOut instance. * @return the "merit" of the subset on the holdOut instance * @exception Exception if the subset cannot be evaluated */ public abstract double evaluateSubset(BitSet subset, Instance holdOut, boolean retrain) throws Exception; } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/InfoGainAttributeEval.java000066400000000000000000000331551214146114000316520ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * InfoGainAttributeEval.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.core.Capabilities; import weka.core.ContingencyTables; import weka.core.Instance; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.Utils; import weka.core.Capabilities.Capability; import weka.filters.Filter; import weka.filters.supervised.attribute.Discretize; import weka.filters.unsupervised.attribute.NumericToBinary; import java.util.Enumeration; import java.util.Vector; /** * InfoGainAttributeEval :
*
* Evaluates the worth of an attribute by measuring the information gain with respect to the class.
*
* InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute).
*

* * Valid options are:

* * 

 -M
 *  treat missing values as a seperate value.
* * 
 -B
 *  just binarize numeric attributes instead 
 *  of properly discretizing them.
* * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.22 $ * @see Discretize * @see NumericToBinary */ public class InfoGainAttributeEval extends ASEvaluation implements AttributeEvaluator, OptionHandler { /** for serialization */ static final long serialVersionUID = -1949849512589218930L; /** Treat missing values as a seperate value */ private boolean m_missing_merge; /** Just binarize numeric attributes */ private boolean m_Binarize; /** The info gain for each attribute */ private double[] m_InfoGains; /** * Returns a string describing this attribute evaluator * @return a description of the evaluator suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "InfoGainAttributeEval :\n\nEvaluates the worth of an attribute " +"by measuring the information gain with respect to the class.\n\n" +"InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute).\n"; } /** * Constructor */ public InfoGainAttributeEval () { resetOptions(); } /** * Returns an enumeration describing the available options. * @return an enumeration of all the available options. **/ public Enumeration listOptions () { Vector newVector = new Vector(2); newVector.addElement(new Option("\ttreat missing values as a seperate " + "value.", "M", 0, "-M")); newVector.addElement(new Option("\tjust binarize numeric attributes instead \n" +"\tof properly discretizing them.", "B", 0, "-B")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -M
   *  treat missing values as a seperate value.
* * 
 -B
   *  just binarize numeric attributes instead 
   *  of properly discretizing them.
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions (String[] options) throws Exception { resetOptions(); setMissingMerge(!(Utils.getFlag('M', options))); setBinarizeNumericAttributes(Utils.getFlag('B', options)); } /** * Gets the current settings of WrapperSubsetEval. * * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { String[] options = new String[2]; int current = 0; if (!getMissingMerge()) { options[current++] = "-M"; } if (getBinarizeNumericAttributes()) { options[current++] = "-B"; } while (current < options.length) { options[current++] = ""; } return options; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String binarizeNumericAttributesTipText() { return "Just binarize numeric attributes instead of properly discretizing them."; } /** * Binarize numeric attributes. * * @param b true=binarize numeric attributes */ public void setBinarizeNumericAttributes (boolean b) { m_Binarize = b; } /** * get whether numeric attributes are just being binarized. * * @return true if missing values are being distributed. */ public boolean getBinarizeNumericAttributes () { return m_Binarize; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String missingMergeTipText() { return "Distribute counts for missing values. Counts are distributed " +"across other values in proportion to their frequency. Otherwise, " +"missing is treated as a separate value."; } /** * distribute the counts for missing values across observed values * * @param b true=distribute missing values. */ public void setMissingMerge (boolean b) { m_missing_merge = b; } /** * get whether missing values are being distributed or not * * @return true if missing values are being distributed. */ public boolean getMissingMerge () { return m_missing_merge; } /** * Returns the capabilities of this evaluator. * * @return the capabilities of this evaluator * @see Capabilities */ public Capabilities getCapabilities() { Capabilities result = super.getCapabilities(); // attributes result.enable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.NUMERIC_ATTRIBUTES); result.enable(Capability.DATE_ATTRIBUTES); result.enable(Capability.MISSING_VALUES); // class result.enable(Capability.NOMINAL_CLASS); result.enable(Capability.MISSING_CLASS_VALUES); return result; } /** * Initializes an information gain attribute evaluator. * Discretizes all attributes that are numeric. * * @param data set of instances serving as training data * @throws Exception if the evaluator has not been * generated successfully */ public void buildEvaluator (Instances data) throws Exception { // can evaluator handle data? getCapabilities().testWithFail(data); int classIndex = data.classIndex(); int numInstances = data.numInstances(); if (!m_Binarize) { Discretize disTransform = new Discretize(); disTransform.setUseBetterEncoding(true); disTransform.setInputFormat(data); data = Filter.useFilter(data, disTransform); } else { NumericToBinary binTransform = new NumericToBinary(); binTransform.setInputFormat(data); data = Filter.useFilter(data, binTransform); } int numClasses = data.attribute(classIndex).numValues(); // Reserve space and initialize counters double[][][] counts = new double[data.numAttributes()][][]; for (int k = 0; k < data.numAttributes(); k++) { if (k != classIndex) { int numValues = data.attribute(k).numValues(); counts[k] = new double[numValues + 1][numClasses + 1]; } } // Initialize counters double[] temp = new double[numClasses + 1]; for (int k = 0; k < numInstances; k++) { Instance inst = data.instance(k); if (inst.classIsMissing()) { temp[numClasses] += inst.weight(); } else { temp[(int)inst.classValue()] += inst.weight(); } } for (int k = 0; k < counts.length; k++) { if (k != classIndex) { for (int i = 0; i < temp.length; i++) { counts[k][0][i] = temp[i]; } } } // Get counts for (int k = 0; k < numInstances; k++) { Instance inst = data.instance(k); for (int i = 0; i < inst.numValues(); i++) { if (inst.index(i) != classIndex) { if (inst.isMissingSparse(i) || inst.classIsMissing()) { if (!inst.isMissingSparse(i)) { counts[inst.index(i)][(int)inst.valueSparse(i)][numClasses] += inst.weight(); counts[inst.index(i)][0][numClasses] -= inst.weight(); } else if (!inst.classIsMissing()) { counts[inst.index(i)][data.attribute(inst.index(i)).numValues()] [(int)inst.classValue()] += inst.weight(); counts[inst.index(i)][0][(int)inst.classValue()] -= inst.weight(); } else { counts[inst.index(i)][data.attribute(inst.index(i)).numValues()] [numClasses] += inst.weight(); counts[inst.index(i)][0][numClasses] -= inst.weight(); } } else { counts[inst.index(i)][(int)inst.valueSparse(i)] [(int)inst.classValue()] += inst.weight(); counts[inst.index(i)][0][(int)inst.classValue()] -= inst.weight(); } } } } // distribute missing counts if required if (m_missing_merge) { for (int k = 0; k < data.numAttributes(); k++) { if (k != classIndex) { int numValues = data.attribute(k).numValues(); // Compute marginals double[] rowSums = new double[numValues]; double[] columnSums = new double[numClasses]; double sum = 0; for (int i = 0; i < numValues; i++) { for (int j = 0; j < numClasses; j++) { rowSums[i] += counts[k][i][j]; columnSums[j] += counts[k][i][j]; } sum += rowSums[i]; } if (Utils.gr(sum, 0)) { double[][] additions = new double[numValues][numClasses]; // Compute what needs to be added to each row for (int i = 0; i < numValues; i++) { for (int j = 0; j < numClasses; j++) { additions[i][j] = (rowSums[i] / sum) * counts[k][numValues][j]; } } // Compute what needs to be added to each column for (int i = 0; i < numClasses; i++) { for (int j = 0; j < numValues; j++) { additions[j][i] += (columnSums[i] / sum) * counts[k][j][numClasses]; } } // Compute what needs to be added to each cell for (int i = 0; i < numClasses; i++) { for (int j = 0; j < numValues; j++) { additions[j][i] += (counts[k][j][i] / sum) * counts[k][numValues][numClasses]; } } // Make new contingency table double[][] newTable = new double[numValues][numClasses]; for (int i = 0; i < numValues; i++) { for (int j = 0; j < numClasses; j++) { newTable[i][j] = counts[k][i][j] + additions[i][j]; } } counts[k] = newTable; } } } } // Compute info gains m_InfoGains = new double[data.numAttributes()]; for (int i = 0; i < data.numAttributes(); i++) { if (i != classIndex) { m_InfoGains[i] = (ContingencyTables.entropyOverColumns(counts[i]) - ContingencyTables.entropyConditionedOnRows(counts[i])); } } } /** * Reset options to their default values */ protected void resetOptions () { m_InfoGains = null; m_missing_merge = true; m_Binarize = false; } /** * evaluates an individual attribute by measuring the amount * of information gained about the class given the attribute. * * @param attribute the index of the attribute to be evaluated * @return the info gain * @throws Exception if the attribute could not be evaluated */ public double evaluateAttribute (int attribute) throws Exception { return m_InfoGains[attribute]; } /** * Describe the attribute evaluator * @return a description of the attribute evaluator as a string */ public String toString () { StringBuffer text = new StringBuffer(); if (m_InfoGains == null) { text.append("Information Gain attribute evaluator has not been built"); } else { text.append("\tInformation Gain Ranking Filter"); if (!m_missing_merge) { text.append("\n\tMissing values treated as seperate"); } if (m_Binarize) { text.append("\n\tNumeric attributes are just binarized"); } } text.append("\n"); return text.toString(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.22 $"); } // ============ // Test method. // ============ /** * Main method for testing this class. * * @param args the options */ public static void main (String[] args) { runEvaluator(new InfoGainAttributeEval(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/LFSMethods.java000066400000000000000000000473041214146114000274350ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * LFSMethods.java * Copyright (C) 2007 Martin Guetlein * */ package weka.attributeSelection; import weka.core.FastVector; import weka.core.Instances; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import weka.core.Utils; import java.io.Serializable; import java.util.BitSet; import java.util.Hashtable; /** * @author Martin Guetlein (martin.guetlein@gmail.com) * @version $Revision: 1.3 $ */ public class LFSMethods implements RevisionHandler { /** max-size of array bestGroupOfSize, should be suffient */ private final static int MAX_SUBSET_SIZE = 200; private BitSet m_bestGroup; private double m_bestMerit; private int m_evalsTotal; private int m_evalsCached; private BitSet[] m_bestGroupOfSize = new BitSet[MAX_SUBSET_SIZE]; /** * empty constructor * * methods are not static because of access to inner class Link2 and * LinkedList2 * */ public LFSMethods() { } /** * @return best group found by forwardSearch/floatingForwardSearch */ public BitSet getBestGroup() { return m_bestGroup; } /** * @return merit of best group found by forwardSearch/floatingForwardSearch */ public double getBestMerit() { return m_bestMerit; } /** * @return best group of size found by forwardSearch */ public BitSet getBestGroupOfSize(int size) { return m_bestGroupOfSize[size]; } /** * @return number of cached / not performed evaluations */ public int getNumEvalsCached() { return m_evalsCached; } /** * @return number totally performed evaluations */ public int getNumEvalsTotal() { return m_evalsTotal; } /** * @return ranking (integer array) of attributes in data with evaluator (sorting is NOT stable!) */ public int[] rankAttributes(Instances data, SubsetEvaluator evaluator, boolean verbose) throws Exception { if (verbose) { System.out.println("Ranking attributes with " + evaluator.getClass().getName()); } double[] merit = new double[data.numAttributes()]; BitSet group = new BitSet(data.numAttributes()); for (int k = 0; k < data.numAttributes(); k++) { if (k != data.classIndex()) { group.set(k); merit[k] -= evaluator.evaluateSubset(group); m_evalsTotal++; group.clear(k); } else { merit[k] = Double.MAX_VALUE; } if (verbose) { System.out.println(k + ": " + merit[k]); } } int[] ranking = Utils.sort(merit); if (verbose) { System.out.print("Ranking [ "); for (int i = 0; i < ranking.length; i++) { System.out.print(ranking[i] + " "); } System.out.println("]\n"); } return ranking; } /** * Performs linear forward selection * * @param cacheSize chacheSize (times number of instances) to store already evaluated sets * @param startGroup start group for search (can be null) * @param ranking ranking of attributes (as produced by rankAttributes), no ranking would be [0,1,2,3,4..] * @param k number of top k attributes that are taken into account * @param incrementK true -> fixed-set, false -> fixed-width * @param maxStale number of times the search proceeds even though no improvement was found (1 = hill-climbing) * @param forceResultSize stopping criteria changed from no-improvement (forceResultSize=-1) to subset-size * @param data * @param evaluator * @param verbose * @return BitSet, that cotains the best-group found * @throws Exception */ public BitSet forwardSearch(int cacheSize, BitSet startGroup, int[] ranking, int k, boolean incrementK, int maxStale, int forceResultSize, Instances data, SubsetEvaluator evaluator, boolean verbose) throws Exception { if ((forceResultSize > 0) && (maxStale > 1)) { throw new Exception("Forcing result size only works for maxStale=1"); } if (verbose) { System.out.println("Starting forward selection"); } BitSet bestGroup; BitSet tempGroup; int bestSize = 0; int tempSize = 0; double bestMerit; double tempMerit = 0; Link2 link; LinkedList2 list = new LinkedList2(maxStale); Hashtable alreadyExpanded = new Hashtable(cacheSize * data.numAttributes()); int insertCount = 0; int stale = 0; boolean improvement; int thisK = k; int evalsTotal = 0; int evalsCached = 0; bestGroup = (BitSet) startGroup.clone(); String hashKey = bestGroup.toString(); bestMerit = evaluator.evaluateSubset(bestGroup); if (verbose) { System.out.print("Group: "); printGroup(bestGroup, data.numAttributes()); System.out.println("Merit: " + tempMerit); System.out.println("----------"); } alreadyExpanded.put(hashKey, new Double(bestMerit)); insertCount++; bestSize = bestGroup.cardinality(); //the list is only used if best-first search is applied if (maxStale > 1) { Object[] best = new Object[1]; best[0] = bestGroup.clone(); list.addToList(best, bestMerit); } while (stale < maxStale) { improvement = false; //best-first: take first elem from list if (maxStale > 1) { if (list.size() == 0) { stale = maxStale; break; } link = list.getLinkAt(0); tempGroup = (BitSet) (link.getData()[0]); tempGroup = (BitSet) tempGroup.clone(); list.removeLinkAt(0); tempSize = 0; for (int i = 0; i < data.numAttributes(); i++) { if (tempGroup.get(i)) { tempSize++; } } } else //hill-climbing { tempGroup = (BitSet) bestGroup.clone(); tempSize = bestSize; } //set number of top k attributes that are taken into account if (incrementK) { thisK = Math.min(Math.max(thisK, k + tempSize), data.numAttributes()); } else { thisK = k; } //temporarilly add attributes to current set for (int i = 0; i < thisK; i++) { if ((ranking[i] == data.classIndex()) || tempGroup.get(ranking[i])) { continue; } tempGroup.set(ranking[i]); tempSize++; hashKey = tempGroup.toString(); if (!alreadyExpanded.containsKey(hashKey)) { evalsTotal++; tempMerit = evaluator.evaluateSubset(tempGroup); if (insertCount > (cacheSize * data.numAttributes())) { alreadyExpanded = new Hashtable(cacheSize * data.numAttributes()); insertCount = 0; } alreadyExpanded.put(hashKey, new Double(tempMerit)); insertCount++; } else { evalsCached++; tempMerit = ((Double) alreadyExpanded.get(hashKey)).doubleValue(); } if (verbose) { System.out.print("Group: "); printGroup(tempGroup, data.numAttributes()); System.out.println("Merit: " + tempMerit); } if (((tempMerit - bestMerit) > 0.00001) || ((forceResultSize >= tempSize) && (tempSize > bestSize))) { improvement = true; stale = 0; bestMerit = tempMerit; bestSize = tempSize; bestGroup = (BitSet) (tempGroup.clone()); m_bestGroupOfSize[bestSize] = (BitSet) (tempGroup.clone()); } if (maxStale > 1) { Object[] add = new Object[1]; add[0] = tempGroup.clone(); list.addToList(add, tempMerit); } tempGroup.clear(ranking[i]); tempSize--; } if (verbose) { System.out.println("----------"); } //handle stopping criteria if (!improvement || (forceResultSize == bestSize)) { stale++; } if ((forceResultSize > 0) && (bestSize == forceResultSize)) { break; } } if (verbose) { System.out.println("Best Group: "); printGroup(bestGroup, data.numAttributes()); System.out.println(); } m_bestGroup = bestGroup; m_bestMerit = bestMerit; m_evalsTotal += evalsTotal; m_evalsCached += evalsCached; return bestGroup; } /** * Performs linear floating forward selection * ( the stopping criteria cannot be changed to a specific size value ) * * * @param cacheSize chacheSize (times number of instances) to store already evaluated sets * @param startGroup start group for search (can be null) * @param ranking ranking of attributes (as produced by rankAttributes), no ranking would be [0,1,2,3,4..] * @param k number of top k attributes that are taken into account * @param incrementK true -> fixed-set, false -> fixed-width * @param maxStale number of times the search proceeds even though no improvement was found (1 = hill-climbing) * @param data * @param evaluator * @param verbose * @return BitSet, that cotains the best-group found * @throws Exception */ public BitSet floatingForwardSearch(int cacheSize, BitSet startGroup, int[] ranking, int k, boolean incrementK, int maxStale, Instances data, SubsetEvaluator evaluator, boolean verbose) throws Exception { if (verbose) { System.out.println("Starting floating forward selection"); } BitSet bestGroup; BitSet tempGroup; int bestSize = 0; int tempSize = 0; double bestMerit; double tempMerit = 0; Link2 link; LinkedList2 list = new LinkedList2(maxStale); Hashtable alreadyExpanded = new Hashtable(cacheSize * data.numAttributes()); int insertCount = 0; int backtrackingSteps = 0; boolean improvement; boolean backward; int thisK = k; int evalsTotal = 0; int evalsCached = 0; bestGroup = (BitSet) startGroup.clone(); String hashKey = bestGroup.toString(); bestMerit = evaluator.evaluateSubset(bestGroup); if (verbose) { System.out.print("Group: "); printGroup(bestGroup, data.numAttributes()); System.out.println("Merit: " + tempMerit); System.out.println("----------"); } alreadyExpanded.put(hashKey, new Double(bestMerit)); insertCount++; bestSize = bestGroup.cardinality(); if (maxStale > 1) { Object[] best = new Object[1]; best[0] = bestGroup.clone(); list.addToList(best, bestMerit); } backward = improvement = true; while (true) { // we are search in backward direction -> // continue backward search as long as a new best set is found if (backward) { if (!improvement) { backward = false; } } // we are searching forward -> // stop search or start backward step else { if (!improvement && (backtrackingSteps >= maxStale)) { break; } backward = true; } improvement = false; // best-first: take first elem from list if (maxStale > 1) { if (list.size() == 0) { backtrackingSteps = maxStale; break; } link = list.getLinkAt(0); tempGroup = (BitSet) (link.getData()[0]); tempGroup = (BitSet) tempGroup.clone(); list.removeLinkAt(0); tempSize = 0; for (int i = 0; i < data.numAttributes(); i++) { if (tempGroup.get(i)) { tempSize++; } } } else //hill-climbing { tempGroup = (BitSet) bestGroup.clone(); tempSize = bestSize; } //backward search only makes sense for set-size bigger than 2 if (backward && (tempSize <= 2)) { backward = false; } //set number of top k attributes that are taken into account if (incrementK) { thisK = Math.max(thisK, Math.min(Math.max(thisK, k + tempSize), data.numAttributes())); } else { thisK = k; } //temporarilly add/remove attributes to/from current set for (int i = 0; i < thisK; i++) { if (ranking[i] == data.classIndex()) { continue; } if (backward) { if (!tempGroup.get(ranking[i])) { continue; } tempGroup.clear(ranking[i]); tempSize--; } else { if ((ranking[i] == data.classIndex()) || tempGroup.get(ranking[i])) { continue; } tempGroup.set(ranking[i]); tempSize++; } hashKey = tempGroup.toString(); if (!alreadyExpanded.containsKey(hashKey)) { evalsTotal++; tempMerit = evaluator.evaluateSubset(tempGroup); if (insertCount > (cacheSize * data.numAttributes())) { alreadyExpanded = new Hashtable(cacheSize * data.numAttributes()); insertCount = 0; } alreadyExpanded.put(hashKey, new Double(tempMerit)); insertCount++; } else { evalsCached++; tempMerit = ((Double) alreadyExpanded.get(hashKey)).doubleValue(); } if (verbose) { System.out.print("Group: "); printGroup(tempGroup, data.numAttributes()); System.out.println("Merit: " + tempMerit); } if ((tempMerit - bestMerit) > 0.00001) { improvement = true; backtrackingSteps = 0; bestMerit = tempMerit; bestSize = tempSize; bestGroup = (BitSet) (tempGroup.clone()); } if (maxStale > 1) { Object[] add = new Object[1]; add[0] = tempGroup.clone(); list.addToList(add, tempMerit); } if (backward) { tempGroup.set(ranking[i]); tempSize++; } else { tempGroup.clear(ranking[i]); tempSize--; } } if (verbose) { System.out.println("----------"); } if ((maxStale > 1) && backward && !improvement) { Object[] add = new Object[1]; add[0] = tempGroup.clone(); list.addToList(add, Double.MAX_VALUE); } if (!backward && !improvement) { backtrackingSteps++; } } if (verbose) { System.out.println("Best Group: "); printGroup(bestGroup, data.numAttributes()); System.out.println(); } m_bestGroup = bestGroup; m_bestMerit = bestMerit; m_evalsTotal += evalsTotal; m_evalsCached += evalsCached; return bestGroup; } /** * Debug-out */ protected static void printGroup(BitSet tt, int numAttribs) { System.out.print("{ "); for (int i = 0; i < numAttribs; i++) { if (tt.get(i) == true) { System.out.print((i + 1) + " "); } } System.out.println("}"); } // Inner classes /** * Class for a node in a linked list. Used in best first search. * Copied from BestFirstSearch * * @author Mark Hall (mhall@cs.waikato.ac.nz) */ public class Link2 implements Serializable, RevisionHandler { /** for serialization. */ private static final long serialVersionUID = -7422719407475185086L; /* BitSet group; */ Object[] m_data; double m_merit; // Constructor public Link2(Object[] data, double mer) { // group = (BitSet)gr.clone(); m_data = data; m_merit = mer; } /** Get a group */ public Object[] getData() { return m_data; } public String toString() { return ("Node: " + m_data.toString() + " " + m_merit); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.3 $"); } } /** * Class for handling a linked list. Used in best first search. Extends the * Vector class. * * @author Mark Hall (mhall@cs.waikato.ac.nz) */ public class LinkedList2 extends FastVector { /** for serialization. */ private static final long serialVersionUID = -7776010892419656105L; // Max number of elements in the list int m_MaxSize; // ================ // Public methods // ================ public LinkedList2(int sz) { super(); m_MaxSize = sz; } /** * removes an element (Link) at a specific index from the list. * * @param index * the index of the element to be removed. */ public void removeLinkAt(int index) throws Exception { if ((index >= 0) && (index < size())) { removeElementAt(index); } else { throw new Exception("index out of range (removeLinkAt)"); } } /** * returns the element (Link) at a specific index from the list. * * @param index * the index of the element to be returned. */ public Link2 getLinkAt(int index) throws Exception { if (size() == 0) { throw new Exception("List is empty (getLinkAt)"); } else { if ((index >= 0) && (index < size())) { return ((Link2) (elementAt(index))); } else { throw new Exception("index out of range (getLinkAt)"); } } } /** * adds an element (Link) to the list. * * @param gr * the attribute set specification * @param mer * the "merit" of this attribute set */ public void addToList(Object[] data, double mer) throws Exception { Link2 newL = new Link2(data, mer); if (size() == 0) { addElement(newL); } else { if (mer > ((Link2) (firstElement())).m_merit) { if (size() == m_MaxSize) { removeLinkAt(m_MaxSize - 1); } // ---------- insertElementAt(newL, 0); } else { int i = 0; int size = size(); boolean done = false; // ------------ // don't insert if list contains max elements an this // is worst than the last if ((size == m_MaxSize) && (mer <= ((Link2) (lastElement())).m_merit)) { } // --------------- else { while ((!done) && (i < size)) { if (mer > ((Link2) (elementAt(i))).m_merit) { if (size == m_MaxSize) { removeLinkAt(m_MaxSize - 1); } // --------------------- insertElementAt(newL, i); done = true; } else { if (i == (size - 1)) { addElement(newL); done = true; } else { i++; } } } } } } } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.3 $"); } } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.3 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/LatentSemanticAnalysis.java000066400000000000000000000664551214146114000321140ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * LatentSemanticAnalysis.java * Copyright (C) 2008 Amri Napolitano * */ package weka.attributeSelection; import weka.core.Attribute; import weka.core.Capabilities; import weka.core.Check; import weka.core.CheckOptionHandler; import weka.core.FastVector; import weka.core.Instance; import weka.core.Instances; import weka.core.matrix.Matrix; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.SparseInstance; import weka.core.Utils; import weka.core.Capabilities.Capability; import weka.core.matrix.SingularValueDecomposition; import weka.filters.Filter; import weka.filters.unsupervised.attribute.NominalToBinary; import weka.filters.unsupervised.attribute.Normalize; import weka.filters.unsupervised.attribute.Remove; import weka.filters.unsupervised.attribute.ReplaceMissingValues; import java.io.BufferedReader; import java.io.File; import java.io.FileReader; import java.util.Enumeration; import java.util.Vector; /** * Performs latent semantic analysis and transformation of the data. * Use in conjunction with a Ranker search. A low-rank approximation * of the full data is found by specifying the number of singular values * to use. The dataset may be transformed to give the relation of either * the attributes or the instances (default) to the concept space created * by the transformation. *

* * Valid options are:

* * 

 -N
 *  Normalize input data.
* * 
 -R
 *  Rank approximation used in LSA. May be actual number of 
 *  LSA attributes to include (if greater than 1) or a proportion 
 *  of total singular values to account for (if between 0 and 1). 
 *  A value less than or equal to zero means use all latent variables.
 *  (default = 0.95)
* * 
 -A
 *  Maximum number of attributes to include in 
 *  transformed attribute names. (-1 = include all)
* * * @author Amri Napolitano * @version $Revision: 1.1 $ */ public class LatentSemanticAnalysis extends UnsupervisedAttributeEvaluator implements AttributeTransformer, OptionHandler { /** For serialization */ static final long serialVersionUID = -8712112988018106198L; /** The data to transform analyse/transform */ private Instances m_trainInstances; /** Keep a copy for the class attribute (if set) */ private Instances m_trainCopy; /** The header for the transformed data format */ private Instances m_transformedFormat; /** Data has a class set */ private boolean m_hasClass; /** Class index */ private int m_classIndex; /** Number of attributes */ private int m_numAttributes; /** Number of instances */ private int m_numInstances; /** Is transpose necessary because numAttributes < numInstances? */ private boolean m_transpose = false; /** Will hold the left singular vectors */ private Matrix m_u = null; /** Will hold the singular values */ private Matrix m_s = null; /** Will hold the right singular values */ private Matrix m_v = null; /** Will hold the matrix used to transform instances to the new feature space */ private Matrix m_transformationMatrix = null; /** Filters for original data */ private ReplaceMissingValues m_replaceMissingFilter; private Normalize m_normalizeFilter; private NominalToBinary m_nominalToBinaryFilter; private Remove m_attributeFilter; /** The number of attributes in the LSA transformed data */ private int m_outputNumAttributes = -1; /** Normalize the input data? */ private boolean m_normalize = false; /** The approximation rank to use (between 0 and 1 means coverage proportion) */ private double m_rank = 0.95; /** The sum of the squares of the singular values */ private double m_sumSquaredSingularValues = 0.0; /** The actual rank number to use for computation */ private int m_actualRank = -1; /** Maximum number of attributes in the transformed attribute name */ private int m_maxAttributesInName = 5; /** * Returns a string describing this attribute transformer * @return a description of the evaluator suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Performs latent semantic analysis and transformation of the data. Use in " + "conjunction with a Ranker search. A low-rank approximation of the full data is " + "found by either specifying the number of singular values to use or specifying a " + "proportion of the singular values to cover."; } /** * Returns an enumeration describing the available options.

* * @return an enumeration of all the available options. **/ public Enumeration listOptions () { Vector options = new Vector(4); options.addElement(new Option("\tNormalize input data.", "N", 0, "-N")); options.addElement(new Option("\tRank approximation used in LSA. \n" + "\tMay be actual number of LSA attributes \n" + "\tto include (if greater than 1) or a \n" + "\tproportion of total singular values to \n" + "\taccount for (if between 0 and 1). \n" + "\tA value less than or equal to zero means \n" + "\tuse all latent variables.(default = 0.95)", "R",1,"-R")); options.addElement(new Option("\tMaximum number of attributes to include\n" + "\tin transformed attribute names.\n" + "\t(-1 = include all)" , "A", 1, "-A")); return options.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -N
   *  Normalize input data.
* * 
 -R
   *  Rank approximation used in LSA. May be actual number of 
   *  LSA attributes to include (if greater than 1) or a proportion 
   *  of total singular values to account for (if between 0 and 1). 
   *  A value less than or equal to zero means use all latent variables.
   *  (default = 0.95)
* * 
 -A
   *  Maximum number of attributes to include in 
   *  transformed attribute names. (-1 = include all)
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions (String[] options) throws Exception { resetOptions(); String optionString; //set approximation rank optionString = Utils.getOption('R', options); if (optionString.length() != 0) { double temp; temp = Double.valueOf(optionString).doubleValue(); setRank(temp); } //set number of attributes to use in transformed names optionString = Utils.getOption('A', options); if (optionString.length() != 0) { setMaximumAttributeNames(Integer.parseInt(optionString)); } //set normalize option setNormalize(Utils.getFlag('N', options)); } /** * Reset to defaults */ private void resetOptions() { m_rank = 0.95; m_normalize = true; m_maxAttributesInName = 5; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String normalizeTipText() { return "Normalize input data."; } /** * Set whether input data will be normalized. * @param newNormalize true if input data is to be normalized */ public void setNormalize(boolean newNormalize) { m_normalize = newNormalize; } /** * Gets whether or not input data is to be normalized * @return true if input data is to be normalized */ public boolean getNormalize() { return m_normalize; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String rankTipText() { return "Matrix rank to use for data reduction. Can be a" + " proportion to indicate desired coverage"; } /** * Sets the desired matrix rank (or coverage proportion) for feature-space reduction * @param newRank the desired rank (or coverage) for feature-space reduction */ public void setRank(double newRank) { m_rank = newRank; } /** * Gets the desired matrix rank (or coverage proportion) for feature-space reduction * @return the rank (or coverage) for feature-space reduction */ public double getRank() { return m_rank; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String maximumAttributeNamesTipText() { return "The maximum number of attributes to include in transformed attribute names."; } /** * Sets maximum number of attributes to include in * transformed attribute names. * @param newMaxAttributes the maximum number of attributes */ public void setMaximumAttributeNames(int newMaxAttributes) { m_maxAttributesInName = newMaxAttributes; } /** * Gets maximum number of attributes to include in * transformed attribute names. * @return the maximum number of attributes */ public int getMaximumAttributeNames() { return m_maxAttributesInName; } /** * Gets the current settings of LatentSemanticAnalysis * * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { String[] options = new String[5]; int current = 0; if (getNormalize()) { options[current++] = "-N"; } options[current++] = "-R"; options[current++] = "" + getRank(); options[current++] = "-A"; options[current++] = "" + getMaximumAttributeNames(); while (current < options.length) { options[current++] = ""; } return options; } /** * Returns the capabilities of this evaluator. * * @return the capabilities of this evaluator * @see Capabilities */ public Capabilities getCapabilities() { Capabilities result = super.getCapabilities(); // attributes result.enable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.NUMERIC_ATTRIBUTES); result.enable(Capability.DATE_ATTRIBUTES); result.enable(Capability.MISSING_VALUES); // class result.enable(Capability.NOMINAL_CLASS); result.enable(Capability.NUMERIC_CLASS); result.enable(Capability.DATE_CLASS); result.enable(Capability.MISSING_CLASS_VALUES); result.enable(Capability.NO_CLASS); return result; } /** * Initializes the singular values/vectors and performs the analysis * @param data the instances to analyse/transform * @throws Exception if analysis fails */ public void buildEvaluator(Instances data) throws Exception { // can evaluator handle data? getCapabilities().testWithFail(data); buildAttributeConstructor(data); } /** * Initializes the singular values/vectors and performs the analysis * @param data the instances to analyse/transform * @throws Exception if analysis fails */ private void buildAttributeConstructor (Instances data) throws Exception { // initialize attributes for performing analysis m_transpose = false; m_s = null; m_u = null; m_v = null; m_outputNumAttributes = -1; m_actualRank = -1; m_sumSquaredSingularValues = 0.0; m_trainInstances = new Instances(data); m_trainCopy = null; m_attributeFilter = null; m_nominalToBinaryFilter = null; m_replaceMissingFilter = new ReplaceMissingValues(); m_replaceMissingFilter.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, m_replaceMissingFilter); // vector to hold indices of attributes to delete (class attribute, // attributes that are all missing, or attributes with one distinct value) Vector attributesToRemove = new Vector(); // if data has a class attribute if (m_trainInstances.classIndex() >= 0) { // make copy of training data so the class values can be appended to final // transformed instances m_trainCopy = new Instances(m_trainInstances); m_hasClass = true; m_classIndex = m_trainInstances.classIndex(); // set class attribute to be removed attributesToRemove.addElement(new Integer(m_classIndex)); } // normalize data if desired if (m_normalize) { m_normalizeFilter = new Normalize(); m_normalizeFilter.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, m_normalizeFilter); } // convert any nominal attributes to binary numeric attributes m_nominalToBinaryFilter = new NominalToBinary(); m_nominalToBinaryFilter.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, m_nominalToBinaryFilter); // delete any attributes with only one distinct value or are all missing for (int i = 0; i < m_trainInstances.numAttributes(); i++) { if (m_trainInstances.numDistinctValues(i) <= 1) { attributesToRemove.addElement(new Integer(i)); } } // remove columns from the data if necessary if (attributesToRemove.size() > 0) { m_attributeFilter = new Remove(); int [] todelete = new int[attributesToRemove.size()]; for (int i = 0; i < attributesToRemove.size(); i++) { todelete[i] = ((Integer)(attributesToRemove.elementAt(i))).intValue(); } m_attributeFilter.setAttributeIndicesArray(todelete); m_attributeFilter.setInvertSelection(false); m_attributeFilter.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, m_attributeFilter); } // can evaluator handle the processed data ? e.g., enough attributes? getCapabilities().testWithFail(m_trainInstances); // record properties of final, ready-to-process data m_numInstances = m_trainInstances.numInstances(); m_numAttributes = m_trainInstances.numAttributes(); // create matrix of attribute values and compute singular value decomposition double [][] trainValues = new double[m_numAttributes][m_numInstances]; for (int i = 0; i < m_numAttributes; i++) { trainValues[i] = m_trainInstances.attributeToDoubleArray(i); } Matrix trainMatrix = new Matrix(trainValues); // svd requires rows >= columns, so transpose data if necessary if (m_numAttributes < m_numInstances) { m_transpose = true; trainMatrix = trainMatrix.transpose(); } SingularValueDecomposition trainSVD = trainMatrix.svd(); m_u = trainSVD.getU(); // left singular vectors m_s = trainSVD.getS(); // singular values m_v = trainSVD.getV(); // right singular vectors // find actual rank to use int maxSingularValues = trainSVD.rank(); for (int i = 0; i < m_s.getRowDimension(); i++) { m_sumSquaredSingularValues += m_s.get(i, i) * m_s.get(i, i); } if (maxSingularValues == 0) { // no nonzero singular values (shouldn't happen) // reset values from computation m_s = null; m_u = null; m_v = null; m_sumSquaredSingularValues = 0.0; throw new Exception("SVD computation produced no non-zero singular values."); } if (m_rank > maxSingularValues || m_rank <= 0) { // adjust rank if too high or too low m_actualRank = maxSingularValues; } else if (m_rank < 1.0) { // determine how many singular values to include for desired coverage double currentSumOfSquaredSingularValues = 0.0; for (int i = 0; i < m_s.getRowDimension() && m_actualRank == -1; i++) { currentSumOfSquaredSingularValues += m_s.get(i, i) * m_s.get(i, i); if (currentSumOfSquaredSingularValues / m_sumSquaredSingularValues >= m_rank) { m_actualRank = i + 1; } } } else { m_actualRank = (int) m_rank; } // lower matrix ranks, adjust for transposition (if necessary), and // compute matrix for transforming future instances if (m_transpose) { Matrix tempMatrix = m_u; m_u = m_v; m_v = tempMatrix; } m_u = m_u.getMatrix(0, m_u.getRowDimension() - 1, 0, m_actualRank - 1); m_s = m_s.getMatrix(0, m_actualRank - 1, 0, m_actualRank - 1); m_v = m_v.getMatrix(0, m_v.getRowDimension() - 1, 0, m_actualRank - 1); m_transformationMatrix = m_u.times(m_s.inverse()); //create dataset header for transformed instances m_transformedFormat = setOutputFormat(); } /** * Set the format for the transformed data * @return a set of empty Instances (header only) in the new format */ private Instances setOutputFormat() { // if analysis hasn't been performed (successfully) yet if (m_s == null) { return null; } // set up transformed attributes if (m_hasClass) { m_outputNumAttributes = m_actualRank + 1; } else { m_outputNumAttributes = m_actualRank; } int numAttributesInName = m_maxAttributesInName; if (numAttributesInName <= 0 || numAttributesInName >= m_numAttributes) { numAttributesInName = m_numAttributes; } FastVector attributes = new FastVector(m_outputNumAttributes); for (int i = 0; i < m_actualRank; i++) { // create attribute name String attributeName = ""; double [] attributeCoefficients = m_transformationMatrix.getMatrix(0, m_numAttributes - 1, i, i).getColumnPackedCopy(); for (int j = 0; j < numAttributesInName; j++) { if (j > 0) { attributeName += "+"; } attributeName += Utils.doubleToString(attributeCoefficients[j], 5, 3); attributeName += m_trainInstances.attribute(j).name(); } if (numAttributesInName < m_numAttributes) { attributeName += "..."; } // add attribute attributes.addElement(new Attribute(attributeName)); } // add original class attribute if present if (m_hasClass) { attributes.addElement(m_trainCopy.classAttribute().copy()); } // create blank header Instances outputFormat = new Instances(m_trainInstances.relationName() + "_LSA", attributes, 0); m_outputNumAttributes = outputFormat.numAttributes(); // set class attribute if applicable if (m_hasClass) { outputFormat.setClassIndex(m_outputNumAttributes - 1); } return outputFormat; } /** * Returns just the header for the transformed data (ie. an empty * set of instances. This is so that AttributeSelection can * determine the structure of the transformed data without actually * having to get all the transformed data through getTransformedData(). * @return the header of the transformed data. * @throws Exception if the header of the transformed data can't * be determined. */ public Instances transformedHeader() throws Exception { if (m_s == null) { throw new Exception("Latent Semantic Analysis hasn't been successfully performed."); } return m_transformedFormat; } /** * Gets the transformed training data. * @return the transformed training data * @throws Exception if transformed data can't be returned */ public Instances transformedData() throws Exception { if (m_s == null) { throw new Exception("Latent Semantic Analysis hasn't been built yet"); } Instances output = new Instances(m_transformedFormat, m_numInstances); // the transformed version of instance i from the training data // is stored as the i'th row vector in v (the right singular vectors) for (int i = 0; i < m_numInstances; i++) { Instance currentInstance = m_trainCopy.instance(i); // record attribute values for converted instance double [] newValues = new double[m_outputNumAttributes]; for (int j = 0; j < m_actualRank; j++) { // fill in values from v newValues[j] = m_v.get(i, j); } if (m_hasClass) { // copy class value if applicable newValues[m_outputNumAttributes - 1] = currentInstance.classValue(); } //create new instance with recorded values and add to output dataset Instance newInstance; if (currentInstance instanceof SparseInstance) { newInstance = new SparseInstance(currentInstance.weight(), newValues); } else { newInstance = new Instance(currentInstance.weight(), newValues); } output.add(newInstance); } return output; } /** * Evaluates the merit of a transformed attribute. This is defined * to be the square of the singular value for the latent variable * corresponding to the transformed attribute. * @param att the attribute to be evaluated * @return the merit of a transformed attribute * @throws Exception if attribute can't be evaluated */ public double evaluateAttribute(int att) throws Exception { if (m_s == null) { throw new Exception("Latent Semantic Analysis hasn't been successfully" + " performed yet!"); } //return the square of the corresponding singular value return (m_s.get(att, att) * m_s.get(att, att)) / m_sumSquaredSingularValues; } /** * Transform an instance in original (unnormalized) format * @param instance an instance in the original (unnormalized) format * @return a transformed instance * @throws Exception if instance can't be transformed */ public Instance convertInstance(Instance instance) throws Exception { if (m_s == null) { throw new Exception("convertInstance: Latent Semantic Analysis not " + "performed yet."); } // array to hold new attribute values double [] newValues = new double[m_outputNumAttributes]; // apply filters so new instance is in same format as training instances Instance tempInstance = (Instance)instance.copy(); if (!instance.equalHeaders(m_trainCopy.instance(0))) { throw new Exception("Can't convert instance: headers don't match: " + "LatentSemanticAnalysis"); } // replace missing values m_replaceMissingFilter.input(tempInstance); m_replaceMissingFilter.batchFinished(); tempInstance = m_replaceMissingFilter.output(); // normalize if (m_normalize) { m_normalizeFilter.input(tempInstance); m_normalizeFilter.batchFinished(); tempInstance = m_normalizeFilter.output(); } // convert nominal attributes to binary m_nominalToBinaryFilter.input(tempInstance); m_nominalToBinaryFilter.batchFinished(); tempInstance = m_nominalToBinaryFilter.output(); // remove class/other attributes if (m_attributeFilter != null) { m_attributeFilter.input(tempInstance); m_attributeFilter.batchFinished(); tempInstance = m_attributeFilter.output(); } // record new attribute values if (m_hasClass) { // copy class value newValues[m_outputNumAttributes - 1] = instance.classValue(); } double [][] oldInstanceValues = new double[1][m_numAttributes]; oldInstanceValues[0] = tempInstance.toDoubleArray(); Matrix instanceVector = new Matrix(oldInstanceValues); // old attribute values instanceVector = instanceVector.times(m_transformationMatrix); // new attribute values for (int i = 0; i < m_actualRank; i++) { newValues[i] = instanceVector.get(0, i); } // return newly transformed instance if (instance instanceof SparseInstance) { return new SparseInstance(instance.weight(), newValues); } else { return new Instance(instance.weight(), newValues); } } /** * Returns a description of this attribute transformer * @return a String describing this attribute transformer */ public String toString() { if (m_s == null) { return "Latent Semantic Analysis hasn't been built yet!"; } else { return "\tLatent Semantic Analysis Attribute Transformer\n\n" + lsaSummary(); } } /** * Return a summary of the analysis * @return a summary of the analysis. */ private String lsaSummary() { StringBuffer result = new StringBuffer(); // print number of latent variables used result.append("Number of latent variables utilized: " + m_actualRank); // print singular values result.append("\n\nSingularValue\tLatentVariable#\n"); // create single array of singular values rather than diagonal matrix for (int i = 0; i < m_actualRank; i++) { result.append(Utils.doubleToString(m_s.get(i, i), 9, 5) + "\t" + (i + 1) + "\n"); } // print attribute vectors result.append("\nAttribute vectors (left singular vectors) -- row vectors show\n" + "the relation between the original attributes and the latent \n" + "variables computed by the singular value decomposition:\n"); for (int i = 0; i < m_actualRank; i++) { result.append("LatentVariable#" + (i + 1) + "\t"); } result.append("AttributeName\n"); for (int i = 0; i < m_u.getRowDimension(); i++) { // for each attribute for (int j = 0; j < m_u.getColumnDimension(); j++) { // for each latent variable result.append(Utils.doubleToString(m_u.get(i, j), 9, 5) + "\t\t"); } result.append(m_trainInstances.attribute(i).name() + "\n"); } // print instance vectors result.append("\n\nInstance vectors (right singular vectors) -- column\n" + "vectors show the relation between the original instances and the\n" + "latent variables computed by the singular value decomposition:\n"); for (int i = 0; i < m_numInstances; i++) { result.append("Instance#" + (i + 1) + "\t"); } result.append("LatentVariable#\n"); for (int i = 0; i < m_v.getColumnDimension(); i++) { // for each instance for (int j = 0; j < m_v.getRowDimension(); j++) { // for each latent variable // going down columns instead of across rows because we're // printing v' but have v stored result.append(Utils.doubleToString(m_v.get(j, i), 9, 5) + "\t"); } result.append((i + 1) + "\n"); } return result.toString(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.1 $"); } /** * Main method for testing this class * @param argv should contain the command line arguments to the * evaluator/transformer (see AttributeSelection) */ public static void main(String [] argv) { runEvaluator(new LatentSemanticAnalysis(), argv); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/LinearForwardSelection.java000066400000000000000000000615171214146114000320740ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * LinearForwardSelection.java * Copyright (C) 2007 Martin Guetlein * */ package weka.attributeSelection; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.Range; import weka.core.RevisionUtils; import weka.core.SelectedTag; import weka.core.Tag; import weka.core.Utils; import weka.core.TechnicalInformation; import weka.core.TechnicalInformationHandler; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; import java.util.BitSet; import java.util.Enumeration; import java.util.Vector; /** * LinearForwardSelection:
*
* Extension of BestFirst. Takes a restricted number of k attributes into account. Fixed-set selects a fixed number k of attributes, whereas k is increased in each step when fixed-width is selected. The search uses either the initial ordering to select the top k attributes, or performs a ranking (with the same evalutator the search uses later on). The search direction can be forward, or floating forward selection (with opitional backward search steps).
*
* For more information see:
*
* Martin Guetlein (2006). Large Scale Attribute Selection Using Wrappers. Freiburg, Germany. *

* * Valid options are:

* * 

 -P <start set>
  *  Specify a starting set of attributes.
  *  Eg. 1,3,5-7.
* * 
 -D <0 = forward selection | 1 = floating forward selection>
  *  Forward selection method. (default = 0).
* * 
 -N <num>
  *  Number of non-improving nodes to
  *  consider before terminating search.
* * 
 -I
  *  Perform initial ranking to select the
  *  top-ranked attributes.
* * 
 -K <num>
  *  Number of top-ranked attributes that are 
  *  taken into account by the search.
* * 
 -T <0 = fixed-set | 1 = fixed-width>
  *  Type of Linear Forward Selection (default = 0).
* * 
 -S <num>
  *  Size of lookup cache for evaluated subsets.
  *  Expressed as a multiple of the number of
  *  attributes in the data set. (default = 1)
* * 
 -Z
  *  verbose on/off
* * * @author Martin Guetlein (martin.guetlein@gmail.com) * @version $Revision: 1.4 $ */ public class LinearForwardSelection extends ASSearch implements OptionHandler, StartSetHandler, TechnicalInformationHandler { /** search directions */ protected static final int SEARCH_METHOD_FORWARD = 0; protected static final int SEARCH_METHOD_FLOATING = 1; public static final Tag[] TAGS_SEARCH_METHOD = { new Tag(SEARCH_METHOD_FORWARD, "Forward selection"), new Tag(SEARCH_METHOD_FLOATING, "Floating forward selection"), }; /** search directions */ protected static final int TYPE_FIXED_SET = 0; protected static final int TYPE_FIXED_WIDTH = 1; public static final Tag[] TAGS_TYPE = { new Tag(TYPE_FIXED_SET, "Fixed-set"), new Tag(TYPE_FIXED_WIDTH, "Fixed-width"), }; // member variables /** maximum number of stale nodes before terminating search */ protected int m_maxStale; /** 0 == forward selection, 1 == floating forward search */ protected int m_forwardSearchMethod; /** perform initial ranking to select top-ranked attributes */ protected boolean m_performRanking; /** * number of top-ranked attributes that are taken into account for the * search */ protected int m_numUsedAttributes; /** 0 == fixed-set, 1 == fixed-width */ protected int m_linearSelectionType; /** holds an array of starting attributes */ protected int[] m_starting; /** holds the start set for the search as a Range */ protected Range m_startRange; /** does the data have a class */ protected boolean m_hasClass; /** holds the class index */ protected int m_classIndex; /** number of attributes in the data */ protected int m_numAttribs; /** total number of subsets evaluated during a search */ protected int m_totalEvals; /** for debugging */ protected boolean m_verbose; /** holds the merit of the best subset found */ protected double m_bestMerit; /** holds the maximum size of the lookup cache for evaluated subsets */ protected int m_cacheSize; /** * Constructor */ public LinearForwardSelection() { resetOptions(); } /** * Returns a string describing this search method * * @return a description of the search method suitable for displaying in the * explorer/experimenter gui */ public String globalInfo() { return "LinearForwardSelection:\n\n" + "Extension of BestFirst. Takes a restricted number of k attributes " + "into account. Fixed-set selects a fixed number k of attributes, " + "whereas k is increased in each step when fixed-width is selected. " + "The search uses either the initial ordering to select the " + "top k attributes, or performs a ranking (with the same evalutator the " + "search uses later on). The search direction can be forward, " + "or floating forward selection (with opitional backward search steps).\n\n" + "For more information see:\n\n" + getTechnicalInformation().toString(); } /** * Returns an instance of a TechnicalInformation object, containing * detailed information about the technical background of this class, * e.g., paper reference or book this class is based on. * * @return the technical information about this class */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.MASTERSTHESIS); result.setValue(Field.AUTHOR, "Martin Guetlein"); result.setValue(Field.YEAR, "2006"); result.setValue(Field.TITLE, "Large Scale Attribute Selection Using Wrappers"); result.setValue(Field.SCHOOL, "Albert-Ludwigs-Universitaet"); result.setValue(Field.ADDRESS, "Freiburg, Germany"); return result; } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. * */ public Enumeration listOptions() { Vector newVector = new Vector(8); newVector.addElement(new Option("\tSpecify a starting set of attributes." + "\n\tEg. 1,3,5-7.", "P", 1, "-P ")); newVector.addElement(new Option( "\tForward selection method. (default = 0).", "D", 1, "-D <0 = forward selection | 1 = floating forward selection>")); newVector.addElement(new Option("\tNumber of non-improving nodes to" + "\n\tconsider before terminating search.", "N", 1, "-N ")); newVector.addElement(new Option("\tPerform initial ranking to select the" + "\n\ttop-ranked attributes.", "I", 0, "-I")); newVector.addElement(new Option( "\tNumber of top-ranked attributes that are " + "\n\ttaken into account by the search.", "K", 1, "-K ")); newVector.addElement(new Option( "\tType of Linear Forward Selection (default = 0).", "T", 1, "-T <0 = fixed-set | 1 = fixed-width>")); newVector.addElement(new Option( "\tSize of lookup cache for evaluated subsets." + "\n\tExpressed as a multiple of the number of" + "\n\tattributes in the data set. (default = 1)", "S", 1, "-S ")); newVector.addElement(new Option("\tverbose on/off", "Z", 0, "-Z")); return newVector.elements(); } /** * Parses a given list of options. * * Valid options are: *

* * -P
* Specify a starting set of attributes. Eg 1,4,7-9. *

* * -D <0 = forward selection | 1 = floating forward selection>
* Forward selection method of the search. (default = 0). *

* * -N
* Number of non improving nodes to consider before terminating search. * (default = 5). *

* * -I
* Perform initial ranking to select top-ranked attributes. *

* * -K
* Number of top-ranked attributes that are taken into account. *

* * -T <0 = fixed-set | 1 = fixed-width>
* Typ of Linear Forward Selection (default = 0). *

* * -S
* Size of lookup cache for evaluated subsets. Expressed as a multiple of * the number of attributes in the data set. (default = 1). *

* * -Z
* verbose on/off. *

* * @param options * the list of options as an array of strings * @exception Exception * if an option is not supported * */ public void setOptions(String[] options) throws Exception { String optionString; resetOptions(); optionString = Utils.getOption('P', options); if (optionString.length() != 0) { setStartSet(optionString); } optionString = Utils.getOption('D', options); if (optionString.length() != 0) { setForwardSelectionMethod(new SelectedTag(Integer.parseInt(optionString), TAGS_SEARCH_METHOD)); } else { setForwardSelectionMethod(new SelectedTag(SEARCH_METHOD_FORWARD, TAGS_SEARCH_METHOD)); } optionString = Utils.getOption('N', options); if (optionString.length() != 0) { setSearchTermination(Integer.parseInt(optionString)); } setPerformRanking(Utils.getFlag('I', options)); optionString = Utils.getOption('K', options); if (optionString.length() != 0) { setNumUsedAttributes(Integer.parseInt(optionString)); } optionString = Utils.getOption('T', options); if (optionString.length() != 0) { setType(new SelectedTag(Integer.parseInt(optionString), TAGS_TYPE)); } else { setType(new SelectedTag(TYPE_FIXED_SET, TAGS_TYPE)); } optionString = Utils.getOption('S', options); if (optionString.length() != 0) { setLookupCacheSize(Integer.parseInt(optionString)); } m_verbose = Utils.getFlag('Z', options); } /** * Set the maximum size of the evaluated subset cache (hashtable). This is * expressed as a multiplier for the number of attributes in the data set. * (default = 1). * * @param size * the maximum size of the hashtable */ public void setLookupCacheSize(int size) { if (size >= 0) { m_cacheSize = size; } } /** * Return the maximum size of the evaluated subset cache (expressed as a * multiplier for the number of attributes in a data set. * * @return the maximum size of the hashtable. */ public int getLookupCacheSize() { return m_cacheSize; } /** * Returns the tip text for this property * * @return tip text for this property suitable for displaying in the * explorer/experimenter gui */ public String lookupCacheSizeTipText() { return "Set the maximum size of the lookup cache of evaluated subsets. This is " + "expressed as a multiplier of the number of attributes in the data set. " + "(default = 1)."; } /** * Returns the tip text for this property * * @return tip text for this property suitable for displaying in the * explorer/experimenter gui */ public String startSetTipText() { return "Set the start point for the search. This is specified as a comma " + "seperated list off attribute indexes starting at 1. It can include " + "ranges. Eg. 1,2,5-9,17."; } /** * Sets a starting set of attributes for the search. It is the search * method's responsibility to report this start set (if any) in its * toString() method. * * @param startSet * a string containing a list of attributes (and or ranges), eg. * 1,2,6,10-15. * @exception Exception * if start set can't be set. */ public void setStartSet(String startSet) throws Exception { m_startRange.setRanges(startSet); } /** * Returns a list of attributes (and or attribute ranges) as a String * * @return a list of attributes (and or attribute ranges) */ public String getStartSet() { return m_startRange.getRanges(); } /** * Returns the tip text for this property * * @return tip text for this property suitable for displaying in the * explorer/experimenter gui */ public String searchTerminationTipText() { return "Set the amount of backtracking. Specify the number of "; } /** * Set the numnber of non-improving nodes to consider before terminating * search. * * @param t * the number of non-improving nodes * @exception Exception * if t is less than 1 */ public void setSearchTermination(int t) throws Exception { if (t < 1) { throw new Exception("Value of -N must be > 0."); } m_maxStale = t; } /** * Get the termination criterion (number of non-improving nodes). * * @return the number of non-improving nodes */ public int getSearchTermination() { return m_maxStale; } /** * Returns the tip text for this property * * @return tip text for this property suitable for displaying in the * explorer/experimenter gui */ public String performRankingTipText() { return "Perform initial ranking to select top-ranked attributes."; } /** * Perform initial ranking to select top-ranked attributes. * * @param b * true if initial ranking should be performed */ public void setPerformRanking(boolean b) { m_performRanking = b; } /** * Get boolean if initial ranking should be performed to select the * top-ranked attributes * * @return true if initial ranking should be performed */ public boolean getPerformRanking() { return m_performRanking; } /** * Returns the tip text for this property * * @return tip text for this property suitable for displaying in the * explorer/experimenter gui */ public String numUsedAttributesTipText() { return "Set the amount of top-ranked attributes that are taken into account by the search process."; } /** * Set the number of top-ranked attributes that taken into account by the * search process. * * @param k * the number of attributes * @exception Exception * if k is less than 2 */ public void setNumUsedAttributes(int k) throws Exception { if (k < 2) { throw new Exception("Value of -K must be >= 2."); } m_numUsedAttributes = k; } /** * Get the number of top-ranked attributes that taken into account by the * search process. * * @return the number of top-ranked attributes that taken into account */ public int getNumUsedAttributes() { return m_numUsedAttributes; } /** * Returns the tip text for this property * * @return tip text for this property suitable for displaying in the * explorer/experimenter gui */ public String forwardSelectionMethodTipText() { return "Set the direction of the search."; } /** * Set the search direction * * @param d * the direction of the search */ public void setForwardSelectionMethod(SelectedTag d) { if (d.getTags() == TAGS_SEARCH_METHOD) { m_forwardSearchMethod = d.getSelectedTag().getID(); } } /** * Get the search direction * * @return the direction of the search */ public SelectedTag getForwardSelectionMethod() { return new SelectedTag(m_forwardSearchMethod, TAGS_SEARCH_METHOD); } /** * Returns the tip text for this property * * @return tip text for this property suitable for displaying in the * explorer/experimenter gui */ public String typeTipText() { return "Set the type of the search."; } /** * Set the type * * @param t * the Linear Forward Selection type */ public void setType(SelectedTag t) { if (t.getTags() == TAGS_TYPE) { m_linearSelectionType = t.getSelectedTag().getID(); } } /** * Get the type * * @return the Linear Forward Selection type */ public SelectedTag getType() { return new SelectedTag(m_linearSelectionType, TAGS_TYPE); } /** * Returns the tip text for this property * * @return tip text for this property suitable for displaying in the * explorer/experimenter gui */ public String verboseTipText() { return "Turn on verbose output for monitoring the search's progress."; } /** * Set whether verbose output should be generated. * * @param d * true if output is to be verbose. */ public void setVerbose(boolean b) { m_verbose = b; } /** * Get whether output is to be verbose * * @return true if output will be verbose */ public boolean getVerbose() { return m_verbose; } /** * Gets the current settings of LinearForwardSelection. * * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions() { String[] options = new String[13]; int current = 0; if (!(getStartSet().equals(""))) { options[current++] = "-P"; options[current++] = "" + startSetToString(); } options[current++] = "-D"; options[current++] = "" + m_forwardSearchMethod; options[current++] = "-N"; options[current++] = "" + m_maxStale; if (m_performRanking) { options[current++] = "-I"; } options[current++] = "-K"; options[current++] = "" + m_numUsedAttributes; options[current++] = "-T"; options[current++] = "" + m_linearSelectionType; options[current++] = "-Z"; options[current++] = "" + m_verbose; while (current < options.length) { options[current++] = ""; } return options; } /** * converts the array of starting attributes to a string. This is used by * getOptions to return the actual attributes specified as the starting set. * This is better than using m_startRanges.getRanges() as the same start set * can be specified in different ways from the command line---eg 1,2,3 == * 1-3. This is to ensure that stuff that is stored in a database is * comparable. * * @return a comma seperated list of individual attribute numbers as a * String */ private String startSetToString() { StringBuffer FString = new StringBuffer(); boolean didPrint; if (m_starting == null) { return getStartSet(); } for (int i = 0; i < m_starting.length; i++) { didPrint = false; if ((m_hasClass == false) || ((m_hasClass == true) && (i != m_classIndex))) { FString.append((m_starting[i] + 1)); didPrint = true; } if (i == (m_starting.length - 1)) { FString.append(""); } else { if (didPrint) { FString.append(","); } } } return FString.toString(); } /** * returns a description of the search as a String * * @return a description of the search */ public String toString() { StringBuffer LFSString = new StringBuffer(); LFSString.append("\tLinear Forward Selection.\n\tStart set: "); if (m_starting == null) { LFSString.append("no attributes\n"); } else { LFSString.append(startSetToString() + "\n"); } LFSString.append("\tForward selection method: "); if (m_forwardSearchMethod == SEARCH_METHOD_FORWARD) { LFSString.append("forward selection\n"); } else { LFSString.append("floating forward selection\n"); } LFSString.append("\tStale search after " + m_maxStale + " node expansions\n"); LFSString.append("\tLinear Forward Selection Type: "); if (m_linearSelectionType == TYPE_FIXED_SET) { LFSString.append("fixed-set\n"); } else { LFSString.append("fixed-width\n"); } LFSString.append("\tNumber of top-ranked attributes that are used: " + m_numUsedAttributes + "\n"); LFSString.append("\tTotal number of subsets evaluated: " + m_totalEvals + "\n"); LFSString.append("\tMerit of best subset found: " + Utils.doubleToString(Math.abs(m_bestMerit), 8, 3) + "\n"); return LFSString.toString(); } /** * Searches the attribute subset space by linear forward selection * * @param ASEvaluator * the attribute evaluator to guide the search * @param data * the training instances. * @return an array (not necessarily ordered) of selected attribute indexes * @exception Exception * if the search can't be completed */ public int[] search(ASEvaluation ASEval, Instances data) throws Exception { m_totalEvals = 0; if (!(ASEval instanceof SubsetEvaluator)) { throw new Exception(ASEval.getClass().getName() + " is not a " + "Subset evaluator!"); } if (ASEval instanceof UnsupervisedSubsetEvaluator) { m_hasClass = false; } else { m_hasClass = true; m_classIndex = data.classIndex(); } ((ASEvaluation) ASEval).buildEvaluator(data); m_numAttribs = data.numAttributes(); if (m_numUsedAttributes > m_numAttribs) { System.out.println( "Decreasing number of top-ranked attributes to total number of attributes: " + data.numAttributes()); m_numUsedAttributes = m_numAttribs; } BitSet start_group = new BitSet(m_numAttribs); m_startRange.setUpper(m_numAttribs - 1); if (!(getStartSet().equals(""))) { m_starting = m_startRange.getSelection(); } // If a starting subset has been supplied, then initialise the bitset if (m_starting != null) { for (int i = 0; i < m_starting.length; i++) { if ((m_starting[i]) != m_classIndex) { start_group.set(m_starting[i]); } } } LFSMethods LFS = new LFSMethods(); int[] ranking; if (m_performRanking) { ranking = LFS.rankAttributes(data, (SubsetEvaluator) ASEval, m_verbose); } else { ranking = new int[m_numAttribs]; for (int i = 0; i < ranking.length; i++) { ranking[i] = i; } } if (m_forwardSearchMethod == SEARCH_METHOD_FORWARD) { LFS.forwardSearch(m_cacheSize, start_group, ranking, m_numUsedAttributes, m_linearSelectionType == TYPE_FIXED_WIDTH, m_maxStale, -1, data, (SubsetEvaluator) ASEval, m_verbose); } else if (m_forwardSearchMethod == SEARCH_METHOD_FLOATING) { LFS.floatingForwardSearch(m_cacheSize, start_group, ranking, m_numUsedAttributes, m_linearSelectionType == TYPE_FIXED_WIDTH, m_maxStale, data, (SubsetEvaluator) ASEval, m_verbose); } m_totalEvals = LFS.getNumEvalsTotal(); m_bestMerit = LFS.getBestMerit(); return attributeList(LFS.getBestGroup()); } /** * Reset options to default values */ protected void resetOptions() { m_maxStale = 5; m_forwardSearchMethod = SEARCH_METHOD_FORWARD; m_performRanking = true; m_numUsedAttributes = 50; m_linearSelectionType = TYPE_FIXED_SET; m_starting = null; m_startRange = new Range(); m_classIndex = -1; m_totalEvals = 0; m_cacheSize = 1; m_verbose = false; } /** * converts a BitSet into a list of attribute indexes * * @param group * the BitSet to convert * @return an array of attribute indexes */ protected int[] attributeList(BitSet group) { int count = 0; // count how many were selected for (int i = 0; i < m_numAttribs; i++) { if (group.get(i)) { count++; } } int[] list = new int[count]; count = 0; for (int i = 0; i < m_numAttribs; i++) { if (group.get(i)) { list[count++] = i; } } return list; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.4 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/OneRAttributeEval.java000066400000000000000000000307401214146114000310200ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * OneRAttributeEval.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.classifiers.Classifier; import weka.classifiers.Evaluation; import weka.core.Capabilities; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.Utils; import weka.core.Capabilities.Capability; import weka.filters.Filter; import weka.filters.unsupervised.attribute.Remove; import java.util.Enumeration; import java.util.Random; import java.util.Vector; /** * OneRAttributeEval :
*
* Evaluates the worth of an attribute by using the OneR classifier.
*

* * Valid options are:

* * 

 -S <seed>
 *  Random number seed for cross validation
 *  (default = 1)
* * 
 -F <folds>
 *  Number of folds for cross validation
 *  (default = 10)
* * 
 -D
 *  Use training data for evaluation rather than cross validaton
* * 
 -B <minimum bucket size>
 *  Minimum number of objects in a bucket
 *  (passed on to OneR, default = 6)
* * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.21 $ */ public class OneRAttributeEval extends ASEvaluation implements AttributeEvaluator, OptionHandler { /** for serialization */ static final long serialVersionUID = 4386514823886856980L; /** The training instances */ private Instances m_trainInstances; /** The class index */ private int m_classIndex; /** The number of attributes */ private int m_numAttribs; /** The number of instances */ private int m_numInstances; /** Random number seed */ private int m_randomSeed; /** Number of folds for cross validation */ private int m_folds; /** Use training data to evaluate merit rather than x-val */ private boolean m_evalUsingTrainingData; /** Passed on to OneR */ private int m_minBucketSize; /** * Returns a string describing this attribute evaluator * @return a description of the evaluator suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "OneRAttributeEval :\n\nEvaluates the worth of an attribute by " +"using the OneR classifier.\n"; } /** * Returns a string for this option suitable for display in the gui * as a tip text * * @return a string describing this option */ public String seedTipText() { return "Set the seed for use in cross validation."; } /** * Set the random number seed for cross validation * * @param seed the seed to use */ public void setSeed(int seed) { m_randomSeed = seed; } /** * Get the random number seed * * @return an int value */ public int getSeed() { return m_randomSeed; } /** * Returns a string for this option suitable for display in the gui * as a tip text * * @return a string describing this option */ public String foldsTipText() { return "Set the number of folds for cross validation."; } /** * Set the number of folds to use for cross validation * * @param folds the number of folds */ public void setFolds(int folds) { m_folds = folds; if (m_folds < 2) { m_folds = 2; } } /** * Get the number of folds used for cross validation * * @return the number of folds */ public int getFolds() { return m_folds; } /** * Returns a string for this option suitable for display in the gui * as a tip text * * @return a string describing this option */ public String evalUsingTrainingDataTipText() { return "Use the training data to evaluate attributes rather than " + "cross validation."; } /** * Use the training data to evaluate attributes rather than cross validation * * @param e true if training data is to be used for evaluation */ public void setEvalUsingTrainingData(boolean e) { m_evalUsingTrainingData = e; } /** * Returns a string for this option suitable for display in the gui * as a tip text * * @return a string describing this option */ public String minimumBucketSizeTipText() { return "The minimum number of objects in a bucket " + "(passed to OneR)."; } /** * Set the minumum bucket size used by OneR * * @param minB the minimum bucket size to use */ public void setMinimumBucketSize(int minB) { m_minBucketSize = minB; } /** * Get the minimum bucket size used by oneR * * @return the minimum bucket size used */ public int getMinimumBucketSize() { return m_minBucketSize; } /** * Returns true if the training data is to be used for evaluation * * @return true if training data is to be used for evaluation */ public boolean getEvalUsingTrainingData() { return m_evalUsingTrainingData; } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector newVector = new Vector(4); newVector.addElement(new Option( "\tRandom number seed for cross validation\n" + "\t(default = 1)", "S", 1, "-S ")); newVector.addElement(new Option( "\tNumber of folds for cross validation\n" + "\t(default = 10)", "F", 1, "-F ")); newVector.addElement(new Option( "\tUse training data for evaluation rather than cross validaton", "D", 0, "-D")); newVector.addElement(new Option( "\tMinimum number of objects in a bucket\n" + "\t(passed on to " +"OneR, default = 6)", "B", 1, "-B ")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -S <seed>
   *  Random number seed for cross validation
   *  (default = 1)
* * 
 -F <folds>
   *  Number of folds for cross validation
   *  (default = 10)
* * 
 -D
   *  Use training data for evaluation rather than cross validaton
* * 
 -B <minimum bucket size>
   *  Minimum number of objects in a bucket
   *  (passed on to OneR, default = 6)
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions(String [] options) throws Exception { String temp = Utils.getOption('S', options); if (temp.length() != 0) { setSeed(Integer.parseInt(temp)); } temp = Utils.getOption('F', options); if (temp.length() != 0) { setFolds(Integer.parseInt(temp)); } temp = Utils.getOption('B', options); if (temp.length() != 0) { setMinimumBucketSize(Integer.parseInt(temp)); } setEvalUsingTrainingData(Utils.getFlag('D', options)); Utils.checkForRemainingOptions(options); } /** * returns the current setup. * * @return the options of the current setup */ public String[] getOptions() { String [] options = new String [7]; int current = 0; if (getEvalUsingTrainingData()) { options[current++] = "-D"; } options[current++] = "-S"; options[current++] = "" + getSeed(); options[current++] = "-F"; options[current++] = "" + getFolds(); options[current++] = "-B"; options[current++] = "" + getMinimumBucketSize(); while (current < options.length) { options[current++] = ""; } return options; } /** * Constructor */ public OneRAttributeEval () { resetOptions(); } /** * Returns the capabilities of this evaluator. * * @return the capabilities of this evaluator * @see Capabilities */ public Capabilities getCapabilities() { Capabilities result = super.getCapabilities(); // attributes result.enable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.NUMERIC_ATTRIBUTES); result.enable(Capability.DATE_ATTRIBUTES); result.enable(Capability.MISSING_VALUES); // class result.enable(Capability.NOMINAL_CLASS); result.enable(Capability.MISSING_CLASS_VALUES); return result; } /** * Initializes a OneRAttribute attribute evaluator. * Discretizes all attributes that are numeric. * * @param data set of instances serving as training data * @throws Exception if the evaluator has not been * generated successfully */ public void buildEvaluator (Instances data) throws Exception { // can evaluator handle data? getCapabilities().testWithFail(data); m_trainInstances = data; m_classIndex = m_trainInstances.classIndex(); m_numAttribs = m_trainInstances.numAttributes(); m_numInstances = m_trainInstances.numInstances(); } /** * rests to defaults. */ protected void resetOptions () { m_trainInstances = null; m_randomSeed = 1; m_folds = 10; m_evalUsingTrainingData = false; m_minBucketSize = 6; // default used by OneR } /** * evaluates an individual attribute by measuring the amount * of information gained about the class given the attribute. * * @param attribute the index of the attribute to be evaluated * @throws Exception if the attribute could not be evaluated */ public double evaluateAttribute (int attribute) throws Exception { int[] featArray = new int[2]; // feat + class double errorRate; Evaluation o_Evaluation; Remove delTransform = new Remove(); delTransform.setInvertSelection(true); // copy the instances Instances trainCopy = new Instances(m_trainInstances); featArray[0] = attribute; featArray[1] = trainCopy.classIndex(); delTransform.setAttributeIndicesArray(featArray); delTransform.setInputFormat(trainCopy); trainCopy = Filter.useFilter(trainCopy, delTransform); o_Evaluation = new Evaluation(trainCopy); String [] oneROpts = { "-B", ""+getMinimumBucketSize()}; Classifier oneR = Classifier.forName("weka.classifiers.rules.OneR", oneROpts); if (m_evalUsingTrainingData) { oneR.buildClassifier(trainCopy); o_Evaluation.evaluateModel(oneR, trainCopy); } else { /* o_Evaluation.crossValidateModel("weka.classifiers.rules.OneR", trainCopy, 10, null, new Random(m_randomSeed)); */ o_Evaluation.crossValidateModel(oneR, trainCopy, m_folds, new Random(m_randomSeed)); } errorRate = o_Evaluation.errorRate(); return (1 - errorRate)*100.0; } /** * Return a description of the evaluator * @return description as a string */ public String toString () { StringBuffer text = new StringBuffer(); if (m_trainInstances == null) { text.append("\tOneR feature evaluator has not been built yet"); } else { text.append("\tOneR feature evaluator.\n\n"); text.append("\tUsing "); if (m_evalUsingTrainingData) { text.append("training data for evaluation of attributes."); } else { text.append(""+getFolds()+" fold cross validation for evaluating " +"attributes."); } text.append("\n\tMinimum bucket size for OneR: " +getMinimumBucketSize()); } text.append("\n"); return text.toString(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.21 $"); } // ============ // Test method. // ============ /** * Main method for testing this class. * * @param args the options */ public static void main (String[] args) { runEvaluator(new OneRAttributeEval(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/PrincipalComponents.java000066400000000000000000000742441214146114000314570ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * PrincipalComponents.java * Copyright (C) 2000 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.core.Attribute; import weka.core.Capabilities; import weka.core.FastVector; import weka.core.Instance; import weka.core.Instances; import weka.core.Matrix; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.SparseInstance; import weka.core.Utils; import weka.core.Capabilities.Capability; import weka.filters.Filter; import weka.filters.unsupervised.attribute.NominalToBinary; import weka.filters.unsupervised.attribute.Normalize; import weka.filters.unsupervised.attribute.Remove; import weka.filters.unsupervised.attribute.ReplaceMissingValues; import java.util.Enumeration; import java.util.Vector; /** * Performs a principal components analysis and transformation of the data. Use in conjunction with a Ranker search. Dimensionality reduction is accomplished by choosing enough eigenvectors to account for some percentage of the variance in the original data---default 0.95 (95%). Attribute noise can be filtered by transforming to the PC space, eliminating some of the worst eigenvectors, and then transforming back to the original space. *

* * Valid options are:

* * 

 -D
 *  Don't normalize input data.
* * 
 -R
 *  Retain enough PC attributes to account 
 *  for this proportion of variance in the original data.
 *  (default = 0.95)
* * 
 -O
 *  Transform through the PC space and 
 *  back to the original space.
* * 
 -A
 *  Maximum number of attributes to include in 
 *  transformed attribute names. (-1 = include all)
* * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @author Gabi Schmidberger (gabi@cs.waikato.ac.nz) * @version $Revision: 1.36 $ */ public class PrincipalComponents extends UnsupervisedAttributeEvaluator implements AttributeTransformer, OptionHandler { /** for serialization */ static final long serialVersionUID = 3310137541055815078L; /** The data to transform analyse/transform */ private Instances m_trainInstances; /** Keep a copy for the class attribute (if set) */ private Instances m_trainCopy; /** The header for the transformed data format */ private Instances m_transformedFormat; /** The header for data transformed back to the original space */ private Instances m_originalSpaceFormat; /** Data has a class set */ private boolean m_hasClass; /** Class index */ private int m_classIndex; /** Number of attributes */ private int m_numAttribs; /** Number of instances */ private int m_numInstances; /** Correlation matrix for the original data */ private double [][] m_correlation; /** Will hold the unordered linear transformations of the (normalized) original data */ private double [][] m_eigenvectors; /** Eigenvalues for the corresponding eigenvectors */ private double [] m_eigenvalues = null; /** Sorted eigenvalues */ private int [] m_sortedEigens; /** sum of the eigenvalues */ private double m_sumOfEigenValues = 0.0; /** Filters for original data */ private ReplaceMissingValues m_replaceMissingFilter; private Normalize m_normalizeFilter; private NominalToBinary m_nominalToBinFilter; private Remove m_attributeFilter; /** used to remove the class column if a class column is set */ private Remove m_attribFilter; /** The number of attributes in the pc transformed data */ private int m_outputNumAtts = -1; /** normalize the input data? */ private boolean m_normalize = true; /** the amount of varaince to cover in the original data when retaining the best n PC's */ private double m_coverVariance = 0.95; /** transform the data through the pc space and back to the original space ? */ private boolean m_transBackToOriginal = false; /** maximum number of attributes in the transformed attribute name */ private int m_maxAttrsInName = 5; /** holds the transposed eigenvectors for converting back to the original space */ private double [][] m_eTranspose; /** * Returns a string describing this attribute transformer * @return a description of the evaluator suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Performs a principal components analysis and transformation of " +"the data. Use in conjunction with a Ranker search. Dimensionality " +"reduction is accomplished by choosing enough eigenvectors to " +"account for some percentage of the variance in the original data---" +"default 0.95 (95%). Attribute noise can be filtered by transforming " +"to the PC space, eliminating some of the worst eigenvectors, and " +"then transforming back to the original space."; } /** * Returns an enumeration describing the available options.

* * @return an enumeration of all the available options. **/ public Enumeration listOptions () { Vector newVector = new Vector(3); newVector.addElement(new Option("\tDon't normalize input data." , "D", 0, "-D")); newVector.addElement(new Option("\tRetain enough PC attributes to account " +"\n\tfor this proportion of variance in " +"the original data.\n" + "\t(default = 0.95)", "R",1,"-R")); newVector.addElement(new Option("\tTransform through the PC space and " +"\n\tback to the original space." , "O", 0, "-O")); newVector.addElement(new Option("\tMaximum number of attributes to include in " + "\n\ttransformed attribute names. (-1 = include all)" , "A", 1, "-A")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -D
   *  Don't normalize input data.
* * 
 -R
   *  Retain enough PC attributes to account 
   *  for this proportion of variance in the original data.
   *  (default = 0.95)
* * 
 -O
   *  Transform through the PC space and 
   *  back to the original space.
* * 
 -A
   *  Maximum number of attributes to include in 
   *  transformed attribute names. (-1 = include all)
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions (String[] options) throws Exception { resetOptions(); String optionString; optionString = Utils.getOption('R', options); if (optionString.length() != 0) { Double temp; temp = Double.valueOf(optionString); setVarianceCovered(temp.doubleValue()); } optionString = Utils.getOption('A', options); if (optionString.length() != 0) { setMaximumAttributeNames(Integer.parseInt(optionString)); } setNormalize(!Utils.getFlag('D', options)); setTransformBackToOriginal(Utils.getFlag('O', options)); } /** * Reset to defaults */ private void resetOptions() { m_coverVariance = 0.95; m_normalize = true; m_sumOfEigenValues = 0.0; m_transBackToOriginal = false; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String normalizeTipText() { return "Normalize input data."; } /** * Set whether input data will be normalized. * @param n true if input data is to be normalized */ public void setNormalize(boolean n) { m_normalize = n; } /** * Gets whether or not input data is to be normalized * @return true if input data is to be normalized */ public boolean getNormalize() { return m_normalize; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String varianceCoveredTipText() { return "Retain enough PC attributes to account for this proportion of " +"variance."; } /** * Sets the amount of variance to account for when retaining * principal components * @param vc the proportion of total variance to account for */ public void setVarianceCovered(double vc) { m_coverVariance = vc; } /** * Gets the proportion of total variance to account for when * retaining principal components * @return the proportion of variance to account for */ public double getVarianceCovered() { return m_coverVariance; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String maximumAttributeNamesTipText() { return "The maximum number of attributes to include in transformed attribute names."; } /** * Sets maximum number of attributes to include in * transformed attribute names. * @param m the maximum number of attributes */ public void setMaximumAttributeNames(int m) { m_maxAttrsInName = m; } /** * Gets maximum number of attributes to include in * transformed attribute names. * @return the maximum number of attributes */ public int getMaximumAttributeNames() { return m_maxAttrsInName; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String transformBackToOriginalTipText() { return "Transform through the PC space and back to the original space. " +"If only the best n PCs are retained (by setting varianceCovered < 1) " +"then this option will give a dataset in the original space but with " +"less attribute noise."; } /** * Sets whether the data should be transformed back to the original * space * @param b true if the data should be transformed back to the * original space */ public void setTransformBackToOriginal(boolean b) { m_transBackToOriginal = b; } /** * Gets whether the data is to be transformed back to the original * space. * @return true if the data is to be transformed back to the original space */ public boolean getTransformBackToOriginal() { return m_transBackToOriginal; } /** * Gets the current settings of PrincipalComponents * * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { String[] options = new String[6]; int current = 0; if (!getNormalize()) { options[current++] = "-D"; } options[current++] = "-R"; options[current++] = ""+getVarianceCovered(); options[current++] = "-A"; options[current++] = ""+getMaximumAttributeNames(); if (getTransformBackToOriginal()) { options[current++] = "-O"; } while (current < options.length) { options[current++] = ""; } return options; } /** * Returns the capabilities of this evaluator. * * @return the capabilities of this evaluator * @see Capabilities */ public Capabilities getCapabilities() { Capabilities result = super.getCapabilities(); // attributes result.enable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.NUMERIC_ATTRIBUTES); result.enable(Capability.DATE_ATTRIBUTES); result.enable(Capability.MISSING_VALUES); // class result.enable(Capability.NOMINAL_CLASS); result.enable(Capability.NUMERIC_CLASS); result.enable(Capability.DATE_CLASS); result.enable(Capability.MISSING_CLASS_VALUES); result.enable(Capability.NO_CLASS); return result; } /** * Initializes principal components and performs the analysis * @param data the instances to analyse/transform * @throws Exception if analysis fails */ public void buildEvaluator(Instances data) throws Exception { // can evaluator handle data? getCapabilities().testWithFail(data); buildAttributeConstructor(data); } private void buildAttributeConstructor (Instances data) throws Exception { m_eigenvalues = null; m_outputNumAtts = -1; m_attributeFilter = null; m_nominalToBinFilter = null; m_sumOfEigenValues = 0.0; m_trainInstances = new Instances(data); // make a copy of the training data so that we can get the class // column to append to the transformed data (if necessary) m_trainCopy = new Instances(m_trainInstances); m_replaceMissingFilter = new ReplaceMissingValues(); m_replaceMissingFilter.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, m_replaceMissingFilter); if (m_normalize) { m_normalizeFilter = new Normalize(); m_normalizeFilter.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, m_normalizeFilter); } m_nominalToBinFilter = new NominalToBinary(); m_nominalToBinFilter.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, m_nominalToBinFilter); // delete any attributes with only one distinct value or are all missing Vector deleteCols = new Vector(); for (int i=0;i=0) { // get rid of the class column m_hasClass = true; m_classIndex = m_trainInstances.classIndex(); deleteCols.addElement(new Integer(m_classIndex)); } // remove columns from the data if necessary if (deleteCols.size() > 0) { m_attributeFilter = new Remove(); int [] todelete = new int [deleteCols.size()]; for (int i=0;i (m_numAttribs - numVectors - 1); i--) { for (int j = 0; j < m_numAttribs; j++) { orderedVectors[j][m_numAttribs - i] = m_eigenvectors[j][m_sortedEigens[i]]; } } // transpose the matrix int nr = orderedVectors.length; int nc = orderedVectors[0].length; m_eTranspose = new double [nc][nr]; for (int i = 0; i < nc; i++) { for (int j = 0; j < nr; j++) { m_eTranspose[i][j] = orderedVectors[j][i]; } } } } /** * Returns just the header for the transformed data (ie. an empty * set of instances. This is so that AttributeSelection can * determine the structure of the transformed data without actually * having to get all the transformed data through getTransformedData(). * @return the header of the transformed data. * @throws Exception if the header of the transformed data can't * be determined. */ public Instances transformedHeader() throws Exception { if (m_eigenvalues == null) { throw new Exception("Principal components hasn't been built yet"); } if (m_transBackToOriginal) { return m_originalSpaceFormat; } else { return m_transformedFormat; } } /** * Gets the transformed training data. * @return the transformed training data * @throws Exception if transformed data can't be returned */ public Instances transformedData() throws Exception { if (m_eigenvalues == null) { throw new Exception("Principal components hasn't been built yet"); } Instances output; if (m_transBackToOriginal) { output = new Instances(m_originalSpaceFormat); } else { output = new Instances(m_transformedFormat); } for (int i=0;i= m_numAttribs - att - 1; i--) { cumulative += m_eigenvalues[m_sortedEigens[i]]; } return 1.0 - cumulative / m_sumOfEigenValues; } /** * Fill the correlation matrix */ private void fillCorrelation() { m_correlation = new double[m_numAttribs][m_numAttribs]; double [] att1 = new double [m_numInstances]; double [] att2 = new double [m_numInstances]; double corr; for (int i = 0; i < m_numAttribs; i++) { for (int j = 0; j < m_numAttribs; j++) { if (i == j) { m_correlation[i][j] = 1.0; } else { for (int k = 0; k < m_numInstances; k++) { att1[k] = m_trainInstances.instance(k).value(i); att2[k] = m_trainInstances.instance(k).value(j); } corr = Utils.correlation(att1,att2,m_numInstances); m_correlation[i][j] = corr; m_correlation[j][i] = corr; } } } } /** * Return a summary of the analysis * @return a summary of the analysis. */ private String principalComponentsSummary() { StringBuffer result = new StringBuffer(); double cumulative = 0.0; Instances output = null; int numVectors=0; try { output = setOutputFormat(); numVectors = (output.classIndex() < 0) ? output.numAttributes() : output.numAttributes()-1; } catch (Exception ex) { } //tomorrow result.append("Correlation matrix\n"+matrixToString(m_correlation) +"\n\n"); result.append("eigenvalue\tproportion\tcumulative\n"); for (int i = m_numAttribs - 1; i > (m_numAttribs - numVectors - 1); i--) { cumulative+=m_eigenvalues[m_sortedEigens[i]]; result.append(Utils.doubleToString(m_eigenvalues[m_sortedEigens[i]],9,5) +"\t"+Utils. doubleToString((m_eigenvalues[m_sortedEigens[i]] / m_sumOfEigenValues), 9,5) +"\t"+Utils.doubleToString((cumulative / m_sumOfEigenValues),9,5) +"\t"+output.attribute(m_numAttribs - i - 1).name()+"\n"); } result.append("\nEigenvectors\n"); for (int j = 1;j <= numVectors;j++) { result.append(" V"+j+'\t'); } result.append("\n"); for (int j = 0; j < m_numAttribs; j++) { for (int i = m_numAttribs - 1; i > (m_numAttribs - numVectors - 1); i--) { result.append(Utils. doubleToString(m_eigenvectors[j][m_sortedEigens[i]],7,4) +"\t"); } result.append(m_trainInstances.attribute(j).name()+'\n'); } if (m_transBackToOriginal) { result.append("\nPC space transformed back to original space.\n" +"(Note: can't evaluate attributes in the original " +"space)\n"); } return result.toString(); } /** * Returns a description of this attribute transformer * @return a String describing this attribute transformer */ public String toString() { if (m_eigenvalues == null) { return "Principal components hasn't been built yet!"; } else { return "\tPrincipal Components Attribute Transformer\n\n" +principalComponentsSummary(); } } /** * Return a matrix as a String * @param matrix that is decribed as a string * @return a String describing a matrix */ private String matrixToString(double [][] matrix) { StringBuffer result = new StringBuffer(); int last = matrix.length - 1; for (int i = 0; i <= last; i++) { for (int j = 0; j <= last; j++) { result.append(Utils.doubleToString(matrix[i][j],6,2)+" "); if (j == last) { result.append('\n'); } } } return result.toString(); } /** * Convert a pc transformed instance back to the original space * * @param inst the instance to convert * @return the processed instance * @throws Exception if something goes wrong */ private Instance convertInstanceToOriginal(Instance inst) throws Exception { double[] newVals = null; if (m_hasClass) { newVals = new double[m_numAttribs+1]; } else { newVals = new double[m_numAttribs]; } if (m_hasClass) { // class is always appended as the last attribute newVals[m_numAttribs] = inst.value(inst.numAttributes() - 1); } for (int i = 0; i < m_eTranspose[0].length; i++) { double tempval = 0.0; for (int j = 1; j < m_eTranspose.length; j++) { tempval += (m_eTranspose[j][i] * inst.value(j - 1)); } newVals[i] = tempval; } if (inst instanceof SparseInstance) { return new SparseInstance(inst.weight(), newVals); } else { return new Instance(inst.weight(), newVals); } } /** * Transform an instance in original (unormalized) format. Convert back * to the original space if requested. * @param instance an instance in the original (unormalized) format * @return a transformed instance * @throws Exception if instance cant be transformed */ public Instance convertInstance(Instance instance) throws Exception { if (m_eigenvalues == null) { throw new Exception("convertInstance: Principal components not " +"built yet"); } double[] newVals = new double[m_outputNumAtts]; Instance tempInst = (Instance)instance.copy(); if (!instance.equalHeaders(m_trainCopy.instance(0))) { throw new Exception("Can't convert instance: header's don't match: " +"PrincipalComponents"); } m_replaceMissingFilter.input(tempInst); m_replaceMissingFilter.batchFinished(); tempInst = m_replaceMissingFilter.output(); if (m_normalize) { m_normalizeFilter.input(tempInst); m_normalizeFilter.batchFinished(); tempInst = m_normalizeFilter.output(); } m_nominalToBinFilter.input(tempInst); m_nominalToBinFilter.batchFinished(); tempInst = m_nominalToBinFilter.output(); if (m_attributeFilter != null) { m_attributeFilter.input(tempInst); m_attributeFilter.batchFinished(); tempInst = m_attributeFilter.output(); } if (m_hasClass) { newVals[m_outputNumAtts - 1] = instance.value(instance.classIndex()); } double cumulative = 0; for (int i = m_numAttribs - 1; i >= 0; i--) { double tempval = 0.0; for (int j = 0; j < m_numAttribs; j++) { tempval += (m_eigenvectors[j][m_sortedEigens[i]] * tempInst.value(j)); } newVals[m_numAttribs - i - 1] = tempval; cumulative+=m_eigenvalues[m_sortedEigens[i]]; if ((cumulative / m_sumOfEigenValues) >= m_coverVariance) { break; } } if (!m_transBackToOriginal) { if (instance instanceof SparseInstance) { return new SparseInstance(instance.weight(), newVals); } else { return new Instance(instance.weight(), newVals); } } else { if (instance instanceof SparseInstance) { return convertInstanceToOriginal(new SparseInstance(instance.weight(), newVals)); } else { return convertInstanceToOriginal(new Instance(instance.weight(), newVals)); } } } /** * Set up the header for the PC->original space dataset * * @return the output format * @throws Exception if something goes wrong */ private Instances setOutputFormatOriginal() throws Exception { FastVector attributes = new FastVector(); for (int i = 0; i < m_numAttribs; i++) { String att = m_trainInstances.attribute(i).name(); attributes.addElement(new Attribute(att)); } if (m_hasClass) { attributes.addElement(m_trainCopy.classAttribute().copy()); } Instances outputFormat = new Instances(m_trainCopy.relationName()+"->PC->original space", attributes, 0); // set the class to be the last attribute if necessary if (m_hasClass) { outputFormat.setClassIndex(outputFormat.numAttributes()-1); } return outputFormat; } /** * Set the format for the transformed data * @return a set of empty Instances (header only) in the new format * @throws Exception if the output format can't be set */ private Instances setOutputFormat() throws Exception { if (m_eigenvalues == null) { return null; } double cumulative = 0.0; FastVector attributes = new FastVector(); for (int i = m_numAttribs - 1; i >= 0; i--) { StringBuffer attName = new StringBuffer(); // build array of coefficients double[] coeff_mags = new double[m_numAttribs]; for (int j = 0; j < m_numAttribs; j++) coeff_mags[j] = -Math.abs(m_eigenvectors[j][m_sortedEigens[i]]); int num_attrs = (m_maxAttrsInName > 0) ? Math.min(m_numAttribs, m_maxAttrsInName) : m_numAttribs; // this array contains the sorted indices of the coefficients int[] coeff_inds; if (m_numAttribs > 0) { // if m_maxAttrsInName > 0, sort coefficients by decreasing magnitude coeff_inds = Utils.sort(coeff_mags); } else { // if m_maxAttrsInName <= 0, use all coeffs in original order coeff_inds = new int[m_numAttribs]; for (int j=0; j 0 && coeff_value >= 0) attName.append("+"); attName.append(Utils.doubleToString(coeff_value,5,3) +m_trainInstances.attribute(coeff_inds[j]).name()); } if (num_attrs < m_numAttribs) attName.append("..."); attributes.addElement(new Attribute(attName.toString())); cumulative+=m_eigenvalues[m_sortedEigens[i]]; if ((cumulative / m_sumOfEigenValues) >= m_coverVariance) { break; } } if (m_hasClass) { attributes.addElement(m_trainCopy.classAttribute().copy()); } Instances outputFormat = new Instances(m_trainInstances.relationName()+"_principal components", attributes, 0); // set the class to be the last attribute if necessary if (m_hasClass) { outputFormat.setClassIndex(outputFormat.numAttributes()-1); } m_outputNumAtts = outputFormat.numAttributes(); return outputFormat; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.36 $"); } /** * Main method for testing this class * @param argv should contain the command line arguments to the * evaluator/transformer (see AttributeSelection) */ public static void main(String [] argv) { runEvaluator(new PrincipalComponents(), argv); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/RaceSearch.java000066400000000000000000001720461214146114000274670ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * RaceSearch.java * Copyright (C) 2000 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.core.Instance; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.SelectedTag; import weka.core.Statistics; import weka.core.Tag; import weka.core.TechnicalInformation; import weka.core.TechnicalInformation.Type; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformationHandler; import weka.core.Utils; import weka.experiment.PairedStats; import weka.experiment.Stats; import java.util.BitSet; import java.util.Enumeration; import java.util.Random; import java.util.Vector; /** * Races the cross validation error of competing attribute subsets. Use in conjuction with a ClassifierSubsetEval. RaceSearch has four modes:
*
* forward selection races all single attribute additions to a base set (initially no attributes), selects the winner to become the new base set and then iterates until there is no improvement over the base set.
*
* Backward elimination is similar but the initial base set has all attributes included and races all single attribute deletions.
*
* Schemata search is a bit different. Each iteration a series of races are run in parallel. Each race in a set determines whether a particular attribute should be included or not---ie the race is between the attribute being "in" or "out". The other attributes for this race are included or excluded randomly at each point in the evaluation. As soon as one race has a clear winner (ie it has been decided whether a particular attribute should be inor not) then the next set of races begins, using the result of the winning race from the previous iteration as new base set.
*
* Rank race first ranks the attributes using an attribute evaluator and then races the ranking. The race includes no attributes, the top ranked attribute, the top two attributes, the top three attributes, etc.
*
* It is also possible to generate a raked list of attributes through the forward racing process. If generateRanking is set to true then a complete forward race will be run---that is, racing continues until all attributes have been selected. The order that they are added in determines a complete ranking of all the attributes.
*
* Racing uses paired and unpaired t-tests on cross-validation errors of competing subsets. When there is a significant difference between the means of the errors of two competing subsets then the poorer of the two can be eliminated from the race. Similarly, if there is no significant difference between the mean errors of two competing subsets and they are within some threshold of each other, then one can be eliminated from the race.
*
* For more information see:
*
* Andrew W. Moore, Mary S. Lee: Efficient Algorithms for Minimizing Cross Validation Error. In: Eleventh International Conference on Machine Learning, 190-198, 1994. *

* * BibTeX: * 

 * @inproceedings{Moore1994,
 *    author = {Andrew W. Moore and Mary S. Lee},
 *    booktitle = {Eleventh International Conference on Machine Learning},
 *    pages = {190-198},
 *    publisher = {Morgan Kaufmann},
 *    title = {Efficient Algorithms for Minimizing Cross Validation Error},
 *    year = {1994}
 * }
 *
*

* * Valid options are:

* * 

 -R <0 = forward | 1 = backward race | 2 = schemata | 3 = rank>
 *  Type of race to perform.
 *  (default = 0).
* * 
 -L <significance>
 *  Significance level for comaparisons
 *  (default = 0.001(forward/backward/rank)/0.01(schemata)).
* * 
 -T <threshold>
 *  Threshold for error comparison.
 *  (default = 0.001).
* * 
 -A <attribute evaluator>
 *  Attribute ranker to use if doing a 
 *  rank search. Place any
 *  evaluator options LAST on 
 *  the command line following a "--".
 *  eg. -A weka.attributeSelection.GainRatioAttributeEval ... -- -M.
 *  (default = GainRatioAttributeEval)
* * 
 -F <0 = 10 fold | 1 = leave-one-out>
 *  Folds for cross validation
 *  (default = 0 (1 if schemata race)
* * 
 -Q
 *  Generate a ranked list of attributes.
 *  Forces the search to be forward
 *  and races until all attributes have
 *  selected, thus producing a ranking.
* * 
 -N <num to select>
 *  Specify number of attributes to retain from 
 *  the ranking. Overides -T. Use in conjunction with -Q
* * 
 -J <threshold>
 *  Specify a theshold by which attributes
 *  may be discarded from the ranking.
 *  Use in conjuction with -Q
* * 
 -Z
 *  Verbose output for monitoring the search.
* * 
 
 * Options specific to evaluator weka.attributeSelection.GainRatioAttributeEval:
 *
* * 
 -M
 *  treat missing values as a seperate value.
* * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.26 $ */ public class RaceSearch extends ASSearch implements RankedOutputSearch, OptionHandler, TechnicalInformationHandler { /** for serialization */ static final long serialVersionUID = 4015453851212985720L; /** the training instances */ private Instances m_Instances = null; /** search types */ private static final int FORWARD_RACE = 0; private static final int BACKWARD_RACE = 1; private static final int SCHEMATA_RACE = 2; private static final int RANK_RACE = 3; public static final Tag [] TAGS_SELECTION = { new Tag(FORWARD_RACE, "Forward selection race"), new Tag(BACKWARD_RACE, "Backward elimination race"), new Tag(SCHEMATA_RACE, "Schemata race"), new Tag(RANK_RACE, "Rank race") }; /** the selected search type */ private int m_raceType = FORWARD_RACE; /** xval types */ private static final int TEN_FOLD = 0; private static final int LEAVE_ONE_OUT = 1; public static final Tag [] XVALTAGS_SELECTION = { new Tag(TEN_FOLD, "10 Fold"), new Tag(LEAVE_ONE_OUT, "Leave-one-out"), }; /** the selected xval type */ private int m_xvalType = TEN_FOLD; /** the class index */ private int m_classIndex; /** the number of attributes in the data */ private int m_numAttribs; /** the total number of partially/fully evaluated subsets */ private int m_totalEvals; /** holds the merit of the best subset found */ private double m_bestMerit = -Double.MAX_VALUE; /** the subset evaluator to use */ private HoldOutSubsetEvaluator m_theEvaluator = null; /** the significance level for comparisons */ private double m_sigLevel = 0.001; /** threshold for comparisons */ private double m_delta = 0.001; /** the number of samples above which to begin testing for similarity between competing subsets */ private int m_samples = 20; /** number of cross validation folds---equal to the number of instances for leave-one-out cv */ private int m_numFolds = 10; /** the attribute evaluator to generate the initial ranking when doing a rank race */ private ASEvaluation m_ASEval = new GainRatioAttributeEval(); /** will hold the attribute ranking produced by the above attribute evaluator if doing a rank search */ private int [] m_Ranking; /** verbose output for monitoring the search and debugging */ private boolean m_debug = false; /** If true then produce a ranked list of attributes by fully traversing a forward hillclimb race */ private boolean m_rankingRequested = false; /** The ranked list of attributes produced if m_rankingRequested is true */ private double [][] m_rankedAtts; /** The number of attributes ranked so far (if ranking is requested) */ private int m_rankedSoFar; /** The number of attributes to retain if a ranking is requested. -1 indicates that all attributes are to be retained. Has precedence over m_threshold */ private int m_numToSelect = -1; private int m_calculatedNumToSelect = -1; /** the threshold for removing attributes if ranking is requested */ private double m_threshold = -Double.MAX_VALUE; /** * Returns a string describing this search method * @return a description of the search method suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Races the cross validation error of competing " +"attribute subsets. Use in conjuction with a ClassifierSubsetEval. " +"RaceSearch has four modes:\n\nforward selection " +"races all single attribute additions to a base set (initially " +" no attributes), selects the winner to become the new base set " +"and then iterates until there is no improvement over the base set. " +"\n\nBackward elimination is similar but the initial base set has all " +"attributes included and races all single attribute deletions. " +"\n\nSchemata search is a bit different. Each iteration a series of " +"races are run in parallel. Each race in a set determines whether " +"a particular attribute should be included or not---ie the race is " +"between the attribute being \"in\" or \"out\". The other attributes " +"for this race are included or excluded randomly at each point in the " +"evaluation. As soon as one race " +"has a clear winner (ie it has been decided whether a particular " +"attribute should be inor not) then the next set of races begins, " +"using the result of the winning race from the previous iteration as " +"new base set.\n\nRank race first ranks the attributes using an " +"attribute evaluator and then races the ranking. The race includes " +"no attributes, the top ranked attribute, the top two attributes, the " +"top three attributes, etc.\n\nIt is also possible to generate a " +"raked list of attributes through the forward racing process. " +"If generateRanking is set to true then a complete forward race will " +"be run---that is, racing continues until all attributes have been " +"selected. The order that they are added in determines a complete " +"ranking of all the attributes.\n\nRacing uses paired and unpaired " +"t-tests on cross-validation errors of competing subsets. When there " +"is a significant difference between the means of the errors of two " +"competing subsets then the poorer of the two can be eliminated from " +"the race. Similarly, if there is no significant difference between " +"the mean errors of two competing subsets and they are within some " +"threshold of each other, then one can be eliminated from the race.\n\n" + "For more information see:\n\n" + getTechnicalInformation().toString(); } /** * Returns an instance of a TechnicalInformation object, containing * detailed information about the technical background of this class, * e.g., paper reference or book this class is based on. * * @return the technical information about this class */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.INPROCEEDINGS); result.setValue(Field.AUTHOR, "Andrew W. Moore and Mary S. Lee"); result.setValue(Field.TITLE, "Efficient Algorithms for Minimizing Cross Validation Error"); result.setValue(Field.BOOKTITLE, "Eleventh International Conference on Machine Learning"); result.setValue(Field.YEAR, "1994"); result.setValue(Field.PAGES, "190-198"); result.setValue(Field.PUBLISHER, "Morgan Kaufmann"); return result; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String raceTypeTipText() { return "Set the type of search."; } /** * Set the race type * * @param d the type of race */ public void setRaceType (SelectedTag d) { if (d.getTags() == TAGS_SELECTION) { m_raceType = d.getSelectedTag().getID(); } if (m_raceType == SCHEMATA_RACE && !m_rankingRequested) { try { setFoldsType(new SelectedTag(LEAVE_ONE_OUT, XVALTAGS_SELECTION)); setSignificanceLevel(0.01); } catch (Exception ex) { } } else { try { setFoldsType(new SelectedTag(TEN_FOLD, XVALTAGS_SELECTION)); setSignificanceLevel(0.001); } catch (Exception ex) { } } } /** * Get the race type * * @return the type of race */ public SelectedTag getRaceType() { return new SelectedTag(m_raceType, TAGS_SELECTION); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String significanceLevelTipText() { return "Set the significance level to use for t-test comparisons."; } /** * Sets the significance level to use * @param sig the significance level */ public void setSignificanceLevel(double sig) { m_sigLevel = sig; } /** * Get the significance level * @return the current significance level */ public double getSignificanceLevel() { return m_sigLevel; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String thresholdTipText() { return "Set the error threshold by which to consider two subsets " +"equivalent."; } /** * Sets the threshold for comparisons * @param t the threshold to use */ public void setThreshold(double t) { m_delta = t; } /** * Get the threshold * @return the current threshold */ public double getThreshold() { return m_delta; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String foldsTypeTipText() { return "Set the number of folds to use for x-val error estimation; " +"leave-one-out is selected automatically for schemata search."; } /** * Set the xfold type * * @param d the type of xval */ public void setFoldsType (SelectedTag d) { if (d.getTags() == XVALTAGS_SELECTION) { m_xvalType = d.getSelectedTag().getID(); } } /** * Get the xfold type * * @return the type of xval */ public SelectedTag getFoldsType () { return new SelectedTag(m_xvalType, XVALTAGS_SELECTION); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String debugTipText() { return "Turn on verbose output for monitoring the search's progress."; } /** * Set whether verbose output should be generated. * @param d true if output is to be verbose. */ public void setDebug(boolean d) { m_debug = d; } /** * Get whether output is to be verbose * @return true if output will be verbose */ public boolean getDebug() { return m_debug; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String attributeEvaluatorTipText() { return "Attribute evaluator to use for generating an initial ranking. " +"Use in conjunction with a rank race"; } /** * Set the attribute evaluator to use for generating the ranking. * @param newEvaluator the attribute evaluator to use. */ public void setAttributeEvaluator(ASEvaluation newEvaluator) { m_ASEval = newEvaluator; } /** * Get the attribute evaluator used to generate the ranking. * @return the evaluator used to generate the ranking. */ public ASEvaluation getAttributeEvaluator() { return m_ASEval; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String generateRankingTipText() { return "Use the racing process to generate a ranked list of attributes. " +"Using this mode forces the race to be a forward type and then races " +"until all attributes have been added, thus giving a ranked list"; } /** * Records whether the user has requested a ranked list of attributes. * @param doRank true if ranking is requested */ public void setGenerateRanking(boolean doRank) { m_rankingRequested = doRank; if (m_rankingRequested) { try { setRaceType(new SelectedTag(FORWARD_RACE, TAGS_SELECTION)); } catch (Exception ex) { } } } /** * Gets whether ranking has been requested. This is used by the * AttributeSelection module to determine if rankedAttributes() * should be called. * @return true if ranking has been requested. */ public boolean getGenerateRanking() { return m_rankingRequested; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String numToSelectTipText() { return "Specify the number of attributes to retain. Use in conjunction " +"with generateRanking. The default value " +"(-1) indicates that all attributes are to be retained. Use either " +"this option or a threshold to reduce the attribute set."; } /** * Specify the number of attributes to select from the ranked list * (if generating a ranking). -1 * indicates that all attributes are to be retained. * @param n the number of attributes to retain */ public void setNumToSelect(int n) { m_numToSelect = n; } /** * Gets the number of attributes to be retained. * @return the number of attributes to retain */ public int getNumToSelect() { return m_numToSelect; } /** * Gets the calculated number of attributes to retain. This is the * actual number of attributes to retain. This is the same as * getNumToSelect if the user specifies a number which is not less * than zero. Otherwise it should be the number of attributes in the * (potentially transformed) data. */ public int getCalculatedNumToSelect() { if (m_numToSelect >= 0) { m_calculatedNumToSelect = m_numToSelect; } return m_calculatedNumToSelect; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String selectionThresholdTipText() { return "Set threshold by which attributes can be discarded. Default value " + "results in no attributes being discarded. Use in conjunction with " + "generateRanking"; } /** * Set the threshold by which the AttributeSelection module can discard * attributes. * @param threshold the threshold. */ public void setSelectionThreshold(double threshold) { m_threshold = threshold; } /** * Returns the threshold so that the AttributeSelection module can * discard attributes from the ranking. */ public double getSelectionThreshold() { return m_threshold; } /** * Returns an enumeration describing the available options. * @return an enumeration of all the available options. **/ public Enumeration listOptions () { Vector newVector = new Vector(); newVector.addElement(new Option( "\tType of race to perform.\n" + "\t(default = 0).", "R", 1 ,"-R <0 = forward | 1 = backward race | 2 = schemata | 3 = rank>")); newVector.addElement(new Option( "\tSignificance level for comaparisons\n" + "\t(default = 0.001(forward/backward/rank)/0.01(schemata)).", "L",1,"-L ")); newVector.addElement(new Option( "\tThreshold for error comparison.\n" + "\t(default = 0.001).", "T",1,"-T ")); newVector.addElement(new Option( "\tAttribute ranker to use if doing a \n" + "\trank search. Place any\n" + "\tevaluator options LAST on \n" + "\tthe command line following a \"--\".\n" + "\teg. -A weka.attributeSelection.GainRatioAttributeEval ... -- -M.\n" + "\t(default = GainRatioAttributeEval)", "A", 1, "-A ")); newVector.addElement(new Option( "\tFolds for cross validation\n" + "\t(default = 0 (1 if schemata race)", "F",1,"-F <0 = 10 fold | 1 = leave-one-out>")); newVector.addElement(new Option( "\tGenerate a ranked list of attributes.\n" +"\tForces the search to be forward\n" +"\tand races until all attributes have\n" +"\tselected, thus producing a ranking.", "Q",0,"-Q")); newVector.addElement(new Option( "\tSpecify number of attributes to retain from \n" + "\tthe ranking. Overides -T. Use in conjunction with -Q", "N", 1, "-N ")); newVector.addElement(new Option( "\tSpecify a theshold by which attributes\n" + "\tmay be discarded from the ranking.\n" +"\tUse in conjuction with -Q", "J",1, "-J ")); newVector.addElement(new Option( "\tVerbose output for monitoring the search.", "Z",0,"-Z")); if ((m_ASEval != null) && (m_ASEval instanceof OptionHandler)) { newVector.addElement(new Option( "", "", 0, "\nOptions specific to evaluator " + m_ASEval.getClass().getName() + ":")); Enumeration enu = ((OptionHandler)m_ASEval).listOptions(); while (enu.hasMoreElements()) { newVector.addElement(enu.nextElement()); } } return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -R <0 = forward | 1 = backward race | 2 = schemata | 3 = rank>
   *  Type of race to perform.
   *  (default = 0).
* * 
 -L <significance>
   *  Significance level for comaparisons
   *  (default = 0.001(forward/backward/rank)/0.01(schemata)).
* * 
 -T <threshold>
   *  Threshold for error comparison.
   *  (default = 0.001).
* * 
 -A <attribute evaluator>
   *  Attribute ranker to use if doing a 
   *  rank search. Place any
   *  evaluator options LAST on 
   *  the command line following a "--".
   *  eg. -A weka.attributeSelection.GainRatioAttributeEval ... -- -M.
   *  (default = GainRatioAttributeEval)
* * 
 -F <0 = 10 fold | 1 = leave-one-out>
   *  Folds for cross validation
   *  (default = 0 (1 if schemata race)
* * 
 -Q
   *  Generate a ranked list of attributes.
   *  Forces the search to be forward
   *  and races until all attributes have
   *  selected, thus producing a ranking.
* * 
 -N <num to select>
   *  Specify number of attributes to retain from 
   *  the ranking. Overides -T. Use in conjunction with -Q
* * 
 -J <threshold>
   *  Specify a theshold by which attributes
   *  may be discarded from the ranking.
   *  Use in conjuction with -Q
* * 
 -Z
   *  Verbose output for monitoring the search.
* * 
 
   * Options specific to evaluator weka.attributeSelection.GainRatioAttributeEval:
   *
* * 
 -M
   *  treat missing values as a seperate value.
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions (String[] options) throws Exception { String optionString; resetOptions(); optionString = Utils.getOption('R', options); if (optionString.length() != 0) { setRaceType(new SelectedTag(Integer.parseInt(optionString), TAGS_SELECTION)); } optionString = Utils.getOption('F', options); if (optionString.length() != 0) { setFoldsType(new SelectedTag(Integer.parseInt(optionString), XVALTAGS_SELECTION)); } optionString = Utils.getOption('L', options); if (optionString.length() !=0) { setSignificanceLevel(Double.parseDouble(optionString)); } optionString = Utils.getOption('T', options); if (optionString.length() !=0) { setThreshold(Double.parseDouble(optionString)); } optionString = Utils.getOption('A', options); if (optionString.length() != 0) { setAttributeEvaluator(ASEvaluation.forName(optionString, Utils.partitionOptions(options))); } setGenerateRanking(Utils.getFlag('Q', options)); optionString = Utils.getOption('J', options); if (optionString.length() != 0) { setSelectionThreshold(Double.parseDouble(optionString)); } optionString = Utils.getOption('N', options); if (optionString.length() != 0) { setNumToSelect(Integer.parseInt(optionString)); } setDebug(Utils.getFlag('Z', options)); } /** * Gets the current settings of BestFirst. * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { int current = 0; String[] evaluatorOptions = new String[0]; if ((m_ASEval != null) && (m_ASEval instanceof OptionHandler)) { evaluatorOptions = ((OptionHandler)m_ASEval).getOptions(); } String[] options = new String[17+evaluatorOptions.length]; options[current++] = "-R"; options[current++] = ""+m_raceType; options[current++] = "-L"; options[current++] = ""+getSignificanceLevel(); options[current++] = "-T"; options[current++] = ""+getThreshold(); options[current++] = "-F"; options[current++] = ""+m_xvalType; if (getGenerateRanking()) { options[current++] = "-Q"; } options[current++] = "-N"; options[current++] = ""+getNumToSelect(); options[current++] = "-J"; options[current++] = ""+getSelectionThreshold(); if (getDebug()) { options[current++] = "-Z"; } if (getAttributeEvaluator() != null) { options[current++] = "-A"; options[current++] = getAttributeEvaluator().getClass().getName(); options[current++] = "--"; System.arraycopy(evaluatorOptions, 0, options, current, evaluatorOptions.length); current += evaluatorOptions.length; } while (current < options.length) { options[current++] = ""; } return options; } /** * Searches the attribute subset space by racing cross validation * errors of competing subsets * * @param ASEval the attribute evaluator to guide the search * @param data the training instances. * @return an array (not necessarily ordered) of selected attribute indexes * @throws Exception if the search can't be completed */ public int[] search (ASEvaluation ASEval, Instances data) throws Exception { if (!(ASEval instanceof SubsetEvaluator)) { throw new Exception(ASEval.getClass().getName() + " is not a " + "Subset evaluator! (RaceSearch)"); } if (ASEval instanceof UnsupervisedSubsetEvaluator) { throw new Exception("Can't use an unsupervised subset evaluator " +"(RaceSearch)."); } if (!(ASEval instanceof HoldOutSubsetEvaluator)) { throw new Exception("Must use a HoldOutSubsetEvaluator, eg. " +"weka.attributeSelection.ClassifierSubsetEval " +"(RaceSearch)"); } if (!(ASEval instanceof ErrorBasedMeritEvaluator)) { throw new Exception("Only error based subset evaluators can be used, " +"eg. weka.attributeSelection.ClassifierSubsetEval " +"(RaceSearch)"); } m_Instances = new Instances(data); m_Instances.deleteWithMissingClass(); if (m_Instances.numInstances() == 0) { throw new Exception("All train instances have missing class! (RaceSearch)"); } if (m_rankingRequested && m_numToSelect > m_Instances.numAttributes()-1) { throw new Exception("More attributes requested than exist in the data " +"(RaceSearch)."); } m_theEvaluator = (HoldOutSubsetEvaluator)ASEval; m_numAttribs = m_Instances.numAttributes(); m_classIndex = m_Instances.classIndex(); if (m_rankingRequested) { m_rankedAtts = new double[m_numAttribs-1][2]; m_rankedSoFar = 0; } if (m_xvalType == LEAVE_ONE_OUT) { m_numFolds = m_Instances.numInstances(); } else { m_numFolds = 10; } Random random = new Random(1); // I guess this should really be a parameter? m_Instances.randomize(random); int [] bestSubset=null; switch (m_raceType) { case FORWARD_RACE: case BACKWARD_RACE: bestSubset = hillclimbRace(m_Instances, random); break; case SCHEMATA_RACE: bestSubset = schemataRace(m_Instances, random); break; case RANK_RACE: bestSubset = rankRace(m_Instances, random); break; } return bestSubset; } public double [][] rankedAttributes() throws Exception { if (!m_rankingRequested) { throw new Exception("Need to request a ranked list of attributes " +"before attributes can be ranked (RaceSearch)."); } if (m_rankedAtts == null) { throw new Exception("Search must be performed before attributes " +"can be ranked (RaceSearch)."); } double [][] final_rank = new double [m_rankedSoFar][2]; for (int i=0;i m_threshold) { count++; } } m_calculatedNumToSelect = count; } /** * Print an attribute set. */ private String printSets(char [][]raceSets) { StringBuffer temp = new StringBuffer(); for (int i=0;i 0) { boolean won = false; for (int i=0;i numInstances) { throw new Exception("raceSchemata: No clear winner after sampling " +sampleCount+" instances."); } */ m_theEvaluator.buildEvaluator(trainCV); // the evaluator must retrain for every test point error = -((HoldOutSubsetEvaluator)m_theEvaluator). evaluateSubset(randomB, testInstance, true); evaluationCount++; // see which racers match this random subset for (int i=0;i (numInstances)) { break raceSet; } for (int j=0;j<2;j++) { boolean matched = true; for (int k =0;k m_samples && raceStats[i][1].count > m_samples) { raceStats[i][0].calculateDerived(); raceStats[i][1].calculateDerived(); // System.err.println(j+" : "+(new String(parallelRaces[i][j]))); // System.err.println(raceStats[i][0]); // System.err.println(raceStats[i][1]); // check the ttest double prob = ttest(raceStats[i][0], raceStats[i][1]); // System.err.println("Prob :"+prob); if (prob < m_sigLevel) { // stop the races we have a winner! if (raceStats[i][0].mean < raceStats[i][1].mean) { base = (char [])parallelRaces[i][0].clone(); m_bestMerit = raceStats[i][0].mean; if (m_debug) { System.err.println("contender 0 won "); } } else { base = (char [])parallelRaces[i][1].clone(); m_bestMerit = raceStats[i][1].mean; if (m_debug) { System.err.println("contender 1 won"); } } if (m_debug) { System.err.println((new String(parallelRaces[i][0])) +" "+(new String(parallelRaces[i][1]))); System.err.println("Means : "+raceStats[i][0].mean +" vs"+raceStats[i][1].mean); System.err.println("Evaluations so far : " +evaluationCount); } won = true; break checkRaces; } } } } } } numRaces--; // set up the next set of races if necessary if (numRaces > 0 && won) { parallelRaces = new char [numRaces][2][m_numAttribs-1]; raceStats = new Stats[numRaces][2]; // update the attribute constraints for (int i=0;i 0) { for (int k=j;k m_samples-1 && (eliminatedCount < raceSets.length-1)) { for (int j=0;j testers[j][k].yStats.mean) { eliminated[j] = true; eliminatedCount++; if (m_debug) { System.err.println("Eliminating (near identical) " +j+" "+raceBitSets[j].toString() +" vs "+k+" " +raceBitSets[k].toString() +" after " +processedCount +" evaluations\n" +"\nerror "+j+" : " +testers[j][k].xStats.mean +" vs "+k+" : " +testers[j][k].yStats.mean +" diff : " +testers[j][k].differencesStats .mean); } break; } else { eliminated[k] = true; eliminatedCount++; if (m_debug) { System.err.println("Eliminating (near identical) " +k+" "+raceBitSets[k].toString() +" vs "+j+" " +raceBitSets[j].toString() +" after " +processedCount +" evaluations\n" +"\nerror "+k+" : " +testers[j][k].yStats.mean +" vs "+j+" : " +testers[j][k].xStats.mean +" diff : " +testers[j][k].differencesStats .mean); } } } } else { // significantly worse ? if (testers[j][k].differencesSignificance != 0) { if (testers[j][k].differencesSignificance > 0) { eliminated[j] = true; eliminatedCount++; if (m_debug) { System.err.println("Eliminating (-worse) " +j+" "+raceBitSets[j].toString() +" vs "+k+" " +raceBitSets[k].toString() +" after " +processedCount +" evaluations" +"\nerror "+j+" : " +testers[j][k].xStats.mean +" vs "+k+" : " +testers[j][k].yStats.mean); } break; } else { eliminated[k] = true; eliminatedCount++; if (m_debug) { System.err.println("Eliminating (worse) " +k+" "+raceBitSets[k].toString() +" vs "+j+" " +raceBitSets[j].toString() +" after " +processedCount +" evaluations" +"\nerror "+k+" : " +testers[j][k].yStats.mean +" vs "+j+" : " +testers[j][k].xStats.mean); } } } } } } } } } // if there is a base set from the previous race and it's the // only remaining subset then terminate the race. if (eliminatedCount == raceSets.length-1 && baseSetIncluded && !eliminated[0] && !m_rankingRequested) { break race; } } } if (m_debug) { System.err.println("*****eliminated count: "+eliminatedCount); } double bestError = Double.MAX_VALUE; int bestIndex=0; // return the index of the winner for (int i=startPt;i 0) { fieldwidth = (int)Math.abs((Math.log(Math.abs(m_bestMerit)) / Math.log(10)))+2; } if (Math.abs(precision) > 0) { precision = Math.abs((Math.log(Math.abs(precision)) / Math.log(10)))+3; } else { precision = 2; } text.append(Utils.doubleToString(Math.abs(m_bestMerit), fieldwidth+(int)precision, (int)precision)+"\n"); return text.toString(); } /** * Reset the search method. */ protected void resetOptions () { m_sigLevel = 0.001; m_delta = 0.001; m_ASEval = new GainRatioAttributeEval(); m_Ranking = null; m_raceType = FORWARD_RACE; m_debug = false; m_theEvaluator = null; m_bestMerit = -Double.MAX_VALUE; m_numFolds = 10; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.26 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/RandomSearch.java000066400000000000000000000453671214146114000300420ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * RandomSearch.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.Range; import weka.core.RevisionUtils; import weka.core.TechnicalInformation; import weka.core.TechnicalInformation.Type; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformationHandler; import weka.core.Utils; import java.util.BitSet; import java.util.Enumeration; import java.util.Random; import java.util.Vector; /** * RandomSearch :
*
* Performs a Random search in the space of attribute subsets. If no start set is supplied, Random search starts from a random point and reports the best subset found. If a start set is supplied, Random searches randomly for subsets that are as good or better than the start point with the same or or fewer attributes. Using RandomSearch in conjunction with a start set containing all attributes equates to the LVF algorithm of Liu and Setiono (ICML-96).
*
* For more information see:
*
* H. Liu, R. Setiono: A probabilistic approach to feature selection - A filter solution. In: 13th International Conference on Machine Learning, 319-327, 1996. *

* * BibTeX: * 

 * @inproceedings{Liu1996,
 *    author = {H. Liu and R. Setiono},
 *    booktitle = {13th International Conference on Machine Learning},
 *    pages = {319-327},
 *    title = {A probabilistic approach to feature selection - A filter solution},
 *    year = {1996}
 * }
 *
*

* * Valid options are:

* * 

 -P <start set>
 *  Specify a starting set of attributes.
 *  Eg. 1,3,5-7.
 *  If a start point is supplied,
 *  random search evaluates the start
 *  point and then randomly looks for
 *  subsets that are as good as or better
 *  than the start point with the same
 *  or lower cardinality.
* * 
 -F <percent> 
 *  Percent of search space to consider.
 *  (default = 25%).
* * 
 -V
 *  Output subsets as the search progresses.
 *  (default = false).
* * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.18 $ */ public class RandomSearch extends ASSearch implements StartSetHandler, OptionHandler, TechnicalInformationHandler { /** for serialization */ static final long serialVersionUID = 7479392617377425484L; /** * holds a starting set as an array of attributes. */ private int[] m_starting; /** holds the start set as a range */ private Range m_startRange; /** the best feature set found during the search */ private BitSet m_bestGroup; /** the merit of the best subset found */ private double m_bestMerit; /** * only accept a feature set as being "better" than the best if its * merit is better or equal to the best, and it contains fewer * features than the best (this allows LVF to be implimented). */ private boolean m_onlyConsiderBetterAndSmaller; /** does the data have a class */ private boolean m_hasClass; /** holds the class index */ private int m_classIndex; /** number of attributes in the data */ private int m_numAttribs; /** seed for random number generation */ private int m_seed; /** percentage of the search space to consider */ private double m_searchSize; /** the number of iterations performed */ private int m_iterations; /** random number object */ private Random m_random; /** output new best subsets as the search progresses */ private boolean m_verbose; /** * Returns a string describing this search method * @return a description of the search suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "RandomSearch : \n\nPerforms a Random search in " +"the space of attribute subsets. If no start set is supplied, Random " +"search starts from a random point and reports the best subset found. " +"If a start set is supplied, Random searches randomly for subsets " +"that are as good or better than the start point with the same or " +"or fewer attributes. Using RandomSearch in conjunction with a start " +"set containing all attributes equates to the LVF algorithm of Liu " +"and Setiono (ICML-96).\n\n" + "For more information see:\n\n" + getTechnicalInformation().toString(); } /** * Returns an instance of a TechnicalInformation object, containing * detailed information about the technical background of this class, * e.g., paper reference or book this class is based on. * * @return the technical information about this class */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.INPROCEEDINGS); result.setValue(Field.AUTHOR, "H. Liu and R. Setiono"); result.setValue(Field.TITLE, "A probabilistic approach to feature selection - A filter solution"); result.setValue(Field.BOOKTITLE, "13th International Conference on Machine Learning"); result.setValue(Field.YEAR, "1996"); result.setValue(Field.PAGES, "319-327"); return result; } /** * Constructor */ public RandomSearch () { resetOptions(); } /** * Returns an enumeration describing the available options. * @return an enumeration of all the available options. **/ public Enumeration listOptions () { Vector newVector = new Vector(3); newVector.addElement(new Option("\tSpecify a starting set of attributes." + "\n\tEg. 1,3,5-7." +"\n\tIf a start point is supplied," +"\n\trandom search evaluates the start" +"\n\tpoint and then randomly looks for" +"\n\tsubsets that are as good as or better" +"\n\tthan the start point with the same" +"\n\tor lower cardinality." ,"P",1 , "-P ")); newVector.addElement(new Option("\tPercent of search space to consider." +"\n\t(default = 25%)." , "F", 1 , "-F ")); newVector.addElement(new Option("\tOutput subsets as the search progresses." +"\n\t(default = false)." , "V", 0 , "-V")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -P <start set>
   *  Specify a starting set of attributes.
   *  Eg. 1,3,5-7.
   *  If a start point is supplied,
   *  random search evaluates the start
   *  point and then randomly looks for
   *  subsets that are as good as or better
   *  than the start point with the same
   *  or lower cardinality.
* * 
 -F <percent> 
   *  Percent of search space to consider.
   *  (default = 25%).
* * 
 -V
   *  Output subsets as the search progresses.
   *  (default = false).
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported * **/ public void setOptions (String[] options) throws Exception { String optionString; resetOptions(); optionString = Utils.getOption('P', options); if (optionString.length() != 0) { setStartSet(optionString); } optionString = Utils.getOption('F',options); if (optionString.length() != 0) { setSearchPercent((new Double(optionString)).doubleValue()); } setVerbose(Utils.getFlag('V',options)); } /** * Gets the current settings of RandomSearch. * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { String[] options = new String[5]; int current = 0; if (m_verbose) { options[current++] = "-V"; } if (!(getStartSet().equals(""))) { options[current++] = "-P"; options[current++] = "" + startSetToString(); } options[current++] = "-F"; options[current++] = "" + getSearchPercent(); while (current < options.length) { options[current++] = ""; } return options; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String startSetTipText() { return "Set the start point for the search. This is specified as a comma " +"seperated list off attribute indexes starting at 1. It can include " +"ranges. Eg. 1,2,5-9,17. If specified, Random searches for subsets " +"of attributes that are as good as or better than the start set with " +"the same or lower cardinality."; } /** * Sets a starting set of attributes for the search. It is the * search method's responsibility to report this start set (if any) * in its toString() method. * @param startSet a string containing a list of attributes (and or ranges), * eg. 1,2,6,10-15. "" indicates no start point. * If a start point is supplied, random search evaluates the * start point and then looks for subsets that are as good as or better * than the start point with the same or lower cardinality. * @throws Exception if start set can't be set. */ public void setStartSet (String startSet) throws Exception { m_startRange.setRanges(startSet); } /** * Returns a list of attributes (and or attribute ranges) as a String * @return a list of attributes (and or attribute ranges) */ public String getStartSet () { return m_startRange.getRanges(); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String verboseTipText() { return "Print progress information. Sends progress info to the terminal " +"as the search progresses."; } /** * set whether or not to output new best subsets as the search proceeds * @param v true if output is to be verbose */ public void setVerbose(boolean v) { m_verbose = v; } /** * get whether or not output is verbose * @return true if output is set to verbose */ public boolean getVerbose() { return m_verbose; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String searchPercentTipText() { return "Percentage of the search space to explore."; } /** * set the percentage of the search space to consider * @param p percent of the search space ( 0 < p <= 100) */ public void setSearchPercent(double p) { p = Math.abs(p); if (p == 0) { p = 25; } if (p > 100.0) { p = 100; } m_searchSize = (p/100.0); } /** * get the percentage of the search space to consider * @return the percent of the search space explored */ public double getSearchPercent() { return m_searchSize * 100; } /** * converts the array of starting attributes to a string. This is * used by getOptions to return the actual attributes specified * as the starting set. This is better than using m_startRanges.getRanges() * as the same start set can be specified in different ways from the * command line---eg 1,2,3 == 1-3. This is to ensure that stuff that * is stored in a database is comparable. * @return a comma seperated list of individual attribute numbers as a String */ private String startSetToString() { StringBuffer FString = new StringBuffer(); boolean didPrint; if (m_starting == null) { return getStartSet(); } for (int i = 0; i < m_starting.length; i++) { didPrint = false; if ((m_hasClass == false) || (m_hasClass == true && i != m_classIndex)) { FString.append((m_starting[i] + 1)); didPrint = true; } if (i == (m_starting.length - 1)) { FString.append(""); } else { if (didPrint) { FString.append(","); } } } return FString.toString(); } /** * prints a description of the search * @return a description of the search as a string */ public String toString() { StringBuffer text = new StringBuffer(); text.append("\tRandom search.\n\tStart set: "); if (m_starting == null) { text.append("no attributes\n"); } else { text.append(startSetToString()+"\n"); } text.append("\tNumber of iterations: "+m_iterations+" (" +(m_searchSize * 100.0)+"% of the search space)\n"); text.append("\tMerit of best subset found: " +Utils.doubleToString(Math.abs(m_bestMerit),8,3)+"\n"); return text.toString(); } /** * Searches the attribute subset space randomly. * * @param ASEval the attribute evaluator to guide the search * @param data the training instances. * @return an array (not necessarily ordered) of selected attribute indexes * @throws Exception if the search can't be completed */ public int[] search (ASEvaluation ASEval, Instances data) throws Exception { double best_merit; int sizeOfBest = m_numAttribs; BitSet temp; m_bestGroup = new BitSet(m_numAttribs); m_onlyConsiderBetterAndSmaller = false; if (!(ASEval instanceof SubsetEvaluator)) { throw new Exception(ASEval.getClass().getName() + " is not a " + "Subset evaluator!"); } m_random = new Random(m_seed); if (ASEval instanceof UnsupervisedSubsetEvaluator) { m_hasClass = false; } else { m_hasClass = true; m_classIndex = data.classIndex(); } SubsetEvaluator ASEvaluator = (SubsetEvaluator)ASEval; m_numAttribs = data.numAttributes(); m_startRange.setUpper(m_numAttribs-1); if (!(getStartSet().equals(""))) { m_starting = m_startRange.getSelection(); } // If a starting subset has been supplied, then initialise the bitset if (m_starting != null) { for (int i = 0; i < m_starting.length; i++) { if ((m_starting[i]) != m_classIndex) { m_bestGroup.set(m_starting[i]); } } m_onlyConsiderBetterAndSmaller = true; best_merit = ASEvaluator.evaluateSubset(m_bestGroup); sizeOfBest = countFeatures(m_bestGroup); } else { // do initial random subset m_bestGroup = generateRandomSubset(); best_merit = ASEvaluator.evaluateSubset(m_bestGroup); } if (m_verbose) { System.out.println("Initial subset (" +Utils.doubleToString(Math. abs(best_merit),8,5) +"): "+printSubset(m_bestGroup)); } int i; if (m_hasClass) { i = m_numAttribs -1; } else { i = m_numAttribs; } m_iterations = (int)((m_searchSize * Math.pow(2, i))); int tempSize; double tempMerit; // main loop for (i=0;i= best_merit) { sizeOfBest = tempSize; m_bestGroup = temp; best_merit = tempMerit; if (m_verbose) { System.out.print("New best subset (" +Utils.doubleToString(Math. abs(best_merit),8,5) +"): "+printSubset(m_bestGroup) + " :"); System.out.println(Utils. doubleToString((((double)i)/ ((double)m_iterations)* 100.0),5,1) +"% done"); } } } } else { tempMerit = ASEvaluator.evaluateSubset(temp); if (tempMerit > best_merit) { m_bestGroup = temp; best_merit = tempMerit; if (m_verbose) { System.out.print("New best subset (" +Utils.doubleToString(Math.abs(best_merit),8,5) +"): "+printSubset(m_bestGroup) + " :"); System.out.println(Utils. doubleToString((((double)i)/ ((double)m_iterations) *100.0),5,1) +"% done"); } } } } m_bestMerit = best_merit; return attributeList(m_bestGroup); } /** * prints a subset as a series of attribute numbers * @param temp the subset to print * @return a subset as a String of attribute numbers */ private String printSubset(BitSet temp) { StringBuffer text = new StringBuffer(); for (int j=0;j * RankSearch :
*
* Uses an attribute/subset evaluator to rank all attributes. If a subset evaluator is specified, then a forward selection search is used to generate a ranked list. From the ranked list of attributes, subsets of increasing size are evaluated, ie. The best attribute, the best attribute plus the next best attribute, etc.... The best attribute set is reported. RankSearch is linear in the number of attributes if a simple attribute evaluator is used such as GainRatioAttributeEval.
*

* * Valid options are:

* * 

 -A <attribute evaluator>
 *  class name of attribute evaluator to use for ranking. Place any
 *  evaluator options LAST on the command line following a "--".
 *  eg.:
 *   -A weka.attributeSelection.GainRatioAttributeEval ... -- -M
 *  (default: weka.attributeSelection.GainRatioAttributeEval)
* * 
 -S <step size>
 *  number of attributes to be added from the
 *  ranking in each iteration (default = 1).
* * 
 -R <start point>
 *  point in the ranking to start evaluating from. 
 *  (default = 0, ie. the head of the ranking).
* * 
 
 * Options specific to evaluator weka.attributeSelection.GainRatioAttributeEval:
 *
* * 
 -M
 *  treat missing values as a seperate value.
* * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.19 $ */ public class RankSearch extends ASSearch implements OptionHandler { /** for serialization */ static final long serialVersionUID = -7992268736874353755L; /** does the data have a class */ private boolean m_hasClass; /** holds the class index */ private int m_classIndex; /** number of attributes in the data */ private int m_numAttribs; /** the best subset found */ private BitSet m_best_group; /** the attribute evaluator to use for generating the ranking */ private ASEvaluation m_ASEval; /** the subset evaluator with which to evaluate the ranking */ private ASEvaluation m_SubsetEval; /** the training instances */ private Instances m_Instances; /** the merit of the best subset found */ private double m_bestMerit; /** will hold the attribute ranking */ private int [] m_Ranking; /** add this many attributes in each iteration from the ranking */ protected int m_add = 1; /** start from this point in the ranking */ protected int m_startPoint = 0; /** * Returns a string describing this search method * @return a description of the search method suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "RankSearch : \n\n" +"Uses an attribute/subset evaluator to rank all attributes. " +"If a subset evaluator is specified, then a forward selection " +"search is used to generate a ranked list. From the ranked " +"list of attributes, subsets of increasing size are evaluated, ie. " +"The best attribute, the best attribute plus the next best attribute, " +"etc.... The best attribute set is reported. RankSearch is linear in " +"the number of attributes if a simple attribute evaluator is used " +"such as GainRatioAttributeEval.\n"; } /** * Constructor */ public RankSearch () { resetOptions(); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String attributeEvaluatorTipText() { return "Attribute evaluator to use for generating a ranking."; } /** * Set the attribute evaluator to use for generating the ranking. * @param newEvaluator the attribute evaluator to use. */ public void setAttributeEvaluator(ASEvaluation newEvaluator) { m_ASEval = newEvaluator; } /** * Get the attribute evaluator used to generate the ranking. * @return the evaluator used to generate the ranking. */ public ASEvaluation getAttributeEvaluator() { return m_ASEval; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String stepSizeTipText() { return "Add this many attributes from the ranking in each iteration."; } /** * Set the number of attributes to add from the rankining * in each iteration * @param ss the number of attribes to add. */ public void setStepSize(int ss) { if (ss > 0) { m_add = ss; } } /** * Get the number of attributes to add from the rankining * in each iteration * @return the number of attributes to add. */ public int getStepSize() { return m_add; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String startPointTipText() { return "Start evaluating from this point in the ranking."; } /** * Set the point at which to start evaluating the ranking * @param sp the position in the ranking to start at */ public void setStartPoint(int sp) { if (sp >= 0) { m_startPoint = sp; } } /** * Get the point at which to start evaluating the ranking * @return the position in the ranking to start at */ public int getStartPoint() { return m_startPoint; } /** * Returns an enumeration describing the available options. * @return an enumeration of all the available options. **/ public Enumeration listOptions () { Vector newVector = new Vector(4); newVector.addElement(new Option( "\tclass name of attribute evaluator to use for ranking. Place any\n" + "\tevaluator options LAST on the command line following a \"--\".\n" + "\teg.:\n" + "\t\t-A weka.attributeSelection.GainRatioAttributeEval ... -- -M\n" + "\t(default: weka.attributeSelection.GainRatioAttributeEval)", "A", 1, "-A ")); newVector.addElement(new Option( "\tnumber of attributes to be added from the" +"\n\tranking in each iteration (default = 1).", "S", 1,"-S ")); newVector.addElement(new Option( "\tpoint in the ranking to start evaluating from. " +"\n\t(default = 0, ie. the head of the ranking).", "R", 1,"-R ")); if ((m_ASEval != null) && (m_ASEval instanceof OptionHandler)) { newVector.addElement(new Option("", "", 0, "\nOptions specific to " + "evaluator " + m_ASEval.getClass().getName() + ":")); Enumeration enu = ((OptionHandler)m_ASEval).listOptions(); while (enu.hasMoreElements()) { newVector.addElement(enu.nextElement()); } } return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -A <attribute evaluator>
   *  class name of attribute evaluator to use for ranking. Place any
   *  evaluator options LAST on the command line following a "--".
   *  eg.:
   *   -A weka.attributeSelection.GainRatioAttributeEval ... -- -M
   *  (default: weka.attributeSelection.GainRatioAttributeEval)
* * 
 -S <step size>
   *  number of attributes to be added from the
   *  ranking in each iteration (default = 1).
* * 
 -R <start point>
   *  point in the ranking to start evaluating from. 
   *  (default = 0, ie. the head of the ranking).
* * 
 
   * Options specific to evaluator weka.attributeSelection.GainRatioAttributeEval:
   *
* * 
 -M
   *  treat missing values as a seperate value.
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions (String[] options) throws Exception { String optionString; resetOptions(); optionString = Utils.getOption('S', options); if (optionString.length() != 0) { setStepSize(Integer.parseInt(optionString)); } optionString = Utils.getOption('R', options); if (optionString.length() != 0) { setStartPoint(Integer.parseInt(optionString)); } optionString = Utils.getOption('A', options); if (optionString.length() == 0) optionString = GainRatioAttributeEval.class.getName(); setAttributeEvaluator(ASEvaluation.forName(optionString, Utils.partitionOptions(options))); } /** * Gets the current settings of WrapperSubsetEval. * * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { String[] evaluatorOptions = new String[0]; if ((m_ASEval != null) && (m_ASEval instanceof OptionHandler)) { evaluatorOptions = ((OptionHandler)m_ASEval).getOptions(); } String[] options = new String[8 + evaluatorOptions.length]; int current = 0; options[current++] = "-S"; options[current++] = ""+getStepSize(); options[current++] = "-R"; options[current++] = ""+getStartPoint(); if (getAttributeEvaluator() != null) { options[current++] = "-A"; options[current++] = getAttributeEvaluator().getClass().getName(); } if (evaluatorOptions.length > 0) { options[current++] = "--"; System.arraycopy(evaluatorOptions, 0, options, current, evaluatorOptions.length); current += evaluatorOptions.length; } while (current < options.length) { options[current++] = ""; } return options; } /** * Reset the search method. */ protected void resetOptions () { m_ASEval = new GainRatioAttributeEval(); m_Ranking = null; } /** * Ranks attributes using the specified attribute evaluator and then * searches the ranking using the supplied subset evaluator. * * @param ASEval the subset evaluator to guide the search * @param data the training instances. * @return an array (not necessarily ordered) of selected attribute indexes * @throws Exception if the search can't be completed */ public int[] search (ASEvaluation ASEval, Instances data) throws Exception { double best_merit = -Double.MAX_VALUE; double temp_merit; BitSet temp_group, best_group=null; if (!(ASEval instanceof SubsetEvaluator)) { throw new Exception(ASEval.getClass().getName() + " is not a " + "Subset evaluator!"); } m_SubsetEval = ASEval; m_Instances = data; m_numAttribs = m_Instances.numAttributes(); /* if (m_ASEval instanceof AttributeTransformer) { throw new Exception("Can't use an attribute transformer " +"with RankSearch"); } */ if (m_ASEval instanceof UnsupervisedAttributeEvaluator || m_ASEval instanceof UnsupervisedSubsetEvaluator) { m_hasClass = false; /* if (!(m_SubsetEval instanceof UnsupervisedSubsetEvaluator)) { throw new Exception("Must use an unsupervised subset evaluator."); } */ } else { m_hasClass = true; m_classIndex = m_Instances.classIndex(); } if (m_ASEval instanceof AttributeEvaluator) { // generate the attribute ranking first Ranker ranker = new Ranker(); m_ASEval.buildEvaluator(m_Instances); if (m_ASEval instanceof AttributeTransformer) { // get the transformed data a rebuild the subset evaluator m_Instances = ((AttributeTransformer)m_ASEval). transformedData(); ((ASEvaluation)m_SubsetEval).buildEvaluator(m_Instances); } m_Ranking = ranker.search(m_ASEval, m_Instances); } else { GreedyStepwise fs = new GreedyStepwise(); double [][]rankres; fs.setGenerateRanking(true); ((ASEvaluation)m_ASEval).buildEvaluator(m_Instances); fs.search(m_ASEval, m_Instances); rankres = fs.rankedAttributes(); m_Ranking = new int[rankres.length]; for (int i=0;i best_merit) { best_merit = temp_merit;; best_group = temp_group; } } m_bestMerit = best_merit; return attributeList(best_group); } /** * converts a BitSet into a list of attribute indexes * @param group the BitSet to convert * @return an array of attribute indexes **/ private int[] attributeList (BitSet group) { int count = 0; // count how many were selected for (int i = 0; i < m_numAttribs; i++) { if (group.get(i)) { count++; } } int[] list = new int[count]; count = 0; for (int i = 0; i < m_numAttribs; i++) { if (group.get(i)) { list[count++] = i; } } return list; } /** * returns a description of the search as a String * @return a description of the search */ public String toString () { StringBuffer text = new StringBuffer(); text.append("\tRankSearch :\n"); text.append("\tAttribute evaluator : " + getAttributeEvaluator().getClass().getName() +" "); if (m_ASEval instanceof OptionHandler) { String[] evaluatorOptions = new String[0]; evaluatorOptions = ((OptionHandler)m_ASEval).getOptions(); for (int i=0;i 0) { fieldwidth = (int)Math.abs((Math.log(Math.abs(m_bestMerit)) / Math.log(10)))+2; } if (Math.abs(precision) > 0) { precision = Math.abs((Math.log(Math.abs(precision)) / Math.log(10)))+3; } else { precision = 2; } text.append(Utils.doubleToString(Math.abs(m_bestMerit), fieldwidth+(int)precision, (int)precision)+"\n"); return text.toString(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.19 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/RankedOutputSearch.java000066400000000000000000000071751214146114000312420ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * RankedOutputSearch.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; /** * Interface for search methods capable of producing a * ranked list of attributes. * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.11 $ */ public interface RankedOutputSearch { // =============== // Public methods. // =============== /** * Returns a X by 2 list of attribute indexes and corresponding * evaluations from best (highest) to worst. * @return the ranked list of attribute indexes in an array of ints * @exception Exception if the ranking can't be produced */ double[][] rankedAttributes() throws Exception; /** * Sets a threshold by which attributes can be discarded from the * ranking. This threshold is used by the AttributeSelection module * which does the actual discarding of attributes---the implementer * of this method needs only to provide a variable in which to store the * supplied threshold. -Double.MAX_VALUE is reserved to mean no threshold, * ie, retain all attributes. * @param threshold the threshold. */ void setThreshold(double threshold); /** * Gets the threshold by which attributes can be discarded. Discarding * of attributes is done by the AttributeSelection module using the * threshold returned by this method. * @return a threshold by which to discard attributes */ double getThreshold(); /** * Specify the number of attributes to select from the ranked list. < 0 * indicates that all attributes are to be retained. NumToSelect has * precedence over threshold, ie. if there is a non -1 value for NumToSelect * then this will take precedence over any threshold value. * @param numToSelect the number of attributes to retain */ void setNumToSelect(int numToSelect); /** * Gets the user specified number of attributes to be retained. * @return the number of attributes to retain */ int getNumToSelect(); /** * Gets the calculated number of attributes to retain. This is the * actual number of attributes to retain. This is the same as * getNumToSelect if the user specifies a number which is not less * than zero. Otherwise it should be the number of attributes in the * (potentially transformed) data. */ int getCalculatedNumToSelect(); /** * Sets whether or not ranking is to be performed. * When a search method is capable of producing a ranked list * of attributes, the user has the choice of seeing the results of a * normal search or seeing a ranked list. * @param doRanking true if ranked list is to be produced */ void setGenerateRanking(boolean doRanking); /** * Gets whether the user has opted to see a ranked list of * attributes rather than the normal result of the search * @return true if a ranked list has been requested. */ boolean getGenerateRanking(); } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/Ranker.java000066400000000000000000000427751214146114000267160ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * Ranker.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.Range; import weka.core.RevisionUtils; import weka.core.Utils; import java.util.Enumeration; import java.util.Vector; /** * Ranker :
*
* Ranks attributes by their individual evaluations. Use in conjunction with attribute evaluators (ReliefF, GainRatio, Entropy etc).
*

* * Valid options are:

* * 

 -P <start set>
 *  Specify a starting set of attributes.
 *  Eg. 1,3,5-7.
 *  Any starting attributes specified are
 *  ignored during the ranking.
* * 
 -T <threshold>
 *  Specify a theshold by which attributes
 *  may be discarded from the ranking.
* * 
 -N <num to select>
 *  Specify number of attributes to select
* * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.26 $ */ public class Ranker extends ASSearch implements RankedOutputSearch, StartSetHandler, OptionHandler { /** for serialization */ static final long serialVersionUID = -9086714848510751934L; /** Holds the starting set as an array of attributes */ private int[] m_starting; /** Holds the start set for the search as a range */ private Range m_startRange; /** Holds the ordered list of attributes */ private int[] m_attributeList; /** Holds the list of attribute merit scores */ private double[] m_attributeMerit; /** Data has class attribute---if unsupervised evaluator then no class */ private boolean m_hasClass; /** Class index of the data if supervised evaluator */ private int m_classIndex; /** The number of attribtes */ private int m_numAttribs; /** * A threshold by which to discard attributes---used by the * AttributeSelection module */ private double m_threshold; /** The number of attributes to select. -1 indicates that all attributes are to be retained. Has precedence over m_threshold */ private int m_numToSelect = -1; /** Used to compute the number to select */ private int m_calculatedNumToSelect = -1; /** * Returns a string describing this search method * @return a description of the search suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Ranker : \n\nRanks attributes by their individual evaluations. " +"Use in conjunction with attribute evaluators (ReliefF, GainRatio, " +"Entropy etc).\n"; } /** * Constructor */ public Ranker () { resetOptions(); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String numToSelectTipText() { return "Specify the number of attributes to retain. The default value " +"(-1) indicates that all attributes are to be retained. Use either " +"this option or a threshold to reduce the attribute set."; } /** * Specify the number of attributes to select from the ranked list. -1 * indicates that all attributes are to be retained. * @param n the number of attributes to retain */ public void setNumToSelect(int n) { m_numToSelect = n; } /** * Gets the number of attributes to be retained. * @return the number of attributes to retain */ public int getNumToSelect() { return m_numToSelect; } /** * Gets the calculated number to select. This might be computed * from a threshold, or if < 0 is set as the number to select then * it is set to the number of attributes in the (transformed) data. * @return the calculated number of attributes to select */ public int getCalculatedNumToSelect() { if (m_numToSelect >= 0) { m_calculatedNumToSelect = m_numToSelect; } return m_calculatedNumToSelect; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String thresholdTipText() { return "Set threshold by which attributes can be discarded. Default value " + "results in no attributes being discarded. Use either this option or " +"numToSelect to reduce the attribute set."; } /** * Set the threshold by which the AttributeSelection module can discard * attributes. * @param threshold the threshold. */ public void setThreshold(double threshold) { m_threshold = threshold; } /** * Returns the threshold so that the AttributeSelection module can * discard attributes from the ranking. */ public double getThreshold() { return m_threshold; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String generateRankingTipText() { return "A constant option. Ranker is only capable of generating " +" attribute rankings."; } /** * This is a dummy set method---Ranker is ONLY capable of producing * a ranked list of attributes for attribute evaluators. * @param doRank this parameter is N/A and is ignored */ public void setGenerateRanking(boolean doRank) { } /** * This is a dummy method. Ranker can ONLY be used with attribute * evaluators and as such can only produce a ranked list of attributes * @return true all the time. */ public boolean getGenerateRanking() { return true; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String startSetTipText() { return "Specify a set of attributes to ignore. " +" When generating the ranking, Ranker will not evaluate the attributes " +" in this list. " +"This is specified as a comma " +"seperated list off attribute indexes starting at 1. It can include " +"ranges. Eg. 1,2,5-9,17."; } /** * Sets a starting set of attributes for the search. It is the * search method's responsibility to report this start set (if any) * in its toString() method. * @param startSet a string containing a list of attributes (and or ranges), * eg. 1,2,6,10-15. * @throws Exception if start set can't be set. */ public void setStartSet (String startSet) throws Exception { m_startRange.setRanges(startSet); } /** * Returns a list of attributes (and or attribute ranges) as a String * @return a list of attributes (and or attribute ranges) */ public String getStartSet () { return m_startRange.getRanges(); } /** * Returns an enumeration describing the available options. * @return an enumeration of all the available options. **/ public Enumeration listOptions () { Vector newVector = new Vector(3); newVector .addElement(new Option("\tSpecify a starting set of attributes.\n" + "\tEg. 1,3,5-7.\n" +"\tAny starting attributes specified are\n" +"\tignored during the ranking." ,"P",1 , "-P ")); newVector .addElement(new Option("\tSpecify a theshold by which attributes\n" + "\tmay be discarded from the ranking.","T",1 , "-T ")); newVector .addElement(new Option("\tSpecify number of attributes to select" ,"N",1 , "-N ")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -P <start set>
   *  Specify a starting set of attributes.
   *  Eg. 1,3,5-7.
   *  Any starting attributes specified are
   *  ignored during the ranking.
* * 
 -T <threshold>
   *  Specify a theshold by which attributes
   *  may be discarded from the ranking.
* * 
 -N <num to select>
   *  Specify number of attributes to select
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions (String[] options) throws Exception { String optionString; resetOptions(); optionString = Utils.getOption('P', options); if (optionString.length() != 0) { setStartSet(optionString); } optionString = Utils.getOption('T', options); if (optionString.length() != 0) { Double temp; temp = Double.valueOf(optionString); setThreshold(temp.doubleValue()); } optionString = Utils.getOption('N', options); if (optionString.length() != 0) { setNumToSelect(Integer.parseInt(optionString)); } } /** * Gets the current settings of ReliefFAttributeEval. * * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { String[] options = new String[6]; int current = 0; if (!(getStartSet().equals(""))) { options[current++] = "-P"; options[current++] = ""+startSetToString(); } options[current++] = "-T"; options[current++] = "" + getThreshold(); options[current++] = "-N"; options[current++] = ""+getNumToSelect(); while (current < options.length) { options[current++] = ""; } return options; } /** * converts the array of starting attributes to a string. This is * used by getOptions to return the actual attributes specified * as the starting set. This is better than using m_startRanges.getRanges() * as the same start set can be specified in different ways from the * command line---eg 1,2,3 == 1-3. This is to ensure that stuff that * is stored in a database is comparable. * @return a comma seperated list of individual attribute numbers as a String */ private String startSetToString() { StringBuffer FString = new StringBuffer(); boolean didPrint; if (m_starting == null) { return getStartSet(); } for (int i = 0; i < m_starting.length; i++) { didPrint = false; if ((m_hasClass == false) || (m_hasClass == true && i != m_classIndex)) { FString.append((m_starting[i] + 1)); didPrint = true; } if (i == (m_starting.length - 1)) { FString.append(""); } else { if (didPrint) { FString.append(","); } } } return FString.toString(); } /** * Kind of a dummy search algorithm. Calls a Attribute evaluator to * evaluate each attribute not included in the startSet and then sorts * them to produce a ranked list of attributes. * * @param ASEval the attribute evaluator to guide the search * @param data the training instances. * @return an array (not necessarily ordered) of selected attribute indexes * @throws Exception if the search can't be completed */ public int[] search (ASEvaluation ASEval, Instances data) throws Exception { int i, j; if (!(ASEval instanceof AttributeEvaluator)) { throw new Exception(ASEval.getClass().getName() + " is not a" + "Attribute evaluator!"); } m_numAttribs = data.numAttributes(); if (ASEval instanceof UnsupervisedAttributeEvaluator) { m_hasClass = false; } else { m_classIndex = data.classIndex(); if (m_classIndex >= 0) { m_hasClass = true; } else { m_hasClass = false; } } // get the transformed data and check to see if the transformer // preserves a class index if (ASEval instanceof AttributeTransformer) { data = ((AttributeTransformer)ASEval).transformedHeader(); if (m_classIndex >= 0 && data.classIndex() >= 0) { m_classIndex = data.classIndex(); m_hasClass = true; } } m_startRange.setUpper(m_numAttribs - 1); if (!(getStartSet().equals(""))) { m_starting = m_startRange.getSelection(); } int sl=0; if (m_starting != null) { sl = m_starting.length; } if ((m_starting != null) && (m_hasClass == true)) { // see if the supplied list contains the class index boolean ok = false; for (i = 0; i < sl; i++) { if (m_starting[i] == m_classIndex) { ok = true; break; } } if (ok == false) { sl++; } } else { if (m_hasClass == true) { sl++; } } m_attributeList = new int[m_numAttribs - sl]; m_attributeMerit = new double[m_numAttribs - sl]; // add in those attributes not in the starting (omit list) for (i = 0, j = 0; i < m_numAttribs; i++) { if (!inStarting(i)) { m_attributeList[j++] = i; } } AttributeEvaluator ASEvaluator = (AttributeEvaluator)ASEval; for (i = 0; i < m_attributeList.length; i++) { m_attributeMerit[i] = ASEvaluator.evaluateAttribute(m_attributeList[i]); } double[][] tempRanked = rankedAttributes(); int[] rankedAttributes = new int[m_attributeList.length]; for (i = 0; i < m_attributeList.length; i++) { rankedAttributes[i] = (int)tempRanked[i][0]; } return rankedAttributes; } /** * Sorts the evaluated attribute list * * @return an array of sorted (highest eval to lowest) attribute indexes * @throws Exception of sorting can't be done. */ public double[][] rankedAttributes () throws Exception { int i, j; if (m_attributeList == null || m_attributeMerit == null) { throw new Exception("Search must be performed before a ranked " + "attribute list can be obtained"); } int[] ranked = Utils.sort(m_attributeMerit); // reverse the order of the ranked indexes double[][] bestToWorst = new double[ranked.length][2]; for (i = ranked.length - 1, j = 0; i >= 0; i--) { bestToWorst[j++][0] = ranked[i]; } // convert the indexes to attribute indexes for (i = 0; i < bestToWorst.length; i++) { int temp = ((int)bestToWorst[i][0]); bestToWorst[i][0] = m_attributeList[temp]; bestToWorst[i][1] = m_attributeMerit[temp]; } if (m_numToSelect > bestToWorst.length) { throw new Exception("More attributes requested than exist in the data"); } if (m_numToSelect <= 0) { if (m_threshold == -Double.MAX_VALUE) { m_calculatedNumToSelect = bestToWorst.length; } else { determineNumToSelectFromThreshold(bestToWorst); } } /* if (m_numToSelect > 0) { determineThreshFromNumToSelect(bestToWorst); } */ return bestToWorst; } private void determineNumToSelectFromThreshold(double [][] ranking) { int count = 0; for (int i = 0; i < ranking.length; i++) { if (ranking[i][1] > m_threshold) { count++; } } m_calculatedNumToSelect = count; } private void determineThreshFromNumToSelect(double [][] ranking) throws Exception { if (m_numToSelect > ranking.length) { throw new Exception("More attributes requested than exist in the data"); } if (m_numToSelect == ranking.length) { return; } m_threshold = (ranking[m_numToSelect-1][1] + ranking[m_numToSelect][1]) / 2.0; } /** * returns a description of the search as a String * @return a description of the search */ public String toString () { StringBuffer BfString = new StringBuffer(); BfString.append("\tAttribute ranking.\n"); if (m_starting != null) { BfString.append("\tIgnored attributes: "); BfString.append(startSetToString()); BfString.append("\n"); } if (m_threshold != -Double.MAX_VALUE) { BfString.append("\tThreshold for discarding attributes: " + Utils.doubleToString(m_threshold,8,4)+"\n"); } return BfString.toString(); } /** * Resets stuff to default values */ protected void resetOptions () { m_starting = null; m_startRange = new Range(); m_attributeList = null; m_attributeMerit = null; m_threshold = -Double.MAX_VALUE; } private boolean inStarting (int feat) { // omit the class from the evaluation if ((m_hasClass == true) && (feat == m_classIndex)) { return true; } if (m_starting == null) { return false; } for (int i = 0; i < m_starting.length; i++) { if (m_starting[i] == feat) { return true; } } return false; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.26 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/ReliefFAttributeEval.java000066400000000000000000001161671214146114000315010ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * ReliefFAttributeEval.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.core.Attribute; import weka.core.Capabilities; import weka.core.Instance; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.TechnicalInformation; import weka.core.TechnicalInformationHandler; import weka.core.Utils; import weka.core.Capabilities.Capability; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; import java.util.Enumeration; import java.util.Random; import java.util.Vector; /** * ReliefFAttributeEval :
*
* Evaluates the worth of an attribute by repeatedly sampling an instance and considering the value of the given attribute for the nearest instance of the same and different class. Can operate on both discrete and continuous class data.
*
* For more information see:
*
* Kenji Kira, Larry A. Rendell: A Practical Approach to Feature Selection. In: Ninth International Workshop on Machine Learning, 249-256, 1992.
*
* Igor Kononenko: Estimating Attributes: Analysis and Extensions of RELIEF. In: European Conference on Machine Learning, 171-182, 1994.
*
* Marko Robnik-Sikonja, Igor Kononenko: An adaptation of Relief for attribute estimation in regression. In: Fourteenth International Conference on Machine Learning, 296-304, 1997. *

* * BibTeX: * 

 * @inproceedings{Kira1992,
 *    author = {Kenji Kira and Larry A. Rendell},
 *    booktitle = {Ninth International Workshop on Machine Learning},
 *    editor = {Derek H. Sleeman and Peter Edwards},
 *    pages = {249-256},
 *    publisher = {Morgan Kaufmann},
 *    title = {A Practical Approach to Feature Selection},
 *    year = {1992}
 * }
 * 
 * @inproceedings{Kononenko1994,
 *    author = {Igor Kononenko},
 *    booktitle = {European Conference on Machine Learning},
 *    editor = {Francesco Bergadano and Luc De Raedt},
 *    pages = {171-182},
 *    publisher = {Springer},
 *    title = {Estimating Attributes: Analysis and Extensions of RELIEF},
 *    year = {1994}
 * }
 * 
 * @inproceedings{Robnik-Sikonja1997,
 *    author = {Marko Robnik-Sikonja and Igor Kononenko},
 *    booktitle = {Fourteenth International Conference on Machine Learning},
 *    editor = {Douglas H. Fisher},
 *    pages = {296-304},
 *    publisher = {Morgan Kaufmann},
 *    title = {An adaptation of Relief for attribute estimation in regression},
 *    year = {1997}
 * }
 *
*

* * Valid options are:

* * 

 -M <num instances>
 *  Specify the number of instances to
 *  sample when estimating attributes.
 *  If not specified, then all instances
 *  will be used.
* * 
 -D <seed>
 *  Seed for randomly sampling instances.
 *  (Default = 1)
* * 
 -K <number of neighbours>
 *  Number of nearest neighbours (k) used
 *  to estimate attribute relevances
 *  (Default = 10).
* * 
 -W
 *  Weight nearest neighbours by distance
* * 
 -A <num>
 *  Specify sigma value (used in an exp
 *  function to control how quickly
 *  weights for more distant instances
 *  decrease. Use in conjunction with -W.
 *  Sensible value=1/5 to 1/10 of the
 *  number of nearest neighbours.
 *  (Default = 2)
* * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.23 $ */ public class ReliefFAttributeEval extends ASEvaluation implements AttributeEvaluator, OptionHandler, TechnicalInformationHandler { /** for serialization */ static final long serialVersionUID = -8422186665795839379L; /** The training instances */ private Instances m_trainInstances; /** The class index */ private int m_classIndex; /** The number of attributes */ private int m_numAttribs; /** The number of instances */ private int m_numInstances; /** Numeric class */ private boolean m_numericClass; /** The number of classes if class is nominal */ private int m_numClasses; /** * Used to hold the probability of a different class val given nearest * instances (numeric class) */ private double m_ndc; /** * Used to hold the prob of different value of an attribute given * nearest instances (numeric class case) */ private double[] m_nda; /** * Used to hold the prob of a different class val and different att * val given nearest instances (numeric class case) */ private double[] m_ndcda; /** Holds the weights that relief assigns to attributes */ private double[] m_weights; /** Prior class probabilities (discrete class case) */ private double[] m_classProbs; /** * The number of instances to sample when estimating attributes * default == -1, use all instances */ private int m_sampleM; /** The number of nearest hits/misses */ private int m_Knn; /** k nearest scores + instance indexes for n classes */ private double[][][] m_karray; /** Upper bound for numeric attributes */ private double[] m_maxArray; /** Lower bound for numeric attributes */ private double[] m_minArray; /** Keep track of the farthest instance for each class */ private double[] m_worst; /** Index in the m_karray of the farthest instance for each class */ private int[] m_index; /** Number of nearest neighbours stored of each class */ private int[] m_stored; /** Random number seed used for sampling instances */ private int m_seed; /** * used to (optionally) weight nearest neighbours by their distance * from the instance in question. Each entry holds * exp(-((rank(r_i, i_j)/sigma)^2)) where rank(r_i,i_j) is the rank of * instance i_j in a sequence of instances ordered by the distance * from r_i. sigma is a user defined parameter, default=20 **/ private double[] m_weightsByRank; private int m_sigma; /** Weight by distance rather than equal weights */ private boolean m_weightByDistance; /** * Constructor */ public ReliefFAttributeEval () { resetOptions(); } /** * Returns a string describing this attribute evaluator * @return a description of the evaluator suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "ReliefFAttributeEval :\n\nEvaluates the worth of an attribute by " +"repeatedly sampling an instance and considering the value of the " +"given attribute for the nearest instance of the same and different " +"class. Can operate on both discrete and continuous class data.\n\n" + "For more information see:\n\n" + getTechnicalInformation().toString(); } /** * Returns an instance of a TechnicalInformation object, containing * detailed information about the technical background of this class, * e.g., paper reference or book this class is based on. * * @return the technical information about this class */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; TechnicalInformation additional; result = new TechnicalInformation(Type.INPROCEEDINGS); result.setValue(Field.AUTHOR, "Kenji Kira and Larry A. Rendell"); result.setValue(Field.TITLE, "A Practical Approach to Feature Selection"); result.setValue(Field.BOOKTITLE, "Ninth International Workshop on Machine Learning"); result.setValue(Field.EDITOR, "Derek H. Sleeman and Peter Edwards"); result.setValue(Field.YEAR, "1992"); result.setValue(Field.PAGES, "249-256"); result.setValue(Field.PUBLISHER, "Morgan Kaufmann"); additional = result.add(Type.INPROCEEDINGS); additional.setValue(Field.AUTHOR, "Igor Kononenko"); additional.setValue(Field.TITLE, "Estimating Attributes: Analysis and Extensions of RELIEF"); additional.setValue(Field.BOOKTITLE, "European Conference on Machine Learning"); additional.setValue(Field.EDITOR, "Francesco Bergadano and Luc De Raedt"); additional.setValue(Field.YEAR, "1994"); additional.setValue(Field.PAGES, "171-182"); additional.setValue(Field.PUBLISHER, "Springer"); additional = result.add(Type.INPROCEEDINGS); additional.setValue(Field.AUTHOR, "Marko Robnik-Sikonja and Igor Kononenko"); additional.setValue(Field.TITLE, "An adaptation of Relief for attribute estimation in regression"); additional.setValue(Field.BOOKTITLE, "Fourteenth International Conference on Machine Learning"); additional.setValue(Field.EDITOR, "Douglas H. Fisher"); additional.setValue(Field.YEAR, "1997"); additional.setValue(Field.PAGES, "296-304"); additional.setValue(Field.PUBLISHER, "Morgan Kaufmann"); return result; } /** * Returns an enumeration describing the available options. * @return an enumeration of all the available options. **/ public Enumeration listOptions () { Vector newVector = new Vector(4); newVector .addElement(new Option("\tSpecify the number of instances to\n" + "\tsample when estimating attributes.\n" + "\tIf not specified, then all instances\n" + "\twill be used.", "M", 1 , "-M ")); newVector. addElement(new Option("\tSeed for randomly sampling instances.\n" + "\t(Default = 1)", "D", 1 , "-D ")); newVector. addElement(new Option("\tNumber of nearest neighbours (k) used\n" + "\tto estimate attribute relevances\n" + "\t(Default = 10).", "K", 1 , "-K ")); newVector. addElement(new Option("\tWeight nearest neighbours by distance", "W" , 0, "-W")); newVector. addElement(new Option("\tSpecify sigma value (used in an exp\n" + "\tfunction to control how quickly\n" + "\tweights for more distant instances\n" + "\tdecrease. Use in conjunction with -W.\n" + "\tSensible value=1/5 to 1/10 of the\n" + "\tnumber of nearest neighbours.\n" + "\t(Default = 2)", "A", 1, "-A ")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -M <num instances>
   *  Specify the number of instances to
   *  sample when estimating attributes.
   *  If not specified, then all instances
   *  will be used.
* * 
 -D <seed>
   *  Seed for randomly sampling instances.
   *  (Default = 1)
* * 
 -K <number of neighbours>
   *  Number of nearest neighbours (k) used
   *  to estimate attribute relevances
   *  (Default = 10).
* * 
 -W
   *  Weight nearest neighbours by distance
* * 
 -A <num>
   *  Specify sigma value (used in an exp
   *  function to control how quickly
   *  weights for more distant instances
   *  decrease. Use in conjunction with -W.
   *  Sensible value=1/5 to 1/10 of the
   *  number of nearest neighbours.
   *  (Default = 2)
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions (String[] options) throws Exception { String optionString; resetOptions(); setWeightByDistance(Utils.getFlag('W', options)); optionString = Utils.getOption('M', options); if (optionString.length() != 0) { setSampleSize(Integer.parseInt(optionString)); } optionString = Utils.getOption('D', options); if (optionString.length() != 0) { setSeed(Integer.parseInt(optionString)); } optionString = Utils.getOption('K', options); if (optionString.length() != 0) { setNumNeighbours(Integer.parseInt(optionString)); } optionString = Utils.getOption('A', options); if (optionString.length() != 0) { setWeightByDistance(true); // turn on weighting by distance setSigma(Integer.parseInt(optionString)); } } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String sigmaTipText() { return "Set influence of nearest neighbours. Used in an exp function to " +"control how quickly weights decrease for more distant instances. " +"Use in conjunction with weightByDistance. Sensible values = 1/5 to " +"1/10 the number of nearest neighbours."; } /** * Sets the sigma value. * * @param s the value of sigma (> 0) * @throws Exception if s is not positive */ public void setSigma (int s) throws Exception { if (s <= 0) { throw new Exception("value of sigma must be > 0!"); } m_sigma = s; } /** * Get the value of sigma. * * @return the sigma value. */ public int getSigma () { return m_sigma; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String numNeighboursTipText() { return "Number of nearest neighbours for attribute estimation."; } /** * Set the number of nearest neighbours * * @param n the number of nearest neighbours. */ public void setNumNeighbours (int n) { m_Knn = n; } /** * Get the number of nearest neighbours * * @return the number of nearest neighbours */ public int getNumNeighbours () { return m_Knn; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String seedTipText() { return "Random seed for sampling instances."; } /** * Set the random number seed for randomly sampling instances. * * @param s the random number seed. */ public void setSeed (int s) { m_seed = s; } /** * Get the seed used for randomly sampling instances. * * @return the random number seed. */ public int getSeed () { return m_seed; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String sampleSizeTipText() { return "Number of instances to sample. Default (-1) indicates that all " +"instances will be used for attribute estimation."; } /** * Set the number of instances to sample for attribute estimation * * @param s the number of instances to sample. */ public void setSampleSize (int s) { m_sampleM = s; } /** * Get the number of instances used for estimating attributes * * @return the number of instances. */ public int getSampleSize () { return m_sampleM; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String weightByDistanceTipText() { return "Weight nearest neighbours by their distance."; } /** * Set the nearest neighbour weighting method * * @param b true nearest neighbours are to be weighted by distance. */ public void setWeightByDistance (boolean b) { m_weightByDistance = b; } /** * Get whether nearest neighbours are being weighted by distance * * @return m_weightByDiffernce */ public boolean getWeightByDistance () { return m_weightByDistance; } /** * Gets the current settings of ReliefFAttributeEval. * * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { String[] options = new String[9]; int current = 0; if (getWeightByDistance()) { options[current++] = "-W"; } options[current++] = "-M"; options[current++] = "" + getSampleSize(); options[current++] = "-D"; options[current++] = "" + getSeed(); options[current++] = "-K"; options[current++] = "" + getNumNeighbours(); if (getWeightByDistance()) { options[current++] = "-A"; options[current++] = "" + getSigma(); } while (current < options.length) { options[current++] = ""; } return options; } /** * Return a description of the ReliefF attribute evaluator. * * @return a description of the evaluator as a String. */ public String toString () { StringBuffer text = new StringBuffer(); if (m_trainInstances == null) { text.append("ReliefF feature evaluator has not been built yet\n"); } else { text.append("\tReliefF Ranking Filter"); text.append("\n\tInstances sampled: "); if (m_sampleM == -1) { text.append("all\n"); } else { text.append(m_sampleM + "\n"); } text.append("\tNumber of nearest neighbours (k): " + m_Knn + "\n"); if (m_weightByDistance) { text.append("\tExponentially decreasing (with distance) " + "influence for\n" + "\tnearest neighbours. Sigma: " + m_sigma + "\n"); } else { text.append("\tEqual influence nearest neighbours\n"); } } return text.toString(); } /** * Returns the capabilities of this evaluator. * * @return the capabilities of this evaluator * @see Capabilities */ public Capabilities getCapabilities() { Capabilities result = super.getCapabilities(); // attributes result.enable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.NUMERIC_ATTRIBUTES); result.enable(Capability.DATE_ATTRIBUTES); result.enable(Capability.MISSING_VALUES); // class result.enable(Capability.NOMINAL_CLASS); result.enable(Capability.NUMERIC_CLASS); result.enable(Capability.DATE_CLASS); result.enable(Capability.MISSING_CLASS_VALUES); return result; } /** * Initializes a ReliefF attribute evaluator. * * @param data set of instances serving as training data * @throws Exception if the evaluator has not been * generated successfully */ public void buildEvaluator (Instances data) throws Exception { int z, totalInstances; Random r = new Random(m_seed); // can evaluator handle data? getCapabilities().testWithFail(data); m_trainInstances = data; m_classIndex = m_trainInstances.classIndex(); m_numAttribs = m_trainInstances.numAttributes(); m_numInstances = m_trainInstances.numInstances(); if (m_trainInstances.attribute(m_classIndex).isNumeric()) { m_numericClass = true; } else { m_numericClass = false; } if (!m_numericClass) { m_numClasses = m_trainInstances.attribute(m_classIndex).numValues(); } else { m_ndc = 0; m_numClasses = 1; m_nda = new double[m_numAttribs]; m_ndcda = new double[m_numAttribs]; } if (m_weightByDistance) // set up the rank based weights { m_weightsByRank = new double[m_Knn]; for (int i = 0; i < m_Knn; i++) { m_weightsByRank[i] = Math.exp(-((i/(double)m_sigma)*(i/(double)m_sigma))); } } // the final attribute weights m_weights = new double[m_numAttribs]; // num classes (1 for numeric class) knn neighbours, // and 0 = distance, 1 = instance index m_karray = new double[m_numClasses][m_Knn][2]; if (!m_numericClass) { m_classProbs = new double[m_numClasses]; for (int i = 0; i < m_numInstances; i++) { m_classProbs[(int)m_trainInstances.instance(i).value(m_classIndex)]++; } for (int i = 0; i < m_numClasses; i++) { m_classProbs[i] /= m_numInstances; } } m_worst = new double[m_numClasses]; m_index = new int[m_numClasses]; m_stored = new int[m_numClasses]; m_minArray = new double[m_numAttribs]; m_maxArray = new double[m_numAttribs]; for (int i = 0; i < m_numAttribs; i++) { m_minArray[i] = m_maxArray[i] = Double.NaN; } for (int i = 0; i < m_numInstances; i++) { updateMinMax(m_trainInstances.instance(i)); } if ((m_sampleM > m_numInstances) || (m_sampleM < 0)) { totalInstances = m_numInstances; } else { totalInstances = m_sampleM; } // process each instance, updating attribute weights for (int i = 0; i < totalInstances; i++) { if (totalInstances == m_numInstances) { z = i; } else { z = r.nextInt()%m_numInstances; } if (z < 0) { z *= -1; } if (!(m_trainInstances.instance(z).isMissing(m_classIndex))) { // first clear the knn and worst index stuff for the classes for (int j = 0; j < m_numClasses; j++) { m_index[j] = m_stored[j] = 0; for (int k = 0; k < m_Knn; k++) { m_karray[j][k][0] = m_karray[j][k][1] = 0; } } findKHitMiss(z); if (m_numericClass) { updateWeightsNumericClass(z); } else { updateWeightsDiscreteClass(z); } } } // now scale weights by 1/m_numInstances (nominal class) or // calculate weights numeric class // System.out.println("num inst:"+m_numInstances+" r_ndc:"+r_ndc); for (int i = 0; i < m_numAttribs; i++) {if (i != m_classIndex) { if (m_numericClass) { m_weights[i] = m_ndcda[i]/m_ndc - ((m_nda[i] - m_ndcda[i])/((double)totalInstances - m_ndc)); } else { m_weights[i] *= (1.0/(double)totalInstances); } // System.out.println(r_weights[i]); } } } /** * Evaluates an individual attribute using ReliefF's instance based approach. * The actual work is done by buildEvaluator which evaluates all features. * * @param attribute the index of the attribute to be evaluated * @throws Exception if the attribute could not be evaluated */ public double evaluateAttribute (int attribute) throws Exception { return m_weights[attribute]; } /** * Reset options to their default values */ protected void resetOptions () { m_trainInstances = null; m_sampleM = -1; m_Knn = 10; m_sigma = 2; m_weightByDistance = false; m_seed = 1; } /** * Normalizes a given value of a numeric attribute. * * @param x the value to be normalized * @param i the attribute's index * @return the normalized value */ private double norm (double x, int i) { if (Double.isNaN(m_minArray[i]) || Utils.eq(m_maxArray[i], m_minArray[i])) { return 0; } else { return (x - m_minArray[i])/(m_maxArray[i] - m_minArray[i]); } } /** * Updates the minimum and maximum values for all the attributes * based on a new instance. * * @param instance the new instance */ private void updateMinMax (Instance instance) { // for (int j = 0; j < m_numAttribs; j++) { try { for (int j = 0; j < instance.numValues(); j++) { if ((instance.attributeSparse(j).isNumeric()) && (!instance.isMissingSparse(j))) { if (Double.isNaN(m_minArray[instance.index(j)])) { m_minArray[instance.index(j)] = instance.valueSparse(j); m_maxArray[instance.index(j)] = instance.valueSparse(j); } else { if (instance.valueSparse(j) < m_minArray[instance.index(j)]) { m_minArray[instance.index(j)] = instance.valueSparse(j); } else { if (instance.valueSparse(j) > m_maxArray[instance.index(j)]) { m_maxArray[instance.index(j)] = instance.valueSparse(j); } } } } } } catch (Exception ex) { System.err.println(ex); ex.printStackTrace(); } } /** * Computes the difference between two given attribute * values. */ private double difference(int index, double val1, double val2) { switch (m_trainInstances.attribute(index).type()) { case Attribute.NOMINAL: // If attribute is nominal if (Instance.isMissingValue(val1) || Instance.isMissingValue(val2)) { return (1.0 - (1.0/((double)m_trainInstances. attribute(index).numValues()))); } else if ((int)val1 != (int)val2) { return 1; } else { return 0; } case Attribute.NUMERIC: // If attribute is numeric if (Instance.isMissingValue(val1) || Instance.isMissingValue(val2)) { if (Instance.isMissingValue(val1) && Instance.isMissingValue(val2)) { return 1; } else { double diff; if (Instance.isMissingValue(val2)) { diff = norm(val1, index); } else { diff = norm(val2, index); } if (diff < 0.5) { diff = 1.0 - diff; } return diff; } } else { return Math.abs(norm(val1, index) - norm(val2, index)); } default: return 0; } } /** * Calculates the distance between two instances * * @param first the first instance * @param second the second instance * @return the distance between the two given instances, between 0 and 1 */ private double distance(Instance first, Instance second) { double distance = 0; int firstI, secondI; for (int p1 = 0, p2 = 0; p1 < first.numValues() || p2 < second.numValues();) { if (p1 >= first.numValues()) { firstI = m_trainInstances.numAttributes(); } else { firstI = first.index(p1); } if (p2 >= second.numValues()) { secondI = m_trainInstances.numAttributes(); } else { secondI = second.index(p2); } if (firstI == m_trainInstances.classIndex()) { p1++; continue; } if (secondI == m_trainInstances.classIndex()) { p2++; continue; } double diff; if (firstI == secondI) { diff = difference(firstI, first.valueSparse(p1), second.valueSparse(p2)); p1++; p2++; } else if (firstI > secondI) { diff = difference(secondI, 0, second.valueSparse(p2)); p2++; } else { diff = difference(firstI, first.valueSparse(p1), 0); p1++; } // distance += diff * diff; distance += diff; } // return Math.sqrt(distance / m_NumAttributesUsed); return distance; } /** * update attribute weights given an instance when the class is numeric * * @param instNum the index of the instance to use when updating weights */ private void updateWeightsNumericClass (int instNum) { int i, j; double temp,temp2; int[] tempSorted = null; double[] tempDist = null; double distNorm = 1.0; int firstI, secondI; Instance inst = m_trainInstances.instance(instNum); // sort nearest neighbours and set up normalization variable if (m_weightByDistance) { tempDist = new double[m_stored[0]]; for (j = 0, distNorm = 0; j < m_stored[0]; j++) { // copy the distances tempDist[j] = m_karray[0][j][0]; // sum normalizer distNorm += m_weightsByRank[j]; } tempSorted = Utils.sort(tempDist); } for (i = 0; i < m_stored[0]; i++) { // P diff prediction (class) given nearest instances if (m_weightByDistance) { temp = difference(m_classIndex, inst.value(m_classIndex), m_trainInstances. instance((int)m_karray[0][tempSorted[i]][1]). value(m_classIndex)); temp *= (m_weightsByRank[i]/distNorm); } else { temp = difference(m_classIndex, inst.value(m_classIndex), m_trainInstances. instance((int)m_karray[0][i][1]). value(m_classIndex)); temp *= (1.0/(double)m_stored[0]); // equal influence } m_ndc += temp; Instance cmp; cmp = (m_weightByDistance) ? m_trainInstances.instance((int)m_karray[0][tempSorted[i]][1]) : m_trainInstances.instance((int)m_karray[0][i][1]); double temp_diffP_diffA_givNearest = difference(m_classIndex, inst.value(m_classIndex), cmp.value(m_classIndex)); // now the attributes for (int p1 = 0, p2 = 0; p1 < inst.numValues() || p2 < cmp.numValues();) { if (p1 >= inst.numValues()) { firstI = m_trainInstances.numAttributes(); } else { firstI = inst.index(p1); } if (p2 >= cmp.numValues()) { secondI = m_trainInstances.numAttributes(); } else { secondI = cmp.index(p2); } if (firstI == m_trainInstances.classIndex()) { p1++; continue; } if (secondI == m_trainInstances.classIndex()) { p2++; continue; } temp = 0.0; temp2 = 0.0; if (firstI == secondI) { j = firstI; temp = difference(j, inst.valueSparse(p1), cmp.valueSparse(p2)); p1++;p2++; } else if (firstI > secondI) { j = secondI; temp = difference(j, 0, cmp.valueSparse(p2)); p2++; } else { j = firstI; temp = difference(j, inst.valueSparse(p1), 0); p1++; } temp2 = temp_diffP_diffA_givNearest * temp; // P of different prediction and different att value given // nearest instances if (m_weightByDistance) { temp2 *= (m_weightsByRank[i]/distNorm); } else { temp2 *= (1.0/(double)m_stored[0]); // equal influence } m_ndcda[j] += temp2; // P of different attribute val given nearest instances if (m_weightByDistance) { temp *= (m_weightsByRank[i]/distNorm); } else { temp *= (1.0/(double)m_stored[0]); // equal influence } m_nda[j] += temp; } } } /** * update attribute weights given an instance when the class is discrete * * @param instNum the index of the instance to use when updating weights */ private void updateWeightsDiscreteClass (int instNum) { int i, j, k; int cl; double temp_diff, w_norm = 1.0; double[] tempDistClass; int[] tempSortedClass = null; double distNormClass = 1.0; double[] tempDistAtt; int[][] tempSortedAtt = null; double[] distNormAtt = null; int firstI, secondI; // store the indexes (sparse instances) of non-zero elements Instance inst = m_trainInstances.instance(instNum); // get the class of this instance cl = (int)m_trainInstances.instance(instNum).value(m_classIndex); // sort nearest neighbours and set up normalization variables if (m_weightByDistance) { // do class (hits) first // sort the distances tempDistClass = new double[m_stored[cl]]; for (j = 0, distNormClass = 0; j < m_stored[cl]; j++) { // copy the distances tempDistClass[j] = m_karray[cl][j][0]; // sum normalizer distNormClass += m_weightsByRank[j]; } tempSortedClass = Utils.sort(tempDistClass); // do misses (other classes) tempSortedAtt = new int[m_numClasses][1]; distNormAtt = new double[m_numClasses]; for (k = 0; k < m_numClasses; k++) { if (k != cl) // already done cl { // sort the distances tempDistAtt = new double[m_stored[k]]; for (j = 0, distNormAtt[k] = 0; j < m_stored[k]; j++) { // copy the distances tempDistAtt[j] = m_karray[k][j][0]; // sum normalizer distNormAtt[k] += m_weightsByRank[j]; } tempSortedAtt[k] = Utils.sort(tempDistAtt); } } } if (m_numClasses > 2) { // the amount of probability space left after removing the // probability of this instance's class value w_norm = (1.0 - m_classProbs[cl]); } // do the k nearest hits of the same class for (j = 0, temp_diff = 0.0; j < m_stored[cl]; j++) { Instance cmp; cmp = (m_weightByDistance) ? m_trainInstances. instance((int)m_karray[cl][tempSortedClass[j]][1]) : m_trainInstances.instance((int)m_karray[cl][j][1]); for (int p1 = 0, p2 = 0; p1 < inst.numValues() || p2 < cmp.numValues();) { if (p1 >= inst.numValues()) { firstI = m_trainInstances.numAttributes(); } else { firstI = inst.index(p1); } if (p2 >= cmp.numValues()) { secondI = m_trainInstances.numAttributes(); } else { secondI = cmp.index(p2); } if (firstI == m_trainInstances.classIndex()) { p1++; continue; } if (secondI == m_trainInstances.classIndex()) { p2++; continue; } if (firstI == secondI) { i = firstI; temp_diff = difference(i, inst.valueSparse(p1), cmp.valueSparse(p2)); p1++;p2++; } else if (firstI > secondI) { i = secondI; temp_diff = difference(i, 0, cmp.valueSparse(p2)); p2++; } else { i = firstI; temp_diff = difference(i, inst.valueSparse(p1), 0); p1++; } if (m_weightByDistance) { temp_diff *= (m_weightsByRank[j]/distNormClass); } else { if (m_stored[cl] > 0) { temp_diff /= (double)m_stored[cl]; } } m_weights[i] -= temp_diff; } } // now do k nearest misses from each of the other classes temp_diff = 0.0; for (k = 0; k < m_numClasses; k++) { if (k != cl) // already done cl { for (j = 0; j < m_stored[k]; j++) { Instance cmp; cmp = (m_weightByDistance) ? m_trainInstances. instance((int)m_karray[k][tempSortedAtt[k][j]][1]) : m_trainInstances.instance((int)m_karray[k][j][1]); for (int p1 = 0, p2 = 0; p1 < inst.numValues() || p2 < cmp.numValues();) { if (p1 >= inst.numValues()) { firstI = m_trainInstances.numAttributes(); } else { firstI = inst.index(p1); } if (p2 >= cmp.numValues()) { secondI = m_trainInstances.numAttributes(); } else { secondI = cmp.index(p2); } if (firstI == m_trainInstances.classIndex()) { p1++; continue; } if (secondI == m_trainInstances.classIndex()) { p2++; continue; } if (firstI == secondI) { i = firstI; temp_diff = difference(i, inst.valueSparse(p1), cmp.valueSparse(p2)); p1++;p2++; } else if (firstI > secondI) { i = secondI; temp_diff = difference(i, 0, cmp.valueSparse(p2)); p2++; } else { i = firstI; temp_diff = difference(i, inst.valueSparse(p1), 0); p1++; } if (m_weightByDistance) { temp_diff *= (m_weightsByRank[j]/distNormAtt[k]); } else { if (m_stored[k] > 0) { temp_diff /= (double)m_stored[k]; } } if (m_numClasses > 2) { m_weights[i] += ((m_classProbs[k]/w_norm)*temp_diff); } else { m_weights[i] += temp_diff; } } } } } } /** * Find the K nearest instances to supplied instance if the class is numeric, * or the K nearest Hits (same class) and Misses (K from each of the other * classes) if the class is discrete. * * @param instNum the index of the instance to find nearest neighbours of */ private void findKHitMiss (int instNum) { int i, j; int cl; double ww; double temp_diff = 0.0; Instance thisInst = m_trainInstances.instance(instNum); for (i = 0; i < m_numInstances; i++) { if (i != instNum) { Instance cmpInst = m_trainInstances.instance(i); temp_diff = distance(cmpInst, thisInst); // class of this training instance or 0 if numeric if (m_numericClass) { cl = 0; } else { cl = (int)m_trainInstances.instance(i).value(m_classIndex); } // add this diff to the list for the class of this instance if (m_stored[cl] < m_Knn) { m_karray[cl][m_stored[cl]][0] = temp_diff; m_karray[cl][m_stored[cl]][1] = i; m_stored[cl]++; // note the worst diff for this class for (j = 0, ww = -1.0; j < m_stored[cl]; j++) { if (m_karray[cl][j][0] > ww) { ww = m_karray[cl][j][0]; m_index[cl] = j; } } m_worst[cl] = ww; } else /* if we already have stored knn for this class then check to see if this instance is better than the worst */ { if (temp_diff < m_karray[cl][m_index[cl]][0]) { m_karray[cl][m_index[cl]][0] = temp_diff; m_karray[cl][m_index[cl]][1] = i; for (j = 0, ww = -1.0; j < m_stored[cl]; j++) { if (m_karray[cl][j][0] > ww) { ww = m_karray[cl][j][0]; m_index[cl] = j; } } m_worst[cl] = ww; } } } } } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.23 $"); } // ============ // Test method. // ============ /** * Main method for testing this class. * * @param args the options */ public static void main (String[] args) { runEvaluator(new ReliefFAttributeEval(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/SVMAttributeEval.java000066400000000000000000000645511214146114000306310ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * SVMAttributeEval.java * Copyright (C) 2002 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.classifiers.functions.SMO; import weka.core.Capabilities; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.SelectedTag; import weka.core.TechnicalInformation; import weka.core.TechnicalInformationHandler; import weka.core.Utils; import weka.core.Capabilities.Capability; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; import weka.filters.Filter; import weka.filters.unsupervised.attribute.MakeIndicator; import java.util.ArrayList; import java.util.Enumeration; import java.util.Iterator; import java.util.Vector; /** * SVMAttributeEval :
*
* Evaluates the worth of an attribute by using an SVM classifier. Attributes are ranked by the square of the weight assigned by the SVM. Attribute selection for multiclass problems is handled by ranking attributes for each class seperately using a one-vs-all method and then "dealing" from the top of each pile to give a final ranking.
*
* For more information see:
*
* I. Guyon, J. Weston, S. Barnhill, V. Vapnik (2002). Gene selection for cancer classification using support vector machines. Machine Learning. 46:389-422. *

* * BibTeX: * 

 * @article{Guyon2002,
 *    author = {I. Guyon and J. Weston and S. Barnhill and V. Vapnik},
 *    journal = {Machine Learning},
 *    pages = {389-422},
 *    title = {Gene selection for cancer classification using support vector machines},
 *    volume = {46},
 *    year = {2002}
 * }
 *
*

* * Valid options are:

* * 

 -X <constant rate of elimination>
 *  Specify the constant rate of attribute
 *  elimination per invocation of
 *  the support vector machine.
 *  Default = 1.
* * 
 -Y <percent rate of elimination>
 *  Specify the percentage rate of attributes to
 *  elimination per invocation of
 *  the support vector machine.
 *  Trumps constant rate (above threshold).
 *  Default = 0.
* * 
 -Z <threshold for percent elimination>
 *  Specify the threshold below which 
 *  percentage attribute elimination
 *  reverts to the constant method.
* * 
 -P <epsilon>
 *  Specify the value of P (epsilon
 *  parameter) to pass on to the
 *  support vector machine.
 *  Default = 1.0e-25
* * 
 -T <tolerance>
 *  Specify the value of T (tolerance
 *  parameter) to pass on to the
 *  support vector machine.
 *  Default = 1.0e-10
* * 
 -C <complexity>
 *  Specify the value of C (complexity
 *  parameter) to pass on to the
 *  support vector machine.
 *  Default = 1.0
* * 
 -N
 *  Whether the SVM should 0=normalize/1=standardize/2=neither.
 *  (default 0=normalize)
* * * @author Eibe Frank (eibe@cs.waikato.ac.nz) * @author Mark Hall (mhall@cs.waikato.ac.nz) * @author Kieran Holland * @version $Revision: 1.28 $ */ public class SVMAttributeEval extends ASEvaluation implements AttributeEvaluator, OptionHandler, TechnicalInformationHandler { /** for serialization */ static final long serialVersionUID = -6489975709033967447L; /** The attribute scores */ private double[] m_attScores; /** Constant rate of attribute elimination per iteration */ private int m_numToEliminate = 1; /** Percentage rate of attribute elimination, trumps constant rate (above threshold), ignored if = 0 */ private int m_percentToEliminate = 0; /** Threshold below which percent elimination switches to constant elimination */ private int m_percentThreshold = 0; /** Complexity parameter to pass on to SMO */ private double m_smoCParameter = 1.0; /** Tolerance parameter to pass on to SMO */ private double m_smoTParameter = 1.0e-10; /** Epsilon parameter to pass on to SMO */ private double m_smoPParameter = 1.0e-25; /** Filter parameter to pass on to SMO */ private int m_smoFilterType = 0; /** * Returns a string describing this attribute evaluator * @return a description of the evaluator suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "SVMAttributeEval :\n\nEvaluates the worth of an attribute by " + "using an SVM classifier. Attributes are ranked by the square of the " + "weight assigned by the SVM. Attribute selection for multiclass " + "problems is handled by ranking attributes for each class seperately " + "using a one-vs-all method and then \"dealing\" from the top of " + "each pile to give a final ranking.\n\n" + "For more information see:\n\n" + getTechnicalInformation().toString(); } /** * Returns an instance of a TechnicalInformation object, containing * detailed information about the technical background of this class, * e.g., paper reference or book this class is based on. * * @return the technical information about this class */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.ARTICLE); result.setValue(Field.AUTHOR, "I. Guyon and J. Weston and S. Barnhill and V. Vapnik"); result.setValue(Field.YEAR, "2002"); result.setValue(Field.TITLE, "Gene selection for cancer classification using support vector machines"); result.setValue(Field.JOURNAL, "Machine Learning"); result.setValue(Field.VOLUME, "46"); result.setValue(Field.PAGES, "389-422"); return result; } /** * Constructor */ public SVMAttributeEval() { resetOptions(); } /** * Returns an enumeration describing all the available options * * @return an enumeration of options */ public Enumeration listOptions() { Vector newVector = new Vector(4); newVector.addElement( new Option( "\tSpecify the constant rate of attribute\n" + "\telimination per invocation of\n" + "\tthe support vector machine.\n" + "\tDefault = 1.", "X", 1, "-X ")); newVector.addElement( new Option( "\tSpecify the percentage rate of attributes to\n" + "\telimination per invocation of\n" + "\tthe support vector machine.\n" + "\tTrumps constant rate (above threshold).\n" + "\tDefault = 0.", "Y", 1, "-Y ")); newVector.addElement( new Option( "\tSpecify the threshold below which \n" + "\tpercentage attribute elimination\n" + "\treverts to the constant method.", "Z", 1, "-Z ")); newVector.addElement( new Option( "\tSpecify the value of P (epsilon\n" + "\tparameter) to pass on to the\n" + "\tsupport vector machine.\n" + "\tDefault = 1.0e-25", "P", 1, "-P ")); newVector.addElement( new Option( "\tSpecify the value of T (tolerance\n" + "\tparameter) to pass on to the\n" + "\tsupport vector machine.\n" + "\tDefault = 1.0e-10", "T", 1, "-T ")); newVector.addElement( new Option( "\tSpecify the value of C (complexity\n" + "\tparameter) to pass on to the\n" + "\tsupport vector machine.\n" + "\tDefault = 1.0", "C", 1, "-C ")); newVector.addElement(new Option("\tWhether the SVM should " + "0=normalize/1=standardize/2=neither.\n" + "\t(default 0=normalize)", "N", 1, "-N")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -X <constant rate of elimination>
   *  Specify the constant rate of attribute
   *  elimination per invocation of
   *  the support vector machine.
   *  Default = 1.
* * 
 -Y <percent rate of elimination>
   *  Specify the percentage rate of attributes to
   *  elimination per invocation of
   *  the support vector machine.
   *  Trumps constant rate (above threshold).
   *  Default = 0.
* * 
 -Z <threshold for percent elimination>
   *  Specify the threshold below which 
   *  percentage attribute elimination
   *  reverts to the constant method.
* * 
 -P <epsilon>
   *  Specify the value of P (epsilon
   *  parameter) to pass on to the
   *  support vector machine.
   *  Default = 1.0e-25
* * 
 -T <tolerance>
   *  Specify the value of T (tolerance
   *  parameter) to pass on to the
   *  support vector machine.
   *  Default = 1.0e-10
* * 
 -C <complexity>
   *  Specify the value of C (complexity
   *  parameter) to pass on to the
   *  support vector machine.
   *  Default = 1.0
* * 
 -N
   *  Whether the SVM should 0=normalize/1=standardize/2=neither.
   *  (default 0=normalize)
* * * @param options the list of options as an array of strings * @throws Exception if an error occurs */ public void setOptions(String[] options) throws Exception { String optionString; optionString = Utils.getOption('X', options); if (optionString.length() != 0) { setAttsToEliminatePerIteration(Integer.parseInt(optionString)); } optionString = Utils.getOption('Y', options); if (optionString.length() != 0) { setPercentToEliminatePerIteration(Integer.parseInt(optionString)); } optionString = Utils.getOption('Z', options); if (optionString.length() != 0) { setPercentThreshold(Integer.parseInt(optionString)); } optionString = Utils.getOption('P', options); if (optionString.length() != 0) { setEpsilonParameter((new Double(optionString)).doubleValue()); } optionString = Utils.getOption('T', options); if (optionString.length() != 0) { setToleranceParameter((new Double(optionString)).doubleValue()); } optionString = Utils.getOption('C', options); if (optionString.length() != 0) { setComplexityParameter((new Double(optionString)).doubleValue()); } optionString = Utils.getOption('N', options); if (optionString.length() != 0) { setFilterType(new SelectedTag(Integer.parseInt(optionString), SMO.TAGS_FILTER)); } else { setFilterType(new SelectedTag(SMO.FILTER_NORMALIZE, SMO.TAGS_FILTER)); } Utils.checkForRemainingOptions(options); } /** * Gets the current settings of SVMAttributeEval * * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions() { String[] options = new String[14]; int current = 0; options[current++] = "-X"; options[current++] = "" + getAttsToEliminatePerIteration(); options[current++] = "-Y"; options[current++] = "" + getPercentToEliminatePerIteration(); options[current++] = "-Z"; options[current++] = "" + getPercentThreshold(); options[current++] = "-P"; options[current++] = "" + getEpsilonParameter(); options[current++] = "-T"; options[current++] = "" + getToleranceParameter(); options[current++] = "-C"; options[current++] = "" + getComplexityParameter(); options[current++] = "-N"; options[current++] = "" + m_smoFilterType; while (current < options.length) { options[current++] = ""; } return options; } //________________________________________________________________________ /** * Returns a tip text for this property suitable for display in the * GUI * * @return tip text string describing this property */ public String attsToEliminatePerIterationTipText() { return "Constant rate of attribute elimination."; } /** * Returns a tip text for this property suitable for display in the * GUI * * @return tip text string describing this property */ public String percentToEliminatePerIterationTipText() { return "Percent rate of attribute elimination."; } /** * Returns a tip text for this property suitable for display in the * GUI * * @return tip text string describing this property */ public String percentThresholdTipText() { return "Threshold below which percent elimination reverts to constant elimination."; } /** * Returns a tip text for this property suitable for display in the * GUI * * @return tip text string describing this property */ public String epsilonParameterTipText() { return "P epsilon parameter to pass to the SVM"; } /** * Returns a tip text for this property suitable for display in the * GUI * * @return tip text string describing this property */ public String toleranceParameterTipText() { return "T tolerance parameter to pass to the SVM"; } /** * Returns a tip text for this property suitable for display in the * GUI * * @return tip text string describing this property */ public String complexityParameterTipText() { return "C complexity parameter to pass to the SVM"; } /** * Returns a tip text for this property suitable for display in the * GUI * * @return tip text string describing this property */ public String filterTypeTipText() { return "filtering used by the SVM"; } //________________________________________________________________________ /** * Set the constant rate of attribute elimination per iteration * * @param cRate the constant rate of attribute elimination per iteration */ public void setAttsToEliminatePerIteration(int cRate) { m_numToEliminate = cRate; } /** * Get the constant rate of attribute elimination per iteration * * @return the constant rate of attribute elimination per iteration */ public int getAttsToEliminatePerIteration() { return m_numToEliminate; } /** * Set the percentage of attributes to eliminate per iteration * * @param pRate percent of attributes to eliminate per iteration */ public void setPercentToEliminatePerIteration(int pRate) { m_percentToEliminate = pRate; } /** * Get the percentage rate of attribute elimination per iteration * * @return the percentage rate of attribute elimination per iteration */ public int getPercentToEliminatePerIteration() { return m_percentToEliminate; } /** * Set the threshold below which percentage elimination reverts to * constant elimination. * * @param pThresh percent of attributes to eliminate per iteration */ public void setPercentThreshold(int pThresh) { m_percentThreshold = pThresh; } /** * Get the threshold below which percentage elimination reverts to * constant elimination. * * @return the threshold below which percentage elimination stops */ public int getPercentThreshold() { return m_percentThreshold; } /** * Set the value of P for SMO * * @param svmP the value of P */ public void setEpsilonParameter(double svmP) { m_smoPParameter = svmP; } /** * Get the value of P used with SMO * * @return the value of P */ public double getEpsilonParameter() { return m_smoPParameter; } /** * Set the value of T for SMO * * @param svmT the value of T */ public void setToleranceParameter(double svmT) { m_smoTParameter = svmT; } /** * Get the value of T used with SMO * * @return the value of T */ public double getToleranceParameter() { return m_smoTParameter; } /** * Set the value of C for SMO * * @param svmC the value of C */ public void setComplexityParameter(double svmC) { m_smoCParameter = svmC; } /** * Get the value of C used with SMO * * @return the value of C */ public double getComplexityParameter() { return m_smoCParameter; } /** * The filtering mode to pass to SMO * * @param newType the new filtering mode */ public void setFilterType(SelectedTag newType) { if (newType.getTags() == SMO.TAGS_FILTER) { m_smoFilterType = newType.getSelectedTag().getID(); } } /** * Get the filtering mode passed to SMO * * @return the filtering mode */ public SelectedTag getFilterType() { return new SelectedTag(m_smoFilterType, SMO.TAGS_FILTER); } //________________________________________________________________________ /** * Returns the capabilities of this evaluator. * * @return the capabilities of this evaluator * @see Capabilities */ public Capabilities getCapabilities() { Capabilities result; result = new SMO().getCapabilities(); result.setOwner(this); // only binary attributes are allowed, otherwise the NominalToBinary // filter inside SMO will increase the number of attributes which in turn // will lead to ArrayIndexOutOfBounds-Exceptions. result.disable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.BINARY_ATTRIBUTES); result.disableAllAttributeDependencies(); return result; } /** * Initializes the evaluator. * * @param data set of instances serving as training data * @throws Exception if the evaluator has not been * generated successfully */ public void buildEvaluator(Instances data) throws Exception { // can evaluator handle data? getCapabilities().testWithFail(data); //System.out.println("Class attribute: " + data.attribute(data.classIndex()).name()); // Check settings m_numToEliminate = (m_numToEliminate > 1) ? m_numToEliminate : 1; m_percentToEliminate = (m_percentToEliminate < 100) ? m_percentToEliminate : 100; m_percentToEliminate = (m_percentToEliminate > 0) ? m_percentToEliminate : 0; m_percentThreshold = (m_percentThreshold < data.numAttributes()) ? m_percentThreshold : data.numAttributes() - 1; m_percentThreshold = (m_percentThreshold > 0) ? m_percentThreshold : 0; // Get ranked attributes for each class seperately, one-vs-all int[][] attScoresByClass; int numAttr = data.numAttributes() - 1; if(data.numClasses()>2) { attScoresByClass = new int[data.numClasses()][numAttr]; for (int i = 0; i < data.numClasses(); i++) { attScoresByClass[i] = rankBySVM(i, data); } } else { attScoresByClass = new int[1][numAttr]; attScoresByClass[0] = rankBySVM(0, data); } // Cycle through class-specific ranked lists, poping top one off for each class // and adding it to the overall ranked attribute list if it's not there already ArrayList ordered = new ArrayList(numAttr); for (int i = 0; i < numAttr; i++) { for (int j = 0; j < (data.numClasses()>2 ? data.numClasses() : 1); j++) { Integer rank = new Integer(attScoresByClass[j][i]); if (!ordered.contains(rank)) ordered.add(rank); } } m_attScores = new double[data.numAttributes()]; Iterator listIt = ordered.iterator(); for (double i = (double) numAttr; listIt.hasNext(); i = i - 1.0) { m_attScores[((Integer) listIt.next()).intValue()] = i; } } /** * Get SVM-ranked attribute indexes (best to worst) selected for * the class attribute indexed by classInd (one-vs-all). */ private int[] rankBySVM(int classInd, Instances data) { // Holds a mapping into the original array of attribute indices int[] origIndices = new int[data.numAttributes()]; for (int i = 0; i < origIndices.length; i++) origIndices[i] = i; // Count down of number of attributes remaining int numAttrLeft = data.numAttributes()-1; // Ranked attribute indices for this class, one vs.all (highest->lowest) int[] attRanks = new int[numAttrLeft]; try { MakeIndicator filter = new MakeIndicator(); filter.setAttributeIndex("" + (data.classIndex() + 1)); filter.setNumeric(false); filter.setValueIndex(classInd); filter.setInputFormat(data); Instances trainCopy = Filter.useFilter(data, filter); double pctToElim = ((double) m_percentToEliminate) / 100.0; while (numAttrLeft > 0) { int numToElim; if (pctToElim > 0) { numToElim = (int) (trainCopy.numAttributes() * pctToElim); numToElim = (numToElim > 1) ? numToElim : 1; if (numAttrLeft - numToElim <= m_percentThreshold) { pctToElim = 0; numToElim = numAttrLeft - m_percentThreshold; } } else { numToElim = (numAttrLeft >= m_numToEliminate) ? m_numToEliminate : numAttrLeft; } // Build the linear SVM with default parameters SMO smo = new SMO(); // SMO seems to get stuck if data not normalised when few attributes remain // smo.setNormalizeData(numAttrLeft < 40); smo.setFilterType(new SelectedTag(m_smoFilterType, SMO.TAGS_FILTER)); smo.setEpsilon(m_smoPParameter); smo.setToleranceParameter(m_smoTParameter); smo.setC(m_smoCParameter); smo.buildClassifier(trainCopy); // Find the attribute with maximum weight^2 double[] weightsSparse = smo.sparseWeights()[0][1]; int[] indicesSparse = smo.sparseIndices()[0][1]; double[] weights = new double[trainCopy.numAttributes()]; for (int j = 0; j < weightsSparse.length; j++) { weights[indicesSparse[j]] = weightsSparse[j] * weightsSparse[j]; } weights[trainCopy.classIndex()] = Double.MAX_VALUE; int minWeightIndex; int[] featArray = new int[numToElim]; boolean[] eliminated = new boolean[origIndices.length]; for (int j = 0; j < numToElim; j++) { minWeightIndex = Utils.minIndex(weights); attRanks[--numAttrLeft] = origIndices[minWeightIndex]; featArray[j] = minWeightIndex; eliminated[minWeightIndex] = true; weights[minWeightIndex] = Double.MAX_VALUE; } // Delete the worst attributes. weka.filters.unsupervised.attribute.Remove delTransform = new weka.filters.unsupervised.attribute.Remove(); delTransform.setInvertSelection(false); delTransform.setAttributeIndicesArray(featArray); delTransform.setInputFormat(trainCopy); trainCopy = Filter.useFilter(trainCopy, delTransform); // Update the array of remaining attribute indices int[] temp = new int[origIndices.length - numToElim]; int k = 0; for (int j = 0; j < origIndices.length; j++) { if (!eliminated[j]) { temp[k++] = origIndices[j]; } } origIndices = temp; } // Carefully handle all exceptions } catch (Exception e) { e.printStackTrace(); } return attRanks; } /** * Resets options to defaults. */ protected void resetOptions() { m_attScores = null; } /** * Evaluates an attribute by returning the rank of the square of its coefficient in a * linear support vector machine. * * @param attribute the index of the attribute to be evaluated * @throws Exception if the attribute could not be evaluated */ public double evaluateAttribute(int attribute) throws Exception { return m_attScores[attribute]; } /** * Return a description of the evaluator * @return description as a string */ public String toString() { StringBuffer text = new StringBuffer(); if (m_attScores == null) { text.append("\tSVM feature evaluator has not been built yet"); } else { text.append("\tSVM feature evaluator"); } text.append("\n"); return text.toString(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.28 $"); } /** * Main method for testing this class. * * @param args the options */ public static void main(String[] args) { runEvaluator(new SVMAttributeEval(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/StartSetHandler.java000066400000000000000000000032061214146114000305250ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * StartSetHandler.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; /** * Interface for search methods capable of doing something sensible * given a starting set of attributes. * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.8 $ */ public interface StartSetHandler { /** * Sets a starting set of attributes for the search. It is the * search method's responsibility to report this start set (if any) * in its toString() method. * @param startSet a string containing a list of attributes (and or ranges), * eg. 1,2,6,10-15. * @exception Exception if start set can't be set. */ void setStartSet (String startSet) throws Exception; /** * Returns a list of attributes (and or attribute ranges) as a String * @return a list of attributes (and or attribute ranges) */ String getStartSet (); } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/SubsetEvaluator.java000066400000000000000000000025461214146114000306140ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * SubsetEvaluator.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import java.util.BitSet; /** * Interface for attribute subset evaluators. * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.11 $ */ public interface SubsetEvaluator { /** * evaluates a subset of attributes * * @param subset a bitset representing the attribute subset to be * evaluated * @return the "merit" of the subset * @exception Exception if the subset could not be evaluated */ double evaluateSubset(BitSet subset) throws Exception; } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/SubsetSizeForwardSelection.java000066400000000000000000000661011214146114000327540ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * SubsetSizeForwardSelection.java * Copyright (C) 2007 Martin Guetlein * */ package weka.attributeSelection; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.SelectedTag; import weka.core.Tag; import weka.core.TechnicalInformation; import weka.core.Utils; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; import java.util.BitSet; import java.util.Enumeration; import java.util.Random; import java.util.Vector; /** * SubsetSizeForwardSelection:
*
* Extension of LinearForwardSelection. The search performs an interior cross-validation (seed and number of folds can be specified). A LinearForwardSelection is performed on each foldto determine the optimal subset-size (using the given SubsetSizeEvaluator). Finally, a LinearForwardSelection up to the optimal subset-size is performed on the whole data.
*
* For more information see:
*
* Martin Guetlein (2006). Large Scale Attribute Selection Using Wrappers. Freiburg, Germany. *

* * Valid options are:

* * 

 -I
 *  Perform initial ranking to select the
 *  top-ranked attributes.
* * 
 -K <num>
 *  Number of top-ranked attributes that are 
 *  taken into account by the search.
* * 
 -T <0 = fixed-set | 1 = fixed-width>
 *  Type of Linear Forward Selection (default = 0).
* * 
 -S <num>
 *  Size of lookup cache for evaluated subsets.
 *  Expressed as a multiple of the number of
 *  attributes in the data set. (default = 1)
* * 
 -E <subset evaluator>
 *  Subset-evaluator used for subset-size determination.-- -M
* * 
 -F <num>
 *  Number of cross validation folds
 *  for subset size determination (default = 5).
* * 
 -R <num>
 *  Seed for cross validation
 *  subset size determination. (default = 1)
* * 
 -Z
 *  verbose on/off
* * 
 
 * Options specific to evaluator weka.attributeSelection.ClassifierSubsetEval:
 *
* * 
 -B <classifier>
 *  class name of the classifier to use for accuracy estimation.
 *  Place any classifier options LAST on the command line
 *  following a "--". eg.:
 *   -B weka.classifiers.bayes.NaiveBayes ... -- -K
 *  (default: weka.classifiers.rules.ZeroR)
* * 
 -T
 *  Use the training data to estimate accuracy.
* * 
 -H <filename>
 *  Name of the hold out/test set to 
 *  estimate accuracy on.
* * 
 
 * Options specific to scheme weka.classifiers.rules.ZeroR:
 *
* * 
 -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
* * * @author Martin Guetlein (martin.guetlein@gmail.com) * @version $Revision: 1.4 $ */ public class SubsetSizeForwardSelection extends ASSearch implements OptionHandler { /** search directions */ protected static final int TYPE_FIXED_SET = 0; protected static final int TYPE_FIXED_WIDTH = 1; public static final Tag[] TAGS_TYPE = { new Tag(TYPE_FIXED_SET, "Fixed-set"), new Tag(TYPE_FIXED_WIDTH, "Fixed-width"), }; // member variables /** perform initial ranking to select top-ranked attributes */ protected boolean m_performRanking; /** * number of top-ranked attributes that are taken into account for the * search */ protected int m_numUsedAttributes; /** 0 == fixed-set, 1 == fixed-width */ protected int m_linearSelectionType; /** the subset evaluator to use for subset size determination */ private ASEvaluation m_setSizeEval; /** * Number of cross validation folds for subset size determination (default = * 5). */ protected int m_numFolds; /** Seed for cross validation subset size determination. (default = 1) */ protected int m_seed; /** number of attributes in the data */ protected int m_numAttribs; /** total number of subsets evaluated during a search */ protected int m_totalEvals; /** for debugging */ protected boolean m_verbose; /** holds the merit of the best subset found */ protected double m_bestMerit; /** holds the maximum size of the lookup cache for evaluated subsets */ protected int m_cacheSize; /** * Constructor */ public SubsetSizeForwardSelection() { resetOptions(); } /** * Returns a string describing this search method * * @return a description of the search method suitable for displaying in the * explorer/experimenter gui */ public String globalInfo() { return "SubsetSizeForwardSelection:\n\n" + "Extension of LinearForwardSelection. The search performs an interior " + "cross-validation (seed and number of folds can be specified). A " + "LinearForwardSelection is performed on each foldto determine the optimal " + "subset-size (using the given SubsetSizeEvaluator). Finally, a " + "LinearForwardSelection up to the optimal subset-size is performed on " + "the whole data.\n\n" + "For more information see:\n\n" + getTechnicalInformation().toString(); } /** * Returns an instance of a TechnicalInformation object, containing * detailed information about the technical background of this class, * e.g., paper reference or book this class is based on. * * @return the technical information about this class */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.MASTERSTHESIS); result.setValue(Field.AUTHOR, "Martin Guetlein"); result.setValue(Field.YEAR, "2006"); result.setValue(Field.TITLE, "Large Scale Attribute Selection Using Wrappers"); result.setValue(Field.SCHOOL, "Albert-Ludwigs-Universitat"); result.setValue(Field.ADDRESS, "Freiburg, Germany"); return result; } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. * */ public Enumeration listOptions() { Vector newVector = new Vector(9); newVector.addElement(new Option("\tPerform initial ranking to select the" + "\n\ttop-ranked attributes.", "I", 0, "-I")); newVector.addElement(new Option( "\tNumber of top-ranked attributes that are " + "\n\ttaken into account by the search.", "K", 1, "-K ")); newVector.addElement(new Option( "\tType of Linear Forward Selection (default = 0).", "T", 1, "-T <0 = fixed-set | 1 = fixed-width>")); newVector.addElement(new Option( "\tSize of lookup cache for evaluated subsets." + "\n\tExpressed as a multiple of the number of" + "\n\tattributes in the data set. (default = 1)", "S", 1, "-S ")); newVector.addElement(new Option( "\tSubset-evaluator used for subset-size determination." + "-- -M", "E", 1, "-E ")); newVector.addElement(new Option("\tNumber of cross validation folds" + "\n\tfor subset size determination (default = 5).", "F", 1, "-F ")); newVector.addElement(new Option("\tSeed for cross validation" + "\n\tsubset size determination. (default = 1)", "R", 1, "-R ")); newVector.addElement(new Option("\tverbose on/off", "Z", 0, "-Z")); if ((m_setSizeEval != null) && (m_setSizeEval instanceof OptionHandler)) { newVector.addElement(new Option("", "", 0, "\nOptions specific to " + "evaluator " + m_setSizeEval.getClass().getName() + ":")); Enumeration enu = ((OptionHandler) m_setSizeEval).listOptions(); while (enu.hasMoreElements()) { newVector.addElement(enu.nextElement()); } } return newVector.elements(); } /** * Parses a given list of options. * * Valid options are: *

* * -I
* Perform initial ranking to select top-ranked attributes. *

* * -K
* Number of top-ranked attributes that are taken into account. *

* * -T <0 = fixed-set | 1 = fixed-width>
* Typ of Linear Forward Selection (default = 0). *

* * -S
* Size of lookup cache for evaluated subsets. Expressed as a multiple of * the number of attributes in the data set. (default = 1). *

* * -E
* class name of subset evaluator to use for subset size determination * (default = null, same subset evaluator as for ranking and final forward * selection is used). Place any evaluator options LAST on the command line * following a "--". eg. -A weka.attributeSelection.ClassifierSubsetEval ... -- * -M * * * * -F
* Number of cross validation folds for subset size determination (default = * 5). *

* * -R
* Seed for cross validation subset size determination. (default = 1) *

* * -Z
* verbose on/off. *

* * @param options * the list of options as an array of strings * @exception Exception * if an option is not supported * */ public void setOptions(String[] options) throws Exception { String optionString; resetOptions(); setPerformRanking(Utils.getFlag('I', options)); optionString = Utils.getOption('K', options); if (optionString.length() != 0) { setNumUsedAttributes(Integer.parseInt(optionString)); } optionString = Utils.getOption('T', options); if (optionString.length() != 0) { setType(new SelectedTag(Integer.parseInt(optionString), TAGS_TYPE)); } else { setType(new SelectedTag(TYPE_FIXED_SET, TAGS_TYPE)); } optionString = Utils.getOption('S', options); if (optionString.length() != 0) { setLookupCacheSize(Integer.parseInt(optionString)); } optionString = Utils.getOption('E', options); if (optionString.length() == 0) { System.out.println( "No subset size evaluator given, using evaluator that is used for final search."); m_setSizeEval = null; } else { setSubsetSizeEvaluator(ASEvaluation.forName(optionString, Utils.partitionOptions(options))); } optionString = Utils.getOption('F', options); if (optionString.length() != 0) { setNumSubsetSizeCVFolds(Integer.parseInt(optionString)); } optionString = Utils.getOption('R', options); if (optionString.length() != 0) { setSeed(Integer.parseInt(optionString)); } m_verbose = Utils.getFlag('Z', options); } /** * Set the maximum size of the evaluated subset cache (hashtable). This is * expressed as a multiplier for the number of attributes in the data set. * (default = 1). * * @param size * the maximum size of the hashtable */ public void setLookupCacheSize(int size) { if (size >= 0) { m_cacheSize = size; } } /** * Return the maximum size of the evaluated subset cache (expressed as a * multiplier for the number of attributes in a data set. * * @return the maximum size of the hashtable. */ public int getLookupCacheSize() { return m_cacheSize; } /** * Returns the tip text for this property * * @return tip text for this property suitable for displaying in the * explorer/experimenter gui */ public String lookupCacheSizeTipText() { return "Set the maximum size of the lookup cache of evaluated subsets. This is " + "expressed as a multiplier of the number of attributes in the data set. " + "(default = 1)."; } /** * Returns the tip text for this property * * @return tip text for this property suitable for displaying in the * explorer/experimenter gui */ public String performRankingTipText() { return "Perform initial ranking to select top-ranked attributes."; } /** * Perform initial ranking to select top-ranked attributes. * * @param b * true if initial ranking should be performed */ public void setPerformRanking(boolean b) { m_performRanking = b; } /** * Get boolean if initial ranking should be performed to select the * top-ranked attributes * * @return true if initial ranking should be performed */ public boolean getPerformRanking() { return m_performRanking; } /** * Returns the tip text for this property * * @return tip text for this property suitable for displaying in the * explorer/experimenter gui */ public String numUsedAttributesTipText() { return "Set the amount of top-ranked attributes that are taken into account by the search process."; } /** * Set the number of top-ranked attributes that taken into account by the * search process. * * @param k * the number of attributes * @exception Exception * if k is less than 2 */ public void setNumUsedAttributes(int k) throws Exception { if (k < 2) { throw new Exception("Value of -K must be >= 2."); } m_numUsedAttributes = k; } /** * Get the number of top-ranked attributes that taken into account by the * search process. * * @return the number of top-ranked attributes that taken into account */ public int getNumUsedAttributes() { return m_numUsedAttributes; } /** * Returns the tip text for this property * * @return tip text for this property suitable for displaying in the * explorer/experimenter gui */ public String typeTipText() { return "Set the type of the search."; } /** * Set the type * * @param t * the Linear Forward Selection type */ public void setType(SelectedTag t) { if (t.getTags() == TAGS_TYPE) { m_linearSelectionType = t.getSelectedTag().getID(); } } /** * Get the type * * @return the Linear Forward Selection type */ public SelectedTag getType() { return new SelectedTag(m_linearSelectionType, TAGS_TYPE); } /** * Returns the tip text for this property * * @return tip text for this property suitable for displaying in the * explorer/experimenter gui */ public String subsetSizeEvaluatorTipText() { return "Subset evaluator to use for subset size determination."; } /** * Set the subset evaluator to use for subset size determination. * * @param eval * the subset evaluator to use for subset size determination. */ public void setSubsetSizeEvaluator(ASEvaluation eval) throws Exception { if (!(eval instanceof SubsetEvaluator)) { throw new Exception(eval.getClass().getName() + " is no subset evaluator."); } m_setSizeEval = eval; } /** * Get the subset evaluator used for subset size determination. * * @return the evaluator used for subset size determination. */ public ASEvaluation getSubsetSizeEvaluator() { return m_setSizeEval; } /** * Returns the tip text for this property * * @return tip text for this property suitable for displaying in the * explorer/experimenter gui */ public String numSubsetSizeCVFoldsTipText() { return "Number of cross validation folds for subset size determination"; } /** * Set the number of cross validation folds for subset size determination * (default = 5). * * @param f * number of folds */ public void setNumSubsetSizeCVFolds(int f) { m_numFolds = f; } /** * Get the number of cross validation folds for subset size determination * (default = 5). * * @return number of folds */ public int getNumSubsetSizeCVFolds() { return m_numFolds; } /** * Returns the tip text for this property * * @return tip text for this property suitable for displaying in the * explorer/experimenter gui */ public String seedTipText() { return "Seed for cross validation subset size determination. (default = 1)"; } /** * Seed for cross validation subset size determination. (default = 1) * * @param s * seed */ public void setSeed(int s) { m_seed = s; } /** * Seed for cross validation subset size determination. (default = 1) * * @return seed */ public int getSeed() { return m_seed; } /** * Returns the tip text for this property * * @return tip text for this property suitable for displaying in the * explorer/experimenter gui */ public String verboseTipText() { return "Turn on verbose output for monitoring the search's progress."; } /** * Set whether verbose output should be generated. * * @param d * true if output is to be verbose. */ public void setVerbose(boolean b) { m_verbose = b; } /** * Get whether output is to be verbose * * @return true if output will be verbose */ public boolean getVerbose() { return m_verbose; } /** * Gets the current settings of LinearForwardSelection. * * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions() { String[] evaluatorOptions = new String[0]; if ((m_setSizeEval != null) && (m_setSizeEval instanceof OptionHandler)) { evaluatorOptions = ((OptionHandler) m_setSizeEval).getOptions(); } String[] options = new String[15 + evaluatorOptions.length]; int current = 0; if (m_performRanking) { options[current++] = "-I"; } options[current++] = "-K"; options[current++] = "" + m_numUsedAttributes; options[current++] = "-T"; options[current++] = "" + m_linearSelectionType; options[current++] = "-F"; options[current++] = "" + m_numFolds; options[current++] = "-S"; options[current++] = "" + m_seed; options[current++] = "-Z"; options[current++] = "" + m_verbose; if (m_setSizeEval != null) { options[current++] = "-E"; options[current++] = m_setSizeEval.getClass().getName(); } options[current++] = "--"; System.arraycopy(evaluatorOptions, 0, options, current, evaluatorOptions.length); current += evaluatorOptions.length; while (current < options.length) { options[current++] = ""; } return options; } /** * returns a description of the search as a String * * @return a description of the search */ public String toString() { StringBuffer LFSString = new StringBuffer(); LFSString.append("\tSubset Size Forward Selection.\n"); LFSString.append("\tLinear Forward Selection Type: "); if (m_linearSelectionType == TYPE_FIXED_SET) { LFSString.append("fixed-set\n"); } else { LFSString.append("fixed-width\n"); } LFSString.append("\tNumber of top-ranked attributes that are used: " + m_numUsedAttributes + "\n"); LFSString.append( "\tNumber of cross validation folds for subset size determination: " + m_numFolds + "\n"); LFSString.append("\tSeed for cross validation subset size determination: " + m_seed + "\n"); LFSString.append("\tTotal number of subsets evaluated: " + m_totalEvals + "\n"); LFSString.append("\tMerit of best subset found: " + Utils.doubleToString(Math.abs(m_bestMerit), 8, 3) + "\n"); return LFSString.toString(); } /** * Searches the attribute subset space by subset size forward selection * * @param ASEvaluator * the attribute evaluator to guide the search * @param data * the training instances. * @return an array (not necessarily ordered) of selected attribute indexes * @exception Exception * if the search can't be completed */ public int[] search(ASEvaluation ASEval, Instances data) throws Exception { m_totalEvals = 0; if (!(ASEval instanceof SubsetEvaluator)) { throw new Exception(ASEval.getClass().getName() + " is not a " + "Subset evaluator!"); } if (m_setSizeEval == null) { m_setSizeEval = ASEval; } m_numAttribs = data.numAttributes(); if (m_numUsedAttributes > m_numAttribs) { System.out.println( "Decreasing number of top-ranked attributes to total number of attributes: " + data.numAttributes()); m_numUsedAttributes = m_numAttribs; } Instances[] trainData = new Instances[m_numFolds]; Instances[] testData = new Instances[m_numFolds]; LFSMethods[] searchResults = new LFSMethods[m_numFolds]; Random random = new Random(m_seed); Instances dataCopy = new Instances(data); dataCopy.randomize(random); if (dataCopy.classAttribute().isNominal()) { dataCopy.stratify(m_numFolds); } for (int f = 0; f < m_numFolds; f++) { trainData[f] = dataCopy.trainCV(m_numFolds, f, random); testData[f] = dataCopy.testCV(m_numFolds, f); } LFSMethods LSF = new LFSMethods(); int[] ranking; if (m_performRanking) { ASEval.buildEvaluator(data); ranking = LSF.rankAttributes(data, (SubsetEvaluator) ASEval, m_verbose); } else { ranking = new int[m_numAttribs]; for (int i = 0; i < ranking.length; i++) { ranking[i] = i; } } int maxSubsetSize = 0; for (int f = 0; f < m_numFolds; f++) { if (m_verbose) { System.out.println("perform search on internal fold: " + (f + 1) + "/" + m_numFolds); } m_setSizeEval.buildEvaluator(trainData[f]); searchResults[f] = new LFSMethods(); searchResults[f].forwardSearch(m_cacheSize, new BitSet(m_numAttribs), ranking, m_numUsedAttributes, m_linearSelectionType == TYPE_FIXED_WIDTH, 1, -1, trainData[f], (SubsetEvaluator)m_setSizeEval, m_verbose); maxSubsetSize = Math.max(maxSubsetSize, searchResults[f].getBestGroup().cardinality()); } if (m_verbose) { System.out.println( "continue searches on internal folds to maxSubsetSize (" + maxSubsetSize + ")"); } for (int f = 0; f < m_numFolds; f++) { if (m_verbose) { System.out.print("perform search on internal fold: " + (f + 1) + "/" + m_numFolds + " with starting set "); LFSMethods.printGroup(searchResults[f].getBestGroup(), trainData[f].numAttributes()); } if (searchResults[f].getBestGroup().cardinality() < maxSubsetSize) { m_setSizeEval.buildEvaluator(trainData[f]); searchResults[f].forwardSearch(m_cacheSize, searchResults[f].getBestGroup(), ranking, m_numUsedAttributes, m_linearSelectionType == TYPE_FIXED_WIDTH, 1, maxSubsetSize, trainData[f], (SubsetEvaluator)m_setSizeEval, m_verbose); } } double[][] testMerit = new double[m_numFolds][maxSubsetSize + 1]; for (int f = 0; f < m_numFolds; f++) { for (int s = 1; s <= maxSubsetSize; s++) { if (HoldOutSubsetEvaluator.class.isInstance(m_setSizeEval)) { m_setSizeEval.buildEvaluator(trainData[f]); testMerit[f][s] = ((HoldOutSubsetEvaluator) m_setSizeEval).evaluateSubset(searchResults[f].getBestGroupOfSize( s), testData[f]); } else { m_setSizeEval.buildEvaluator(testData[f]); testMerit[f][s] = ((SubsetEvaluator)m_setSizeEval).evaluateSubset(searchResults[f].getBestGroupOfSize( s)); } } } double[] avgTestMerit = new double[maxSubsetSize + 1]; int finalSubsetSize = -1; for (int s = 1; s <= maxSubsetSize; s++) { for (int f = 0; f < m_numFolds; f++) { avgTestMerit[s] = ((avgTestMerit[s] * f) + testMerit[f][s]) / (double) (f + 1); } if ((finalSubsetSize == -1) || (avgTestMerit[s] > avgTestMerit[finalSubsetSize])) { finalSubsetSize = s; } if (m_verbose) { System.out.println("average merit for subset-size " + s + ": " + avgTestMerit[s]); } } if (m_verbose) { System.out.println("performing final forward selection to subset-size: " + finalSubsetSize); } ASEval.buildEvaluator(data); LSF.forwardSearch(m_cacheSize, new BitSet(m_numAttribs), ranking, m_numUsedAttributes, m_linearSelectionType == TYPE_FIXED_WIDTH, 1, finalSubsetSize, data, (SubsetEvaluator) ASEval, m_verbose); m_totalEvals = LSF.getNumEvalsTotal(); m_bestMerit = LSF.getBestMerit(); return attributeList(LSF.getBestGroup()); } /** * Reset options to default values */ protected void resetOptions() { m_performRanking = true; m_numUsedAttributes = 50; m_linearSelectionType = TYPE_FIXED_SET; m_setSizeEval = new ClassifierSubsetEval(); m_numFolds = 5; m_seed = 1; m_totalEvals = 0; m_cacheSize = 1; m_verbose = false; } /** * converts a BitSet into a list of attribute indexes * * @param group * the BitSet to convert * @return an array of attribute indexes */ protected int[] attributeList(BitSet group) { int count = 0; // count how many were selected for (int i = 0; i < m_numAttribs; i++) { if (group.get(i)) { count++; } } int[] list = new int[count]; count = 0; for (int i = 0; i < m_numAttribs; i++) { if (group.get(i)) { list[count++] = i; } } return list; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.4 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/SymmetricalUncertAttributeEval.java000066400000000000000000000257421214146114000336350ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * SymmetricalUncertAttributeEval.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.core.Capabilities; import weka.core.ContingencyTables; import weka.core.Instance; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.Utils; import weka.core.Capabilities.Capability; import weka.filters.Filter; import weka.filters.supervised.attribute.Discretize; import java.util.Enumeration; import java.util.Vector; /** * SymmetricalUncertAttributeEval :
*
* Evaluates the worth of an attribute by measuring the symmetrical uncertainty with respect to the class.
*
* SymmU(Class, Attribute) = 2 * (H(Class) - H(Class | Attribute)) / H(Class) + H(Attribute).
*

* * Valid options are:

* * 

 -M
 *  treat missing values as a seperate value.
* * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.22 $ * @see Discretize */ public class SymmetricalUncertAttributeEval extends ASEvaluation implements AttributeEvaluator, OptionHandler { /** for serialization */ static final long serialVersionUID = -8096505776132296416L; /** The training instances */ private Instances m_trainInstances; /** The class index */ private int m_classIndex; /** The number of attributes */ private int m_numAttribs; /** The number of instances */ private int m_numInstances; /** The number of classes */ private int m_numClasses; /** Treat missing values as a seperate value */ private boolean m_missing_merge; /** * Returns a string describing this attribute evaluator * @return a description of the evaluator suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "SymmetricalUncertAttributeEval :\n\nEvaluates the worth of an attribute " +"by measuring the symmetrical uncertainty with respect to the class. " +"\n\n SymmU(Class, Attribute) = 2 * (H(Class) - H(Class | Attribute)) " +"/ H(Class) + H(Attribute).\n"; } /** * Constructor */ public SymmetricalUncertAttributeEval () { resetOptions(); } /** * Returns an enumeration describing the available options. * @return an enumeration of all the available options. **/ public Enumeration listOptions () { Vector newVector = new Vector(1); newVector.addElement(new Option("\ttreat missing values as a seperate " + "value.", "M", 0, "-M")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -M
   *  treat missing values as a seperate value.
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported **/ public void setOptions (String[] options) throws Exception { resetOptions(); setMissingMerge(!(Utils.getFlag('M', options))); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String missingMergeTipText() { return "Distribute counts for missing values. Counts are distributed " +"across other values in proportion to their frequency. Otherwise, " +"missing is treated as a separate value."; } /** * distribute the counts for missing values across observed values * * @param b true=distribute missing values. */ public void setMissingMerge (boolean b) { m_missing_merge = b; } /** * get whether missing values are being distributed or not * * @return true if missing values are being distributed. */ public boolean getMissingMerge () { return m_missing_merge; } /** * Gets the current settings of WrapperSubsetEval. * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { String[] options = new String[1]; int current = 0; if (!getMissingMerge()) { options[current++] = "-M"; } while (current < options.length) { options[current++] = ""; } return options; } /** * Returns the capabilities of this evaluator. * * @return the capabilities of this evaluator * @see Capabilities */ public Capabilities getCapabilities() { Capabilities result = super.getCapabilities(); // attributes result.enable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.NUMERIC_ATTRIBUTES); result.enable(Capability.DATE_ATTRIBUTES); result.enable(Capability.MISSING_VALUES); // class result.enable(Capability.NOMINAL_CLASS); result.enable(Capability.MISSING_CLASS_VALUES); return result; } /** * Initializes a symmetrical uncertainty attribute evaluator. * Discretizes all attributes that are numeric. * * @param data set of instances serving as training data * @throws Exception if the evaluator has not been * generated successfully */ public void buildEvaluator (Instances data) throws Exception { // can evaluator handle data? getCapabilities().testWithFail(data); m_trainInstances = data; m_classIndex = m_trainInstances.classIndex(); m_numAttribs = m_trainInstances.numAttributes(); m_numInstances = m_trainInstances.numInstances(); Discretize disTransform = new Discretize(); disTransform.setUseBetterEncoding(true); disTransform.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, disTransform); m_numClasses = m_trainInstances.attribute(m_classIndex).numValues(); } /** * set options to default values */ protected void resetOptions () { m_trainInstances = null; m_missing_merge = true; } /** * evaluates an individual attribute by measuring the symmetrical * uncertainty between it and the class. * * @param attribute the index of the attribute to be evaluated * @return the uncertainty * @throws Exception if the attribute could not be evaluated */ public double evaluateAttribute (int attribute) throws Exception { int i, j, ii, jj; int nnj, nni, ni, nj; double sum = 0.0; ni = m_trainInstances.attribute(attribute).numValues() + 1; nj = m_numClasses + 1; double[] sumi, sumj; Instance inst; double temp = 0.0; sumi = new double[ni]; sumj = new double[nj]; double[][] counts = new double[ni][nj]; sumi = new double[ni]; sumj = new double[nj]; for (i = 0; i < ni; i++) { sumi[i] = 0.0; for (j = 0; j < nj; j++) { sumj[j] = 0.0; counts[i][j] = 0.0; } } // Fill the contingency table for (i = 0; i < m_numInstances; i++) { inst = m_trainInstances.instance(i); if (inst.isMissing(attribute)) { ii = ni - 1; } else { ii = (int)inst.value(attribute); } if (inst.isMissing(m_classIndex)) { jj = nj - 1; } else { jj = (int)inst.value(m_classIndex); } counts[ii][jj]++; } // get the row totals for (i = 0; i < ni; i++) { sumi[i] = 0.0; for (j = 0; j < nj; j++) { sumi[i] += counts[i][j]; sum += counts[i][j]; } } // get the column totals for (j = 0; j < nj; j++) { sumj[j] = 0.0; for (i = 0; i < ni; i++) { sumj[j] += counts[i][j]; } } // distribute missing counts if (m_missing_merge && (sumi[ni-1] < m_numInstances) && (sumj[nj-1] < m_numInstances)) { double[] i_copy = new double[sumi.length]; double[] j_copy = new double[sumj.length]; double[][] counts_copy = new double[sumi.length][sumj.length]; for (i = 0; i < ni; i++) { System.arraycopy(counts[i], 0, counts_copy[i], 0, sumj.length); } System.arraycopy(sumi, 0, i_copy, 0, sumi.length); System.arraycopy(sumj, 0, j_copy, 0, sumj.length); double total_missing = (sumi[ni - 1] + sumj[nj - 1] - counts[ni - 1][nj - 1]); // do the missing i's if (sumi[ni - 1] > 0.0) { for (j = 0; j < nj - 1; j++) { if (counts[ni - 1][j] > 0.0) { for (i = 0; i < ni - 1; i++) { temp = ((i_copy[i]/(sum - i_copy[ni - 1])) * counts[ni - 1][j]); counts[i][j] += temp; sumi[i] += temp; } counts[ni - 1][j] = 0.0; } } } sumi[ni - 1] = 0.0; // do the missing j's if (sumj[nj - 1] > 0.0) { for (i = 0; i < ni - 1; i++) { if (counts[i][nj - 1] > 0.0) { for (j = 0; j < nj - 1; j++) { temp = ((j_copy[j]/(sum - j_copy[nj - 1]))*counts[i][nj - 1]); counts[i][j] += temp; sumj[j] += temp; } counts[i][nj - 1] = 0.0; } } } sumj[nj - 1] = 0.0; // do the both missing if (counts[ni - 1][nj - 1] > 0.0 && total_missing != sum) { for (i = 0; i < ni - 1; i++) { for (j = 0; j < nj - 1; j++) { temp = (counts_copy[i][j]/(sum - total_missing)) * counts_copy[ni - 1][nj - 1]; counts[i][j] += temp; sumi[i] += temp; sumj[j] += temp; } } counts[ni - 1][nj - 1] = 0.0; } } return ContingencyTables.symmetricalUncertainty(counts); } /** * Return a description of the evaluator * @return description as a string */ public String toString () { StringBuffer text = new StringBuffer(); if (m_trainInstances == null) { text.append("\tSymmetrical Uncertainty evaluator has not been built"); } else { text.append("\tSymmetrical Uncertainty Ranking Filter"); if (!m_missing_merge) { text.append("\n\tMissing values treated as seperate"); } } text.append("\n"); return text.toString(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.22 $"); } // ============ // Test method. // ============ /** * Main method for testing this class. * * @param argv should contain the following arguments: * -t training file */ public static void main (String[] argv) { runEvaluator(new SymmetricalUncertAttributeEval(), argv); } } SymmetricalUncertAttributeSetEval.java000066400000000000000000000525561214146114000342350ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * RELEASE INFORMATION (December 27, 2004) * * FCBF algorithm: * Template obtained from Weka * Developed for Weka by Zheng Alan Zhao * December 27, 2004 * * FCBF algorithm is a feature selection method based on Symmetrical Uncertainty Measurement for * relevance redundancy analysis. The details of FCBF algorithm are in: * * Lei Yu, Huan Liu: Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution. In: Proceedings of the Twentieth International Conference on Machine Learning, 856-863, 2003. * * * * CONTACT INFORMATION * * For algorithm implementation: * Zheng Zhao: zhaozheng at asu.edu * * For the algorithm: * Lei Yu: leiyu at asu.edu * Huan Liu: hliu at asu.edu * * Data Mining and Machine Learning Lab * Computer Science and Engineering Department * Fulton School of Engineering * Arizona State University * Tempe, AZ 85287 * * SymmetricalUncertAttributeSetEval.java * * Copyright (C) 2004 Data Mining and Machine Learning Lab, * Computer Science and Engineering Department, * Fulton School of Engineering, * Arizona State University * */ package weka.attributeSelection; import weka.core.Capabilities; import weka.core.ContingencyTables; import weka.core.Instance; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.TechnicalInformation; import weka.core.TechnicalInformationHandler; import weka.core.Utils; import weka.core.Capabilities.Capability; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; import weka.filters.Filter; import weka.filters.supervised.attribute.Discretize; import java.util.Enumeration; import java.util.Vector; /** * SymmetricalUncertAttributeSetEval :
*
* Evaluates the worth of a set attributes by measuring the symmetrical uncertainty with respect to another set of attributes.
*
* SymmU(AttributeSet2, AttributeSet1) = 2 * (H(AttributeSet2) - H(AttributeSet1 | AttributeSet2)) / H(AttributeSet2) + H(AttributeSet1).
*
* For more information see:
*
* Lei Yu, Huan Liu: Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution. In: Proceedings of the Twentieth International Conference on Machine Learning, 856-863, 2003. *

* * BibTeX: * 

 * @inproceedings{Yu2003,
 *    author = {Lei Yu and Huan Liu},
 *    booktitle = {Proceedings of the Twentieth International Conference on Machine Learning},
 *    pages = {856-863},
 *    publisher = {AAAI Press},
 *    title = {Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution},
 *    year = {2003}
 * }
 *
*

* * Valid options are:

* * 

 -M
 *  treat missing values as a seperate value.
* * * @author Zheng Zhao: zhaozheng at asu.edu * @version $Revision: 1.7 $ * @see Discretize */ public class SymmetricalUncertAttributeSetEval extends AttributeSetEvaluator implements OptionHandler, TechnicalInformationHandler { /** for serialization */ static final long serialVersionUID = 8351377335495873202L; /** The training instances */ private Instances m_trainInstances; /** The class index */ private int m_classIndex; /** The number of attributes */ private int m_numAttribs; /** The number of instances */ private int m_numInstances; /** The number of classes */ private int m_numClasses; /** Treat missing values as a seperate value */ private boolean m_missing_merge; /** * Returns a string describing this attribute evaluator * @return a description of the evaluator suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "SymmetricalUncertAttributeSetEval :\n\nEvaluates the worth of a set attributes " +"by measuring the symmetrical uncertainty with respect to another set of attributes. " +"\n\n SymmU(AttributeSet2, AttributeSet1) = 2 * (H(AttributeSet2) - H(AttributeSet1 | AttributeSet2)) " +"/ H(AttributeSet2) + H(AttributeSet1).\n\n" + "For more information see:\n\n" + getTechnicalInformation().toString(); } /** * Returns an instance of a TechnicalInformation object, containing * detailed information about the technical background of this class, * e.g., paper reference or book this class is based on. * * @return the technical information about this class */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.INPROCEEDINGS); result.setValue(Field.AUTHOR, "Lei Yu and Huan Liu"); result.setValue(Field.TITLE, "Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution"); result.setValue(Field.BOOKTITLE, "Proceedings of the Twentieth International Conference on Machine Learning"); result.setValue(Field.YEAR, "2003"); result.setValue(Field.PAGES, "856-863"); result.setValue(Field.PUBLISHER, "AAAI Press"); return result; } /** * Constructor */ public SymmetricalUncertAttributeSetEval () { resetOptions(); } /** * Returns an enumeration describing the available options. * @return an enumeration of all the available options. **/ public Enumeration listOptions () { Vector newVector = new Vector(1); newVector.addElement(new Option("\ttreat missing values as a seperate " + "value.", "M", 0, "-M")); return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -M
   *  treat missing values as a seperate value.
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions (String[] options) throws Exception { resetOptions(); setMissingMerge(!(Utils.getFlag('M', options))); } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String missingMergeTipText() { return "Distribute counts for missing values. Counts are distributed " +"across other values in proportion to their frequency. Otherwise, " +"missing is treated as a separate value."; } /** * distribute the counts for missing values across observed values * * @param b true=distribute missing values. */ public void setMissingMerge (boolean b) { m_missing_merge = b; } /** * get whether missing values are being distributed or not * * @return true if missing values are being distributed. */ public boolean getMissingMerge () { return m_missing_merge; } /** * Gets the current settings of WrapperSubsetEval. * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { String[] options = new String[1]; int current = 0; if (!getMissingMerge()) { options[current++] = "-M"; } while (current < options.length) { options[current++] = ""; } return options; } /** * Returns the capabilities of this evaluator. * * @return the capabilities of this evaluator * @see Capabilities */ public Capabilities getCapabilities() { Capabilities result = super.getCapabilities(); // attributes result.enable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.NUMERIC_ATTRIBUTES); result.enable(Capability.DATE_ATTRIBUTES); result.enable(Capability.MISSING_VALUES); // class result.enable(Capability.NOMINAL_CLASS); result.enable(Capability.MISSING_CLASS_VALUES); return result; } /** * Initializes a symmetrical uncertainty attribute evaluator. * Discretizes all attributes that are numeric. * * @param data set of instances serving as training data * @throws Exception if the evaluator has not been * generated successfully */ public void buildEvaluator (Instances data) throws Exception { // can evaluator handle data? getCapabilities().testWithFail(data); m_trainInstances = data; m_classIndex = m_trainInstances.classIndex(); m_numAttribs = m_trainInstances.numAttributes(); m_numInstances = m_trainInstances.numInstances(); Discretize disTransform = new Discretize(); disTransform.setUseBetterEncoding(true); disTransform.setInputFormat(m_trainInstances); m_trainInstances = Filter.useFilter(m_trainInstances, disTransform); m_numClasses = m_trainInstances.attribute(m_classIndex).numValues(); } /** * set options to default values */ protected void resetOptions () { m_trainInstances = null; m_missing_merge = true; } /** * evaluates an individual attribute by measuring the symmetrical * uncertainty between it and the class. * * @param attribute the index of the attribute to be evaluated * @return the uncertainty * @throws Exception if the attribute could not be evaluated */ public double evaluateAttribute (int attribute) throws Exception { int i, j, ii, jj; int ni, nj; double sum = 0.0; ni = m_trainInstances.attribute(attribute).numValues() + 1; nj = m_numClasses + 1; double[] sumi, sumj; Instance inst; double temp = 0.0; sumi = new double[ni]; sumj = new double[nj]; double[][] counts = new double[ni][nj]; sumi = new double[ni]; sumj = new double[nj]; for (i = 0; i < ni; i++) { sumi[i] = 0.0; for (j = 0; j < nj; j++) { sumj[j] = 0.0; counts[i][j] = 0.0; } } // Fill the contingency table for (i = 0; i < m_numInstances; i++) { inst = m_trainInstances.instance(i); if (inst.isMissing(attribute)) { ii = ni - 1; } else { ii = (int)inst.value(attribute); } if (inst.isMissing(m_classIndex)) { jj = nj - 1; } else { jj = (int)inst.value(m_classIndex); } counts[ii][jj]++; } // get the row totals for (i = 0; i < ni; i++) { sumi[i] = 0.0; for (j = 0; j < nj; j++) { //there are how many happen of a special feature value sumi[i] += counts[i][j]; sum += counts[i][j]; } } // get the column totals for (j = 0; j < nj; j++) { sumj[j] = 0.0; for (i = 0; i < ni; i++) { //a class value include how many instance. sumj[j] += counts[i][j]; } } // distribute missing counts if (m_missing_merge && (sumi[ni-1] < m_numInstances) && (sumj[nj-1] < m_numInstances)) { double[] i_copy = new double[sumi.length]; double[] j_copy = new double[sumj.length]; double[][] counts_copy = new double[sumi.length][sumj.length]; for (i = 0; i < ni; i++) { System.arraycopy(counts[i], 0, counts_copy[i], 0, sumj.length); } System.arraycopy(sumi, 0, i_copy, 0, sumi.length); System.arraycopy(sumj, 0, j_copy, 0, sumj.length); double total_missing = (sumi[ni - 1] + sumj[nj - 1] - counts[ni - 1][nj - 1]); // do the missing i's if (sumi[ni - 1] > 0.0) { //sumi[ni - 1]: missing value contains how many values. for (j = 0; j < nj - 1; j++) { if (counts[ni - 1][j] > 0.0) { for (i = 0; i < ni - 1; i++) { temp = ((i_copy[i]/(sum - i_copy[ni - 1])) * counts[ni - 1][j]); counts[i][j] += temp; //according to the probability of value i we distribute account of the missing degree of a class lable to it sumi[i] += temp; } counts[ni - 1][j] = 0.0; } } } sumi[ni - 1] = 0.0; // do the missing j's if (sumj[nj - 1] > 0.0) { for (i = 0; i < ni - 1; i++) { if (counts[i][nj - 1] > 0.0) { for (j = 0; j < nj - 1; j++) { temp = ((j_copy[j]/(sum - j_copy[nj - 1]))*counts[i][nj - 1]); counts[i][j] += temp; sumj[j] += temp; } counts[i][nj - 1] = 0.0; } } } sumj[nj - 1] = 0.0; // do the both missing if (counts[ni - 1][nj - 1] > 0.0 && total_missing != sum) { for (i = 0; i < ni - 1; i++) { for (j = 0; j < nj - 1; j++) { temp = (counts_copy[i][j]/(sum - total_missing)) * counts_copy[ni - 1][nj - 1]; counts[i][j] += temp; sumi[i] += temp; sumj[j] += temp; } } counts[ni - 1][nj - 1] = 0.0; } } return ContingencyTables.symmetricalUncertainty(counts); } /** * calculate symmetrical uncertainty between sets of attributes * * @param attributes the indexes of the attributes * @param classAttributes the indexes of the attributes whose combination will * be used as class label * @return the uncertainty * @throws Exception if the attribute could not be evaluated */ public double evaluateAttribute (int[] attributes, int[] classAttributes) throws Exception { int i, j; //variable for looping. int p; //variable for looping. int ii, jj; //specifying the position in the contingency table. int nnj, nni; //counting base for attributes[]. int ni, nj; //the nubmer of rows and columns in the ContingencyTables. double sum = 0.0; boolean b_missing_attribute = false; boolean b_missing_classAtrribute = false; if(attributes.length==0) { throw new Exception("the parameter attributes[] is empty;SEQ:W-FS-Eval-SUAS-001"); } if(classAttributes.length==0) { throw new Exception("the parameter classAttributes[] is empty;SEQ:W-FS-Eval-SUAS-002"); } /*calculate the number of the rows in ContingencyTable*/ ni = m_trainInstances.attribute(attributes[0]).numValues(); if (ni == 0) { throw new Exception("an attribute is empty;SEQ:W-FS-Eval-SUAS-003;"+1); } for (i = 1;i=0; p--) { if (inst.isMissing(attributes[p])) { b_missing_attribute = true; } ii = ((int)inst.value(attributes[p])*nni)+ii; if (p=0; p--) { if (inst.isMissing(classAttributes[p])) { b_missing_classAtrribute = true; } jj = ((int)inst.value(classAttributes[p])*nnj)+jj; if (p 0.0) { //sumi[ni - 1]: missing value contains how many values. for (j = 0; j < nj - 1; j++) { if (counts[ni - 1][j] > 0.0) { for (i = 0; i < ni - 1; i++) { temp = ((i_copy[i]/(sum - i_copy[ni - 1])) * counts[ni - 1][j]); counts[i][j] += temp; //according to the probability of value i we distribute account of the missing degree of a class lable to it sumi[i] += temp; } counts[ni - 1][j] = 0.0; } } } sumi[ni - 1] = 0.0; // do the missing j's if (sumj[nj - 1] > 0.0) { for (i = 0; i < ni - 1; i++) { if (counts[i][nj - 1] > 0.0) { for (j = 0; j < nj - 1; j++) { temp = ((j_copy[j]/(sum - j_copy[nj - 1]))*counts[i][nj - 1]); counts[i][j] += temp; sumj[j] += temp; } counts[i][nj - 1] = 0.0; } } } sumj[nj - 1] = 0.0; // do the both missing if (counts[ni - 1][nj - 1] > 0.0 && total_missing != sum) { for (i = 0; i < ni - 1; i++) { for (j = 0; j < nj - 1; j++) { temp = (counts_copy[i][j]/(sum - total_missing)) * counts_copy[ni - 1][nj - 1]; counts[i][j] += temp; sumi[i] += temp; sumj[j] += temp; } } counts[ni - 1][nj - 1] = 0.0; } } return ContingencyTables.symmetricalUncertainty(counts); } /** * Return a description of the evaluator * @return description as a string */ public String toString () { StringBuffer text = new StringBuffer(); if (m_trainInstances == null) { text.append("\tSymmetrical Uncertainty evaluator has not been built"); } else { text.append("\tSymmetrical Uncertainty Ranking Filter"); if (!m_missing_merge) { text.append("\n\tMissing values treated as seperate"); } } text.append("\n"); return text.toString(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.7 $"); } // ============ // Test method. // ============ /** * Main method for testing this class. * * @param argv should contain the following arguments: * -t training file */ public static void main (String[] argv) { runEvaluator(new SymmetricalUncertAttributeSetEval(), argv); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/UnsupervisedAttributeEvaluator.java000066400000000000000000000023431214146114000337220ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * UnsupervisedAttributeEvaluator.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; /** * Abstract unsupervised attribute evaluator. * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.10 $ */ public abstract class UnsupervisedAttributeEvaluator extends ASEvaluation implements AttributeEvaluator { /** for serialization */ private static final long serialVersionUID = -4100897318675336178L; } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/UnsupervisedSubsetEvaluator.java000066400000000000000000000033071214146114000332250ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * UnsupervisedSubsetEvaluator.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.clusterers.Clusterer; /** * Abstract unsupervised attribute subset evaluator. * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.10 $ */ public abstract class UnsupervisedSubsetEvaluator extends ASEvaluation implements SubsetEvaluator { /** for serialization */ static final long serialVersionUID = 627934376267488763L; /** * Return the number of clusters used by the subset evaluator * * @return the number of clusters used * @exception Exception if an error occurs */ public abstract int getNumClusters() throws Exception; /** * Get the clusterer * * @return the clusterer */ public abstract Clusterer getClusterer(); /** * Set the clusterer to use * * @param d the clusterer to use */ public abstract void setClusterer(Clusterer d); } rlichtenwalter-LPmade-ba322c3/weka/src/weka/attributeSelection/WrapperSubsetEval.java000066400000000000000000000450111214146114000310740ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * WrapperSubsetEval.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.attributeSelection; import weka.classifiers.Classifier; import weka.classifiers.Evaluation; import weka.classifiers.rules.ZeroR; import weka.core.Capabilities; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.TechnicalInformation; import weka.core.TechnicalInformationHandler; import weka.core.Utils; import weka.core.Capabilities.Capability; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; import weka.filters.Filter; import weka.filters.unsupervised.attribute.Remove; import java.util.BitSet; import java.util.Enumeration; import java.util.Random; import java.util.Vector; /** * WrapperSubsetEval:
*
* Evaluates attribute sets by using a learning scheme. Cross validation is used to estimate the accuracy of the learning scheme for a set of attributes.
*
* For more information see:
*
* Ron Kohavi, George H. John (1997). Wrappers for feature subset selection. Artificial Intelligence. 97(1-2):273-324. *

* * BibTeX: * 

 * @article{Kohavi1997,
 *    author = {Ron Kohavi and George H. John},
 *    journal = {Artificial Intelligence},
 *    note = {Special issue on relevance},
 *    number = {1-2},
 *    pages = {273-324},
 *    title = {Wrappers for feature subset selection},
 *    volume = {97},
 *    year = {1997},
 *    ISSN = {0004-3702}
 * }
 *
*

* * Valid options are:

* * 

 -B <base learner>
 *  class name of base learner to use for  accuracy estimation.
 *  Place any classifier options LAST on the command line
 *  following a "--". eg.:
 *   -B weka.classifiers.bayes.NaiveBayes ... -- -K
 *  (default: weka.classifiers.rules.ZeroR)
* * 
 -F <num>
 *  number of cross validation folds to use for estimating accuracy.
 *  (default=5)
* * 
 -R <seed>
 *  Seed for cross validation accuracy testimation.
 *  (default = 1)
* * 
 -T <num>
 *  threshold by which to execute another cross validation
 *  (standard deviation---expressed as a percentage of the mean).
 *  (default: 0.01 (1%))
* * 
 
 * Options specific to scheme weka.classifiers.rules.ZeroR:
 *
* * 
 -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
* * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @version $Revision: 1.31 $ */ public class WrapperSubsetEval extends ASEvaluation implements SubsetEvaluator, OptionHandler, TechnicalInformationHandler { /** for serialization */ static final long serialVersionUID = -4573057658746728675L; /** training instances */ private Instances m_trainInstances; /** class index */ private int m_classIndex; /** number of attributes in the training data */ private int m_numAttribs; /** number of instances in the training data */ private int m_numInstances; /** holds an evaluation object */ private Evaluation m_Evaluation; /** holds the base classifier object */ private Classifier m_BaseClassifier; /** number of folds to use for cross validation */ private int m_folds; /** random number seed */ private int m_seed; /** * the threshold by which to do further cross validations when * estimating the accuracy of a subset */ private double m_threshold; /** * Returns a string describing this attribute evaluator * @return a description of the evaluator suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "WrapperSubsetEval:\n\n" +"Evaluates attribute sets by using a learning scheme. Cross " +"validation is used to estimate the accuracy of the learning " +"scheme for a set of attributes.\n\n" + "For more information see:\n\n" + getTechnicalInformation().toString(); } /** * Returns an instance of a TechnicalInformation object, containing * detailed information about the technical background of this class, * e.g., paper reference or book this class is based on. * * @return the technical information about this class */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.ARTICLE); result.setValue(Field.AUTHOR, "Ron Kohavi and George H. John"); result.setValue(Field.YEAR, "1997"); result.setValue(Field.TITLE, "Wrappers for feature subset selection"); result.setValue(Field.JOURNAL, "Artificial Intelligence"); result.setValue(Field.VOLUME, "97"); result.setValue(Field.NUMBER, "1-2"); result.setValue(Field.PAGES, "273-324"); result.setValue(Field.NOTE, "Special issue on relevance"); result.setValue(Field.ISSN, "0004-3702"); return result; } /** * Constructor. Calls restOptions to set default options **/ public WrapperSubsetEval () { resetOptions(); } /** * Returns an enumeration describing the available options. * @return an enumeration of all the available options. **/ public Enumeration listOptions () { Vector newVector = new Vector(4); newVector.addElement(new Option( "\tclass name of base learner to use for \taccuracy estimation.\n" + "\tPlace any classifier options LAST on the command line\n" + "\tfollowing a \"--\". eg.:\n" + "\t\t-B weka.classifiers.bayes.NaiveBayes ... -- -K\n" + "\t(default: weka.classifiers.rules.ZeroR)", "B", 1, "-B ")); newVector.addElement(new Option( "\tnumber of cross validation folds to use for estimating accuracy.\n" + "\t(default=5)", "F", 1, "-F ")); newVector.addElement(new Option( "\tSeed for cross validation accuracy testimation.\n" + "\t(default = 1)", "R", 1,"-R ")); newVector.addElement(new Option( "\tthreshold by which to execute another cross validation\n" + "\t(standard deviation---expressed as a percentage of the mean).\n" + "\t(default: 0.01 (1%))", "T", 1, "-T ")); if ((m_BaseClassifier != null) && (m_BaseClassifier instanceof OptionHandler)) { newVector.addElement(new Option("", "", 0, "\nOptions specific to scheme " + m_BaseClassifier.getClass().getName() + ":")); Enumeration enu = ((OptionHandler)m_BaseClassifier).listOptions(); while (enu.hasMoreElements()) { newVector.addElement(enu.nextElement()); } } return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -B <base learner>
   *  class name of base learner to use for  accuracy estimation.
   *  Place any classifier options LAST on the command line
   *  following a "--". eg.:
   *   -B weka.classifiers.bayes.NaiveBayes ... -- -K
   *  (default: weka.classifiers.rules.ZeroR)
* * 
 -F <num>
   *  number of cross validation folds to use for estimating accuracy.
   *  (default=5)
* * 
 -R <seed>
   *  Seed for cross validation accuracy testimation.
   *  (default = 1)
* * 
 -T <num>
   *  threshold by which to execute another cross validation
   *  (standard deviation---expressed as a percentage of the mean).
   *  (default: 0.01 (1%))
* * 
 
   * Options specific to scheme weka.classifiers.rules.ZeroR:
   *
* * 
 -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions (String[] options) throws Exception { String optionString; resetOptions(); optionString = Utils.getOption('B', options); if (optionString.length() == 0) optionString = ZeroR.class.getName(); setClassifier(Classifier.forName(optionString, Utils.partitionOptions(options))); optionString = Utils.getOption('F', options); if (optionString.length() != 0) { setFolds(Integer.parseInt(optionString)); } optionString = Utils.getOption('R', options); if (optionString.length() != 0) { setSeed(Integer.parseInt(optionString)); } // optionString = Utils.getOption('S',options); // if (optionString.length() != 0) // { // seed = Integer.parseInt(optionString); // } optionString = Utils.getOption('T', options); if (optionString.length() != 0) { Double temp; temp = Double.valueOf(optionString); setThreshold(temp.doubleValue()); } } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String thresholdTipText() { return "Repeat xval if stdev of mean exceeds this value."; } /** * Set the value of the threshold for repeating cross validation * * @param t the value of the threshold */ public void setThreshold (double t) { m_threshold = t; } /** * Get the value of the threshold * * @return the threshold as a double */ public double getThreshold () { return m_threshold; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String foldsTipText() { return "Number of xval folds to use when estimating subset accuracy."; } /** * Set the number of folds to use for accuracy estimation * * @param f the number of folds */ public void setFolds (int f) { m_folds = f; } /** * Get the number of folds used for accuracy estimation * * @return the number of folds */ public int getFolds () { return m_folds; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String seedTipText() { return "Seed to use for randomly generating xval splits."; } /** * Set the seed to use for cross validation * * @param s the seed */ public void setSeed (int s) { m_seed = s; } /** * Get the random number seed used for cross validation * * @return the seed */ public int getSeed () { return m_seed; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String classifierTipText() { return "Classifier to use for estimating the accuracy of subsets"; } /** * Set the classifier to use for accuracy estimation * * @param newClassifier the Classifier to use. */ public void setClassifier (Classifier newClassifier) { m_BaseClassifier = newClassifier; } /** * Get the classifier used as the base learner. * * @return the classifier used as the classifier */ public Classifier getClassifier () { return m_BaseClassifier; } /** * Gets the current settings of WrapperSubsetEval. * * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions () { String[] classifierOptions = new String[0]; if ((m_BaseClassifier != null) && (m_BaseClassifier instanceof OptionHandler)) { classifierOptions = ((OptionHandler)m_BaseClassifier).getOptions(); } String[] options = new String[9 + classifierOptions.length]; int current = 0; if (getClassifier() != null) { options[current++] = "-B"; options[current++] = getClassifier().getClass().getName(); } options[current++] = "-F"; options[current++] = "" + getFolds(); options[current++] = "-T"; options[current++] = "" + getThreshold(); options[current++] = "-R"; options[current++] = "" + getSeed(); options[current++] = "--"; System.arraycopy(classifierOptions, 0, options, current, classifierOptions.length); current += classifierOptions.length; while (current < options.length) { options[current++] = ""; } return options; } protected void resetOptions () { m_trainInstances = null; m_Evaluation = null; m_BaseClassifier = new ZeroR(); m_folds = 5; m_seed = 1; m_threshold = 0.01; } /** * Returns the capabilities of this evaluator. * * @return the capabilities of this evaluator * @see Capabilities */ public Capabilities getCapabilities() { Capabilities result; if (getClassifier() == null) result = super.getCapabilities(); else result = getClassifier().getCapabilities(); // set dependencies for (Capability cap: Capability.values()) result.enableDependency(cap); result.setMinimumNumberInstances(getFolds()); return result; } /** * Generates a attribute evaluator. Has to initialize all fields of the * evaluator that are not being set via options. * * @param data set of instances serving as training data * @throws Exception if the evaluator has not been * generated successfully */ public void buildEvaluator (Instances data) throws Exception { // can evaluator handle data? getCapabilities().testWithFail(data); m_trainInstances = data; m_classIndex = m_trainInstances.classIndex(); m_numAttribs = m_trainInstances.numAttributes(); m_numInstances = m_trainInstances.numInstances(); } /** * Evaluates a subset of attributes * * @param subset a bitset representing the attribute subset to be * evaluated * @return the error rate * @throws Exception if the subset could not be evaluated */ public double evaluateSubset (BitSet subset) throws Exception { double errorRate = 0; double[] repError = new double[5]; int numAttributes = 0; int i, j; Random Rnd = new Random(m_seed); Remove delTransform = new Remove(); delTransform.setInvertSelection(true); // copy the instances Instances trainCopy = new Instances(m_trainInstances); // count attributes set in the BitSet for (i = 0; i < m_numAttribs; i++) { if (subset.get(i)) { numAttributes++; } } // set up an array of attribute indexes for the filter (+1 for the class) int[] featArray = new int[numAttributes + 1]; for (i = 0, j = 0; i < m_numAttribs; i++) { if (subset.get(i)) { featArray[j++] = i; } } featArray[j] = m_classIndex; delTransform.setAttributeIndicesArray(featArray); delTransform.setInputFormat(trainCopy); trainCopy = Filter.useFilter(trainCopy, delTransform); // max of 5 repititions ofcross validation for (i = 0; i < 5; i++) { m_Evaluation = new Evaluation(trainCopy); m_Evaluation.crossValidateModel(m_BaseClassifier, trainCopy, m_folds, Rnd); repError[i] = m_Evaluation.errorRate(); // check on the standard deviation if (!repeat(repError, i + 1)) { i++; break; } } for (j = 0; j < i; j++) { errorRate += repError[j]; } errorRate /= (double)i; m_Evaluation = null; return -errorRate; } /** * Returns a string describing the wrapper * * @return the description as a string */ public String toString () { StringBuffer text = new StringBuffer(); if (m_trainInstances == null) { text.append("\tWrapper subset evaluator has not been built yet\n"); } else { text.append("\tWrapper Subset Evaluator\n"); text.append("\tLearning scheme: " + getClassifier().getClass().getName() + "\n"); text.append("\tScheme options: "); String[] classifierOptions = new String[0]; if (m_BaseClassifier instanceof OptionHandler) { classifierOptions = ((OptionHandler)m_BaseClassifier).getOptions(); for (int i = 0; i < classifierOptions.length; i++) { text.append(classifierOptions[i] + " "); } } text.append("\n"); if (m_trainInstances.attribute(m_classIndex).isNumeric()) { text.append("\tAccuracy estimation: RMSE\n"); } else { text.append("\tAccuracy estimation: classification error\n"); } text.append("\tNumber of folds for accuracy estimation: " + m_folds + "\n"); } return text.toString(); } /** * decides whether to do another repeat of cross validation. If the * standard deviation of the cross validations * is greater than threshold% of the mean (default 1%) then another * repeat is done. * * @param repError an array of cross validation results * @param entries the number of cross validations done so far * @return true if another cv is to be done */ private boolean repeat (double[] repError, int entries) { int i; double mean = 0; double variance = 0; if (entries == 1) { return true; } for (i = 0; i < entries; i++) { mean += repError[i]; } mean /= (double)entries; for (i = 0; i < entries; i++) { variance += ((repError[i] - mean)*(repError[i] - mean)); } variance /= (double)entries; if (variance > 0) { variance = Math.sqrt(variance); } if ((variance/mean) > m_threshold) { return true; } return false; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.31 $"); } /** * Main method for testing this class. * * @param args the options */ public static void main (String[] args) { runEvaluator(new WrapperSubsetEval(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/build.xml000066400000000000000000000526201214146114000225670ustar00rootroot00000000000000 rlichtenwalter-LPmade-ba322c3/weka/src/weka/classifiers/000077500000000000000000000000001214146114000232505ustar00rootroot00000000000000rlichtenwalter-LPmade-ba322c3/weka/src/weka/classifiers/BVDecompose.java000066400000000000000000000474001214146114000262660ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * BVDecompose.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.classifiers; import weka.core.Attribute; import weka.core.Instance; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import weka.core.TechnicalInformation; import weka.core.TechnicalInformationHandler; import weka.core.Utils; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; import java.io.BufferedReader; import java.io.FileReader; import java.io.Reader; import java.util.Enumeration; import java.util.Random; import java.util.Vector; /** * Class for performing a Bias-Variance decomposition on any classifier using the method specified in:
*
* Ron Kohavi, David H. Wolpert: Bias Plus Variance Decomposition for Zero-One Loss Functions. In: Machine Learning: Proceedings of the Thirteenth International Conference, 275-283, 1996. *

* * BibTeX: * 

 * @inproceedings{Kohavi1996,
 *    author = {Ron Kohavi and David H. Wolpert},
 *    booktitle = {Machine Learning: Proceedings of the Thirteenth International Conference},
 *    editor = {Lorenza Saitta},
 *    pages = {275-283},
 *    publisher = {Morgan Kaufmann},
 *    title = {Bias Plus Variance Decomposition for Zero-One Loss Functions},
 *    year = {1996},
 *    PS = {http://robotics.stanford.edu/\~ronnyk/biasVar.ps}
 * }
 *
*

* * Valid options are:

* * 

 -c <class index>
 *  The index of the class attribute.
 *  (default last)
* * 
 -t <name of arff file>
 *  The name of the arff file used for the decomposition.
* * 
 -T <training pool size>
 *  The number of instances placed in the training pool.
 *  The remainder will be used for testing. (default 100)
* * 
 -s <seed>
 *  The random number seed used.
* * 
 -x <num>
 *  The number of training repetitions used.
 *  (default 50)
* * 
 -D
 *  Turn on debugging output.
* * 
 -W <classifier class name>
 *  Full class name of the learner used in the decomposition.
 *  eg: weka.classifiers.bayes.NaiveBayes
* * 
 
 * Options specific to learner weka.classifiers.rules.ZeroR:
 *
* * 
 -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
* * * Options after -- are passed to the designated sub-learner.

* * @author Len Trigg (trigg@cs.waikato.ac.nz) * @version $Revision: 1.15 $ */ public class BVDecompose implements OptionHandler, TechnicalInformationHandler, RevisionHandler { /** Debugging mode, gives extra output if true */ protected boolean m_Debug; /** An instantiated base classifier used for getting and testing options. */ protected Classifier m_Classifier = new weka.classifiers.rules.ZeroR(); /** The options to be passed to the base classifier. */ protected String [] m_ClassifierOptions; /** The number of train iterations */ protected int m_TrainIterations = 50; /** The name of the data file used for the decomposition */ protected String m_DataFileName; /** The index of the class attribute */ protected int m_ClassIndex = -1; /** The random number seed */ protected int m_Seed = 1; /** The calculated bias (squared) */ protected double m_Bias; /** The calculated variance */ protected double m_Variance; /** The calculated sigma (squared) */ protected double m_Sigma; /** The error rate */ protected double m_Error; /** The number of instances used in the training pool */ protected int m_TrainPoolSize = 100; /** * Returns a string describing this object * @return a description of the classifier suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "Class for performing a Bias-Variance decomposition on any classifier " + "using the method specified in:\n\n" + getTechnicalInformation().toString(); } /** * Returns an instance of a TechnicalInformation object, containing * detailed information about the technical background of this class, * e.g., paper reference or book this class is based on. * * @return the technical information about this class */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.INPROCEEDINGS); result.setValue(Field.AUTHOR, "Ron Kohavi and David H. Wolpert"); result.setValue(Field.YEAR, "1996"); result.setValue(Field.TITLE, "Bias Plus Variance Decomposition for Zero-One Loss Functions"); result.setValue(Field.BOOKTITLE, "Machine Learning: Proceedings of the Thirteenth International Conference"); result.setValue(Field.PUBLISHER, "Morgan Kaufmann"); result.setValue(Field.EDITOR, "Lorenza Saitta"); result.setValue(Field.PAGES, "275-283"); result.setValue(Field.PS, "http://robotics.stanford.edu/~ronnyk/biasVar.ps"); return result; } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector newVector = new Vector(7); newVector.addElement(new Option( "\tThe index of the class attribute.\n"+ "\t(default last)", "c", 1, "-c ")); newVector.addElement(new Option( "\tThe name of the arff file used for the decomposition.", "t", 1, "-t ")); newVector.addElement(new Option( "\tThe number of instances placed in the training pool.\n" + "\tThe remainder will be used for testing. (default 100)", "T", 1, "-T ")); newVector.addElement(new Option( "\tThe random number seed used.", "s", 1, "-s ")); newVector.addElement(new Option( "\tThe number of training repetitions used.\n" +"\t(default 50)", "x", 1, "-x ")); newVector.addElement(new Option( "\tTurn on debugging output.", "D", 0, "-D")); newVector.addElement(new Option( "\tFull class name of the learner used in the decomposition.\n" +"\teg: weka.classifiers.bayes.NaiveBayes", "W", 1, "-W ")); if ((m_Classifier != null) && (m_Classifier instanceof OptionHandler)) { newVector.addElement(new Option( "", "", 0, "\nOptions specific to learner " + m_Classifier.getClass().getName() + ":")); Enumeration enu = ((OptionHandler)m_Classifier).listOptions(); while (enu.hasMoreElements()) { newVector.addElement(enu.nextElement()); } } return newVector.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -c <class index>
   *  The index of the class attribute.
   *  (default last)
* * 
 -t <name of arff file>
   *  The name of the arff file used for the decomposition.
* * 
 -T <training pool size>
   *  The number of instances placed in the training pool.
   *  The remainder will be used for testing. (default 100)
* * 
 -s <seed>
   *  The random number seed used.
* * 
 -x <num>
   *  The number of training repetitions used.
   *  (default 50)
* * 
 -D
   *  Turn on debugging output.
* * 
 -W <classifier class name>
   *  Full class name of the learner used in the decomposition.
   *  eg: weka.classifiers.bayes.NaiveBayes
* * 
 
   * Options specific to learner weka.classifiers.rules.ZeroR:
   *
* * 
 -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console
* * * Options after -- are passed to the designated sub-learner.

* * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { setDebug(Utils.getFlag('D', options)); String classIndex = Utils.getOption('c', options); if (classIndex.length() != 0) { if (classIndex.toLowerCase().equals("last")) { setClassIndex(0); } else if (classIndex.toLowerCase().equals("first")) { setClassIndex(1); } else { setClassIndex(Integer.parseInt(classIndex)); } } else { setClassIndex(0); } String trainIterations = Utils.getOption('x', options); if (trainIterations.length() != 0) { setTrainIterations(Integer.parseInt(trainIterations)); } else { setTrainIterations(50); } String trainPoolSize = Utils.getOption('T', options); if (trainPoolSize.length() != 0) { setTrainPoolSize(Integer.parseInt(trainPoolSize)); } else { setTrainPoolSize(100); } String seedString = Utils.getOption('s', options); if (seedString.length() != 0) { setSeed(Integer.parseInt(seedString)); } else { setSeed(1); } String dataFile = Utils.getOption('t', options); if (dataFile.length() == 0) { throw new Exception("An arff file must be specified" + " with the -t option."); } setDataFileName(dataFile); String classifierName = Utils.getOption('W', options); if (classifierName.length() == 0) { throw new Exception("A learner must be specified with the -W option."); } setClassifier(Classifier.forName(classifierName, Utils.partitionOptions(options))); } /** * Gets the current settings of the CheckClassifier. * * @return an array of strings suitable for passing to setOptions */ public String [] getOptions() { String [] classifierOptions = new String [0]; if ((m_Classifier != null) && (m_Classifier instanceof OptionHandler)) { classifierOptions = ((OptionHandler)m_Classifier).getOptions(); } String [] options = new String [classifierOptions.length + 14]; int current = 0; if (getDebug()) { options[current++] = "-D"; } options[current++] = "-c"; options[current++] = "" + getClassIndex(); options[current++] = "-x"; options[current++] = "" + getTrainIterations(); options[current++] = "-T"; options[current++] = "" + getTrainPoolSize(); options[current++] = "-s"; options[current++] = "" + getSeed(); if (getDataFileName() != null) { options[current++] = "-t"; options[current++] = "" + getDataFileName(); } if (getClassifier() != null) { options[current++] = "-W"; options[current++] = getClassifier().getClass().getName(); } options[current++] = "--"; System.arraycopy(classifierOptions, 0, options, current, classifierOptions.length); current += classifierOptions.length; while (current < options.length) { options[current++] = ""; } return options; } /** * Get the number of instances in the training pool. * * @return number of instances in the training pool. */ public int getTrainPoolSize() { return m_TrainPoolSize; } /** * Set the number of instances in the training pool. * * @param numTrain number of instances in the training pool. */ public void setTrainPoolSize(int numTrain) { m_TrainPoolSize = numTrain; } /** * Set the classifiers being analysed * * @param newClassifier the Classifier to use. */ public void setClassifier(Classifier newClassifier) { m_Classifier = newClassifier; } /** * Gets the name of the classifier being analysed * * @return the classifier being analysed. */ public Classifier getClassifier() { return m_Classifier; } /** * Sets debugging mode * * @param debug true if debug output should be printed */ public void setDebug(boolean debug) { m_Debug = debug; } /** * Gets whether debugging is turned on * * @return true if debugging output is on */ public boolean getDebug() { return m_Debug; } /** * Sets the random number seed * * @param seed the random number seed */ public void setSeed(int seed) { m_Seed = seed; } /** * Gets the random number seed * * @return the random number seed */ public int getSeed() { return m_Seed; } /** * Sets the maximum number of boost iterations * * @param trainIterations the number of boost iterations */ public void setTrainIterations(int trainIterations) { m_TrainIterations = trainIterations; } /** * Gets the maximum number of boost iterations * * @return the maximum number of boost iterations */ public int getTrainIterations() { return m_TrainIterations; } /** * Sets the name of the data file used for the decomposition * * @param dataFileName the data file to use */ public void setDataFileName(String dataFileName) { m_DataFileName = dataFileName; } /** * Get the name of the data file used for the decomposition * * @return the name of the data file */ public String getDataFileName() { return m_DataFileName; } /** * Get the index (starting from 1) of the attribute used as the class. * * @return the index of the class attribute */ public int getClassIndex() { return m_ClassIndex + 1; } /** * Sets index of attribute to discretize on * * @param classIndex the index (starting from 1) of the class attribute */ public void setClassIndex(int classIndex) { m_ClassIndex = classIndex - 1; } /** * Get the calculated bias squared * * @return the bias squared */ public double getBias() { return m_Bias; } /** * Get the calculated variance * * @return the variance */ public double getVariance() { return m_Variance; } /** * Get the calculated sigma squared * * @return the sigma squared */ public double getSigma() { return m_Sigma; } /** * Get the calculated error rate * * @return the error rate */ public double getError() { return m_Error; } /** * Carry out the bias-variance decomposition * * @throws Exception if the decomposition couldn't be carried out */ public void decompose() throws Exception { Reader dataReader = new BufferedReader(new FileReader(m_DataFileName)); Instances data = new Instances(dataReader); if (m_ClassIndex < 0) { data.setClassIndex(data.numAttributes() - 1); } else { data.setClassIndex(m_ClassIndex); } if (data.classAttribute().type() != Attribute.NOMINAL) { throw new Exception("Class attribute must be nominal"); } int numClasses = data.numClasses(); data.deleteWithMissingClass(); if (data.checkForStringAttributes()) { throw new Exception("Can't handle string attributes!"); } if (data.numInstances() < 2 * m_TrainPoolSize) { throw new Exception("The dataset must contain at least " + (2 * m_TrainPoolSize) + " instances"); } Random random = new Random(m_Seed); data.randomize(random); Instances trainPool = new Instances(data, 0, m_TrainPoolSize); Instances test = new Instances(data, m_TrainPoolSize, data.numInstances() - m_TrainPoolSize); int numTest = test.numInstances(); double [][] instanceProbs = new double [numTest][numClasses]; m_Error = 0; for (int i = 0; i < m_TrainIterations; i++) { if (m_Debug) { System.err.println("Iteration " + (i + 1)); } trainPool.randomize(random); Instances train = new Instances(trainPool, 0, m_TrainPoolSize / 2); Classifier current = Classifier.makeCopy(m_Classifier); current.buildClassifier(train); //// Evaluate the classifier on test, updating BVD stats for (int j = 0; j < numTest; j++) { int pred = (int)current.classifyInstance(test.instance(j)); if (pred != test.instance(j).classValue()) { m_Error++; } instanceProbs[j][pred]++; } } m_Error /= (m_TrainIterations * numTest); // Average the BV over each instance in test. m_Bias = 0; m_Variance = 0; m_Sigma = 0; for (int i = 0; i < numTest; i++) { Instance current = test.instance(i); double [] predProbs = instanceProbs[i]; double pActual, pPred; double bsum = 0, vsum = 0, ssum = 0; for (int j = 0; j < numClasses; j++) { pActual = (current.classValue() == j) ? 1 : 0; // Or via 1NN from test data? pPred = predProbs[j] / m_TrainIterations; bsum += (pActual - pPred) * (pActual - pPred) - pPred * (1 - pPred) / (m_TrainIterations - 1); vsum += pPred * pPred; ssum += pActual * pActual; } m_Bias += bsum; m_Variance += (1 - vsum); m_Sigma += (1 - ssum); } m_Bias /= (2 * numTest); m_Variance /= (2 * numTest); m_Sigma /= (2 * numTest); if (m_Debug) { System.err.println("Decomposition finished"); } } /** * Returns description of the bias-variance decomposition results. * * @return the bias-variance decomposition results as a string */ public String toString() { String result = "\nBias-Variance Decomposition\n"; if (getClassifier() == null) { return "Invalid setup"; } result += "\nClassifier : " + getClassifier().getClass().getName(); if (getClassifier() instanceof OptionHandler) { result += Utils.joinOptions(((OptionHandler)m_Classifier).getOptions()); } result += "\nData File : " + getDataFileName(); result += "\nClass Index : "; if (getClassIndex() == 0) { result += "last"; } else { result += getClassIndex(); } result += "\nTraining Pool: " + getTrainPoolSize(); result += "\nIterations : " + getTrainIterations(); result += "\nSeed : " + getSeed(); result += "\nError : " + Utils.doubleToString(getError(), 6, 4); result += "\nSigma^2 : " + Utils.doubleToString(getSigma(), 6, 4); result += "\nBias^2 : " + Utils.doubleToString(getBias(), 6, 4); result += "\nVariance : " + Utils.doubleToString(getVariance(), 6, 4); return result + "\n"; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.15 $"); } /** * Test method for this class * * @param args the command line arguments */ public static void main(String [] args) { try { BVDecompose bvd = new BVDecompose(); try { bvd.setOptions(args); Utils.checkForRemainingOptions(args); } catch (Exception ex) { String result = ex.getMessage() + "\nBVDecompose Options:\n\n"; Enumeration enu = bvd.listOptions(); while (enu.hasMoreElements()) { Option option = (Option) enu.nextElement(); result += option.synopsis() + "\n" + option.description() + "\n"; } throw new Exception(result); } bvd.decompose(); System.out.println(bvd.toString()); } catch (Exception ex) { System.err.println(ex.getMessage()); } } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/classifiers/BVDecomposeSegCVSub.java000066400000000000000000001151561214146114000276340ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * BVDecomposeSegCVSub.java * Copyright (C) 2003 Paul Conilione * * Based on the class: BVDecompose.java by Len Trigg (1999) */ /* * DEDICATION * * Paul Conilione would like to express his deep gratitude and appreciation * to his Chinese Buddhist Taoist Master Sifu Chow Yuk Nen for the abilities * and insight that he has been taught, which have allowed him to program in * a clear and efficient manner. * * Master Sifu Chow Yuk Nen's Teachings are unique and precious. They are * applicable to any field of human endeavour. Through his unique and powerful * ability to skilfully apply Chinese Buddhist Teachings, people have achieved * success in; Computing, chemical engineering, business, accounting, philosophy * and more. * */ package weka.classifiers; import weka.core.Attribute; import weka.core.Instance; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import weka.core.TechnicalInformation; import weka.core.TechnicalInformationHandler; import weka.core.Utils; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; import java.io.BufferedReader; import java.io.FileReader; import java.io.Reader; import java.util.Enumeration; import java.util.Random; import java.util.Vector; /** * This class performs Bias-Variance decomposion on any classifier using the sub-sampled cross-validation procedure as specified in (1).
* The Kohavi and Wolpert definition of bias and variance is specified in (2).
* The Webb definition of bias and variance is specified in (3).
*
* Geoffrey I. Webb, Paul Conilione (2002). Estimating bias and variance from data. School of Computer Science and Software Engineering, Victoria, Australia.
*
* Ron Kohavi, David H. Wolpert: Bias Plus Variance Decomposition for Zero-One Loss Functions. In: Machine Learning: Proceedings of the Thirteenth International Conference, 275-283, 1996.
*
* Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. 40(2):159-196. *

* * BibTeX: * 

 * @misc{Webb2002,
 *    address = {School of Computer Science and Software Engineering, Victoria, Australia},
 *    author = {Geoffrey I. Webb and Paul Conilione},
 *    institution = {Monash University},
 *    title = {Estimating bias and variance from data},
 *    year = {2002},
 *    PDF = {http://www.csse.monash.edu.au/\~webb/Files/WebbConilione04.pdf}
 * }
 * 
 * @inproceedings{Kohavi1996,
 *    author = {Ron Kohavi and David H. Wolpert},
 *    booktitle = {Machine Learning: Proceedings of the Thirteenth International Conference},
 *    editor = {Lorenza Saitta},
 *    pages = {275-283},
 *    publisher = {Morgan Kaufmann},
 *    title = {Bias Plus Variance Decomposition for Zero-One Loss Functions},
 *    year = {1996},
 *    PS = {http://robotics.stanford.edu/\~ronnyk/biasVar.ps}
 * }
 * 
 * @article{Webb2000,
 *    author = {Geoffrey I. Webb},
 *    journal = {Machine Learning},
 *    number = {2},
 *    pages = {159-196},
 *    title = {MultiBoosting: A Technique for Combining Boosting and Wagging},
 *    volume = {40},
 *    year = {2000}
 * }
 *
*

* * Valid options are:

* * 

 -c <class index>
 *  The index of the class attribute.
 *  (default last)
* * 
 -D
 *  Turn on debugging output.
* * 
 -l <num>
 *  The number of times each instance is classified.
 *  (default 10)
* * 
 -p <proportion of objects in common>
 *  The average proportion of instances common between any two training sets
* * 
 -s <seed>
 *  The random number seed used.
* * 
 -t <name of arff file>
 *  The name of the arff file used for the decomposition.
* * 
 -T <number of instances in training set>
 *  The number of instances in the training set.
* * 
 -W <classifier class name>
 *  Full class name of the learner used in the decomposition.
 *  eg: weka.classifiers.bayes.NaiveBayes
* * 
 
 * Options specific to learner weka.classifiers.rules.ZeroR:
 *
* * 
 -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
* * * Options after -- are passed to the designated sub-learner.

* * @author Paul Conilione (paulc4321@yahoo.com.au) * @version $Revision: 1.7 $ */ public class BVDecomposeSegCVSub implements OptionHandler, TechnicalInformationHandler, RevisionHandler { /** Debugging mode, gives extra output if true. */ protected boolean m_Debug; /** An instantiated base classifier used for getting and testing options. */ protected Classifier m_Classifier = new weka.classifiers.rules.ZeroR(); /** The options to be passed to the base classifier. */ protected String [] m_ClassifierOptions; /** The number of times an instance is classified*/ protected int m_ClassifyIterations; /** The name of the data file used for the decomposition */ protected String m_DataFileName; /** The index of the class attribute */ protected int m_ClassIndex = -1; /** The random number seed */ protected int m_Seed = 1; /** The calculated Kohavi & Wolpert bias (squared) */ protected double m_KWBias; /** The calculated Kohavi & Wolpert variance */ protected double m_KWVariance; /** The calculated Kohavi & Wolpert sigma */ protected double m_KWSigma; /** The calculated Webb bias */ protected double m_WBias; /** The calculated Webb variance */ protected double m_WVariance; /** The error rate */ protected double m_Error; /** The training set size */ protected int m_TrainSize; /** Proportion of instances common between any two training sets. */ protected double m_P; /** * Returns a string describing this object * @return a description of the classifier suitable for * displaying in the explorer/experimenter gui */ public String globalInfo() { return "This class performs Bias-Variance decomposion on any classifier using the " + "sub-sampled cross-validation procedure as specified in (1).\n" + "The Kohavi and Wolpert definition of bias and variance is specified in (2).\n" + "The Webb definition of bias and variance is specified in (3).\n\n" + getTechnicalInformation().toString(); } /** * Returns an instance of a TechnicalInformation object, containing * detailed information about the technical background of this class, * e.g., paper reference or book this class is based on. * * @return the technical information about this class */ public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; TechnicalInformation additional; result = new TechnicalInformation(Type.MISC); result.setValue(Field.AUTHOR, "Geoffrey I. Webb and Paul Conilione"); result.setValue(Field.YEAR, "2002"); result.setValue(Field.TITLE, "Estimating bias and variance from data"); result.setValue(Field.INSTITUTION, "Monash University"); result.setValue(Field.ADDRESS, "School of Computer Science and Software Engineering, Victoria, Australia"); result.setValue(Field.PDF, "http://www.csse.monash.edu.au/~webb/Files/WebbConilione04.pdf"); additional = result.add(Type.INPROCEEDINGS); additional.setValue(Field.AUTHOR, "Ron Kohavi and David H. Wolpert"); additional.setValue(Field.YEAR, "1996"); additional.setValue(Field.TITLE, "Bias Plus Variance Decomposition for Zero-One Loss Functions"); additional.setValue(Field.BOOKTITLE, "Machine Learning: Proceedings of the Thirteenth International Conference"); additional.setValue(Field.PUBLISHER, "Morgan Kaufmann"); additional.setValue(Field.EDITOR, "Lorenza Saitta"); additional.setValue(Field.PAGES, "275-283"); additional.setValue(Field.PS, "http://robotics.stanford.edu/~ronnyk/biasVar.ps"); additional = result.add(Type.ARTICLE); additional.setValue(Field.AUTHOR, "Geoffrey I. Webb"); additional.setValue(Field.YEAR, "2000"); additional.setValue(Field.TITLE, "MultiBoosting: A Technique for Combining Boosting and Wagging"); additional.setValue(Field.JOURNAL, "Machine Learning"); additional.setValue(Field.VOLUME, "40"); additional.setValue(Field.NUMBER, "2"); additional.setValue(Field.PAGES, "159-196"); return result; } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector newVector = new Vector(8); newVector.addElement(new Option( "\tThe index of the class attribute.\n"+ "\t(default last)", "c", 1, "-c ")); newVector.addElement(new Option( "\tTurn on debugging output.", "D", 0, "-D")); newVector.addElement(new Option( "\tThe number of times each instance is classified.\n" +"\t(default 10)", "l", 1, "-l ")); newVector.addElement(new Option( "\tThe average proportion of instances common between any two training sets", "p", 1, "-p ")); newVector.addElement(new Option( "\tThe random number seed used.", "s", 1, "-s ")); newVector.addElement(new Option( "\tThe name of the arff file used for the decomposition.", "t", 1, "-t ")); newVector.addElement(new Option( "\tThe number of instances in the training set.", "T", 1, "-T ")); newVector.addElement(new Option( "\tFull class name of the learner used in the decomposition.\n" +"\teg: weka.classifiers.bayes.NaiveBayes", "W", 1, "-W ")); if ((m_Classifier != null) && (m_Classifier instanceof OptionHandler)) { newVector.addElement(new Option( "", "", 0, "\nOptions specific to learner " + m_Classifier.getClass().getName() + ":")); Enumeration enu = ((OptionHandler)m_Classifier).listOptions(); while (enu.hasMoreElements()) { newVector.addElement(enu.nextElement()); } } return newVector.elements(); } /** * Sets the OptionHandler's options using the given list. All options * will be set (or reset) during this call (i.e. incremental setting * of options is not possible).

* * Valid options are:

* * 

 -c <class index>
     *  The index of the class attribute.
     *  (default last)
* * 
 -D
     *  Turn on debugging output.
* * 
 -l <num>
     *  The number of times each instance is classified.
     *  (default 10)
* * 
 -p <proportion of objects in common>
     *  The average proportion of instances common between any two training sets
* * 
 -s <seed>
     *  The random number seed used.
* * 
 -t <name of arff file>
     *  The name of the arff file used for the decomposition.
* * 
 -T <number of instances in training set>
     *  The number of instances in the training set.
* * 
 -W <classifier class name>
     *  Full class name of the learner used in the decomposition.
     *  eg: weka.classifiers.bayes.NaiveBayes
* * 
 
     * Options specific to learner weka.classifiers.rules.ZeroR:
     *
* * 
 -D
     *  If set, classifier is run in debug mode and
     *  may output additional info to the console
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { setDebug(Utils.getFlag('D', options)); String classIndex = Utils.getOption('c', options); if (classIndex.length() != 0) { if (classIndex.toLowerCase().equals("last")) { setClassIndex(0); } else if (classIndex.toLowerCase().equals("first")) { setClassIndex(1); } else { setClassIndex(Integer.parseInt(classIndex)); } } else { setClassIndex(0); } String classifyIterations = Utils.getOption('l', options); if (classifyIterations.length() != 0) { setClassifyIterations(Integer.parseInt(classifyIterations)); } else { setClassifyIterations(10); } String prob = Utils.getOption('p', options); if (prob.length() != 0) { setP( Double.parseDouble(prob)); } else { setP(-1); } //throw new Exception("A proportion must be specified" + " with a -p option."); String seedString = Utils.getOption('s', options); if (seedString.length() != 0) { setSeed(Integer.parseInt(seedString)); } else { setSeed(1); } String dataFile = Utils.getOption('t', options); if (dataFile.length() != 0) { setDataFileName(dataFile); } else { throw new Exception("An arff file must be specified" + " with the -t option."); } String trainSize = Utils.getOption('T', options); if (trainSize.length() != 0) { setTrainSize(Integer.parseInt(trainSize)); } else { setTrainSize(-1); } //throw new Exception("A training set size must be specified" + " with a -T option."); String classifierName = Utils.getOption('W', options); if (classifierName.length() != 0) { setClassifier(Classifier.forName(classifierName, Utils.partitionOptions(options))); } else { throw new Exception("A learner must be specified with the -W option."); } } /** * Gets the current settings of the CheckClassifier. * * @return an array of strings suitable for passing to setOptions */ public String [] getOptions() { String [] classifierOptions = new String [0]; if ((m_Classifier != null) && (m_Classifier instanceof OptionHandler)) { classifierOptions = ((OptionHandler)m_Classifier).getOptions(); } String [] options = new String [classifierOptions.length + 14]; int current = 0; if (getDebug()) { options[current++] = "-D"; } options[current++] = "-c"; options[current++] = "" + getClassIndex(); options[current++] = "-l"; options[current++] = "" + getClassifyIterations(); options[current++] = "-p"; options[current++] = "" + getP(); options[current++] = "-s"; options[current++] = "" + getSeed(); if (getDataFileName() != null) { options[current++] = "-t"; options[current++] = "" + getDataFileName(); } options[current++] = "-T"; options[current++] = "" + getTrainSize(); if (getClassifier() != null) { options[current++] = "-W"; options[current++] = getClassifier().getClass().getName(); } options[current++] = "--"; System.arraycopy(classifierOptions, 0, options, current, classifierOptions.length); current += classifierOptions.length; while (current < options.length) { options[current++] = ""; } return options; } /** * Set the classifiers being analysed * * @param newClassifier the Classifier to use. */ public void setClassifier(Classifier newClassifier) { m_Classifier = newClassifier; } /** * Gets the name of the classifier being analysed * * @return the classifier being analysed. */ public Classifier getClassifier() { return m_Classifier; } /** * Sets debugging mode * * @param debug true if debug output should be printed */ public void setDebug(boolean debug) { m_Debug = debug; } /** * Gets whether debugging is turned on * * @return true if debugging output is on */ public boolean getDebug() { return m_Debug; } /** * Sets the random number seed * * @param seed the random number seed */ public void setSeed(int seed) { m_Seed = seed; } /** * Gets the random number seed * * @return the random number seed */ public int getSeed() { return m_Seed; } /** * Sets the number of times an instance is classified * * @param classifyIterations number of times an instance is classified */ public void setClassifyIterations(int classifyIterations) { m_ClassifyIterations = classifyIterations; } /** * Gets the number of times an instance is classified * * @return the maximum number of times an instance is classified */ public int getClassifyIterations() { return m_ClassifyIterations; } /** * Sets the name of the dataset file. * * @param dataFileName name of dataset file. */ public void setDataFileName(String dataFileName) { m_DataFileName = dataFileName; } /** * Get the name of the data file used for the decomposition * * @return the name of the data file */ public String getDataFileName() { return m_DataFileName; } /** * Get the index (starting from 1) of the attribute used as the class. * * @return the index of the class attribute */ public int getClassIndex() { return m_ClassIndex + 1; } /** * Sets index of attribute to discretize on * * @param classIndex the index (starting from 1) of the class attribute */ public void setClassIndex(int classIndex) { m_ClassIndex = classIndex - 1; } /** * Get the calculated bias squared according to the Kohavi and Wolpert definition * * @return the bias squared */ public double getKWBias() { return m_KWBias; } /** * Get the calculated bias according to the Webb definition * * @return the bias * */ public double getWBias() { return m_WBias; } /** * Get the calculated variance according to the Kohavi and Wolpert definition * * @return the variance */ public double getKWVariance() { return m_KWVariance; } /** * Get the calculated variance according to the Webb definition * * @return the variance according to Webb * */ public double getWVariance() { return m_WVariance; } /** * Get the calculated sigma according to the Kohavi and Wolpert definition * * @return the sigma * */ public double getKWSigma() { return m_KWSigma; } /** * Set the training size. * * @param size the size of the training set * */ public void setTrainSize(int size) { m_TrainSize = size; } /** * Get the training size * * @return the size of the training set * */ public int getTrainSize() { return m_TrainSize; } /** * Set the proportion of instances that are common between two training sets * used to train a classifier. * * @param proportion the proportion of instances that are common between training * sets. * */ public void setP(double proportion) { m_P = proportion; } /** * Get the proportion of instances that are common between two training sets. * * @return the proportion * */ public double getP() { return m_P; } /** * Get the calculated error rate * * @return the error rate */ public double getError() { return m_Error; } /** * Carry out the bias-variance decomposition using the sub-sampled cross-validation method. * * @throws Exception if the decomposition couldn't be carried out */ public void decompose() throws Exception { Reader dataReader; Instances data; int tps; // training pool size, size of segment E. int k; // number of folds in segment E. int q; // number of segments of size tps. dataReader = new BufferedReader(new FileReader(m_DataFileName)); //open file data = new Instances(dataReader); // encapsulate in wrapper class called weka.Instances() if (m_ClassIndex < 0) { data.setClassIndex(data.numAttributes() - 1); } else { data.setClassIndex(m_ClassIndex); } if (data.classAttribute().type() != Attribute.NOMINAL) { throw new Exception("Class attribute must be nominal"); } int numClasses = data.numClasses(); data.deleteWithMissingClass(); if ( data.checkForStringAttributes() ) { throw new Exception("Can't handle string attributes!"); } // Dataset size must be greater than 2 if ( data.numInstances() <= 2 ){ throw new Exception("Dataset size must be greater than 2."); } if ( m_TrainSize == -1 ){ // default value m_TrainSize = (int) Math.floor( (double) data.numInstances() / 2.0 ); }else if ( m_TrainSize < 0 || m_TrainSize >= data.numInstances() - 1 ) { // Check if 0 < training Size < D - 1 throw new Exception("Training set size of "+m_TrainSize+" is invalid."); } if ( m_P == -1 ){ // default value m_P = (double) m_TrainSize / ( (double)data.numInstances() - 1 ); }else if ( m_P < ( m_TrainSize / ( (double)data.numInstances() - 1 ) ) || m_P >= 1.0 ) { //Check if p is in range: m/(|D|-1) <= p < 1.0 throw new Exception("Proportion is not in range: "+ (m_TrainSize / ((double) data.numInstances() - 1 )) +" <= p < 1.0 "); } //roundup tps from double to integer tps = (int) Math.ceil( ((double)m_TrainSize / (double)m_P) + 1 ); k = (int) Math.ceil( tps / (tps - (double) m_TrainSize)); // number of folds cannot be more than the number of instances in the training pool if ( k > tps ) { throw new Exception("The required number of folds is too many." + "Change p or the size of the training set."); } // calculate the number of segments, round down. q = (int) Math.floor( (double) data.numInstances() / (double)tps ); //create confusion matrix, columns = number of instances in data set, as all will be used, by rows = number of classes. double [][] instanceProbs = new double [data.numInstances()][numClasses]; int [][] foldIndex = new int [ k ][ 2 ]; Vector segmentList = new Vector(q + 1); //Set random seed Random random = new Random(m_Seed); data.randomize(random); //create index arrays for different segments int currentDataIndex = 0; for( int count = 1; count <= (q + 1); count++ ){ if( count > q){ int [] segmentIndex = new int [ (data.numInstances() - (q * tps)) ]; for(int index = 0; index < segmentIndex.length; index++, currentDataIndex++){ segmentIndex[index] = currentDataIndex; } segmentList.add(segmentIndex); } else { int [] segmentIndex = new int [ tps ]; for(int index = 0; index < segmentIndex.length; index++, currentDataIndex++){ segmentIndex[index] = currentDataIndex; } segmentList.add(segmentIndex); } } int remainder = tps % k; // remainder is used to determine when to shrink the fold size by 1. //foldSize = ROUNDUP( tps / k ) (round up, eg 3 -> 3, 3.3->4) int foldSize = (int) Math.ceil( (double)tps /(double) k); //roundup fold size double to integer int index = 0; int currentIndex; for( int count = 0; count < k; count ++){ if( remainder != 0 && count == remainder ){ foldSize -= 1; } foldIndex[count][0] = index; foldIndex[count][1] = foldSize; index += foldSize; } for( int l = 0; l < m_ClassifyIterations; l++) { for(int i = 1; i <= q; i++){ int [] currentSegment = (int[]) segmentList.get(i - 1); randomize(currentSegment, random); //CROSS FOLD VALIDATION for current Segment for( int j = 1; j <= k; j++){ Instances TP = null; for(int foldNum = 1; foldNum <= k; foldNum++){ if( foldNum != j){ int startFoldIndex = foldIndex[ foldNum - 1 ][ 0 ]; //start index foldSize = foldIndex[ foldNum - 1 ][ 1 ]; int endFoldIndex = startFoldIndex + foldSize - 1; for(int currentFoldIndex = startFoldIndex; currentFoldIndex <= endFoldIndex; currentFoldIndex++){ if( TP == null ){ TP = new Instances(data, currentSegment[ currentFoldIndex ], 1); }else{ TP.add( data.instance( currentSegment[ currentFoldIndex ] ) ); } } } } TP.randomize(random); if( getTrainSize() > TP.numInstances() ){ throw new Exception("The training set size of " + getTrainSize() + ", is greater than the training pool " + TP.numInstances() ); } Instances train = new Instances(TP, 0, m_TrainSize); Classifier current = Classifier.makeCopy(m_Classifier); current.buildClassifier(train); // create a clssifier using the instances in train. int currentTestIndex = foldIndex[ j - 1 ][ 0 ]; //start index int testFoldSize = foldIndex[ j - 1 ][ 1 ]; //size int endTestIndex = currentTestIndex + testFoldSize - 1; while( currentTestIndex <= endTestIndex ){ Instance testInst = data.instance( currentSegment[currentTestIndex] ); int pred = (int)current.classifyInstance( testInst ); if(pred != testInst.classValue()) { m_Error++; // add 1 to mis-classifications. } instanceProbs[ currentSegment[ currentTestIndex ] ][ pred ]++; currentTestIndex++; } if( i == 1 && j == 1){ int[] segmentElast = (int[])segmentList.lastElement(); for( currentIndex = 0; currentIndex < segmentElast.length; currentIndex++){ Instance testInst = data.instance( segmentElast[currentIndex] ); int pred = (int)current.classifyInstance( testInst ); if(pred != testInst.classValue()) { m_Error++; // add 1 to mis-classifications. } instanceProbs[ segmentElast[ currentIndex ] ][ pred ]++; } } } } } m_Error /= (double)( m_ClassifyIterations * data.numInstances() ); m_KWBias = 0.0; m_KWVariance = 0.0; m_KWSigma = 0.0; m_WBias = 0.0; m_WVariance = 0.0; for (int i = 0; i < data.numInstances(); i++) { Instance current = data.instance( i ); double [] predProbs = instanceProbs[ i ]; double pActual, pPred; double bsum = 0, vsum = 0, ssum = 0; double wBSum = 0, wVSum = 0; Vector centralTendencies = findCentralTendencies( predProbs ); if( centralTendencies == null ){ throw new Exception("Central tendency was null."); } for (int j = 0; j < numClasses; j++) { pActual = (current.classValue() == j) ? 1 : 0; pPred = predProbs[j] / m_ClassifyIterations; bsum += (pActual - pPred) * (pActual - pPred) - pPred * (1 - pPred) / (m_ClassifyIterations - 1); vsum += pPred * pPred; ssum += pActual * pActual; } m_KWBias += bsum; m_KWVariance += (1 - vsum); m_KWSigma += (1 - ssum); for( int count = 0; count < centralTendencies.size(); count++ ) { int wB = 0, wV = 0; int centralTendency = ((Integer)centralTendencies.get(count)).intValue(); // For a single instance xi, find the bias and variance. for (int j = 0; j < numClasses; j++) { //Webb definition if( j != (int)current.classValue() && j == centralTendency ) { wB += predProbs[j]; } if( j != (int)current.classValue() && j != centralTendency ) { wV += predProbs[j]; } } wBSum += (double) wB; wVSum += (double) wV; } // calculate bais by dividing bSum by the number of central tendencies and // total number of instances. (effectively finding the average and dividing // by the number of instances to get the nominalised probability). m_WBias += ( wBSum / ((double) ( centralTendencies.size() * m_ClassifyIterations ))); // calculate variance by dividing vSum by the total number of interations m_WVariance += ( wVSum / ((double) ( centralTendencies.size() * m_ClassifyIterations ))); } m_KWBias /= (2.0 * (double) data.numInstances()); m_KWVariance /= (2.0 * (double) data.numInstances()); m_KWSigma /= (2.0 * (double) data.numInstances()); // bias = bias / number of data instances m_WBias /= (double) data.numInstances(); // variance = variance / number of data instances. m_WVariance /= (double) data.numInstances(); if (m_Debug) { System.err.println("Decomposition finished"); } } /** Finds the central tendency, given the classifications for an instance. * * Where the central tendency is defined as the class that was most commonly * selected for a given instance.

* * For example, instance 'x' may be classified out of 3 classes y = {1, 2, 3}, * so if x is classified 10 times, and is classified as follows, '1' = 2 times, '2' = 5 times * and '3' = 3 times. Then the central tendency is '2'.

* * However, it is important to note that this method returns a list of all classes * that have the highest number of classifications. * * In cases where there are several classes with the largest number of classifications, then * all of these classes are returned. For example if 'x' is classified '1' = 4 times, * '2' = 4 times and '3' = 2 times. Then '1' and '2' are returned.

* * @param predProbs the array of classifications for a single instance. * * @return a Vector containing Integer objects which store the class(s) which * are the central tendency. */ public Vector findCentralTendencies(double[] predProbs) { int centralTValue = 0; int currentValue = 0; //array to store the list of classes the have the greatest number of classifictions. Vector centralTClasses; centralTClasses = new Vector(); //create an array with size of the number of classes. // Go through array, finding the central tendency. for( int i = 0; i < predProbs.length; i++) { currentValue = (int) predProbs[i]; // if current value is greater than the central tendency value then // clear vector and add new class to vector array. if( currentValue > centralTValue) { centralTClasses.clear(); centralTClasses.addElement( new Integer(i) ); centralTValue = currentValue; } else if( currentValue != 0 && currentValue == centralTValue) { centralTClasses.addElement( new Integer(i) ); } } //return all classes that have the greatest number of classifications. if( centralTValue != 0){ return centralTClasses; } else { return null; } } /** * Returns description of the bias-variance decomposition results. * * @return the bias-variance decomposition results as a string */ public String toString() { String result = "\nBias-Variance Decomposition Segmentation, Cross Validation\n" + "with subsampling.\n"; if (getClassifier() == null) { return "Invalid setup"; } result += "\nClassifier : " + getClassifier().getClass().getName(); if (getClassifier() instanceof OptionHandler) { result += Utils.joinOptions(((OptionHandler)m_Classifier).getOptions()); } result += "\nData File : " + getDataFileName(); result += "\nClass Index : "; if (getClassIndex() == 0) { result += "last"; } else { result += getClassIndex(); } result += "\nIterations : " + getClassifyIterations(); result += "\np : " + getP(); result += "\nTraining Size : " + getTrainSize(); result += "\nSeed : " + getSeed(); result += "\n\nDefinition : " +"Kohavi and Wolpert"; result += "\nError :" + Utils.doubleToString(getError(), 4); result += "\nBias^2 :" + Utils.doubleToString(getKWBias(), 4); result += "\nVariance :" + Utils.doubleToString(getKWVariance(), 4); result += "\nSigma^2 :" + Utils.doubleToString(getKWSigma(), 4); result += "\n\nDefinition : " +"Webb"; result += "\nError :" + Utils.doubleToString(getError(), 4); result += "\nBias :" + Utils.doubleToString(getWBias(), 4); result += "\nVariance :" + Utils.doubleToString(getWVariance(), 4); return result; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.7 $"); } /** * Test method for this class * * @param args the command line arguments */ public static void main(String [] args) { try { BVDecomposeSegCVSub bvd = new BVDecomposeSegCVSub(); try { bvd.setOptions(args); Utils.checkForRemainingOptions(args); } catch (Exception ex) { String result = ex.getMessage() + "\nBVDecompose Options:\n\n"; Enumeration enu = bvd.listOptions(); while (enu.hasMoreElements()) { Option option = (Option) enu.nextElement(); result += option.synopsis() + "\n" + option.description() + "\n"; } throw new Exception(result); } bvd.decompose(); System.out.println(bvd.toString()); } catch (Exception ex) { System.err.println(ex.getMessage()); } } /** * Accepts an array of ints and randomises the values in the array, using the * random seed. * *@param index is the array of integers *@param random is the Random seed. */ public final void randomize(int[] index, Random random) { for( int j = index.length - 1; j > 0; j-- ){ int k = random.nextInt( j + 1 ); int temp = index[j]; index[j] = index[k]; index[k] = temp; } } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/classifiers/CheckClassifier.java000066400000000000000000002166221214146114000271460ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * CheckClassifier.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.classifiers; import weka.core.Attribute; import weka.core.CheckScheme; import weka.core.FastVector; import weka.core.Instance; import weka.core.Instances; import weka.core.MultiInstanceCapabilitiesHandler; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.SerializationHelper; import weka.core.TestInstances; import weka.core.Utils; import weka.core.WeightedInstancesHandler; import java.util.Enumeration; import java.util.Random; import java.util.Vector; /** * Class for examining the capabilities and finding problems with * classifiers. If you implement a classifier using the WEKA.libraries, * you should run the checks on it to ensure robustness and correct * operation. Passing all the tests of this object does not mean * bugs in the classifier don't exist, but this will help find some * common ones.

* * Typical usage:

* java weka.classifiers.CheckClassifier -W classifier_name * classifier_options

* * CheckClassifier reports on the following: *

    *
  • Classifier abilities *
      *
    • Possible command line options to the classifier
      • *
    • Whether the classifier can predict nominal, numeric, string, * date or relational class attributes. Warnings will be displayed if * performance is worse than ZeroR
      • *
    • Whether the classifier can be trained incrementally
      • *
    • Whether the classifier can handle numeric predictor attributes
      • *
    • Whether the classifier can handle nominal predictor attributes
      • *
    • Whether the classifier can handle string predictor attributes
      • *
    • Whether the classifier can handle date predictor attributes
      • *
    • Whether the classifier can handle relational predictor attributes
      • *
    • Whether the classifier can handle multi-instance data
      • *
    • Whether the classifier can handle missing predictor values
      • *
    • Whether the classifier can handle missing class values
      • *
    • Whether a nominal classifier only handles 2 class problems
      • *
    • Whether the classifier can handle instance weights
      • *
    *
    • *
  • Correct functioning *
      *
    • Correct initialisation during buildClassifier (i.e. no result * changes when buildClassifier called repeatedly)
      • *
    • Whether incremental training produces the same results * as during non-incremental training (which may or may not * be OK)
      • *
    • Whether the classifier alters the data pased to it * (number of instances, instance order, instance weights, etc)
      • *
    • Whether the toString() method works correctly before the * classifier has been built.
      • *
    *
    • *
  • Degenerate cases *
      *
    • building classifier with zero training instances
      • *
    • all but one predictor attribute values missing
      • *
    • all predictor attribute values missing
      • *
    • all but one class values missing
      • *
    • all class values missing
      • *
    *
    • *
* Running CheckClassifier with the debug option set will output the * training and test datasets for any failed tests.

* * The weka.classifiers.AbstractClassifierTest uses this * class to test all the classifiers. Any changes here, have to be * checked in that abstract test class, too.

* * Valid options are:

* * 

 -D
 *  Turn on debugging output.
* * 
 -S
 *  Silent mode - prints nothing to stdout.
* * 
 -N <num>
 *  The number of instances in the datasets (default 20).
* * 
 -nominal <num>
 *  The number of nominal attributes (default 2).
* * 
 -nominal-values <num>
 *  The number of values for nominal attributes (default 1).
* * 
 -numeric <num>
 *  The number of numeric attributes (default 1).
* * 
 -string <num>
 *  The number of string attributes (default 1).
* * 
 -date <num>
 *  The number of date attributes (default 1).
* * 
 -relational <num>
 *  The number of relational attributes (default 1).
* * 
 -num-instances-relational <num>
 *  The number of instances in relational/bag attributes (default 10).
* * 
 -words <comma-separated-list>
 *  The words to use in string attributes.
* * 
 -word-separators <chars>
 *  The word separators to use in string attributes.
* * 
 -W
 *  Full name of the classifier analysed.
 *  eg: weka.classifiers.bayes.NaiveBayes
 *  (default weka.classifiers.rules.ZeroR)
* * 
 
 * Options specific to classifier weka.classifiers.rules.ZeroR:
 *
* * 
 -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
* * * Options after -- are passed to the designated classifier.

* * @author Len Trigg (trigg@cs.waikato.ac.nz) * @author FracPete (fracpete at waikato dot ac dot nz) * @version $Revision: 1.33 $ * @see TestInstances */ public class CheckClassifier extends CheckScheme { /* * Note about test methods: * - methods return array of booleans * - first index: success or not * - second index: acceptable or not (e.g., Exception is OK) * - in case the performance is worse than that of ZeroR both indices are true * * FracPete (fracpete at waikato dot ac dot nz) */ /*** The classifier to be examined */ protected Classifier m_Classifier = new weka.classifiers.rules.ZeroR(); /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector result = new Vector(); Enumeration en = super.listOptions(); while (en.hasMoreElements()) result.addElement(en.nextElement()); result.addElement(new Option( "\tFull name of the classifier analysed.\n" +"\teg: weka.classifiers.bayes.NaiveBayes\n" + "\t(default weka.classifiers.rules.ZeroR)", "W", 1, "-W")); if ((m_Classifier != null) && (m_Classifier instanceof OptionHandler)) { result.addElement(new Option("", "", 0, "\nOptions specific to classifier " + m_Classifier.getClass().getName() + ":")); Enumeration enu = ((OptionHandler)m_Classifier).listOptions(); while (enu.hasMoreElements()) result.addElement(enu.nextElement()); } return result.elements(); } /** * Parses a given list of options. * * Valid options are:

* * 

 -D
   *  Turn on debugging output.
* * 
 -S
   *  Silent mode - prints nothing to stdout.
* * 
 -N <num>
   *  The number of instances in the datasets (default 20).
* * 
 -nominal <num>
   *  The number of nominal attributes (default 2).
* * 
 -nominal-values <num>
   *  The number of values for nominal attributes (default 1).
* * 
 -numeric <num>
   *  The number of numeric attributes (default 1).
* * 
 -string <num>
   *  The number of string attributes (default 1).
* * 
 -date <num>
   *  The number of date attributes (default 1).
* * 
 -relational <num>
   *  The number of relational attributes (default 1).
* * 
 -num-instances-relational <num>
   *  The number of instances in relational/bag attributes (default 10).
* * 
 -words <comma-separated-list>
   *  The words to use in string attributes.
* * 
 -word-separators <chars>
   *  The word separators to use in string attributes.
* * 
 -W
   *  Full name of the classifier analysed.
   *  eg: weka.classifiers.bayes.NaiveBayes
   *  (default weka.classifiers.rules.ZeroR)
* * 
 
   * Options specific to classifier weka.classifiers.rules.ZeroR:
   *
* * 
 -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console
* * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { String tmpStr; super.setOptions(options); tmpStr = Utils.getOption('W', options); if (tmpStr.length() == 0) tmpStr = weka.classifiers.rules.ZeroR.class.getName(); setClassifier( (Classifier) forName( "weka.classifiers", Classifier.class, tmpStr, Utils.partitionOptions(options))); } /** * Gets the current settings of the CheckClassifier. * * @return an array of strings suitable for passing to setOptions */ public String[] getOptions() { Vector result; String[] options; int i; result = new Vector(); options = super.getOptions(); for (i = 0; i < options.length; i++) result.add(options[i]); if (getClassifier() != null) { result.add("-W"); result.add(getClassifier().getClass().getName()); } if ((m_Classifier != null) && (m_Classifier instanceof OptionHandler)) options = ((OptionHandler) m_Classifier).getOptions(); else options = new String[0]; if (options.length > 0) { result.add("--"); for (i = 0; i < options.length; i++) result.add(options[i]); } return (String[]) result.toArray(new String[result.size()]); } /** * Begin the tests, reporting results to System.out */ public void doTests() { if (getClassifier() == null) { println("\n=== No classifier set ==="); return; } println("\n=== Check on Classifier: " + getClassifier().getClass().getName() + " ===\n"); // Start tests m_ClasspathProblems = false; println("--> Checking for interfaces"); canTakeOptions(); boolean updateableClassifier = updateableClassifier()[0]; boolean weightedInstancesHandler = weightedInstancesHandler()[0]; boolean multiInstanceHandler = multiInstanceHandler()[0]; println("--> Classifier tests"); declaresSerialVersionUID(); testToString(); testsPerClassType(Attribute.NOMINAL, updateableClassifier, weightedInstancesHandler, multiInstanceHandler); testsPerClassType(Attribute.NUMERIC, updateableClassifier, weightedInstancesHandler, multiInstanceHandler); testsPerClassType(Attribute.DATE, updateableClassifier, weightedInstancesHandler, multiInstanceHandler); testsPerClassType(Attribute.STRING, updateableClassifier, weightedInstancesHandler, multiInstanceHandler); testsPerClassType(Attribute.RELATIONAL, updateableClassifier, weightedInstancesHandler, multiInstanceHandler); } /** * Set the classifier for boosting. * * @param newClassifier the Classifier to use. */ public void setClassifier(Classifier newClassifier) { m_Classifier = newClassifier; } /** * Get the classifier used as the classifier * * @return the classifier used as the classifier */ public Classifier getClassifier() { return m_Classifier; } /** * Run a battery of tests for a given class attribute type * * @param classType true if the class attribute should be numeric * @param updateable true if the classifier is updateable * @param weighted true if the classifier says it handles weights * @param multiInstance true if the classifier is a multi-instance classifier */ protected void testsPerClassType(int classType, boolean updateable, boolean weighted, boolean multiInstance) { boolean PNom = canPredict(true, false, false, false, false, multiInstance, classType)[0]; boolean PNum = canPredict(false, true, false, false, false, multiInstance, classType)[0]; boolean PStr = canPredict(false, false, true, false, false, multiInstance, classType)[0]; boolean PDat = canPredict(false, false, false, true, false, multiInstance, classType)[0]; boolean PRel; if (!multiInstance) PRel = canPredict(false, false, false, false, true, multiInstance, classType)[0]; else PRel = false; if (PNom || PNum || PStr || PDat || PRel) { if (weighted) instanceWeights(PNom, PNum, PStr, PDat, PRel, multiInstance, classType); canHandleOnlyClass(PNom, PNum, PStr, PDat, PRel, classType); if (classType == Attribute.NOMINAL) canHandleNClasses(PNom, PNum, PStr, PDat, PRel, multiInstance, 4); if (!multiInstance) { canHandleClassAsNthAttribute(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, 0); canHandleClassAsNthAttribute(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, 1); } canHandleZeroTraining(PNom, PNum, PStr, PDat, PRel, multiInstance, classType); boolean handleMissingPredictors = canHandleMissing(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, true, false, 20)[0]; if (handleMissingPredictors) canHandleMissing(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, true, false, 100); boolean handleMissingClass = canHandleMissing(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, false, true, 20)[0]; if (handleMissingClass) canHandleMissing(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, false, true, 100); correctBuildInitialisation(PNom, PNum, PStr, PDat, PRel, multiInstance, classType); datasetIntegrity(PNom, PNum, PStr, PDat, PRel, multiInstance, classType, handleMissingPredictors, handleMissingClass); doesntUseTestClassVal(PNom, PNum, PStr, PDat, PRel, multiInstance, classType); if (updateable) updatingEquality(PNom, PNum, PStr, PDat, PRel, multiInstance, classType); } } /** * Checks whether the scheme's toString() method works even though the * classifies hasn't been built yet. * * @return index 0 is true if the toString() method works fine */ protected boolean[] testToString() { boolean[] result = new boolean[2]; print("toString..."); try { Classifier copy = (Classifier) m_Classifier.getClass().newInstance(); copy.toString(); result[0] = true; println("yes"); } catch (Exception e) { result[0] = false; println("no"); if (m_Debug) { println("\n=== Full report ==="); e.printStackTrace(); println("\n"); } } return result; } /** * tests for a serialVersionUID. Fails in case the scheme doesn't declare * a UID. * * @return index 0 is true if the scheme declares a UID */ protected boolean[] declaresSerialVersionUID() { boolean[] result = new boolean[2]; print("serialVersionUID..."); result[0] = !SerializationHelper.needsUID(m_Classifier.getClass()); if (result[0]) println("yes"); else println("no"); return result; } /** * Checks whether the scheme can take command line options. * * @return index 0 is true if the classifier can take options */ protected boolean[] canTakeOptions() { boolean[] result = new boolean[2]; print("options..."); if (m_Classifier instanceof OptionHandler) { println("yes"); if (m_Debug) { println("\n=== Full report ==="); Enumeration enu = ((OptionHandler)m_Classifier).listOptions(); while (enu.hasMoreElements()) { Option option = (Option) enu.nextElement(); print(option.synopsis() + "\n" + option.description() + "\n"); } println("\n"); } result[0] = true; } else { println("no"); result[0] = false; } return result; } /** * Checks whether the scheme can build models incrementally. * * @return index 0 is true if the classifier can train incrementally */ protected boolean[] updateableClassifier() { boolean[] result = new boolean[2]; print("updateable classifier..."); if (m_Classifier instanceof UpdateableClassifier) { println("yes"); result[0] = true; } else { println("no"); result[0] = false; } return result; } /** * Checks whether the scheme says it can handle instance weights. * * @return true if the classifier handles instance weights */ protected boolean[] weightedInstancesHandler() { boolean[] result = new boolean[2]; print("weighted instances classifier..."); if (m_Classifier instanceof WeightedInstancesHandler) { println("yes"); result[0] = true; } else { println("no"); result[0] = false; } return result; } /** * Checks whether the scheme handles multi-instance data. * * @return true if the classifier handles multi-instance data */ protected boolean[] multiInstanceHandler() { boolean[] result = new boolean[2]; print("multi-instance classifier..."); if (m_Classifier instanceof MultiInstanceCapabilitiesHandler) { println("yes"); result[0] = true; } else { println("no"); result[0] = false; } return result; } /** * Checks basic prediction of the scheme, for simple non-troublesome * datasets. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NOMINAL, NUMERIC, etc.) * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable */ protected boolean[] canPredict( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType) { print("basic predict"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); FastVector accepts = new FastVector(); accepts.addElement("unary"); accepts.addElement("binary"); accepts.addElement("nominal"); accepts.addElement("numeric"); accepts.addElement("string"); accepts.addElement("date"); accepts.addElement("relational"); accepts.addElement("multi-instance"); accepts.addElement("not in classpath"); int numTrain = getNumInstances(), numTest = getNumInstances(), numClasses = 2, missingLevel = 0; boolean predictorMissing = false, classMissing = false; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, missingLevel, predictorMissing, classMissing, numTrain, numTest, numClasses, accepts); } /** * Checks whether the scheme can handle data that contains only the class * attribute. If a scheme cannot build a proper model with that data, it * should default back to a ZeroR model. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param classType the class type (NOMINAL, NUMERIC, etc.) * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable */ protected boolean[] canHandleOnlyClass( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, int classType) { print("only class in data"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, false, classType); print("..."); FastVector accepts = new FastVector(); accepts.addElement("class"); accepts.addElement("zeror"); int numTrain = getNumInstances(), numTest = getNumInstances(), missingLevel = 0; boolean predictorMissing = false, classMissing = false; return runBasicTest(false, false, false, false, false, false, classType, missingLevel, predictorMissing, classMissing, numTrain, numTest, 2, accepts); } /** * Checks whether nominal schemes can handle more than two classes. * If a scheme is only designed for two-class problems it should * throw an appropriate exception for multi-class problems. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param numClasses the number of classes to test * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable */ protected boolean[] canHandleNClasses( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int numClasses) { print("more than two class problems"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, Attribute.NOMINAL); print("..."); FastVector accepts = new FastVector(); accepts.addElement("number"); accepts.addElement("class"); int numTrain = getNumInstances(), numTest = getNumInstances(), missingLevel = 0; boolean predictorMissing = false, classMissing = false; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, Attribute.NOMINAL, missingLevel, predictorMissing, classMissing, numTrain, numTest, numClasses, accepts); } /** * Checks whether the scheme can handle class attributes as Nth attribute. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param classIndex the index of the class attribute (0-based, -1 means last attribute) * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable * @see TestInstances#CLASS_IS_LAST */ protected boolean[] canHandleClassAsNthAttribute( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType, int classIndex) { if (classIndex == TestInstances.CLASS_IS_LAST) print("class attribute as last attribute"); else print("class attribute as " + (classIndex + 1) + ". attribute"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); FastVector accepts = new FastVector(); int numTrain = getNumInstances(), numTest = getNumInstances(), numClasses = 2, missingLevel = 0; boolean predictorMissing = false, classMissing = false; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, classIndex, missingLevel, predictorMissing, classMissing, numTrain, numTest, numClasses, accepts); } /** * Checks whether the scheme can handle zero training instances. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable */ protected boolean[] canHandleZeroTraining( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType) { print("handle zero training instances"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); FastVector accepts = new FastVector(); accepts.addElement("train"); accepts.addElement("value"); int numTrain = 0, numTest = getNumInstances(), numClasses = 2, missingLevel = 0; boolean predictorMissing = false, classMissing = false; return runBasicTest( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, missingLevel, predictorMissing, classMissing, numTrain, numTest, numClasses, accepts); } /** * Checks whether the scheme correctly initialises models when * buildClassifier is called. This test calls buildClassifier with * one training dataset and records performance on a test set. * buildClassifier is then called on a training set with different * structure, and then again with the original training set. The * performance on the test set is compared with the original results * and any performance difference noted as incorrect build initialisation. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @return index 0 is true if the test was passed, index 1 is true if the * scheme performs worse than ZeroR, but without error (index 0 is * false) */ protected boolean[] correctBuildInitialisation( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType) { boolean[] result = new boolean[2]; print("correct initialisation during buildClassifier"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); int numTrain = getNumInstances(), numTest = getNumInstances(), numClasses = 2, missingLevel = 0; boolean predictorMissing = false, classMissing = false; Instances train1 = null; Instances test1 = null; Instances train2 = null; Instances test2 = null; Classifier classifier = null; Evaluation evaluation1A = null; Evaluation evaluation1B = null; Evaluation evaluation2 = null; boolean built = false; int stage = 0; try { // Make two sets of train/test splits with different // numbers of attributes train1 = makeTestDataset(42, numTrain, nominalPredictor ? getNumNominal() : 0, numericPredictor ? getNumNumeric() : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, multiInstance); train2 = makeTestDataset(84, numTrain, nominalPredictor ? getNumNominal() + 1 : 0, numericPredictor ? getNumNumeric() + 1 : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, multiInstance); test1 = makeTestDataset(24, numTest, nominalPredictor ? getNumNominal() : 0, numericPredictor ? getNumNumeric() : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, multiInstance); test2 = makeTestDataset(48, numTest, nominalPredictor ? getNumNominal() + 1 : 0, numericPredictor ? getNumNumeric() + 1 : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, multiInstance); if (missingLevel > 0) { addMissing(train1, missingLevel, predictorMissing, classMissing); addMissing(test1, Math.min(missingLevel,50), predictorMissing, classMissing); addMissing(train2, missingLevel, predictorMissing, classMissing); addMissing(test2, Math.min(missingLevel,50), predictorMissing, classMissing); } classifier = Classifier.makeCopies(getClassifier(), 1)[0]; evaluation1A = new Evaluation(train1); evaluation1B = new Evaluation(train1); evaluation2 = new Evaluation(train2); } catch (Exception ex) { throw new Error("Error setting up for tests: " + ex.getMessage()); } try { stage = 0; classifier.buildClassifier(train1); built = true; if (!testWRTZeroR(classifier, evaluation1A, train1, test1)[0]) { throw new Exception("Scheme performs worse than ZeroR"); } stage = 1; built = false; classifier.buildClassifier(train2); built = true; if (!testWRTZeroR(classifier, evaluation2, train2, test2)[0]) { throw new Exception("Scheme performs worse than ZeroR"); } stage = 2; built = false; classifier.buildClassifier(train1); built = true; if (!testWRTZeroR(classifier, evaluation1B, train1, test1)[0]) { throw new Exception("Scheme performs worse than ZeroR"); } stage = 3; if (!evaluation1A.equals(evaluation1B)) { if (m_Debug) { println("\n=== Full report ===\n" + evaluation1A.toSummaryString("\nFirst buildClassifier()", true) + "\n\n"); println( evaluation1B.toSummaryString("\nSecond buildClassifier()", true) + "\n\n"); } throw new Exception("Results differ between buildClassifier calls"); } println("yes"); result[0] = true; if (false && m_Debug) { println("\n=== Full report ===\n" + evaluation1A.toSummaryString("\nFirst buildClassifier()", true) + "\n\n"); println( evaluation1B.toSummaryString("\nSecond buildClassifier()", true) + "\n\n"); } } catch (Exception ex) { String msg = ex.getMessage().toLowerCase(); if (msg.indexOf("worse than zeror") >= 0) { println("warning: performs worse than ZeroR"); result[0] = (stage < 1); result[1] = (stage < 1); } else { println("no"); result[0] = false; } if (m_Debug) { println("\n=== Full Report ==="); print("Problem during"); if (built) { print(" testing"); } else { print(" training"); } switch (stage) { case 0: print(" of dataset 1"); break; case 1: print(" of dataset 2"); break; case 2: print(" of dataset 1 (2nd build)"); break; case 3: print(", comparing results from builds of dataset 1"); break; } println(": " + ex.getMessage() + "\n"); println("here are the datasets:\n"); println("=== Train1 Dataset ===\n" + train1.toString() + "\n"); println("=== Test1 Dataset ===\n" + test1.toString() + "\n\n"); println("=== Train2 Dataset ===\n" + train2.toString() + "\n"); println("=== Test2 Dataset ===\n" + test2.toString() + "\n\n"); } } return result; } /** * Checks basic missing value handling of the scheme. If the missing * values cause an exception to be thrown by the scheme, this will be * recorded. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param predictorMissing true if the missing values may be in * the predictors * @param classMissing true if the missing values may be in the class * @param missingLevel the percentage of missing values * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable */ protected boolean[] canHandleMissing( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType, boolean predictorMissing, boolean classMissing, int missingLevel) { if (missingLevel == 100) print("100% "); print("missing"); if (predictorMissing) { print(" predictor"); if (classMissing) print(" and"); } if (classMissing) print(" class"); print(" values"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); FastVector accepts = new FastVector(); accepts.addElement("missing"); accepts.addElement("value"); accepts.addElement("train"); int numTrain = getNumInstances(), numTest = getNumInstances(), numClasses = 2; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, missingLevel, predictorMissing, classMissing, numTrain, numTest, numClasses, accepts); } /** * Checks whether an updateable scheme produces the same model when * trained incrementally as when batch trained. The model itself * cannot be compared, so we compare the evaluation on test data * for both models. It is possible to get a false positive on this * test (likelihood depends on the classifier). * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @return index 0 is true if the test was passed */ protected boolean[] updatingEquality( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType) { print("incremental training produces the same results" + " as batch training"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); int numTrain = getNumInstances(), numTest = getNumInstances(), numClasses = 2, missingLevel = 0; boolean predictorMissing = false, classMissing = false; boolean[] result = new boolean[2]; Instances train = null; Instances test = null; Classifier [] classifiers = null; Evaluation evaluationB = null; Evaluation evaluationI = null; boolean built = false; try { train = makeTestDataset(42, numTrain, nominalPredictor ? getNumNominal() : 0, numericPredictor ? getNumNumeric() : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, multiInstance); test = makeTestDataset(24, numTest, nominalPredictor ? getNumNominal() : 0, numericPredictor ? getNumNumeric() : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, multiInstance); if (missingLevel > 0) { addMissing(train, missingLevel, predictorMissing, classMissing); addMissing(test, Math.min(missingLevel, 50), predictorMissing, classMissing); } classifiers = Classifier.makeCopies(getClassifier(), 2); evaluationB = new Evaluation(train); evaluationI = new Evaluation(train); classifiers[0].buildClassifier(train); testWRTZeroR(classifiers[0], evaluationB, train, test); } catch (Exception ex) { throw new Error("Error setting up for tests: " + ex.getMessage()); } try { classifiers[1].buildClassifier(new Instances(train, 0)); for (int i = 0; i < train.numInstances(); i++) { ((UpdateableClassifier)classifiers[1]).updateClassifier( train.instance(i)); } built = true; testWRTZeroR(classifiers[1], evaluationI, train, test); if (!evaluationB.equals(evaluationI)) { println("no"); result[0] = false; if (m_Debug) { println("\n=== Full Report ==="); println("Results differ between batch and " + "incrementally built models.\n" + "Depending on the classifier, this may be OK"); println("Here are the results:\n"); println(evaluationB.toSummaryString( "\nbatch built results\n", true)); println(evaluationI.toSummaryString( "\nincrementally built results\n", true)); println("Here are the datasets:\n"); println("=== Train Dataset ===\n" + train.toString() + "\n"); println("=== Test Dataset ===\n" + test.toString() + "\n\n"); } } else { println("yes"); result[0] = true; } } catch (Exception ex) { result[0] = false; print("Problem during"); if (built) print(" testing"); else print(" training"); println(": " + ex.getMessage() + "\n"); } return result; } /** * Checks whether the classifier erroneously uses the class * value of test instances (if provided). Runs the classifier with * test instance class values set to missing and compares with results * when test instance class values are left intact. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @return index 0 is true if the test was passed */ protected boolean[] doesntUseTestClassVal( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType) { print("classifier ignores test instance class vals"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); int numTrain = 2*getNumInstances(), numTest = getNumInstances(), numClasses = 2, missingLevel = 0; boolean predictorMissing = false, classMissing = false; boolean[] result = new boolean[2]; Instances train = null; Instances test = null; Classifier [] classifiers = null; boolean evalFail = false; try { train = makeTestDataset(42, numTrain, nominalPredictor ? getNumNominal() + 1 : 0, numericPredictor ? getNumNumeric() + 1 : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, multiInstance); test = makeTestDataset(24, numTest, nominalPredictor ? getNumNominal() + 1 : 0, numericPredictor ? getNumNumeric() + 1 : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, multiInstance); if (missingLevel > 0) { addMissing(train, missingLevel, predictorMissing, classMissing); addMissing(test, Math.min(missingLevel, 50), predictorMissing, classMissing); } classifiers = Classifier.makeCopies(getClassifier(), 2); classifiers[0].buildClassifier(train); classifiers[1].buildClassifier(train); } catch (Exception ex) { throw new Error("Error setting up for tests: " + ex.getMessage()); } try { // Now set test values to missing when predicting for (int i = 0; i < test.numInstances(); i++) { Instance testInst = test.instance(i); Instance classMissingInst = (Instance)testInst.copy(); classMissingInst.setDataset(test); classMissingInst.setClassMissing(); double [] dist0 = classifiers[0].distributionForInstance(testInst); double [] dist1 = classifiers[1].distributionForInstance(classMissingInst); for (int j = 0; j < dist0.length; j++) { // ignore, if both are NaNs if (Double.isNaN(dist0[j]) && Double.isNaN(dist1[j])) { if (getDebug()) System.out.println("Both predictions are NaN!"); continue; } // distribution different? if (dist0[j] != dist1[j]) { throw new Exception("Prediction different for instance " + (i + 1)); } } } println("yes"); result[0] = true; } catch (Exception ex) { println("no"); result[0] = false; if (m_Debug) { println("\n=== Full Report ==="); if (evalFail) { println("Results differ between non-missing and " + "missing test class values."); } else { print("Problem during testing"); println(": " + ex.getMessage() + "\n"); } println("Here are the datasets:\n"); println("=== Train Dataset ===\n" + train.toString() + "\n"); println("=== Train Weights ===\n"); for (int i = 0; i < train.numInstances(); i++) { println(" " + (i + 1) + " " + train.instance(i).weight()); } println("=== Test Dataset ===\n" + test.toString() + "\n\n"); println("(test weights all 1.0\n"); } } return result; } /** * Checks whether the classifier can handle instance weights. * This test compares the classifier performance on two datasets * that are identical except for the training weights. If the * results change, then the classifier must be using the weights. It * may be possible to get a false positive from this test if the * weight changes aren't significant enough to induce a change * in classifier performance (but the weights are chosen to minimize * the likelihood of this). * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @return index 0 true if the test was passed */ protected boolean[] instanceWeights( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType) { print("classifier uses instance weights"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); int numTrain = 2*getNumInstances(), numTest = getNumInstances(), numClasses = 2, missingLevel = 0; boolean predictorMissing = false, classMissing = false; boolean[] result = new boolean[2]; Instances train = null; Instances test = null; Classifier [] classifiers = null; Evaluation evaluationB = null; Evaluation evaluationI = null; boolean built = false; boolean evalFail = false; try { train = makeTestDataset(42, numTrain, nominalPredictor ? getNumNominal() + 1 : 0, numericPredictor ? getNumNumeric() + 1 : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, multiInstance); test = makeTestDataset(24, numTest, nominalPredictor ? getNumNominal() + 1 : 0, numericPredictor ? getNumNumeric() + 1 : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, multiInstance); if (missingLevel > 0) { addMissing(train, missingLevel, predictorMissing, classMissing); addMissing(test, Math.min(missingLevel, 50), predictorMissing, classMissing); } classifiers = Classifier.makeCopies(getClassifier(), 2); evaluationB = new Evaluation(train); evaluationI = new Evaluation(train); classifiers[0].buildClassifier(train); testWRTZeroR(classifiers[0], evaluationB, train, test); } catch (Exception ex) { throw new Error("Error setting up for tests: " + ex.getMessage()); } try { // Now modify instance weights and re-built/test for (int i = 0; i < train.numInstances(); i++) { train.instance(i).setWeight(0); } Random random = new Random(1); for (int i = 0; i < train.numInstances() / 2; i++) { int inst = Math.abs(random.nextInt()) % train.numInstances(); int weight = Math.abs(random.nextInt()) % 10 + 1; train.instance(inst).setWeight(weight); } classifiers[1].buildClassifier(train); built = true; testWRTZeroR(classifiers[1], evaluationI, train, test); if (evaluationB.equals(evaluationI)) { // println("no"); evalFail = true; throw new Exception("evalFail"); } println("yes"); result[0] = true; } catch (Exception ex) { println("no"); result[0] = false; if (m_Debug) { println("\n=== Full Report ==="); if (evalFail) { println("Results don't differ between non-weighted and " + "weighted instance models."); println("Here are the results:\n"); println(evaluationB.toSummaryString("\nboth methods\n", true)); } else { print("Problem during"); if (built) { print(" testing"); } else { print(" training"); } println(": " + ex.getMessage() + "\n"); } println("Here are the datasets:\n"); println("=== Train Dataset ===\n" + train.toString() + "\n"); println("=== Train Weights ===\n"); for (int i = 0; i < train.numInstances(); i++) { println(" " + (i + 1) + " " + train.instance(i).weight()); } println("=== Test Dataset ===\n" + test.toString() + "\n\n"); println("(test weights all 1.0\n"); } } return result; } /** * Checks whether the scheme alters the training dataset during * training. If the scheme needs to modify the training * data it should take a copy of the training data. Currently checks * for changes to header structure, number of instances, order of * instances, instance weights. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param predictorMissing true if we know the classifier can handle * (at least) moderate missing predictor values * @param classMissing true if we know the classifier can handle * (at least) moderate missing class values * @return index 0 is true if the test was passed */ protected boolean[] datasetIntegrity( boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType, boolean predictorMissing, boolean classMissing) { print("classifier doesn't alter original datasets"); printAttributeSummary( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); int numTrain = getNumInstances(), numTest = getNumInstances(), numClasses = 2, missingLevel = 20; boolean[] result = new boolean[2]; Instances train = null; Instances test = null; Classifier classifier = null; Evaluation evaluation = null; boolean built = false; try { train = makeTestDataset(42, numTrain, nominalPredictor ? getNumNominal() : 0, numericPredictor ? getNumNumeric() : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, multiInstance); test = makeTestDataset(24, numTest, nominalPredictor ? getNumNominal() : 0, numericPredictor ? getNumNumeric() : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, multiInstance); if (missingLevel > 0) { addMissing(train, missingLevel, predictorMissing, classMissing); addMissing(test, Math.min(missingLevel, 50), predictorMissing, classMissing); } classifier = Classifier.makeCopies(getClassifier(), 1)[0]; evaluation = new Evaluation(train); } catch (Exception ex) { throw new Error("Error setting up for tests: " + ex.getMessage()); } try { Instances trainCopy = new Instances(train); Instances testCopy = new Instances(test); classifier.buildClassifier(trainCopy); compareDatasets(train, trainCopy); built = true; testWRTZeroR(classifier, evaluation, trainCopy, testCopy); compareDatasets(test, testCopy); println("yes"); result[0] = true; } catch (Exception ex) { println("no"); result[0] = false; if (m_Debug) { println("\n=== Full Report ==="); print("Problem during"); if (built) { print(" testing"); } else { print(" training"); } println(": " + ex.getMessage() + "\n"); println("Here are the datasets:\n"); println("=== Train Dataset ===\n" + train.toString() + "\n"); println("=== Test Dataset ===\n" + test.toString() + "\n\n"); } } return result; } /** * Runs a text on the datasets with the given characteristics. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param missingLevel the percentage of missing values * @param predictorMissing true if the missing values may be in * the predictors * @param classMissing true if the missing values may be in the class * @param numTrain the number of instances in the training set * @param numTest the number of instaces in the test set * @param numClasses the number of classes * @param accepts the acceptable string in an exception * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable */ protected boolean[] runBasicTest(boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType, int missingLevel, boolean predictorMissing, boolean classMissing, int numTrain, int numTest, int numClasses, FastVector accepts) { return runBasicTest( nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, TestInstances.CLASS_IS_LAST, missingLevel, predictorMissing, classMissing, numTrain, numTest, numClasses, accepts); } /** * Runs a text on the datasets with the given characteristics. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param classIndex the attribute index of the class * @param missingLevel the percentage of missing values * @param predictorMissing true if the missing values may be in * the predictors * @param classMissing true if the missing values may be in the class * @param numTrain the number of instances in the training set * @param numTest the number of instaces in the test set * @param numClasses the number of classes * @param accepts the acceptable string in an exception * @return index 0 is true if the test was passed, index 1 is true if test * was acceptable */ protected boolean[] runBasicTest(boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType, int classIndex, int missingLevel, boolean predictorMissing, boolean classMissing, int numTrain, int numTest, int numClasses, FastVector accepts) { boolean[] result = new boolean[2]; Instances train = null; Instances test = null; Classifier classifier = null; Evaluation evaluation = null; boolean built = false; try { train = makeTestDataset(42, numTrain, nominalPredictor ? getNumNominal() : 0, numericPredictor ? getNumNumeric() : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, classIndex, multiInstance); test = makeTestDataset(24, numTest, nominalPredictor ? getNumNominal() : 0, numericPredictor ? getNumNumeric() : 0, stringPredictor ? getNumString() : 0, datePredictor ? getNumDate() : 0, relationalPredictor ? getNumRelational() : 0, numClasses, classType, classIndex, multiInstance); if (missingLevel > 0) { addMissing(train, missingLevel, predictorMissing, classMissing); addMissing(test, Math.min(missingLevel, 50), predictorMissing, classMissing); } classifier = Classifier.makeCopies(getClassifier(), 1)[0]; evaluation = new Evaluation(train); } catch (Exception ex) { ex.printStackTrace(); throw new Error("Error setting up for tests: " + ex.getMessage()); } try { classifier.buildClassifier(train); built = true; if (!testWRTZeroR(classifier, evaluation, train, test)[0]) { result[0] = true; result[1] = true; throw new Exception("Scheme performs worse than ZeroR"); } println("yes"); result[0] = true; } catch (Exception ex) { boolean acceptable = false; String msg; if (ex.getMessage() == null) msg = ""; else msg = ex.getMessage().toLowerCase(); if (msg.indexOf("not in classpath") > -1) m_ClasspathProblems = true; if (msg.indexOf("worse than zeror") >= 0) { println("warning: performs worse than ZeroR"); result[0] = true; result[1] = true; } else { for (int i = 0; i < accepts.size(); i++) { if (msg.indexOf((String)accepts.elementAt(i)) >= 0) { acceptable = true; } } println("no" + (acceptable ? " (OK error message)" : "")); result[1] = acceptable; } if (m_Debug) { println("\n=== Full Report ==="); print("Problem during"); if (built) { print(" testing"); } else { print(" training"); } println(": " + ex.getMessage() + "\n"); if (!acceptable) { if (accepts.size() > 0) { print("Error message doesn't mention "); for (int i = 0; i < accepts.size(); i++) { if (i != 0) { print(" or "); } print('"' + (String)accepts.elementAt(i) + '"'); } } println("here are the datasets:\n"); println("=== Train Dataset ===\n" + train.toString() + "\n"); println("=== Test Dataset ===\n" + test.toString() + "\n\n"); } } } return result; } /** * Determine whether the scheme performs worse than ZeroR during testing * * @param classifier the pre-trained classifier * @param evaluation the classifier evaluation object * @param train the training data * @param test the test data * @return index 0 is true if the scheme performs better than ZeroR * @throws Exception if there was a problem during the scheme's testing */ protected boolean[] testWRTZeroR(Classifier classifier, Evaluation evaluation, Instances train, Instances test) throws Exception { boolean[] result = new boolean[2]; evaluation.evaluateModel(classifier, test); try { // Tested OK, compare with ZeroR Classifier zeroR = new weka.classifiers.rules.ZeroR(); zeroR.buildClassifier(train); Evaluation zeroREval = new Evaluation(train); zeroREval.evaluateModel(zeroR, test); result[0] = Utils.grOrEq(zeroREval.errorRate(), evaluation.errorRate()); } catch (Exception ex) { throw new Error("Problem determining ZeroR performance: " + ex.getMessage()); } return result; } /** * Make a simple set of instances, which can later be modified * for use in specific tests. * * @param seed the random number seed * @param numInstances the number of instances to generate * @param numNominal the number of nominal attributes * @param numNumeric the number of numeric attributes * @param numString the number of string attributes * @param numDate the number of date attributes * @param numRelational the number of relational attributes * @param numClasses the number of classes (if nominal class) * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param multiInstance whether the dataset should a multi-instance dataset * @return the test dataset * @throws Exception if the dataset couldn't be generated * @see #process(Instances) */ protected Instances makeTestDataset(int seed, int numInstances, int numNominal, int numNumeric, int numString, int numDate, int numRelational, int numClasses, int classType, boolean multiInstance) throws Exception { return makeTestDataset( seed, numInstances, numNominal, numNumeric, numString, numDate, numRelational, numClasses, classType, TestInstances.CLASS_IS_LAST, multiInstance); } /** * Make a simple set of instances with variable position of the class * attribute, which can later be modified for use in specific tests. * * @param seed the random number seed * @param numInstances the number of instances to generate * @param numNominal the number of nominal attributes * @param numNumeric the number of numeric attributes * @param numString the number of string attributes * @param numDate the number of date attributes * @param numRelational the number of relational attributes * @param numClasses the number of classes (if nominal class) * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param classIndex the index of the class (0-based, -1 as last) * @param multiInstance whether the dataset should a multi-instance dataset * @return the test dataset * @throws Exception if the dataset couldn't be generated * @see TestInstances#CLASS_IS_LAST * @see #process(Instances) */ protected Instances makeTestDataset(int seed, int numInstances, int numNominal, int numNumeric, int numString, int numDate, int numRelational, int numClasses, int classType, int classIndex, boolean multiInstance) throws Exception { TestInstances dataset = new TestInstances(); dataset.setSeed(seed); dataset.setNumInstances(numInstances); dataset.setNumNominal(numNominal); dataset.setNumNumeric(numNumeric); dataset.setNumString(numString); dataset.setNumDate(numDate); dataset.setNumRelational(numRelational); dataset.setNumClasses(numClasses); dataset.setClassType(classType); dataset.setClassIndex(classIndex); dataset.setNumClasses(numClasses); dataset.setMultiInstance(multiInstance); dataset.setWords(getWords()); dataset.setWordSeparators(getWordSeparators()); return process(dataset.generate()); } /** * Print out a short summary string for the dataset characteristics * * @param nominalPredictor true if nominal predictor attributes are present * @param numericPredictor true if numeric predictor attributes are present * @param stringPredictor true if string predictor attributes are present * @param datePredictor true if date predictor attributes are present * @param relationalPredictor true if relational predictor attributes are present * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) */ protected void printAttributeSummary(boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType) { String str = ""; if (numericPredictor) str += " numeric"; if (nominalPredictor) { if (str.length() > 0) str += " &"; str += " nominal"; } if (stringPredictor) { if (str.length() > 0) str += " &"; str += " string"; } if (datePredictor) { if (str.length() > 0) str += " &"; str += " date"; } if (relationalPredictor) { if (str.length() > 0) str += " &"; str += " relational"; } str += " predictors)"; switch (classType) { case Attribute.NUMERIC: str = " (numeric class," + str; break; case Attribute.NOMINAL: str = " (nominal class," + str; break; case Attribute.STRING: str = " (string class," + str; break; case Attribute.DATE: str = " (date class," + str; break; case Attribute.RELATIONAL: str = " (relational class," + str; break; } print(str); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.33 $"); } /** * Test method for this class * * @param args the commandline parameters */ public static void main(String [] args) { runCheck(new CheckClassifier(), args); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/classifiers/CheckSource.java000066400000000000000000000275251214146114000263240ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * CheckSource.java * Copyright (C) 2007 University of Waikato, Hamilton, New Zealand */ package weka.classifiers; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import weka.core.Utils; import weka.core.converters.ConverterUtils.DataSource; import java.io.File; import java.util.Enumeration; import java.util.Vector; /** * A simple class for checking the source generated from Classifiers * implementing the weka.classifiers.Sourcable interface. * It takes a classifier, the classname of the generated source * and the dataset the source was generated with as parameters and tests * the output of the built classifier against the output of the generated * source. Use option '-h' to display all available commandline options. * * Valid options are:

* * 

 -W <classname and options>
 *  The classifier (incl. options) that was used to generate
 *  the source code.
* * 
 -S <classname>
 *  The classname of the generated source code.
* * 
 -t <file>
 *  The training set with which the source code was generated.
* * 
 -c <index>
 *  The class index of the training set. 'first' and 'last' are
 *  valid indices.
 *  (default: last)
* * * Options after -- are passed to the designated classifier (specified with -W). * * @author fracpete (fracpete at waikato dot ac dot nz) * @version $Revision: 1.4 $ * @see weka.classifiers.Sourcable */ public class CheckSource implements OptionHandler, RevisionHandler { /** the classifier used for generating the source code */ protected Classifier m_Classifier = null; /** the generated source code */ protected Classifier m_SourceCode = null; /** the dataset to use for testing */ protected File m_Dataset = null; /** the class index */ protected int m_ClassIndex = -1; /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector result = new Vector(); result.addElement(new Option( "\tThe classifier (incl. options) that was used to generate\n" + "\tthe source code.", "W", 1, "-W ")); result.addElement(new Option( "\tThe classname of the generated source code.", "S", 1, "-S ")); result.addElement(new Option( "\tThe training set with which the source code was generated.", "t", 1, "-t ")); result.addElement(new Option( "\tThe class index of the training set. 'first' and 'last' are\n" + "\tvalid indices.\n" + "\t(default: last)", "c", 1, "-c ")); return result.elements(); } /** * Parses a given list of options.

* * Valid options are:

* * 

 -W <classname and options>
   *  The classifier (incl. options) that was used to generate
   *  the source code.
* * 
 -S <classname>
   *  The classname of the generated source code.
* * 
 -t <file>
   *  The training set with which the source code was generated.
* * 
 -c <index>
   *  The class index of the training set. 'first' and 'last' are
   *  valid indices.
   *  (default: last)
* * * Options after -- are passed to the designated classifier (specified with * -W). * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { String tmpStr; String[] spec; String classname; tmpStr = Utils.getOption('W', options); if (tmpStr.length() > 0) { spec = Utils.splitOptions(tmpStr); if (spec.length == 0) throw new IllegalArgumentException("Invalid classifier specification string"); classname = spec[0]; spec[0] = ""; setClassifier((Classifier) Utils.forName(Classifier.class, classname, spec)); } else { throw new Exception("No classifier (classname + options) provided!"); } tmpStr = Utils.getOption('S', options); if (tmpStr.length() > 0) { spec = Utils.splitOptions(tmpStr); if (spec.length != 1) throw new IllegalArgumentException("Invalid source code specification string"); classname = spec[0]; spec[0] = ""; setSourceCode((Classifier) Utils.forName(Classifier.class, classname, spec)); } else { throw new Exception("No source code (classname) provided!"); } tmpStr = Utils.getOption('t', options); if (tmpStr.length() != 0) setDataset(new File(tmpStr)); else throw new Exception("No dataset provided!"); tmpStr = Utils.getOption('c', options); if (tmpStr.length() != 0) { if (tmpStr.equals("first")) setClassIndex(0); else if (tmpStr.equals("last")) setClassIndex(-1); else setClassIndex(Integer.parseInt(tmpStr) - 1); } else { setClassIndex(-1); } } /** * Gets the current settings of the Classifier. * * @return an array of strings suitable for passing to setOptions */ public String[] getOptions() { Vector result; result = new Vector(); if (getClassifier() != null) { result.add("-W"); result.add(getClassifier().getClass().getName() + " " + Utils.joinOptions(((OptionHandler) getClassifier()).getOptions())); } if (getSourceCode() != null) { result.add("-S"); result.add(getSourceCode().getClass().getName()); } if (getDataset() != null) { result.add("-t"); result.add(m_Dataset.getAbsolutePath()); } result.add("-c"); if (getClassIndex() == -1) result.add("last"); else if (getClassIndex() == 0) result.add("first"); else result.add("" + (getClassIndex() + 1)); return result.toArray(new String[result.size()]); } /** * Sets the classifier to use for the comparison. * * @param value the classifier to use */ public void setClassifier(Classifier value) { m_Classifier = value; } /** * Gets the classifier being used for the tests, can be null. * * @return the currently set classifier */ public Classifier getClassifier() { return m_Classifier; } /** * Sets the class to test. * * @param value the class to test */ public void setSourceCode(Classifier value) { m_SourceCode = value; } /** * Gets the class to test. * * @return the currently set class, can be null. */ public Classifier getSourceCode() { return m_SourceCode; } /** * Sets the dataset to use for testing. * * @param value the dataset to use. */ public void setDataset(File value) { if (!value.exists()) throw new IllegalArgumentException( "Dataset '" + value.getAbsolutePath() + "' does not exist!"); else m_Dataset = value; } /** * Gets the dataset to use for testing, can be null. * * @return the dataset to use. */ public File getDataset() { return m_Dataset; } /** * Sets the class index of the dataset. * * @param value the class index of the dataset. */ public void setClassIndex(int value) { m_ClassIndex = value; } /** * Gets the class index of the dataset. * * @return the current class index. */ public int getClassIndex() { return m_ClassIndex; } /** * performs the comparison test * * @return true if tests were successful * @throws Exception if tests fail */ public boolean execute() throws Exception { boolean result; Classifier cls; Classifier code; int i; Instances data; DataSource source; boolean numeric; boolean different; double predClassifier; double predSource; result = true; // a few checks if (getClassifier() == null) throw new Exception("No classifier set!"); if (getSourceCode() == null) throw new Exception("No source code set!"); if (getDataset() == null) throw new Exception("No dataset set!"); if (!getDataset().exists()) throw new Exception( "Dataset '" + getDataset().getAbsolutePath() + "' does not exist!"); // load data source = new DataSource(getDataset().getAbsolutePath()); data = source.getDataSet(); if (getClassIndex() == -1) data.setClassIndex(data.numAttributes() - 1); else data.setClassIndex(getClassIndex()); numeric = data.classAttribute().isNumeric(); // build classifier cls = Classifier.makeCopy(getClassifier()); cls.buildClassifier(data); code = getSourceCode(); // compare predictions for (i = 0; i < data.numInstances(); i++) { // perform predictions predClassifier = cls.classifyInstance(data.instance(i)); predSource = code.classifyInstance(data.instance(i)); // compare both results if (Double.isNaN(predClassifier) && Double.isNaN(predSource)) { different = false; } else { if (numeric) different = !Utils.eq(predClassifier, predSource); else different = ((int) predClassifier != (int) predSource); } if (different) { result = false; if (numeric) System.out.println( (i+1) + ". instance (Classifier/Source code): " + predClassifier + " != " + predSource); else System.out.println( (i+1) + ". instance (Classifier/Source code): " + data.classAttribute().value((int) predClassifier) + " != " + data.classAttribute().value((int) predSource)); } } return result; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.4 $"); } /** * Executes the tests, use "-h" to list the commandline options. * * @param args the commandline parameters * @throws Exception if something goes wrong */ public static void main(String[] args) throws Exception{ CheckSource check; StringBuffer text; Enumeration enm; check = new CheckSource(); if (Utils.getFlag('h', args)) { text = new StringBuffer(); text.append("\nHelp requested:\n\n"); enm = check.listOptions(); while (enm.hasMoreElements()) { Option option = (Option) enm.nextElement(); text.append(option.synopsis() + "\n"); text.append(option.description() + "\n"); } System.out.println("\n" + text + "\n"); } else { check.setOptions(args); if (check.execute()) System.out.println("Tests OK!"); else System.out.println("Tests failed!"); } } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/classifiers/Classifier.java000066400000000000000000000216061214146114000262040ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * Classifier.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.classifiers; import weka.core.Attribute; import weka.core.Capabilities; import weka.core.CapabilitiesHandler; import weka.core.Instance; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionHandler; import weka.core.SerializedObject; import weka.core.Utils; import java.io.Serializable; import java.util.Enumeration; import java.util.Vector; /** * Abstract classifier. All schemes for numeric or nominal prediction in * Weka extend this class. Note that a classifier MUST either implement * distributionForInstance() or classifyInstance(). * * @author Eibe Frank (eibe@cs.waikato.ac.nz) * @author Len Trigg (trigg@cs.waikato.ac.nz) * @version $Revision: 1.18 $ */ public abstract class Classifier implements Cloneable, Serializable, OptionHandler, CapabilitiesHandler, RevisionHandler { /** for serialization */ private static final long serialVersionUID = 6502780192411755341L; /** Whether the classifier is run in debug mode. */ protected boolean m_Debug = false; /** * Generates a classifier. Must initialize all fields of the classifier * that are not being set via options (ie. multiple calls of buildClassifier * must always lead to the same result). Must not change the dataset * in any way. * * @param data set of instances serving as training data * @exception Exception if the classifier has not been * generated successfully */ public abstract void buildClassifier( Instances data ) throws Exception; /** * Classifies the given test instance. The instance has to belong to a * dataset when it's being classified. Note that a classifier MUST * implement either this or distributionForInstance(). * * @param instance the instance to be classified * @return the predicted most likely class for the instance or * Instance.missingValue() if no prediction is made * @exception Exception if an error occurred during the prediction */ public double classifyInstance( Instance instance ) throws Exception { double[] dist = distributionForInstance( instance ); if( dist == null ) { throw new Exception( "Null distribution predicted" ); } switch( instance.classAttribute().type() ) { case Attribute.NOMINAL: double max = 0; int maxIndex = 0; for( int i = 0; i < dist.length; i++ ) { if( dist[i] > max ) { maxIndex = i; max = dist[i]; } } if( max > 0 ) { return maxIndex; } else { return Instance.missingValue(); } case Attribute.NUMERIC: return dist[0]; default: return Instance.missingValue(); } } /** * Predicts the class memberships for a given instance. If * an instance is unclassified, the returned array elements * must be all zero. If the class is numeric, the array * must consist of only one element, which contains the * predicted value. Note that a classifier MUST implement * either this or classifyInstance(). * * @param instance the instance to be classified * @return an array containing the estimated membership * probabilities of the test instance in each class * or the numeric prediction * @exception Exception if distribution could not be * computed successfully */ public double[] distributionForInstance( Instance instance ) throws Exception { double[] dist = new double[instance.numClasses()]; switch( instance.classAttribute().type() ) { case Attribute.NOMINAL: double classification = classifyInstance( instance ); if( Instance.isMissingValue( classification ) ) { return dist; } else { dist[(int) classification] = 1.0; } return dist; case Attribute.NUMERIC: dist[0] = classifyInstance( instance ); return dist; default: return dist; } } /** * Creates a new instance of a classifier given it's class name and * (optional) arguments to pass to it's setOptions method. If the * classifier implements OptionHandler and the options parameter is * non-null, the classifier will have it's options set. * * @param classifierName the fully qualified class name of the classifier * @param options an array of options suitable for passing to setOptions. May * be null. * @return the newly created classifier, ready for use. * @exception Exception if the classifier name is invalid, or the options * supplied are not acceptable to the classifier */ public static Classifier forName( String classifierName, String[] options ) throws Exception { return (Classifier) Utils.forName( Classifier.class, classifierName, options ); } /** * Creates a deep copy of the given classifier using serialization. * * @param model the classifier to copy * @return a deep copy of the classifier * @exception Exception if an error occurs */ public static Classifier makeCopy( Classifier model ) throws Exception { return (Classifier) new SerializedObject( model ).getObject(); } /** * Creates a given number of deep copies of the given classifier using serialization. * * @param model the classifier to copy * @param num the number of classifier copies to create. * @return an array of classifiers. * @exception Exception if an error occurs */ public static Classifier[] makeCopies( Classifier model, int num ) throws Exception { if( model == null ) { throw new Exception( "No model classifier set" ); } Classifier[] classifiers = new Classifier[num]; SerializedObject so = new SerializedObject( model ); for( int i = 0; i < classifiers.length; i++ ) { classifiers[i] = (Classifier) so.getObject(); } return classifiers; } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector newVector = new Vector( 1 ); newVector.addElement( new Option( "\tIf set, classifier is run in debug mode and\n" + "\tmay output additional info to the console", "D", 0, "-D" ) ); return newVector.elements(); } /** * Parses a given list of options. Valid options are:

* * -D
* If set, classifier is run in debug mode and * may output additional info to the console.

* * @param options the list of options as an array of strings * @exception Exception if an option is not supported */ public void setOptions( String[] options ) throws Exception { setDebug( Utils.getFlag( 'D', options ) ); } /** * Gets the current settings of the Classifier. * * @return an array of strings suitable for passing to setOptions */ public String[] getOptions() { String[] options; if( getDebug() ) { options = new String[1]; options[0] = "-D"; } else { options = new String[0]; } return options; } /** * Set debugging mode. * * @param debug true if debug output should be printed */ public void setDebug( boolean debug ) { m_Debug = debug; } /** * Get whether debugging is turned on. * * @return true if debugging output is on */ public boolean getDebug() { return m_Debug; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String debugTipText() { return "If set to true, classifier may output additional info to " + "the console."; } /** * Returns the Capabilities of this classifier. Derived classifiers have to * override this method to enable capabilities. * * @return the capabilities of this object * @see Capabilities */ public Capabilities getCapabilities() { return new Capabilities( this ); } /** * runs the classifier instance with the given options. * * @param classifier the classifier to run * @param options the commandline options */ protected static void runClassifier( Classifier classifier, String[] options ) { try { System.out.println( Evaluation.evaluateModel( classifier, options ) ); } catch( Exception e ) { if( ((e.getMessage() != null) && (e.getMessage().indexOf( "General options" ) == -1)) || (e.getMessage() == null) ) { e.printStackTrace(); } else { System.err.println( e.getMessage() ); } } } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/classifiers/CostMatrix.java000066400000000000000000000575221214146114000262230ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * CostMatrix.java * Copyright (C) 2006 University of Waikato, Hamilton, New Zealand * */ package weka.classifiers; import weka.core.AttributeExpression; import weka.core.Instance; import weka.core.Instances; import weka.core.Matrix; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import weka.core.Utils; import java.io.LineNumberReader; import java.io.Reader; import java.io.Serializable; import java.io.StreamTokenizer; import java.io.Writer; import java.util.Random; import java.util.StringTokenizer; /** * Class for storing and manipulating a misclassification cost matrix. * The element at position i,j in the matrix is the penalty for classifying * an instance of class j as class i. Cost values can be fixed or * computed on a per-instance basis (cost sensitive evaluation only) * from the value of an attribute or an expression involving * attribute(s). * * @author Mark Hall * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz) * @version $Revision: 1.17 $ */ public class CostMatrix implements Serializable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = -1973792250544554965L; private int m_size; /** [rows][columns] */ protected Object [][] m_matrix; /** The deafult file extension for cost matrix files */ public static String FILE_EXTENSION = ".cost"; /** * Creates a default cost matrix of a particular size. * All diagonal values will be 0 and all non-diagonal values 1. * * @param numOfClasses the number of classes that the cost matrix holds. */ public CostMatrix(int numOfClasses) { m_size = numOfClasses; initialize(); } /** * Creates a cost matrix that is a copy of another. * * @param toCopy the matrix to copy. */ public CostMatrix(CostMatrix toCopy) { this(toCopy.size()); for (int i = 0; i < m_size; i++) { for (int j = 0; j < m_size; j++) { setCell(i, j, toCopy.getCell(i, j)); } } } /** * Initializes the matrix */ public void initialize() { m_matrix = new Object[m_size][m_size]; for (int i = 0; i < m_size; i++) { for (int j = 0; j < m_size; j++) { setCell(i, j, i == j ? new Double(0.0) : new Double(1.0)); } } } /** * The number of rows (and columns) * @return the size of the matrix */ public int size() { return m_size; } /** * Same as size * @return the number of columns */ public int numColumns() { return size(); } /** * Same as size * @return the number of rows */ public int numRows() { return size(); } private boolean replaceStrings() throws Exception { boolean nonDouble = false; for (int i = 0; i < m_size; i++) { for (int j = 0; j < m_size; j++) { if (getCell(i, j) instanceof String) { AttributeExpression temp = new AttributeExpression(); temp.convertInfixToPostfix((String)getCell(i, j)); setCell(i, j, temp); nonDouble = true; } else if (getCell(i, j) instanceof AttributeExpression) { nonDouble = true; } } } return nonDouble; } /** * Applies the cost matrix to a set of instances. If a random number generator is * supplied the instances will be resampled, otherwise they will be rewighted. * Adapted from code once sitting in Instances.java * * @param data the instances to reweight. * @param random a random number generator for resampling, if null then instances are * rewighted. * @return a new dataset reflecting the cost of misclassification. * @exception Exception if the data has no class or the matrix in inappropriate. */ public Instances applyCostMatrix(Instances data, Random random) throws Exception { double sumOfWeightFactors = 0, sumOfMissClassWeights, sumOfWeights; double [] weightOfInstancesInClass, weightFactor, weightOfInstances; Instances newData; if (data.classIndex() < 0) { throw new Exception("Class index is not set!"); } if (size() != data.numClasses()) { throw new Exception("Misclassification cost matrix has "+ "wrong format!"); } // are there any non-fixed, per-instance costs defined in the matrix? if (replaceStrings()) { // could reweight in the two class case if (data.classAttribute().numValues() > 2) { throw new Exception("Can't resample/reweight instances using " +"non-fixed cost values when there are more " +"than two classes!"); } else { // Store new weights weightOfInstances = new double[data.numInstances()]; for (int i = 0; i < data.numInstances(); i++) { Instance inst = data.instance(i); int classValIndex = (int)inst.classValue(); double factor = 1.0; Object element = (classValIndex == 0) ? getCell(classValIndex, 1) : getCell(classValIndex, 0); if (element instanceof Double) { factor = ((Double)element).doubleValue(); } else { factor = ((AttributeExpression)element).evaluateExpression(inst); } weightOfInstances[i] = inst.weight() * factor; /* System.err.println("Multiplying " + inst.classAttribute().value((int)inst.classValue()) +" by factor " + factor); */ } // Change instances weight or do resampling if (random != null) { return data.resampleWithWeights(random, weightOfInstances); } else { Instances instances = new Instances(data); for (int i = 0; i < data.numInstances(); i++) { instances.instance(i).setWeight(weightOfInstances[i]); } return instances; } } } weightFactor = new double[data.numClasses()]; weightOfInstancesInClass = new double[data.numClasses()]; for (int j = 0; j < data.numInstances(); j++) { weightOfInstancesInClass[(int)data.instance(j).classValue()] += data.instance(j).weight(); } sumOfWeights = Utils.sum(weightOfInstancesInClass); // normalize the matrix if not already for (int i=0; i< m_size; i++) { if (!Utils.eq(((Double)getCell(i, i)).doubleValue(), 0)) { CostMatrix normMatrix = new CostMatrix(this); normMatrix.normalize(); return normMatrix.applyCostMatrix(data, random); } } for (int i = 0; i < data.numClasses(); i++) { // Using Kai Ming Ting's formula for deriving weights for // the classes and Breiman's heuristic for multiclass // problems. sumOfMissClassWeights = 0; for (int j = 0; j < data.numClasses(); j++) { if (Utils.sm(((Double)getCell(i,j)).doubleValue(),0)) { throw new Exception("Neg. weights in misclassification "+ "cost matrix!"); } sumOfMissClassWeights += ((Double)getCell(i,j)).doubleValue(); } weightFactor[i] = sumOfMissClassWeights * sumOfWeights; sumOfWeightFactors += sumOfMissClassWeights * weightOfInstancesInClass[i]; } for (int i = 0; i < data.numClasses(); i++) { weightFactor[i] /= sumOfWeightFactors; } // Store new weights weightOfInstances = new double[data.numInstances()]; for (int i = 0; i < data.numInstances(); i++) { weightOfInstances[i] = data.instance(i).weight()* weightFactor[(int)data.instance(i).classValue()]; } // Change instances weight or do resampling if (random != null) { return data.resampleWithWeights(random, weightOfInstances); } else { Instances instances = new Instances(data); for (int i = 0; i < data.numInstances(); i++) { instances.instance(i).setWeight(weightOfInstances[i]); } return instances; } } /** * Calculates the expected misclassification cost for each possible class value, * given class probability estimates. * * @param classProbs the class probability estimates. * @return the expected costs. * @exception Exception if the wrong number of class probabilities is supplied. */ public double[] expectedCosts(double[] classProbs) throws Exception { if (classProbs.length != m_size) { throw new Exception("Length of probability estimates don't " +"match cost matrix"); } double[] costs = new double[m_size]; for (int x = 0; x < m_size; x++) { for (int y = 0; y < m_size; y++) { Object element = getCell(y, x); if (!(element instanceof Double)) { throw new Exception("Can't use non-fixed costs in " +"computing expected costs."); } costs[x] += classProbs[y] * ((Double)element).doubleValue(); } } return costs; } /** * Calculates the expected misclassification cost for each possible class value, * given class probability estimates. * * @param classProbs the class probability estimates. * @param inst the current instance for which the class probabilites * apply. Is used for computing any non-fixed cost values. * @return the expected costs. * @exception Exception if something goes wrong */ public double[] expectedCosts(double [] classProbs, Instance inst) throws Exception { if (classProbs.length != m_size) { throw new Exception("Length of probability estimates don't " +"match cost matrix"); } if (!replaceStrings()) { return expectedCosts(classProbs); } double[] costs = new double[m_size]; for (int x = 0; x < m_size; x++) { for (int y = 0; y < m_size; y++) { Object element = getCell(y, x); double costVal; if (!(element instanceof Double)) { costVal = ((AttributeExpression)element).evaluateExpression(inst); } else { costVal = ((Double)element).doubleValue(); } costs[x] += classProbs[y] * costVal; } } return costs; } /** * Gets the maximum cost for a particular class value. * * @param classVal the class value. * @return the maximum cost. * @exception Exception if cost matrix contains non-fixed * costs */ public double getMaxCost(int classVal) throws Exception { double maxCost = Double.NEGATIVE_INFINITY; for (int i = 0; i < m_size; i++) { Object element = getCell(classVal, i); if (!(element instanceof Double)) { throw new Exception("Can't use non-fixed costs when " +"getting max cost."); } double cost = ((Double)element).doubleValue(); if (cost > maxCost) maxCost = cost; } return maxCost; } /** * Gets the maximum cost for a particular class value. * * @param classVal the class value. * @return the maximum cost. * @exception Exception if cost matrix contains non-fixed * costs */ public double getMaxCost(int classVal, Instance inst) throws Exception { if (!replaceStrings()) { return getMaxCost(classVal); } double maxCost = Double.NEGATIVE_INFINITY; double cost; for (int i = 0; i < m_size; i++) { Object element = getCell(classVal, i); if (!(element instanceof Double)) { cost = ((AttributeExpression)element).evaluateExpression(inst); } else { cost = ((Double)element).doubleValue(); } if (cost > maxCost) maxCost = cost; } return maxCost; } /** * Normalizes the matrix so that the diagonal contains zeros. * */ public void normalize() { for (int y=0; y= size()) { throw new Exception("Class index out of range!"); } // Get index of second class. if (StreamTokenizer.TT_EOF == (currentToken = tokenizer.nextToken())) { throw new Exception("Premature end of file!"); } if (currentToken == StreamTokenizer.TT_EOL) { throw new Exception("Premature end of line!"); } if (currentToken != StreamTokenizer.TT_NUMBER) { throw new Exception("Only numbers and comments allowed "+ "in cost file!"); } secondIndex = tokenizer.nval; if (!Utils.eq((double)(int)secondIndex,secondIndex)) { throw new Exception("Second number in line has to be "+ "index of a class!"); } if ((int)secondIndex >= size()) { throw new Exception("Class index out of range!"); } if ((int)secondIndex == (int)firstIndex) { throw new Exception("Diagonal of cost matrix non-zero!"); } // Get cost factor. if (StreamTokenizer.TT_EOF == (currentToken = tokenizer.nextToken())) { throw new Exception("Premature end of file!"); } if (currentToken == StreamTokenizer.TT_EOL) { throw new Exception("Premature end of line!"); } if (currentToken != StreamTokenizer.TT_NUMBER) { throw new Exception("Only numbers and comments allowed "+ "in cost file!"); } weight = tokenizer.nval; if (!Utils.gr(weight,0)) { throw new Exception("Only positive weights allowed!"); } setCell((int)firstIndex, (int)secondIndex, new Double(weight)); } } /** * Reads a matrix from a reader. The first line in the file should * contain the number of rows and columns. Subsequent lines * contain elements of the matrix. * (FracPete: taken from old weka.core.Matrix class) * * @param reader the reader containing the matrix * @throws Exception if an error occurs * @see #write(Writer) */ public CostMatrix(Reader reader) throws Exception { LineNumberReader lnr = new LineNumberReader(reader); String line; int currentRow = -1; while ((line = lnr.readLine()) != null) { // Comments if (line.startsWith("%")) { continue; } StringTokenizer st = new StringTokenizer(line); // Ignore blank lines if (!st.hasMoreTokens()) { continue; } if (currentRow < 0) { int rows = Integer.parseInt(st.nextToken()); if (!st.hasMoreTokens()) { throw new Exception("Line " + lnr.getLineNumber() + ": expected number of columns"); } int cols = Integer.parseInt(st.nextToken()); if (rows != cols) { throw new Exception("Trying to create a non-square cost " +"matrix"); } // m_matrix = new Object[rows][cols]; m_size = rows; initialize(); currentRow++; continue; } else { if (currentRow == m_size) { throw new Exception("Line " + lnr.getLineNumber() + ": too many rows provided"); } for (int i = 0; i < m_size; i++) { if (!st.hasMoreTokens()) { throw new Exception("Line " + lnr.getLineNumber() + ": too few matrix elements provided"); } String nextTok = st.nextToken(); // try to parse as a double first Double val = null; try { val = new Double(nextTok); double value = val.doubleValue(); } catch (Exception ex) { val = null; } if (val == null) { setCell(currentRow, i, nextTok); } else { setCell(currentRow, i, val); } } currentRow++; } } if (currentRow == -1) { throw new Exception("Line " + lnr.getLineNumber() + ": expected number of rows"); } else if (currentRow != m_size) { throw new Exception("Line " + lnr.getLineNumber() + ": too few rows provided"); } } /** * Writes out a matrix. The format can be read via the * CostMatrix(Reader) constructor. * (FracPete: taken from old weka.core.Matrix class) * * @param w the output Writer * @throws Exception if an error occurs */ public void write(Writer w) throws Exception { w.write("% Rows\tColumns\n"); w.write("" + m_size + "\t" + m_size + "\n"); w.write("% Matrix elements\n"); for(int i = 0; i < m_size; i++) { for(int j = 0; j < m_size; j++) { w.write("" + getCell(i, j) + "\t"); } w.write("\n"); } w.flush(); } /** * converts the Matrix into a single line Matlab string: matrix is enclosed * by parentheses, rows are separated by semicolon and single cells by * blanks, e.g., [1 2; 3 4]. * @return the matrix in Matlab single line format */ public String toMatlab() { StringBuffer result; int i; int n; result = new StringBuffer(); result.append("["); for (i = 0; i < m_size; i++) { if (i > 0) { result.append("; "); } for (n = 0; n < m_size; n++) { if (n > 0) { result.append(" "); } result.append(getCell(i, n)); } } result.append("]"); return result.toString(); } /** * Set the value of a particular cell in the matrix * * @param rowIndex the row * @param columnIndex the column * @param value the value to set */ public final void setCell(int rowIndex, int columnIndex, Object value) { m_matrix[rowIndex][columnIndex] = value; } /** * Return the contents of a particular cell. Note: this * method returns the Object stored at a particular cell. * * @param rowIndex the row * @param columnIndex the column * @return the value at the cell */ public final Object getCell(int rowIndex, int columnIndex) { return m_matrix[rowIndex][columnIndex]; } /** * Return the value of a cell as a double (for legacy code) * * @param rowIndex the row * @param columnIndex the column * @return the value at a particular cell as a double * @exception Exception if the value is not a double */ public final double getElement(int rowIndex, int columnIndex) throws Exception { if (!(m_matrix[rowIndex][columnIndex] instanceof Double)) { throw new Exception("Cost matrix contains non-fixed costs!"); } return ((Double)m_matrix[rowIndex][columnIndex]).doubleValue(); } /** * Return the value of a cell as a double. Computes the * value for non-fixed costs using the supplied Instance * * @param rowIndex the row * @param columnIndex the column * @return the value from a particular cell * @exception Exception if something goes wrong */ public final double getElement(int rowIndex, int columnIndex, Instance inst) throws Exception { if (m_matrix[rowIndex][columnIndex] instanceof Double) { return ((Double)m_matrix[rowIndex][columnIndex]).doubleValue(); } else if (m_matrix[rowIndex][columnIndex] instanceof String) { replaceStrings(); } return ((AttributeExpression)m_matrix[rowIndex][columnIndex]). evaluateExpression(inst); } /** * Set the value of a cell as a double * * @param rowIndex the row * @param columnIndex the column * @param value the value (double) to set */ public final void setElement(int rowIndex, int columnIndex, double value) { m_matrix[rowIndex][columnIndex] = new Double(value); } /** * creates a matrix from the given Matlab string. * @param matlab the matrix in matlab format * @return the matrix represented by the given string */ public static Matrix parseMatlab(String matlab) throws Exception { return Matrix.parseMatlab(matlab); } /** * Converts a matrix to a string. * (FracPete: taken from old weka.core.Matrix class) * * @return the converted string */ public String toString() { // Determine the width required for the maximum element, // and check for fractional display requirement. double maxval = 0; boolean fractional = false; Object element = null; int widthNumber = 0; int widthExpression = 0; for (int i = 0; i < size(); i++) { for (int j = 0; j < size(); j++) { element = getCell(i, j); if (element instanceof Double) { double current = ((Double)element).doubleValue(); if (current < 0) current *= -11; if (current > maxval) maxval = current; double fract = Math.abs(current - Math.rint(current)); if (!fractional && ((Math.log(fract) / Math.log(10)) >= -2)) { fractional = true; } } else { if (element.toString().length() > widthExpression) { widthExpression = element.toString().length(); } } } } if (maxval > 0) { widthNumber = (int)(Math.log(maxval) / Math.log(10) + (fractional ? 4 : 1)); } int width = (widthNumber > widthExpression) ? widthNumber : widthExpression; StringBuffer text = new StringBuffer(); for (int i = 0; i < size(); i++) { for (int j = 0; j < size(); j++) { element = getCell(i, j); if (element instanceof Double) { text.append(" "). append(Utils.doubleToString(((Double)element). doubleValue(), width, (fractional ? 2 : 0))); } else { int diff = width - element.toString().length(); if (diff > 0) { int left = diff % 2; left += diff / 2; String temp = Utils.padLeft(element.toString(), element.toString().length()+left); temp = Utils.padRight(temp, width); text.append(" ").append(temp); } else { text.append(" "). append(element.toString()); } } } text.append("\n"); } return text.toString(); } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.17 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/classifiers/EnsembleLibrary.java000066400000000000000000000263141214146114000272000ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * EnsembleLibrary.java * Copyright (C) 2006 Robert Jung * */ package weka.classifiers; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import weka.gui.ensembleLibraryEditor.LibrarySerialization; import java.beans.PropertyChangeListener; import java.beans.PropertyChangeSupport; import java.io.BufferedReader; import java.io.BufferedWriter; import java.io.File; import java.io.FileReader; import java.io.FileWriter; import java.io.InputStream; import java.io.Serializable; import java.io.Writer; import java.util.Iterator; import java.util.TreeSet; import java.util.Vector; import javax.swing.JComponent; import javax.swing.JOptionPane; /** * This class represents a library of classifiers. This class * follows the factory design pattern of creating LibraryModels * when asked. It also has the methods necessary for saving * and loading models from lists. * * @author Robert Jung (mrbobjung@gmail.com) * @version $Revision: 1.2 $ */ public class EnsembleLibrary implements Serializable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = -7987178904923706760L; /** The default file extension for model list files */ public static final String XML_FILE_EXTENSION = ".model.xml"; /** The flat file extension for model list files */ public static final String FLAT_FILE_EXTENSION = ".mlf"; /** the set of classifiers that constitute the library */ public TreeSet m_Models; /** A helper class for notifying listeners when the library changes */ private transient PropertyChangeSupport m_LibraryPropertySupport = new PropertyChangeSupport(this); /** * Constructor is responsible for initializing the data * structure hoilding all of the models * */ public EnsembleLibrary() { m_Models = new TreeSet(new EnsembleLibraryModelComparator()); } /** * Returns the number of models in the ensemble library * * @return the number of models */ public int size() { if (m_Models != null) return m_Models.size(); else return 0; } /** * adds a LibraryModel to the Library * * @param model the model to add */ public void addModel(EnsembleLibraryModel model) { m_Models.add(model); if (m_LibraryPropertySupport != null) m_LibraryPropertySupport.firePropertyChange(null, null, null); } /** * adds a LibraryModel to the Library * * @param modelString the model to add */ public void addModel(String modelString) { m_Models.add(createModel(modelString)); m_LibraryPropertySupport.firePropertyChange(null, null, null); } /** * removes a LibraryModel from the Library * * @param model the model to remove */ public void removeModel(EnsembleLibraryModel model) { m_Models.remove(model); m_LibraryPropertySupport.firePropertyChange(null, null, null); } /** * creates a LibraryModel from a string representing the command * line invocation * * @param classifier the classifier to create a model from * @return the generated model */ public EnsembleLibraryModel createModel(Classifier classifier) { EnsembleLibraryModel model = new EnsembleLibraryModel(classifier); return model; } /** * This method takes a String argument defining a classifier and * uses it to create a base Classifier. * * @param modelString the classifier string * @return the generated model */ public EnsembleLibraryModel createModel(String modelString) { String[] splitString = modelString.split("\\s+"); String className = splitString[0]; String argString = modelString.replaceAll(splitString[0], ""); String[] optionStrings = argString.split("\\s+"); EnsembleLibraryModel model = null; try { model = new EnsembleLibraryModel(Classifier.forName(className, optionStrings)); } catch (Exception e) { e.printStackTrace(); } return model; } /** * getter for the set of models in this library * * @return the current models */ public TreeSet getModels() { return m_Models; } /** * setter for the set of models in this library * * @param models the models to use */ public void setModels(TreeSet models) { m_Models = models; m_LibraryPropertySupport.firePropertyChange(null, null, null); } /** * removes all models from the current library */ public void clearModels() { m_Models.clear(); m_LibraryPropertySupport.firePropertyChange(null, null, null); } /** * Loads and returns a library from the specified file * * @param selectedFile the file to load from * @param dialogParent the parent component * @param library will contain the data after loading */ public static void loadLibrary(File selectedFile, JComponent dialogParent, EnsembleLibrary library) { try { loadLibrary(selectedFile, library); } catch (Exception ex) { JOptionPane.showMessageDialog(dialogParent, "Error reading file '" + selectedFile.getName() + "':\n" + ex.getMessage(), "Load failed", JOptionPane.ERROR_MESSAGE); System.err.println(ex.getMessage()); } } /** * This method takes a model list file and a library object as arguments and * Instantiates all of the models in the library list file. It is assumed * that the passed library was an associated working directory and can take * care of creating the model objects itself. * * @param selectedFile the file to load * @param library the library * @throws Exception if something goes wrong */ public static void loadLibrary(File selectedFile, EnsembleLibrary library) throws Exception { //decide what type of model file list we are dealing with and //then load accordingly //deal with XML extension for xml files if (selectedFile.getName().toLowerCase().endsWith( EnsembleLibrary.XML_FILE_EXTENSION)) { LibrarySerialization librarySerialization; Vector classifiers = null; try { librarySerialization = new LibrarySerialization(); classifiers = (Vector) librarySerialization.read(selectedFile.getPath()); } catch (Exception e) { e.printStackTrace(); } //library.setClassifiers(classifiers); for (Iterator it = classifiers.iterator(); it.hasNext();) { EnsembleLibraryModel model = library.createModel((Classifier) it.next()); model.testOptions(); library.addModel(model); } //deal with MLF extesion for flat files } else if (selectedFile.getName().toLowerCase().endsWith( EnsembleLibrary.FLAT_FILE_EXTENSION)) { BufferedReader reader = null; reader = new BufferedReader(new FileReader(selectedFile)); String modelString; while ((modelString = reader.readLine()) != null) { EnsembleLibraryModel model = library.createModel(modelString); if (model != null) { model.testOptions(); library.addModel(model); } else { System.err.println("Failed to create model: " + modelString); } } reader.close(); } } /** * This method takes an XML input stream and a library object as arguments * and Instantiates all of the models in the stream. It is assumed * that the passed library was an associated working directory and can take * care of creating the model objects itself. * * @param stream the XML stream to load * @param library the library * @throws Exception if something goes wrong */ public static void loadLibrary(InputStream stream, EnsembleLibrary library) throws Exception { Vector classifiers = null; try { LibrarySerialization librarySerialization = new LibrarySerialization(); classifiers = (Vector) librarySerialization.read(stream); } catch (Exception e) { e.printStackTrace(); } for (int i = 0; i < classifiers.size(); i++) { EnsembleLibraryModel model = library.createModel((Classifier) classifiers.get(i)); model.testOptions(); library.addModel(model); } } /** * Saves the given library in the specified file. This saves only * the specification of the models as a model list. * * @param selectedFile the file to save to * @param library the library to save * @param dialogParent the component parent */ public static void saveLibrary(File selectedFile, EnsembleLibrary library, JComponent dialogParent) { //save decide what type of model file list we are dealing with and //then save accordingly //System.out.println("writing to file: "+selectedFile.getPath()); //deal with XML extension for xml files if (selectedFile.getName().toLowerCase().endsWith( EnsembleLibrary.XML_FILE_EXTENSION)) { LibrarySerialization librarySerialization; Vector classifiers = new Vector(); for (Iterator it = library.getModels().iterator(); it.hasNext();) { EnsembleLibraryModel model = (EnsembleLibraryModel) it.next(); classifiers.add(model.getClassifier()); } try { librarySerialization = new LibrarySerialization(); librarySerialization.write(selectedFile.getPath(), classifiers); } catch (Exception e) { e.printStackTrace(); } //deal with MLF extesion for flat files } else if (selectedFile.getName().toLowerCase().endsWith( EnsembleLibrary.FLAT_FILE_EXTENSION)) { Writer writer = null; try { writer = new BufferedWriter(new FileWriter(selectedFile)); Iterator it = library.getModels().iterator(); while (it.hasNext()) { EnsembleLibraryModel model = (EnsembleLibraryModel) it.next(); writer.write(model.getStringRepresentation() + "\n"); } writer.close(); } catch (Exception ex) { JOptionPane.showMessageDialog(dialogParent, "Error writing file '" + selectedFile.getName() + "':\n" + ex.getMessage(), "Save failed", JOptionPane.ERROR_MESSAGE); System.err.println(ex.getMessage()); } } } /** * Adds an object to the list of those that wish to be informed when the * library changes. * * @param listener a new listener to add to the list */ public void addPropertyChangeListener(PropertyChangeListener listener) { if (m_LibraryPropertySupport != null) { m_LibraryPropertySupport.addPropertyChangeListener(listener); } } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.2 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/classifiers/EnsembleLibraryModel.java000066400000000000000000000250771214146114000301660ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * EnsembleLibraryModel.java * Copyright (C) 2006 Robert Jung * */ package weka.classifiers; import weka.core.OptionHandler; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import weka.core.Utils; import weka.gui.GenericObjectEditor; import java.beans.BeanInfo; import java.beans.IntrospectionException; import java.beans.Introspector; import java.beans.MethodDescriptor; import java.beans.PropertyDescriptor; import java.beans.PropertyEditor; import java.beans.PropertyEditorManager; import java.io.IOException; import java.io.ObjectInputStream; import java.io.ObjectOutputStream; import java.io.Serializable; import java.lang.reflect.InvocationTargetException; import java.lang.reflect.Method; /** * This class is a light wrapper that is meant to represent a * classifier in a classifier library. This simple base class * is intended only to store the class type and the parameters * for a specific "type" of classifier. You will need to * extend this class to add functionality specific to models * that have been trained on data (as we have for Ensemble Selection) * * @author Robert Jung (mrbobjung@gmail.com) * @version $Revision: 1.2 $ */ public class EnsembleLibraryModel implements Serializable, RevisionHandler { /** this stores the class of the classifier */ //private Class m_ModelClass; /** * This is the serialVersionUID that SHOULD stay the same * so that future modified versions of this class will be * backwards compatible with older model versions. */ private static final long serialVersionUID = 7932816660173443200L; /** this is an array of options*/ protected Classifier m_Classifier; /** the description of this classifier*/ protected String m_DescriptionText; /** this is also saved separately */ protected String m_ErrorText; /** a boolean variable tracking whether or not this classifier was * able to be built successfully with the given set of options*/ protected boolean m_OptionsWereValid; /** this is stores redundantly to speed up the many operations that frequently need to get the model string representations (like JList renderers) */ protected String m_StringRepresentation; /** * Default Constructor */ public EnsembleLibraryModel() { super(); } /** * Initializes the class with the given classifier. * * @param classifier the classifier to use */ public EnsembleLibraryModel(Classifier classifier) { m_Classifier = classifier; //this may seem redundant and stupid, but it really is a good idea //to cache the stringRepresentation to minimize the amount of work //that needs to be done when these Models are rendered m_StringRepresentation = toString(); updateDescriptionText(); } /** * This method will attempt to instantiate this classifier with the given * options. If an exception is thrown from the setOptions method of the * classifier then the resulting exception text will be saved in the * description text string. */ public void testOptions() { Classifier testClassifier = null; try { testClassifier = ((Classifier) m_Classifier.getClass() .newInstance()); } catch (InstantiationException e) { e.printStackTrace(); } catch (IllegalAccessException e) { e.printStackTrace(); } setOptionsWereValid(true); setErrorText(null); updateDescriptionText(); try { testClassifier.setOptions(m_Classifier.getOptions()); } catch (Exception e) { setOptionsWereValid(false); setErrorText(e.getMessage()); } updateDescriptionText(); } /** * Returns the base classifier this library model represents. * * @return the base classifier */ public Classifier getClassifier() { return m_Classifier; } /** * getter for the string representation * * @return the string representation */ public String getStringRepresentation() { return m_StringRepresentation; } /** * setter for the description text * * @param descriptionText the description */ public void setDescriptionText(String descriptionText) { m_DescriptionText = descriptionText; } /** * getter for the string representation * * @return the description */ public String getDescriptionText() { return m_DescriptionText; } /** * setter for the error text * * @param errorText the error text */ public void setErrorText(String errorText) { this.m_ErrorText = errorText; } /** * getter for the error text * * @return the error text */ public String getErrorText() { return m_ErrorText; } /** * setter for the optionsWereValid member variable * * @param optionsWereValid if true, the options were valid */ public void setOptionsWereValid(boolean optionsWereValid) { this.m_OptionsWereValid = optionsWereValid; } /** * getter for the optionsWereValid member variable * * @return true if the options were valid */ public boolean getOptionsWereValid() { return m_OptionsWereValid; } /** * This method converts the current set of arguments and the * class name to a string value representing the command line * invocation * * @return a string representation of classname and options */ public String toString() { String str = m_Classifier.getClass().getName(); str += " " + Utils .joinOptions(((OptionHandler) m_Classifier) .getOptions()); return str; } /** * getter for the modelClass * * @return the model class */ public Class getModelClass() { return m_Classifier.getClass(); } /** * getter for the classifier options * * @return the classifier options */ public String[] getOptions() { return m_Classifier.getOptions(); } /** * Custom serialization method * * @param stream the stream to write the object to * @throws IOException if something goes wrong IO-wise */ private void writeObject(ObjectOutputStream stream) throws IOException { //stream.defaultWriteObject(); //stream.writeObject(b); //serialize the LibraryModel fields stream.writeObject(m_Classifier); stream.writeObject(m_DescriptionText); stream.writeObject(m_ErrorText); stream.writeObject(new Boolean(m_OptionsWereValid)); stream.writeObject(m_StringRepresentation); } /** * Custom serialization method * * @param stream the stream to write to * @throws IOException if something goes wrong IO-wise * @throws ClassNotFoundException if class couldn't be found */ private void readObject(ObjectInputStream stream) throws IOException, ClassNotFoundException { m_Classifier = (Classifier) stream.readObject(); m_DescriptionText = (String) stream.readObject(); m_ErrorText = (String) stream.readObject(); m_OptionsWereValid = ((Boolean) stream.readObject()).booleanValue(); m_StringRepresentation = (String) stream.readObject(); } /** * This method loops through all of the properties of a classifier to * build the html toolTipText that will show all of the property values * for this model. * * Note that this code is copied+adapted from the PropertySheetPanel * class. */ public void updateDescriptionText() { String toolTipText = new String(""); if (!m_OptionsWereValid) { toolTipText += "" + "Invalid Model:
" + m_ErrorText + "
"; } toolTipText += ""; PropertyDescriptor properties[] = null; MethodDescriptor methods[] = null; try { BeanInfo bi = Introspector.getBeanInfo(m_Classifier.getClass()); properties = bi.getPropertyDescriptors(); methods = bi.getMethodDescriptors(); } catch (IntrospectionException ex) { System.err.println("LibraryModel: Couldn't introspect"); return; } for (int i = 0; i < properties.length; i++) { if (properties[i].isHidden() || properties[i].isExpert()) { continue; } String name = properties[i].getDisplayName(); Class type = properties[i].getPropertyType(); Method getter = properties[i].getReadMethod(); Method setter = properties[i].getWriteMethod(); // Only display read/write properties. if (getter == null || setter == null) { continue; } try { Object args[] = {}; Object value = getter.invoke(m_Classifier, args); PropertyEditor editor = null; Class pec = properties[i].getPropertyEditorClass(); if (pec != null) { try { editor = (PropertyEditor) pec.newInstance(); } catch (Exception ex) { // Drop through. } } if (editor == null) { editor = PropertyEditorManager.findEditor(type); } //editors[i] = editor; // If we can't edit this component, skip it. if (editor == null) { continue; } if (editor instanceof GenericObjectEditor) { ((GenericObjectEditor) editor).setClassType(type); } // Don't try to set null values: if (value == null) { continue; } toolTipText += ""; } catch (InvocationTargetException ex) { System.err.println("Skipping property " + name + " ; exception on target: " + ex.getTargetException()); ex.getTargetException().printStackTrace(); continue; } catch (Exception ex) { System.err.println("Skipping property " + name + " ; exception: " + ex); ex.printStackTrace(); continue; } } toolTipText += "
" + name + "" + value.toString() + "
"; toolTipText += ""; m_DescriptionText = toolTipText; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.2 $"); } } rlichtenwalter-LPmade-ba322c3/weka/src/weka/classifiers/EnsembleLibraryModelComparator.java000066400000000000000000000043171214146114000322100ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * EnsembleLibraryModelComparator.java * Copyright (C) 2006 Robert Jung * */ package weka.classifiers; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import java.io.Serializable; import java.util.Comparator; /** * This class is a Comparator for the LibraryModel class. It basically ranks * them alphabetically based on their String Representations * * @author Robert Jung (mrbobjung@gmail.com) * @version $Revision: 1.2 $ */ public final class EnsembleLibraryModelComparator implements Comparator, Serializable, RevisionHandler { /** for serialization */ private static final long serialVersionUID = -6522464740036141188L; /** * Compares its two arguments for order. * * @param o1 first object * @param o2 second object * @return a negative integer, zero, or a positive integer as the * first argument is less than, equal to, or greater than * the second. */ public int compare(Object o1, Object o2) { int comparison = 0; if (o1 instanceof EnsembleLibraryModel && o2 instanceof EnsembleLibraryModel) { comparison = ((String) ((EnsembleLibraryModel) o1) .getStringRepresentation()) .compareTo(((String) ((EnsembleLibraryModel) o2) .getStringRepresentation())); } return comparison; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 1.2 $"); } }rlichtenwalter-LPmade-ba322c3/weka/src/weka/classifiers/Evaluation.java000066400000000000000000003261711214146114000262340ustar00rootroot00000000000000/* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * Evaluation.java * Copyright (C) 1999 University of Waikato, Hamilton, New Zealand * */ package weka.classifiers; import weka.classifiers.evaluation.NominalPrediction; import weka.classifiers.evaluation.ThresholdCurve; import weka.classifiers.xml.XMLClassifier; import weka.core.Drawable; import weka.core.FastVector; import weka.core.Instance; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.Range; import weka.core.RevisionHandler; import weka.core.RevisionUtils; import weka.core.Summarizable; import weka.core.Utils; import weka.core.Version; import weka.core.converters.ConverterUtils.DataSink; import weka.core.converters.ConverterUtils.DataSource; import weka.core.xml.KOML; import weka.core.xml.XMLOptions; import weka.core.xml.XMLSerialization; import weka.estimators.Estimator; import weka.estimators.KernelEstimator; import java.io.BufferedInputStream; import java.io.BufferedOutputStream; import java.io.BufferedReader; import java.io.FileInputStream; import java.io.FileOutputStream; import java.io.FileReader; import java.io.InputStream; import java.io.ObjectInputStream; import java.io.ObjectOutputStream; import java.io.OutputStream; import java.io.Reader; import java.util.Date; import java.util.Enumeration; import java.util.Random; import java.util.zip.GZIPInputStream; import java.util.zip.GZIPOutputStream; /** * Class for evaluating machine learning models.

* * -------------------------------------------------------------------

* * General options when evaluating a learning scheme from the command-line:

* * -t filename
* Name of the file with the training data. (required)

* * -T filename
* Name of the file with the test data. If missing a cross-validation * is performed.

* * -c index
* Index of the class attribute (1, 2, ...; default: last).

* * -x number
* The number of folds for the cross-validation (default: 10).

* * -no-cv
* No cross validation. If no test file is provided, no evaluation * is done.

* * -split-percentage percentage
* Sets the percentage for the train/test set split, e.g., 66.

* * -preserve-order
* Preserves the order in the percentage split instead of randomizing * the data first with the seed value ('-s').

* * -s seed
* Random number seed for the cross-validation and percentage split * (default: 1).

* * -m filename
* The name of a file containing a cost matrix.

* * -l filename
* Loads classifier from the given file. In case the filename ends with ".xml" * the options are loaded from XML.

* * -d filename
* Saves classifier built from the training data into the given file. In case * the filename ends with ".xml" the options are saved XML, not the model.

* * -v
* Outputs no statistics for the training data.

* * -o
* Outputs statistics only, not the classifier.

* * -i
* Outputs information-retrieval statistics per class.

* * -k
* Outputs information-theoretic statistics.

* * -p range
* Outputs predictions for test instances (or the train instances if no test * instances provided and -no-cv is used), along with the attributes in the specified range * (and nothing else). Use '-p 0' if no attributes are desired.

* * -distribution
* Outputs the distribution instead of only the prediction * in conjunction with the '-p' option (only nominal classes).

* * -r
* Outputs cumulative margin distribution (and nothing else).

* * -g
* Only for classifiers that implement "Graphable." Outputs * the graph representation of the classifier (and nothing * else).

* * -xml filename | xml-string
* Retrieves the options from the XML-data instead of the command line.

* * -threshold-file file
* The file to save the threshold data to. * The format is determined by the extensions, e.g., '.arff' for ARFF * format or '.csv' for CSV.

* * -threshold-label label
* The class label to determine the threshold data for * (default is the first label)

* * -------------------------------------------------------------------

* * Example usage as the main of a classifier (called FunkyClassifier): * 

 * public static void main(String [] args) {
 *   runClassifier(new FunkyClassifier(), args);
 * }
 *
*

* * ------------------------------------------------------------------

* * Example usage from within an application: * 

 * Instances trainInstances = ... instances got from somewhere
 * Instances testInstances = ... instances got from somewhere
 * Classifier scheme = ... scheme got from somewhere
 *
 * Evaluation evaluation = new Evaluation(trainInstances);
 * evaluation.evaluateModel(scheme, testInstances);
 * System.out.println(evaluation.toSummaryString());
 *
* * * @author Eibe Frank (eibe@cs.waikato.ac.nz) * @author Len Trigg (trigg@cs.waikato.ac.nz) * @version $Revision: 1.88 $ */ public class Evaluation implements Summarizable, RevisionHandler { /** The number of classes. */ protected int m_NumClasses; /** The number of folds for a cross-validation. */ protected int m_NumFolds; /** The weight of all incorrectly classified instances. */ protected double m_Incorrect; /** The weight of all correctly classified instances. */ protected double m_Correct; /** The weight of all unclassified instances. */ protected double m_Unclassified; /*** The weight of all instances that had no class assigned to them. */ protected double m_MissingClass; /** The weight of all instances that had a class assigned to them. */ protected double m_WithClass; /** Array for storing the confusion matrix. */ protected double[][] m_ConfusionMatrix; /** The names of the classes. */ protected String[] m_ClassNames; /** Is the class nominal or numeric? */ protected boolean m_ClassIsNominal; /** The prior probabilities of the classes */ protected double[] m_ClassPriors; /** The sum of counts for priors */ protected double m_ClassPriorsSum; /** The cost matrix (if given). */ protected CostMatrix m_CostMatrix; /** The total cost of predictions (includes instance weights) */ protected double m_TotalCost; /** Sum of errors. */ protected double m_SumErr; /** Sum of absolute errors. */ protected double m_SumAbsErr; /** Sum of squared errors. */ protected double m_SumSqrErr; /** Sum of class values. */ protected double m_SumClass; /** Sum of squared class values. */ protected double m_SumSqrClass; /*** Sum of predicted values. */ protected double m_SumPredicted; /** Sum of squared predicted values. */ protected double m_SumSqrPredicted; /** Sum of predicted * class values. */ protected double m_SumClassPredicted; /** Sum of absolute errors of the prior */ protected double m_SumPriorAbsErr; /** Sum of absolute errors of the prior */ protected double m_SumPriorSqrErr; /** Total Kononenko & Bratko Information */ protected double m_SumKBInfo; /*** Resolution of the margin histogram */ protected static int k_MarginResolution = 500; /** Cumulative margin distribution */ protected double m_MarginCounts[]; /** Number of non-missing class training instances seen */ protected int m_NumTrainClassVals; /** Array containing all numeric training class values seen */ protected double[] m_TrainClassVals; /** Array containing all numeric training class weights */ protected double[] m_TrainClassWeights; /** Numeric class error estimator for prior */ protected Estimator m_PriorErrorEstimator; /** Numeric class error estimator for scheme */ protected Estimator m_ErrorEstimator; /** * The minimum probablility accepted from an estimator to avoid * taking log(0) in Sf calculations. */ protected static final double MIN_SF_PROB = Double.MIN_VALUE; /** Total entropy of prior predictions */ protected double m_SumPriorEntropy; /** Total entropy of scheme predictions */ protected double m_SumSchemeEntropy; /** The list of predictions that have been generated (for computing AUC) */ private FastVector m_Predictions; /** enables/disables the use of priors, e.g., if no training set is * present in case of de-serialized schemes */ protected boolean m_NoPriors = false; /** * Initializes all the counters for the evaluation. * Use useNoPriors() if the dataset is the test set and you * can't initialize with the priors from the training set via * setPriors(Instances). * * @param data set of training instances, to get some header * information and prior class distribution information * @throws Exception if the class is not defined * @see #useNoPriors() * @see #setPriors(Instances) */ public Evaluation(Instances data) throws Exception { this(data, null); } /** * Initializes all the counters for the evaluation and also takes a * cost matrix as parameter. * Use useNoPriors() if the dataset is the test set and you * can't initialize with the priors from the training set via * setPriors(Instances). * * @param data set of training instances, to get some header * information and prior class distribution information * @param costMatrix the cost matrix---if null, default costs will be used * @throws Exception if cost matrix is not compatible with * data, the class is not defined or the class is numeric * @see #useNoPriors() * @see #setPriors(Instances) */ public Evaluation(Instances data, CostMatrix costMatrix) throws Exception { m_NumClasses = data.numClasses(); m_NumFolds = 1; m_ClassIsNominal = data.classAttribute().isNominal(); if (m_ClassIsNominal) { m_ConfusionMatrix = new double[m_NumClasses][m_NumClasses]; m_ClassNames = new String[m_NumClasses]; for (int i = 0; i < m_NumClasses; i++) { m_ClassNames[i] = data.classAttribute().value(i); } } m_CostMatrix = costMatrix; if (m_CostMatrix != null) { if (!m_ClassIsNominal) { throw new Exception("Class has to be nominal if cost matrix " + "given!"); } if (m_CostMatrix.size() != m_NumClasses) { throw new Exception("Cost matrix not compatible with data!"); } } m_ClassPriors = new double[m_NumClasses]; setPriors(data); m_MarginCounts = new double[k_MarginResolution + 1]; } /** * Returns the area under ROC for those predictions that have been collected * in the evaluateClassifier(Classifier, Instances) method. Returns * Instance.missingValue() if the area is not available. * * @param classIndex the index of the class to consider as "positive" * @return the area under the ROC curve or not a number */ public double areaUnderROC(int classIndex) { // Check if any predictions have been collected if (m_Predictions == null) { return Instance.missingValue(); } else { ThresholdCurve tc = new ThresholdCurve(); Instances result = tc.getCurve(m_Predictions, classIndex); return ThresholdCurve.getROCArea(result); } } /** * Returns a copy of the confusion matrix. * * @return a copy of the confusion matrix as a two-dimensional array */ public double[][] confusionMatrix() { double[][] newMatrix = new double[m_ConfusionMatrix.length][0]; for (int i = 0; i < m_ConfusionMatrix.length; i++) { newMatrix[i] = new double[m_ConfusionMatrix[i].length]; System.arraycopy(m_ConfusionMatrix[i], 0, newMatrix[i], 0, m_ConfusionMatrix[i].length); } return newMatrix; } /** * Performs a (stratified if class is nominal) cross-validation * for a classifier on a set of instances. Now performs * a deep copy of the classifier before each call to * buildClassifier() (just in case the classifier is not * initialized properly). * * @param classifier the classifier with any options set. * @param data the data on which the cross-validation is to be * performed * @param numFolds the number of folds for the cross-validation * @param random random number generator for randomization * @param forPredictionsString varargs parameter that, if supplied, is * expected to hold a StringBuffer to print predictions to, * a Range of attributes to output and a Boolean (true if the distribution * is to be printed) * @throws Exception if a classifier could not be generated * successfully or the class is not defined */ public void crossValidateModel(Classifier classifier, Instances data, int numFolds, Random random, Object... forPredictionsPrinting) throws Exception { // Make a copy of the data we can reorder data = new Instances(data); data.randomize(random); if (data.classAttribute().isNominal()) { data.stratify(numFolds); } // We assume that the first element is a StringBuffer, the second a Range (attributes // to output) and the third a Boolean (whether or not to output a distribution instead // of just a classification) if (forPredictionsPrinting.length > 0) { // print the header first StringBuffer buff = (StringBuffer) forPredictionsPrinting[0]; Range attsToOutput = (Range) forPredictionsPrinting[1]; boolean printDist = ((Boolean) forPredictionsPrinting[2]).booleanValue(); printClassificationsHeader(data, attsToOutput, printDist, buff); } // Do the folds for (int i = 0; i < numFolds; i++) { Instances train = data.trainCV(numFolds, i, random); setPriors(train); Classifier copiedClassifier = Classifier.makeCopy(classifier); copiedClassifier.buildClassifier(train); Instances test = data.testCV(numFolds, i); evaluateModel(copiedClassifier, test, forPredictionsPrinting); } m_NumFolds = numFolds; } /** * Performs a (stratified if class is nominal) cross-validation * for a classifier on a set of instances. * * @param classifierString a string naming the class of the classifier * @param data the data on which the cross-validation is to be * performed * @param numFolds the number of folds for the cross-validation * @param options the options to the classifier. Any options * @param random the random number generator for randomizing the data * accepted by the classifier will be removed from this array. * @throws Exception if a classifier could not be generated * successfully or the class is not defined */ public void crossValidateModel(String classifierString, Instances data, int numFolds, String[] options, Random random) throws Exception { crossValidateModel(Classifier.forName(classifierString, options), data, numFolds, random); } /** * Evaluates a classifier with the options given in an array of * strings.

* * Valid options are:

* * -t filename
* Name of the file with the training data. (required)

* * -T filename
* Name of the file with the test data. If missing a cross-validation * is performed.

* * -c index
* Index of the class attribute (1, 2, ...; default: last).

* * -x number
* The number of folds for the cross-validation (default: 10).

* * -no-cv
* No cross validation. If no test file is provided, no evaluation * is done.

* * -split-percentage percentage
* Sets the percentage for the train/test set split, e.g., 66.

* * -preserve-order
* Preserves the order in the percentage split instead of randomizing * the data first with the seed value ('-s').

* * -s seed
* Random number seed for the cross-validation and percentage split * (default: 1).

* * -m filename
* The name of a file containing a cost matrix.

* * -l filename
* Loads classifier from the given file. In case the filename ends with * ".xml" the options are loaded from XML.

* * -d filename
* Saves classifier built from the training data into the given file. In case * the filename ends with ".xml" the options are saved XML, not the model.

* * -v
* Outputs no statistics for the training data.

* * -o
* Outputs statistics only, not the classifier.

* * -i
* Outputs detailed information-retrieval statistics per class.

* * -k
* Outputs information-theoretic statistics.

* * -p range
* Outputs predictions for test instances (or the train instances if no test * instances provided and -no-cv is used), along with the attributes in the specified range (and * nothing else). Use '-p 0' if no attributes are desired.

* * -distribution
* Outputs the distribution instead of only the prediction * in conjunction with the '-p' option (only nominal classes).

* * -r
* Outputs cumulative margin distribution (and nothing else).

* * -g
* Only for classifiers that implement "Graphable." Outputs * the graph representation of the classifier (and nothing * else).

* * -xml filename | xml-string
* Retrieves the options from the XML-data instead of the command line.

* * -threshold-file file
* The file to save the threshold data to. * The format is determined by the extensions, e.g., '.arff' for ARFF * format or '.csv' for CSV.

* * -threshold-label label
* The class label to determine the threshold data for * (default is the first label)

* * @param classifierString class of machine learning classifier as a string * @param options the array of string containing the options * @throws Exception if model could not be evaluated successfully * @return a string describing the results */ public static String evaluateModel(String classifierString, String[] options) throws Exception { Classifier classifier; // Create classifier try { classifier = (Classifier) Class.forName(classifierString).newInstance(); } catch (Exception e) { throw new Exception("Can't find class with name " + classifierString + '.'); } return evaluateModel(classifier, options); } /** * A test method for this class. Just extracts the first command line * argument as a classifier class name and calls evaluateModel. * @param args an array of command line arguments, the first of which * must be the class name of a classifier. */ public static void main(String[] args) { try { if (args.length == 0) { throw new Exception("The first argument must be the class name" + " of a classifier"); } String classifier = args[0]; args[0] = ""; System.out.println(evaluateModel(classifier, args)); } catch (Exception ex) { ex.printStackTrace(); System.err.println(ex.getMessage()); } } /** * Evaluates a classifier with the options given in an array of * strings.

* * Valid options are:

* * -t name of training file
* Name of the file with the training data. (required)

* * -T name of test file
* Name of the file with the test data. If missing a cross-validation * is performed.

* * -c class index
* Index of the class attribute (1, 2, ...; default: last).

* * -x number of folds
* The number of folds for the cross-validation (default: 10).

* * -no-cv
* No cross validation. If no test file is provided, no evaluation * is done.

* * -split-percentage percentage
* Sets the percentage for the train/test set split, e.g., 66.

* * -preserve-order
* Preserves the order in the percentage split instead of randomizing * the data first with the seed value ('-s').

* * -s seed
* Random number seed for the cross-validation and percentage split * (default: 1).

* * -m file with cost matrix
* The name of a file containing a cost matrix.

* * -l filename
* Loads classifier from the given file. In case the filename ends with * ".xml" the options are loaded from XML.

* * -d filename
* Saves classifier built from the training data into the given file. In case * the filename ends with ".xml" the options are saved XML, not the model.

* * -v
* Outputs no statistics for the training data.

* * -o
* Outputs statistics only, not the classifier.

* * -i
* Outputs detailed information-retrieval statistics per class.

* * -k
* Outputs information-theoretic statistics.

* * -p range
* Outputs predictions for test instances (or the train instances if no test * instances provided and -no-cv is used), along with the attributes in the specified range * (and nothing else). Use '-p 0' if no attributes are desired.

* * -distribution
* Outputs the distribution instead of only the prediction * in conjunction with the '-p' option (only nominal classes).

* * -r
* Outputs cumulative margin distribution (and nothing else).

* * -g
* Only for classifiers that implement "Graphable." Outputs * the graph representation of the classifier (and nothing * else).

* * -xml filename | xml-string
* Retrieves the options from the XML-data instead of the command line.

* * @param classifier machine learning classifier * @param options the array of string containing the options * @throws Exception if model could not be evaluated successfully * @return a string describing the results */ public static String evaluateModel(Classifier classifier, String[] options) throws Exception { Instances train = null, tempTrain, test = null, template = null; int seed = 1, folds = 10, classIndex = -1; boolean noCrossValidation = false; String trainFileName, testFileName, sourceClass, classIndexString, seedString, foldsString, objectInputFileName, objectOutputFileName, attributeRangeString; boolean noOutput = false, printClassifications = false, trainStatistics = true, printMargins = false, printComplexityStatistics = false, printGraph = false, classStatistics = false, printSource = false; StringBuffer text = new StringBuffer(); DataSource trainSource = null, testSource = null; ObjectInputStream objectInputStream = null; BufferedInputStream xmlInputStream = null; CostMatrix costMatrix = null; StringBuffer schemeOptionsText = null; Range attributesToOutput = null; long trainTimeStart = 0, trainTimeElapsed = 0, testTimeStart = 0, testTimeElapsed = 0; String xml = ""; String[] optionsTmp = null; Classifier classifierBackup; Classifier classifierClassifications = null; boolean printDistribution = false; int actualClassIndex = -1; // 0-based class index String splitPercentageString = ""; int splitPercentage = -1; boolean preserveOrder = false; boolean trainSetPresent = false; boolean testSetPresent = false; String thresholdFile; String thresholdLabel; StringBuffer predsBuff = null; // predictions from cross-validation // help requested? if (Utils.getFlag("h", options) || Utils.getFlag("help", options)) { throw new Exception("\nHelp requested." + makeOptionString(classifier)); } try { // do we get the input from XML instead of normal parameters? xml = Utils.getOption("xml", options); if (!xml.equals("")) { options = new XMLOptions(xml).toArray(); } // is the input model only the XML-Options, i.e. w/o built model? optionsTmp = new String[options.length]; for (int i = 0; i < options.length; i++) { optionsTmp[i] = options[i]; } if (Utils.getOption('l', optionsTmp).toLowerCase().endsWith(".xml")) { // load options from serialized data ('-l' is automatically erased!) XMLClassifier xmlserial = new XMLClassifier(); Classifier cl = (Classifier) xmlserial.read(Utils.getOption('l', options)); // merge options optionsTmp = new String[options.length + cl.getOptions().length]; System.arraycopy(cl.getOptions(), 0, optionsTmp, 0, cl.getOptions().length); System.arraycopy(options, 0, optionsTmp, cl.getOptions().length, options.length); options = optionsTmp; } noCrossValidation = Utils.getFlag("no-cv", options); // Get basic options (options the same for all schemes) classIndexString = Utils.getOption('c', options); if (classIndexString.length() != 0) { if (classIndexString.equals("first")) { classIndex = 1; } else if (classIndexString.equals("last")) { classIndex = -1; } else { classIndex = Integer.parseInt(classIndexString); } } trainFileName = Utils.getOption('t', options); objectInputFileName = Utils.getOption('l', options); objectOutputFileName = Utils.getOption('d', options); testFileName = Utils.getOption('T', options); foldsString = Utils.getOption('x', options); if (foldsString.length() != 0) { folds = Integer.parseInt(foldsString); } seedString = Utils.getOption('s', options); if (seedString.length() != 0) { seed = Integer.parseInt(seedString); } if (trainFileName.length() == 0) { if (objectInputFileName.length() == 0) { throw new Exception("No training file and no object " + "input file given."); } if (testFileName.length() == 0) { throw new Exception("No training file and no test " + "file given."); } } else if ((objectInputFileName.length() != 0) && ((!(classifier instanceof UpdateableClassifier)) || (testFileName.length() == 0))) { throw new Exception("Classifier not incremental, or no " + "test file provided: can't " + "use both train and model file."); } try { if (trainFileName.length() != 0) { trainSetPresent = true; trainSource = new DataSource(trainFileName); } if (testFileName.length() != 0) { testSetPresent = true; testSource = new DataSource(testFileName); } if (objectInputFileName.length() != 0) { InputStream is = new FileInputStream(objectInputFileName); if (objectInputFileName.endsWith(".gz")) { is = new GZIPInputStream(is); } // load from KOML? if (!(objectInputFileName.endsWith(".koml") && KOML.isPresent())) { objectInputStream = new ObjectInputStream(is); xmlInputStream = null; } else { objectInputStream = null; xmlInputStream = new BufferedInputStream(is); } } } catch (Exception e) { throw new Exception("Can't open file " + e.getMessage() + '.'); } if (testSetPresent) { template = test = testSource.getStructure(); if (classIndex != -1) { test.setClassIndex(classIndex - 1); } else { if ((test.classIndex() == -1) || (classIndexString.length() != 0)) { test.setClassIndex(test.numAttributes() - 1); } } actualClassIndex = test.classIndex(); } else { // percentage split splitPercentageString = Utils.getOption("split-percentage", options); if (splitPercentageString.length() != 0) { if (foldsString.length() != 0) { throw new Exception( "Percentage split cannot be used in conjunction with " + "cross-validation ('-x')."); } splitPercentage = Integer.parseInt(splitPercentageString); if ((splitPercentage <= 0) || (splitPercentage >= 100)) { throw new Exception("Percentage split value needs be >0 and <100."); } } else { splitPercentage = -1; } preserveOrder = Utils.getFlag("preserve-order", options); if (preserveOrder) { if (splitPercentage == -1) { throw new Exception("Percentage split ('-percentage-split') is missing."); } } // create new train/test sources if (splitPercentage > 0) { testSetPresent = true; Instances tmpInst = trainSource.getDataSet(actualClassIndex); if (!preserveOrder) { tmpInst.randomize(new Random(seed)); } int trainSize = tmpInst.numInstances() * splitPercentage / 100; int testSize = tmpInst.numInstances() - trainSize; Instances trainInst = new Instances(tmpInst, 0, trainSize); Instances testInst = new Instances(tmpInst, trainSize, testSize); trainSource = new DataSource(trainInst); testSource = new DataSource(testInst); template = test = testSource.getStructure(); if (classIndex != -1) { test.setClassIndex(classIndex - 1); } else { if ((test.classIndex() == -1) || (classIndexString.length() != 0)) { test.setClassIndex(test.numAttributes() - 1); } } actualClassIndex = test.classIndex(); } } if (trainSetPresent) { template = train = trainSource.getStructure(); if (classIndex != -1) { train.setClassIndex(classIndex - 1); } else { if ((train.classIndex() == -1) || (classIndexString.length() != 0)) { train.setClassIndex(train.numAttributes() - 1); } } actualClassIndex = train.classIndex(); if ((testSetPresent) && !test.equalHeaders(train)) { throw new IllegalArgumentException("Train and test file not compatible!"); } } if (template == null) { throw new Exception("No actual dataset provided to use as template"); } costMatrix = handleCostOption( Utils.getOption('m', options), template.numClasses()); classStatistics = Utils.getFlag('i', options); noOutput = Utils.getFlag('o', options); trainStatistics = !Utils.getFlag('v', options); printComplexityStatistics = Utils.getFlag('k', options); printMargins = Utils.getFlag('r', options); printGraph = Utils.getFlag('g', options); sourceClass = Utils.getOption('z', options); printSource = (sourceClass.length() != 0); printDistribution = Utils.getFlag("distribution", options); thresholdFile = Utils.getOption("threshold-file", options); thresholdLabel = Utils.getOption("threshold-label", options); // Check -p option try { attributeRangeString = Utils.getOption('p', options); } catch (Exception e) { throw new Exception(e.getMessage() + "\nNOTE: the -p option has changed. " + "It now expects a parameter specifying a range of attributes " + "to list with the predictions. Use '-p 0' for none."); } if (attributeRangeString.length() != 0) { printClassifications = true; if (!attributeRangeString.equals("0")) { attributesToOutput = new Range(attributeRangeString); } } if (!printClassifications && printDistribution) { throw new Exception("Cannot print distribution without '-p' option!"); } // if no training file given, we don't have any priors if ((!trainSetPresent) && (printComplexityStatistics)) { throw new Exception("Cannot print complexity statistics ('-k') without training file ('-t')!"); } // If a model file is given, we can't process // scheme-specific options if (objectInputFileName.length() != 0) { Utils.checkForRemainingOptions(options); } else { // Set options for classifier if (classifier instanceof OptionHandler) { for (int i = 0; i < options.length; i++) { if (options[i].length() != 0) { if (schemeOptionsText == null) { schemeOptionsText = new StringBuffer(); } if (options[i].indexOf(' ') != -1) { schemeOptionsText.append('"' + options[i] + "\" "); } else { schemeOptionsText.append(options[i] + " "); } } } ((OptionHandler) classifier).setOptions(options); } } Utils.checkForRemainingOptions(options); } catch (Exception e) { throw new Exception("\nWeka exception: " + e.getMessage() + makeOptionString(classifier)); } // Setup up evaluation objects Evaluation trainingEvaluation = new Evaluation(new Instances(template, 0), costMatrix); Evaluation testingEvaluation = new Evaluation(new Instances(template, 0), costMatrix); // disable use of priors if no training file given if (!trainSetPresent) { testingEvaluation.useNoPriors(); } if (objectInputFileName.length() != 0) { // Load classifier from file if (objectInputStream != null) { classifier = (Classifier) objectInputStream.readObject(); // try and read a header (if present) Instances savedStructure = null; try { savedStructure = (Instances) objectInputStream.readObject(); } catch (Exception ex) { // don't make a fuss } if (savedStructure != null) { // test for compatibility with template if (!template.equalHeaders(savedStructure)) { throw new Exception("training and test set are not compatible"); } } objectInputStream.close(); } else { // whether KOML is available has already been checked (objectInputStream would null otherwise)! classifier = (Classifier) KOML.read(xmlInputStream); xmlInputStream.close(); } } // backup of fully setup classifier for cross-validation classifierBackup = Classifier.makeCopy(classifier); // Build the classifier if no object file provided if ((classifier instanceof UpdateableClassifier) && (testSetPresent || noCrossValidation) && (costMatrix == null) && (trainSetPresent)) { // Build classifier incrementally trainingEvaluation.setPriors(train); testingEvaluation.setPriors(train); trainTimeStart = System.currentTimeMillis(); if (objectInputFileName.length() == 0) { classifier.buildClassifier(train); } Instance trainInst; while (trainSource.hasMoreElements(train)) { trainInst = trainSource.nextElement(train); trainingEvaluation.updatePriors(trainInst); testingEvaluation.updatePriors(trainInst); ((UpdateableClassifier) classifier).updateClassifier(trainInst); } trainTimeElapsed = System.currentTimeMillis() - trainTimeStart; } else if (objectInputFileName.length() == 0) { // Build classifier in one go tempTrain = trainSource.getDataSet(actualClassIndex); trainingEvaluation.setPriors(tempTrain); testingEvaluation.setPriors(tempTrain); trainTimeStart = System.currentTimeMillis(); classifier.buildClassifier(tempTrain); trainTimeElapsed = System.currentTimeMillis() - trainTimeStart; } /* FOR LARGE DATA SETS // backup of fully trained classifier for printing the classifications if (printClassifications) { classifierClassifications = Classifier.makeCopy(classifier); } */ // Save the classifier if an object output file is provided if (objectOutputFileName.length() != 0) { OutputStream os = new FileOutputStream(objectOutputFileName); // binary if (!(objectOutputFileName.endsWith(".xml") || (objectOutputFileName.endsWith(".koml") && KOML.isPresent()))) { if (objectOutputFileName.endsWith(".gz")) { os = new GZIPOutputStream(os); } ObjectOutputStream objectOutputStream = new ObjectOutputStream(os); objectOutputStream.writeObject(classifier); if (template != null) { objectOutputStream.writeObject(template); } objectOutputStream.flush(); objectOutputStream.close(); } // KOML/XML else { BufferedOutputStream xmlOutputStream = new BufferedOutputStream(os); if (objectOutputFileName.endsWith(".xml")) { XMLSerialization xmlSerial = new XMLClassifier(); xmlSerial.write(xmlOutputStream, classifier); } else // whether KOML is present has already been checked // if not present -> ".koml" is interpreted as binary - see above if (objectOutputFileName.endsWith(".koml")) { KOML.write(xmlOutputStream, classifier); } xmlOutputStream.close(); } } /* FOR LARGE DATA SETS // If classifier is drawable output string describing graph if ((classifier instanceof Drawable) && (printGraph)) { return ((Drawable) classifier).graph(); } // Output the classifier as equivalent source if ((classifier instanceof Sourcable) && (printSource)) { return wekaStaticWrapper((Sourcable) classifier, sourceClass); } // Output model if (!(noOutput || printMargins)) { if (classifier instanceof OptionHandler) { if (schemeOptionsText != null) { text.append("\nOptions: " + schemeOptionsText); text.append("\n"); } } text.append("\n" + classifier.toString() + "\n"); } if (!printMargins && (costMatrix != null)) { text.append("\n=== Evaluation Cost Matrix ===\n\n"); text.append(costMatrix.toString()); } */ // FOR LARGE DATA SETS // Output test instance predictions only if (printClassifications) { DataSource source = testSource; predsBuff = new StringBuffer(); // no test set -> use train set if (source == null && noCrossValidation) { source = trainSource; predsBuff.append("\n=== Predictions on training data ===\n\n"); } else { predsBuff.append("\n=== Predictions on test data ===\n\n"); } if (source != null) { /* return printClassifications(classifierClassifications, new Instances(template, 0), source, actualClassIndex + 1, attributesToOutput, printDistribution); */ printClassifications(classifier, new Instances(template, 0), source, actualClassIndex + 1, attributesToOutput, printDistribution, predsBuff); /* FOR LARGE DATA SETS printClassifications(classifierClassifications, new Instances(template, 0), source, actualClassIndex + 1, attributesToOutput, printDistribution, predsBuff); */ // return predsText.toString(); } } /* FOR LARGE DATA SETS // Compute error estimate from training data if ((trainStatistics) && (trainSetPresent)) { if ((classifier instanceof UpdateableClassifier) && (testSetPresent) && (costMatrix == null)) { // Classifier was trained incrementally, so we have to // reset the source. trainSource.reset(); // Incremental testing train = trainSource.getStructure(actualClassIndex); testTimeStart = System.currentTimeMillis(); Instance trainInst; while (trainSource.hasMoreElements(train)) { trainInst = trainSource.nextElement(train); trainingEvaluation.evaluateModelOnce((Classifier) classifier, trainInst); } testTimeElapsed = System.currentTimeMillis() - testTimeStart; } else { testTimeStart = System.currentTimeMillis(); trainingEvaluation.evaluateModel( classifier, trainSource.getDataSet(actualClassIndex)); testTimeElapsed = System.currentTimeMillis() - testTimeStart; } // Print the results of the training evaluation if (printMargins) { return trainingEvaluation.toCumulativeMarginDistributionString(); } else { text.append("\nTime taken to build model: " + Utils.doubleToString(trainTimeElapsed / 1000.0, 2) + " seconds"); if (splitPercentage > 0) { text.append("\nTime taken to test model on training split: "); } else { text.append("\nTime taken to test model on training data: "); } text.append(Utils.doubleToString(testTimeElapsed / 1000.0, 2) + " seconds"); if (splitPercentage > 0) { text.append(trainingEvaluation.toSummaryString("\n\n=== Error on training" + " split ===\n", printComplexityStatistics)); } else { text.append(trainingEvaluation.toSummaryString("\n\n=== Error on training" + " data ===\n", printComplexityStatistics)); } if (template.classAttribute().isNominal()) { if (classStatistics) { text.append("\n\n" + trainingEvaluation.toClassDetailsString()); } if (!noCrossValidation) { text.append("\n\n" + trainingEvaluation.toMatrixString()); } } } } // Compute proper error estimates if (testSource != null) { // Testing is on the supplied test data testSource.reset(); test = testSource.getStructure(test.classIndex()); Instance testInst; while (testSource.hasMoreElements(test)) { testInst = testSource.nextElement(test); testingEvaluation.evaluateModelOnceAndRecordPrediction( (Classifier) classifier, testInst); } if (splitPercentage > 0) { text.append("\n\n" + testingEvaluation.toSummaryString("=== Error on test split ===\n", printComplexityStatistics)); } else { text.append("\n\n" + testingEvaluation.toSummaryString("=== Error on test data ===\n", printComplexityStatistics)); } } else if (trainSource != null) { if (!noCrossValidation) { // Testing is via cross-validation on training data Random random = new Random(seed); // use untrained (!) classifier for cross-validation classifier = Classifier.makeCopy(classifierBackup); if (!printClassifications) { testingEvaluation.crossValidateModel(classifier, trainSource.getDataSet(actualClassIndex), folds, random); if (template.classAttribute().isNumeric()) { text.append("\n\n\n" + testingEvaluation.toSummaryString("=== Cross-validation ===\n", printComplexityStatistics)); } else { text.append("\n\n\n" + testingEvaluation.toSummaryString("=== Stratified " + "cross-validation ===\n", printComplexityStatistics)); } } else { predsBuff = new StringBuffer(); predsBuff.append("\n=== Predictions under cross-validation ===\n\n"); testingEvaluation.crossValidateModel(classifier, trainSource.getDataSet(actualClassIndex), folds, random, predsBuff, attributesToOutput, new Boolean(printDistribution)); if (template.classAttribute().isNumeric()) { text.append("\n\n\n" + testingEvaluation.toSummaryString("=== Cross-validation ===\n", printComplexityStatistics)); } else { text.append("\n\n\n" + testingEvaluation.toSummaryString("=== Stratified " + "cross-validation ===\n", printComplexityStatistics)); } } } } if (template.classAttribute().isNominal()) { if (classStatistics && !noCrossValidation) { text.append("\n\n" + testingEvaluation.toClassDetailsString()); } if (!noCrossValidation) { text.append("\n\n" + testingEvaluation.toMatrixString()); } // predictions from cross-validation? if (predsBuff != null) { text.append("\n" + predsBuff); } } if ((thresholdFile.length() != 0) && template.classAttribute().isNominal()) { int labelIndex = 0; if (thresholdLabel.length() != 0) { labelIndex = template.classAttribute().indexOfValue(thresholdLabel); } if (labelIndex == -1) { throw new IllegalArgumentException( "Class label '" + thresholdLabel + "' is unknown!"); } ThresholdCurve tc = new ThresholdCurve(); Instances result = tc.getCurve(testingEvaluation.predictions(), labelIndex); DataSink.write(thresholdFile, result); } */ // FOR LARGE DATA SETS return text.toString(); } /** * Attempts to load a cost matrix. * * @param costFileName the filename of the cost matrix * @param numClasses the number of classes that should be in the cost matrix * (only used if the cost file is in old format). * @return a CostMatrix value, or null if costFileName is empty * @throws Exception if an error occurs. */ protected static CostMatrix handleCostOption(String costFileName, int numClasses) throws Exception { if ((costFileName != null) && (costFileName.length() != 0)) { System.out.println( "NOTE: The behaviour of the -m option has changed between WEKA 3.0" + " and WEKA 3.1. -m now carries out cost-sensitive *evaluation*" + " only. For cost-sensitive *prediction*, use one of the" + " cost-sensitive metaschemes such as" + " weka.classifiers.meta.CostSensitiveClassifier or" + " weka.classifiers.meta.MetaCost"); Reader costReader = null; try { costReader = new BufferedReader(new FileReader(costFileName)); } catch (Exception e) { throw new Exception("Can't open file " + e.getMessage() + '.'); } try { // First try as a proper cost matrix format return new CostMatrix(costReader); } catch (Exception ex) { try { // Now try as the poxy old format :-) //System.err.println("Attempting to read old format cost file"); try { costReader.close(); // Close the old one costReader = new BufferedReader(new FileReader(costFileName)); } catch (Exception e) { throw new Exception("Can't open file " + e.getMessage() + '.'); } CostMatrix costMatrix = new CostMatrix(numClasses); //System.err.println("Created default cost matrix"); costMatrix.readOldFormat(costReader); return costMatrix; //System.err.println("Read old format"); } catch (Exception e2) { // re-throw the original exception //System.err.println("Re-throwing original exception"); throw ex; } } } else { return null; } } /** * Evaluates the classifier on a given set of instances. Note that * the data must have exactly the same format (e.g. order of * attributes) as the data used to train the classifier! Otherwise * the results will generally be meaningless. * * @param classifier machine learning classifier * @param data set of test instances for evaluation * @param forPredictionsString varargs parameter that, if supplied, is * expected to hold a StringBuffer to print predictions to, * a Range of attributes to output and a Boolean (true if the distribution * is to be printed) * @return the predictions * @throws Exception if model could not be evaluated * successfully */ public double[] evaluateModel(Classifier classifier, Instances data, Object... forPredictionsPrinting) throws Exception { // for predictions printing StringBuffer buff = null; Range attsToOutput = null; boolean printDist = false; double predictions[] = new double[data.numInstances()]; if (forPredictionsPrinting.length > 0) { buff = (StringBuffer) forPredictionsPrinting[0]; attsToOutput = (Range) forPredictionsPrinting[1]; printDist = ((Boolean) forPredictionsPrinting[2]).booleanValue(); } // Need to be able to collect predictions if appropriate (for AUC) for (int i = 0; i < data.numInstances(); i++) { predictions[i] = evaluateModelOnceAndRecordPrediction((Classifier) classifier, data.instance(i)); if (buff != null) { buff.append(predictionText(classifier, data.instance(i), i, attsToOutput, printDist)); } } return predictions; } /** * Evaluates the classifier on a single instance and records the * prediction (if the class is nominal). * * @param classifier machine learning classifier * @param instance the test instance to be classified * @return the prediction made by the clasifier * @throws Exception if model could not be evaluated * successfully or the data contains string attributes */ public double evaluateModelOnceAndRecordPrediction(Classifier classifier, Instance instance) throws Exception { Instance classMissing = (Instance) instance.copy(); double pred = 0; classMissing.setDataset(instance.dataset()); classMissing.setClassMissing(); if (m_ClassIsNominal) { if (m_Predictions == null) { m_Predictions = new FastVector(); } double[] dist = classifier.distributionForInstance(classMissing); pred = Utils.maxIndex(dist); if (dist[(int) pred] <= 0) { pred = Instance.missingValue(); } updateStatsForClassifier(dist, instance); m_Predictions.addElement(new NominalPrediction(instance.classValue(), dist, instance.weight())); } else { pred = classifier.classifyInstance(classMissing); updateStatsForPredictor(pred, instance); } return pred; } /** * Evaluates the classifier on a single instance. * * @param classifier machine learning classifier * @param instance the test instance to be classified * @return the prediction made by the clasifier * @throws Exception if model could not be evaluated * successfully or the data contains string attributes */ public double evaluateModelOnce(Classifier classifier, Instance instance) throws Exception { Instance classMissing = (Instance) instance.copy(); double pred = 0; classMissing.setDataset(instance.dataset()); classMissing.setClassMissing(); if (m_ClassIsNominal) { double[] dist = classifier.distributionForInstance(classMissing); pred = Utils.maxIndex(dist); if (dist[(int) pred] <= 0) { pred = Instance.missingValue(); } updateStatsForClassifier(dist, instance); } else { pred = classifier.classifyInstance(classMissing); updateStatsForPredictor(pred, instance); } return pred; } /** * Evaluates the supplied distribution on a single instance. * * @param dist the supplied distribution * @param instance the test instance to be classified * @return the prediction * @throws Exception if model could not be evaluated * successfully */ public double evaluateModelOnce(double[] dist, Instance instance) throws Exception { double pred; if (m_ClassIsNominal) { pred = Utils.maxIndex(dist); if (dist[(int) pred] <= 0) { pred = Instance.missingValue(); } updateStatsForClassifier(dist, instance); } else { pred = dist[0]; updateStatsForPredictor(pred, instance); } return pred; } /** * Evaluates the supplied distribution on a single instance. * * @param dist the supplied distribution * @param instance the test instance to be classified * @return the prediction * @throws Exception if model could not be evaluated * successfully */ public double evaluateModelOnceAndRecordPrediction(double[] dist, Instance instance) throws Exception { double pred; if (m_ClassIsNominal) { if (m_Predictions == null) { m_Predictions = new FastVector(); } pred = Utils.maxIndex(dist); if (dist[(int) pred] <= 0) { pred = Instance.missingValue(); } updateStatsForClassifier(dist, instance); m_Predictions.addElement(new NominalPrediction(instance.classValue(), dist, instance.weight())); } else { pred = dist[0]; updateStatsForPredictor(pred, instance); } return pred; } /** * Evaluates the supplied prediction on a single instance. * * @param prediction the supplied prediction * @param instance the test instance to be classified * @throws Exception if model could not be evaluated * successfully */ public void evaluateModelOnce(double prediction, Instance instance) throws Exception { if (m_ClassIsNominal) { updateStatsForClassifier(makeDistribution(prediction), instance); } else { updateStatsForPredictor(prediction, instance); } } /** * Returns the predictions that have been collected. * * @return a reference to the FastVector containing the predictions * that have been collected. This should be null if no predictions * have been collected (e.g. if the class is numeric). */ public FastVector predictions() { return m_Predictions; } /** * Wraps a static classifier in enough source to test using the weka * class libraries. * * @param classifier a Sourcable Classifier * @param className the name to give to the source code class * @return the source for a static classifier that can be tested with * weka libraries. * @throws Exception if code-generation fails */ public static String wekaStaticWrapper(Sourcable classifier, String className) throws Exception { StringBuffer result = new StringBuffer(); String staticClassifier = classifier.toSource(className); result.append("// Generated with Weka " + Version.VERSION + "\n"); result.append("//\n"); result.append("// This code is public domain and comes with no warranty.\n"); result.append("//\n"); result.append("// Timestamp: " + new Date() + "\n"); result.append("\n"); result.append("package weka.classifiers;\n"); result.append("\n"); result.append("import weka.core.Attribute;\n"); result.append("import weka.core.Capabilities;\n"); result.append("import weka.core.Capabilities.Capability;\n"); result.append("import weka.core.Instance;\n"); result.append("import weka.core.Instances;\n"); result.append("import weka.classifiers.Classifier;\n"); result.append("\n"); result.append("public class WekaWrapper\n"); result.append(" extends Classifier {\n"); // globalInfo result.append("\n"); result.append(" /**\n"); result.append(" * Returns only the toString() method.\n"); result.append(" *\n"); result.append(" * @return a string describing the classifier\n"); result.append(" */\n"); result.append(" public String globalInfo() {\n"); result.append(" return toString();\n"); result.append(" }\n"); // getCapabilities result.append("\n"); result.append(" /**\n"); result.append(" * Returns the capabilities of this classifier.\n"); result.append(" *\n"); result.append(" * @return the capabilities\n"); result.append(" */\n"); result.append(" public Capabilities getCapabilities() {\n"); result.append(((Classifier) classifier).getCapabilities().toSource("result", 4)); result.append(" return result;\n"); result.append(" }\n"); // buildClassifier result.append("\n"); result.append(" /**\n"); result.append(" * only checks the data against its capabilities.\n"); result.append(" *\n"); result.append(" * @param i the training data\n"); result.append(" */\n"); result.append(" public void buildClassifier(Instances i) throws Exception {\n"); result.append(" // can classifier handle the data?\n"); result.append(" getCapabilities().testWithFail(i);\n"); result.append(" }\n"); // classifyInstance result.append("\n"); result.append(" /**\n"); result.append(" * Classifies the given instance.\n"); result.append(" *\n"); result.append(" * @param i the instance to classify\n"); result.append(" * @return the classification result\n"); result.append(" */\n"); result.append(" public double classifyInstance(Instance i) throws Exception {\n"); result.append(" Object[] s = new Object[i.numAttributes()];\n"); result.append(" \n"); result.append(" for (int j = 0; j < s.length; j++) {\n"); result.append(" if (!i.isMissing(j)) {\n"); result.append(" if (i.attribute(j).isNominal())\n"); result.append(" s[j] = new String(i.stringValue(j));\n"); result.append(" else if (i.attribute(j).isNumeric())\n"); result.append(" s[j] = new Double(i.value(j));\n"); result.append(" }\n"); result.append(" }\n"); result.append(" \n"); result.append(" // set class value to missing\n"); result.append(" s[i.classIndex()] = null;\n"); result.append(" \n"); result.append(" return " + className + ".classify(s);\n"); result.append(" }\n"); // toString result.append("\n"); result.append(" /**\n"); result.append(" * Returns only the classnames and what classifier it is based on.\n"); result.append(" *\n"); result.append(" * @return a short description\n"); result.append(" */\n"); result.append(" public String toString() {\n"); result.append(" return \"Auto-generated classifier wrapper, based on " + classifier.getClass().getName() + " (generated with Weka " + Version.VERSION + ").\\n" + "\" + this.getClass().getName() + \"/" + className + "\";\n"); result.append(" }\n"); // main result.append("\n"); result.append(" /**\n"); result.append(" * Runs the classfier from commandline.\n"); result.append(" *\n"); result.append(" * @param args the commandline arguments\n"); result.append(" */\n"); result.append(" public static void main(String args[]) {\n"); result.append(" runClassifier(new WekaWrapper(), args);\n"); result.append(" }\n"); result.append("}\n"); // actual classifier code result.append("\n"); result.append(staticClassifier); return result.toString(); } /** * Gets the number of test instances that had a known class value * (actually the sum of the weights of test instances with known * class value). * * @return the number of test instances with known class */ public final double numInstances() { return m_WithClass; } /** * Gets the number of instances incorrectly classified (that is, for * which an incorrect prediction was made). (Actually the sum of the weights * of these instances) * * @return the number of incorrectly classified instances */ public final double incorrect() { return m_Incorrect; } /** * Gets the percentage of instances incorrectly classified (that is, for * which an incorrect prediction was made). * * @return the percent of incorrectly classified instances * (between 0 and 100) */ public final double pctIncorrect() { return 100 * m_Incorrect / m_WithClass; } /** * Gets the total cost, that is, the cost of each prediction times the * weight of the instance, summed over all instances. * * @return the total cost */ public final double totalCost() { return m_TotalCost; } /** * Gets the average cost, that is, total cost of misclassifications * (incorrect plus unclassified) over the total number of instances. * * @return the average cost. */ public final double avgCost() { return m_TotalCost / m_WithClass; } /** * Gets the number of instances correctly classified (that is, for * which a correct prediction was made). (Actually the sum of the weights * of these instances) * * @return the number of correctly classified instances */ public final double correct() { return m_Correct; } /** * Gets the percentage of instances correctly classified (that is, for * which a correct prediction was made). * * @return the percent of correctly classified instances (between 0 and 100) */ public final double pctCorrect() { return 100 * m_Correct / m_WithClass; } /** * Gets the number of instances not classified (that is, for * which no prediction was made by the classifier). (Actually the sum * of the weights of these instances) * * @return the number of unclassified instances */ public final double unclassified() { return m_Unclassified; } /** * Gets the percentage of instances not classified (that is, for * which no prediction was made by the classifier). * * @return the percent of unclassified instances (between 0 and 100) */ public final double pctUnclassified() { return 100 * m_Unclassified / m_WithClass; } /** * Returns the estimated error rate or the root mean squared error * (if the class is numeric). If a cost matrix was given this * error rate gives the average cost. * * @return the estimated error rate (between 0 and 1, or between 0 and * maximum cost) */ public final double errorRate() { if (!m_ClassIsNominal) { return Math.sqrt(m_SumSqrErr / (m_WithClass - m_Unclassified)); } if (m_CostMatrix == null) { return m_Incorrect / m_WithClass; } else { return avgCost(); } } /** * Returns value of kappa statistic if class is nominal. * * @return the value of the kappa statistic */ public final double kappa() { double[] sumRows = new double[m_ConfusionMatrix.length]; double[] sumColumns = new double[m_ConfusionMatrix.length]; double sumOfWeights = 0; for (int i = 0; i < m_ConfusionMatrix.length; i++) { for (int j = 0; j < m_ConfusionMatrix.length; j++) { sumRows[i] += m_ConfusionMatrix[i][j]; sumColumns[j] += m_ConfusionMatrix[i][j]; sumOfWeights += m_ConfusionMatrix[i][j]; } } double correct = 0, chanceAgreement = 0; for (int i = 0; i < m_ConfusionMatrix.length; i++) { chanceAgreement += (sumRows[i] * sumColumns[i]); correct += m_ConfusionMatrix[i][i]; } chanceAgreement /= (sumOfWeights * sumOfWeights); correct /= sumOfWeights; if (chanceAgreement < 1) { return (correct - chanceAgreement) / (1 - chanceAgreement); } else { return 1; } } /** * Returns the correlation coefficient if the class is numeric. * * @return the correlation coefficient * @throws Exception if class is not numeric */ public final double correlationCoefficient() throws Exception { if (m_ClassIsNominal) { throw new Exception("Can't compute correlation coefficient: " + "class is nominal!"); } double correlation = 0; double varActual = m_SumSqrClass - m_SumClass * m_SumClass / (m_WithClass - m_Unclassified); double varPredicted = m_SumSqrPredicted - m_SumPredicted * m_SumPredicted / (m_WithClass - m_Unclassified); double varProd = m_SumClassPredicted - m_SumClass * m_SumPredicted / (m_WithClass - m_Unclassified); if (varActual * varPredicted <= 0) { correlation = 0.0; } else { correlation = varProd / Math.sqrt(varActual * varPredicted); } return correlation; } /** * Returns the mean absolute error. Refers to the error of the * predicted values for numeric classes, and the error of the * predicted probability distribution for nominal classes. * * @return the mean absolute error */ public final double meanAbsoluteError() { return m_SumAbsErr / (m_WithClass - m_Unclassified); } /** * Returns the mean absolute error of the prior. * * @return the mean absolute error */ public final double meanPriorAbsoluteError() { if (m_NoPriors) { return Double.NaN; } return m_SumPriorAbsErr / m_WithClass; } /** * Returns the relative absolute error. * * @return the relative absolute error * @throws Exception if it can't be computed */ public final double relativeAbsoluteError() throws Exception { if (m_NoPriors) { return Double.NaN; } return 100 * meanAbsoluteError() / meanPriorAbsoluteError(); } /** * Returns the root mean squared error. * * @return the root mean squared error */ public final double rootMeanSquaredError() { return Math.sqrt(m_SumSqrErr / (m_WithClass - m_Unclassified)); } /** * Returns the mean squared error. * * @return the mean squared error */ public final double meanSquaredError() { return m_SumSqrErr / (m_WithClass - m_Unclassified); } /** * Returns the root mean prior squared error. * * @return the root mean prior squared error */ public final double rootMeanPriorSquaredError() { if (m_NoPriors) { return Double.NaN; } return Math.sqrt(m_SumPriorSqrErr / m_WithClass); } /** * Returns the root relative squared error if the class is numeric. * * @return the root relative squared error */ public final double rootRelativeSquaredError() { if (m_NoPriors) { return Double.NaN; } return 100.0 * rootMeanSquaredError() / rootMeanPriorSquaredError(); } /** * Calculate the entropy of the prior distribution * * @return the entropy of the prior distribution * @throws Exception if the class is not nominal */ public final double priorEntropy() throws Exception { if (!m_ClassIsNominal) { throw new Exception("Can't compute entropy of class prior: " + "class numeric!"); } if (m_NoPriors) { return Double.NaN; } double entropy = 0; for (int i = 0; i < m_NumClasses; i++) { entropy -= m_ClassPriors[i] / m_ClassPriorsSum * Utils.log2(m_ClassPriors[i] / m_ClassPriorsSum); } return entropy; } /** * Return the total Kononenko & Bratko Information score in bits * * @return the K&B information score * @throws Exception if the class is not nominal */ public final double KBInformation() throws Exception { if (!m_ClassIsNominal) { throw new Exception("Can't compute K&B Info score: " + "class numeric!"); } if (m_NoPriors) { return Double.NaN; } return m_SumKBInfo; } /** * Return the Kononenko & Bratko Information score in bits per * instance. * * @return the K&B information score * @throws Exception if the class is not nominal */ public final double KBMeanInformation() throws Exception { if (!m_ClassIsNominal) { throw new Exception("Can't compute K&B Info score: " + "class numeric!"); } if (m_NoPriors) { return Double.NaN; } return m_SumKBInfo / (m_WithClass - m_Unclassified); } /** * Return the Kononenko & Bratko Relative Information score * * @return the K&B relative information score * @throws Exception if the class is not nominal */ public final double KBRelativeInformation() throws Exception { if (!m_ClassIsNominal) { throw new Exception("Can't compute K&B Info score: " + "class numeric!"); } if (m_NoPriors) { return Double.NaN; } return 100.0 * KBInformation() / priorEntropy(); } /** * Returns the total entropy for the null model * * @return the total null model entropy */ public final double SFPriorEntropy() { if (m_NoPriors) { return Double.NaN; } return m_SumPriorEntropy; } /** * Returns the entropy per instance for the null model * * @return the null model entropy per instance */ public final double SFMeanPriorEntropy() { if (m_NoPriors) { return Double.NaN; } return m_SumPriorEntropy / m_WithClass; } /** * Returns the total entropy for the scheme * * @return the total scheme entropy */ public final double SFSchemeEntropy() { if (m_NoPriors) { return Double.NaN; } return m_SumSchemeEntropy; } /** * Returns the entropy per instance for the scheme * * @return the scheme entropy per instance */ public final double SFMeanSchemeEntropy() { if (m_NoPriors) { return Double.NaN; } return m_SumSchemeEntropy / (m_WithClass - m_Unclassified); } /** * Returns the total SF, which is the null model entropy minus * the scheme entropy. * * @return the total SF */ public final double SFEntropyGain() { if (m_NoPriors) { return Double.NaN; } return m_SumPriorEntropy - m_SumSchemeEntropy; } /** * Returns the SF per instance, which is the null model entropy * minus the scheme entropy, per instance. * * @return the SF per instance */ public final double SFMeanEntropyGain() { if (m_NoPriors) { return Double.NaN; } return (m_SumPriorEntropy - m_SumSchemeEntropy) / (m_WithClass - m_Unclassified); } /** * Output the cumulative margin distribution as a string suitable * for input for gnuplot or similar package. * * @return the cumulative margin distribution * @throws Exception if the class attribute is nominal */ public String toCumulativeMarginDistributionString() throws Exception { if (!m_ClassIsNominal) { throw new Exception("Class must be nominal for margin distributions"); } String result = ""; double cumulativeCount = 0; double margin; for (int i = 0; i <= k_MarginResolution; i++) { if (m_MarginCounts[i] != 0) { cumulativeCount += m_MarginCounts[i]; margin = (double) i * 2.0 / k_MarginResolution - 1.0; result = result + Utils.doubleToString(margin, 7, 3) + ' ' + Utils.doubleToString(cumulativeCount * 100 / m_WithClass, 7, 3) + '\n'; } else if (i == 0) { result = Utils.doubleToString(-1.0, 7, 3) + ' ' + Utils.doubleToString(0, 7, 3) + '\n'; } } return result; } /** * Calls toSummaryString() with no title and no complexity stats * * @return a summary description of the classifier evaluation */ public String toSummaryString() { return toSummaryString("", false); } /** * Calls toSummaryString() with a default title. * * @param printComplexityStatistics if true, complexity statistics are * returned as well * @return the summary string */ public String toSummaryString(boolean printComplexityStatistics) { return toSummaryString("=== Summary ===\n", printComplexityStatistics); } /** * Outputs the performance statistics in summary form. Lists * number (and percentage) of instances classified correctly, * incorrectly and unclassified. Outputs the total number of * instances classified, and the number of instances (if any) * that had no class value provided. * * @param title the title for the statistics * @param printComplexityStatistics if true, complexity statistics are * returned as well * @return the summary as a String */ public String toSummaryString(String title, boolean printComplexityStatistics) { StringBuffer text = new StringBuffer(); if (printComplexityStatistics && m_NoPriors) { printComplexityStatistics = false; System.err.println("Priors disabled, cannot print complexity statistics!"); } text.append(title + "\n"); try { if (m_WithClass > 0) { if (m_ClassIsNominal) { text.append("Correctly Classified Instances "); text.append(Utils.doubleToString(correct(), 12, 4) + " " + Utils.doubleToString(pctCorrect(), 12, 4) + " %\n"); text.append("Incorrectly Classified Instances "); text.append(Utils.doubleToString(incorrect(), 12, 4) + " " + Utils.doubleToString(pctIncorrect(), 12, 4) + " %\n"); text.append("Kappa statistic "); text.append(Utils.doubleToString(kappa(), 12, 4) + "\n"); if (m_CostMatrix != null) { text.append("Total Cost "); text.append(Utils.doubleToString(totalCost(), 12, 4) + "\n"); text.append("Average Cost "); text.append(Utils.doubleToString(avgCost(), 12, 4) + "\n"); } if (printComplexityStatistics) { text.append("K&B Relative Info Score "); text.append(Utils.doubleToString(KBRelativeInformation(), 12, 4) + " %\n"); text.append("K&B Information Score "); text.append(Utils.doubleToString(KBInformation(), 12, 4) + " bits"); text.append(Utils.doubleToString(KBMeanInformation(), 12, 4) + " bits/instance\n"); } } else { text.append("Correlation coefficient "); text.append(Utils.doubleToString(correlationCoefficient(), 12, 4) + "\n"); } if (printComplexityStatistics) { text.append("Class complexity | order 0 "); text.append(Utils.doubleToString(SFPriorEntropy(), 12, 4) + " bits"); text.append(Utils.doubleToString(SFMeanPriorEntropy(), 12, 4) + " bits/instance\n"); text.append("Class complexity | scheme "); text.append(Utils.doubleToString(SFSchemeEntropy(), 12, 4) + " bits"); text.append(Utils.doubleToString(SFMeanSchemeEntropy(), 12, 4) + " bits/instance\n"); text.append("Complexity improvement (Sf) "); text.append(Utils.doubleToString(SFEntropyGain(), 12, 4) + " bits"); text.append(Utils.doubleToString(SFMeanEntropyGain(), 12, 4) + " bits/instance\n"); } text.append("Mean absolute error "); text.append(Utils.doubleToString(meanAbsoluteError(), 12, 4) + "\n"); text.append("Root mean squared error "); text.append(Utils.doubleToString(rootMeanSquaredError(), 12, 4) + "\n"); if (!m_NoPriors) { text.append("Relative absolute error "); text.append(Utils.doubleToString(relativeAbsoluteError(), 12, 4) + " %\n"); text.append("Root relative squared error "); text.append(Utils.doubleToString(rootRelativeSquaredError(), 12, 4) + " %\n"); } } if (Utils.gr(unclassified(), 0)) { text.append("UnClassified Instances "); text.append(Utils.doubleToString(unclassified(), 12, 4) + " " + Utils.doubleToString(pctUnclassified(), 12, 4) + " %\n"); } text.append("Total Number of Instances "); text.append(Utils.doubleToString(m_WithClass, 12, 4) + "\n"); if (m_MissingClass > 0) { text.append("Ignored Class Unknown Instances "); text.append(Utils.doubleToString(m_MissingClass, 12, 4) + "\n"); } } catch (Exception ex) { // Should never occur since the class is known to be nominal // here System.err.println("Arggh - Must be a bug in Evaluation class"); } return text.toString(); } /** * Calls toMatrixString() with a default title. * * @return the confusion matrix as a string * @throws Exception if the class is numeric */ public String toMatrixString() throws Exception { return toMatrixString("=== Confusion Matrix ===\n"); } /** * Outputs the performance statistics as a classification confusion * matrix. For each class value, shows the distribution of * predicted class values. * * @param title the title for the confusion matrix * @return the confusion matrix as a String * @throws Exception if the class is numeric */ public String toMatrixString(String title) throws Exception { StringBuffer text = new StringBuffer(); char[] IDChars = {'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'}; int IDWidth; boolean fractional = false; if (!m_ClassIsNominal) { throw new Exception("Evaluation: No confusion matrix possible!"); } // Find the maximum value in the matrix // and check for fractional display requirement double maxval = 0; for (int i = 0; i < m_NumClasses; i++) { for (int j = 0; j < m_NumClasses; j++) { double current = m_ConfusionMatrix[i][j]; if (current < 0) { current *= -10; } if (current > maxval) { maxval = current; } double fract = current - Math.rint(current); if (!fractional && ((Math.log(fract) / Math.log(10)) >= -2)) { fractional = true; } } } IDWidth = 1 + Math.max((int) (Math.log(maxval) / Math.log(10) + (fractional ? 3 : 0)), (int) (Math.log(m_NumClasses) / Math.log(IDChars.length))); text.append(title).append("\n"); for (int i = 0; i < m_NumClasses; i++) { if (fractional) { text.append(" ").append(num2ShortID(i, IDChars, IDWidth - 3)).append(" "); } else { text.append(" ").append(num2ShortID(i, IDChars, IDWidth)); } } text.append(" <-- classified as\n"); for (int i = 0; i < m_NumClasses; i++) { for (int j = 0; j < m_NumClasses; j++) { text.append(" ").append( Utils.doubleToString(m_ConfusionMatrix[i][j], IDWidth, (fractional ? 2 : 0))); } text.append(" | ").append(num2ShortID(i, IDChars, IDWidth)).append(" = ").append(m_ClassNames[i]).append("\n"); } return text.toString(); } /** * Generates a breakdown of the accuracy for each class (with default title), * incorporating various information-retrieval statistics, such as * true/false positive rate, precision/recall/F-Measure. Should be * useful for ROC curves, recall/precision curves. * * @return the statistics presented as a string * @throws Exception if class is not nominal */ public String toClassDetailsString() throws Exception { return toClassDetailsString("=== Detailed Accuracy By Class ===\n"); } /** * Generates a breakdown of the accuracy for each class, * incorporating various information-retrieval statistics, such as * true/false positive rate, precision/recall/F-Measure. Should be * useful for ROC curves, recall/precision curves. * * @param title the title to prepend the stats string with * @return the statistics presented as a string * @throws Exception if class is not nominal */ public String toClassDetailsString(String title) throws Exception { if (!m_ClassIsNominal) { throw new Exception("Evaluation: No confusion matrix possible!"); } StringBuffer text = new StringBuffer(title + "\nTP Rate FP Rate" + " Precision Recall" + " F-Measure ROC Area Class\n"); for (int i = 0; i < m_NumClasses; i++) { text.append(Utils.doubleToString(truePositiveRate(i), 7, 3)).append(" "); text.append(Utils.doubleToString(falsePositiveRate(i), 7, 3)).append(" "); text.append(Utils.doubleToString(precision(i), 7, 3)).append(" "); text.append(Utils.doubleToString(recall(i), 7, 3)).append(" "); text.append(Utils.doubleToString(fMeasure(i), 7, 3)).append(" "); double rocVal = areaUnderROC(i); if (Instance.isMissingValue(rocVal)) { text.append(" ? ").append(" "); } else { text.append(Utils.doubleToString(rocVal, 7, 3)).append(" "); } text.append(m_ClassNames[i]).append('\n'); } return text.toString(); } /** * Calculate the number of true positives with respect to a particular class. * This is defined as

* 

	 * correctly classified positives
	 *
* * @param classIndex the index of the class to consider as "positive" * @return the true positive rate */ public double numTruePositives(int classIndex) { double correct = 0; for (int j = 0; j < m_NumClasses; j++) { if (j == classIndex) { correct += m_ConfusionMatrix[classIndex][j]; } } return correct; } /** * Calculate the true positive rate with respect to a particular class. * This is defined as

* 

	 * correctly classified positives
	 * ------------------------------
	 *       total positives
	 *
* * @param classIndex the index of the class to consider as "positive" * @return the true positive rate */ public double truePositiveRate(int classIndex) { double correct = 0, total = 0; for (int j = 0; j < m_NumClasses; j++) { if (j == classIndex) { correct += m_ConfusionMatrix[classIndex][j]; } total += m_ConfusionMatrix[classIndex][j]; } if (total == 0) { return 0; } return correct / total; } /** * Calculate the number of true negatives with respect to a particular class. * This is defined as

* 

	 * correctly classified negatives
	 *
* * @param classIndex the index of the class to consider as "positive" * @return the true positive rate */ public double numTrueNegatives(int classIndex) { double correct = 0; for (int i = 0; i < m_NumClasses; i++) { if (i != classIndex) { for (int j = 0; j < m_NumClasses; j++) { if (j != classIndex) { correct += m_ConfusionMatrix[i][j]; } } } } return correct; } /** * Calculate the true negative rate with respect to a particular class. * This is defined as

* 

	 * correctly classified negatives
	 * ------------------------------
	 *       total negatives
	 *
* * @param classIndex the index of the class to consider as "positive" * @return the true positive rate */ public double trueNegativeRate(int classIndex) { double correct = 0, total = 0; for (int i = 0; i < m_NumClasses; i++) { if (i != classIndex) { for (int j = 0; j < m_NumClasses; j++) { if (j != classIndex) { correct += m_ConfusionMatrix[i][j]; } total += m_ConfusionMatrix[i][j]; } } } if (total == 0) { return 0; } return correct / total; } /** * Calculate number of false positives with respect to a particular class. * This is defined as

* 

	 * incorrectly classified negatives
	 *
* * @param classIndex the index of the class to consider as "positive" * @return the false positive rate */ public double numFalsePositives(int classIndex) { double incorrect = 0; for (int i = 0; i < m_NumClasses; i++) { if (i != classIndex) { for (int j = 0; j < m_NumClasses; j++) { if (j == classIndex) { incorrect += m_ConfusionMatrix[i][j]; } } } } return incorrect; } /** * Calculate the false positive rate with respect to a particular class. * This is defined as

* 

	 * incorrectly classified negatives
	 * --------------------------------
	 *        total negatives
	 *
* * @param classIndex the index of the class to consider as "positive" * @return the false positive rate */ public double falsePositiveRate(int classIndex) { double incorrect = 0, total = 0; for (int i = 0; i < m_NumClasses; i++) { if (i != classIndex) { for (int j = 0; j < m_NumClasses; j++) { if (j == classIndex) { incorrect += m_ConfusionMatrix[i][j]; } total += m_ConfusionMatrix[i][j]; } } } if (total == 0) { return 0; } return incorrect / total; } /** * Calculate number of false negatives with respect to a particular class. * This is defined as

* 

	 * incorrectly classified positives
	 *
* * @param classIndex the index of the class to consider as "positive" * @return the false positive rate */ public double numFalseNegatives(int classIndex) { double incorrect = 0; for (int i = 0; i < m_NumClasses; i++) { if (i == classIndex) { for (int j = 0; j < m_NumClasses; j++) { if (j != classIndex) { incorrect += m_ConfusionMatrix[i][j]; } } } } return incorrect; } /** * Calculate the false negative rate with respect to a particular class. * This is defined as

* 

	 * incorrectly classified positives
	 * --------------------------------
	 *        total positives
	 *
* * @param classIndex the index of the class to consider as "positive" * @return the false positive rate */ public double falseNegativeRate(int classIndex) { double incorrect = 0, total = 0; for (int i = 0; i < m_NumClasses; i++) { if (i == classIndex) { for (int j = 0; j < m_NumClasses; j++) { if (j != classIndex) { incorrect += m_ConfusionMatrix[i][j]; } total += m_ConfusionMatrix[i][j]; } } } if (total == 0) { return 0; } return incorrect / total; } /** * Calculate the recall with respect to a particular class. * This is defined as

* 

	 * correctly classified positives
	 * ------------------------------
	 *       total positives
	 *

* (Which is also the same as the truePositiveRate.) * * @param classIndex the index of the class to consider as "positive" * @return the recall */ public double recall(int classIndex) { return truePositiveRate(classIndex); } /** * Calculate the precision with respect to a particular class. * This is defined as

* 

	 * correctly classified positives
	 * ------------------------------
	 *  total predicted as positive
	 *
* * @param classIndex the index of the class to consider as "positive" * @return the precision */ public double precision(int classIndex) { double correct = 0, total = 0; for (int i = 0; i < m_NumClasses; i++) { if (i == classIndex) { correct += m_ConfusionMatrix[i][classIndex]; } total += m_ConfusionMatrix[i][classIndex]; } if (total == 0) { return 0; } return correct / total; } /** * Calculate the F-Measure with respect to a particular class. * This is defined as

* 

	 * 2 * recall * precision
	 * ----------------------
	 *   recall + precision
	 *
* * @param classIndex the index of the class to consider as "positive" * @return the F-Measure */ public double fMeasure(int classIndex) { double precision = precision(classIndex); double recall = recall(classIndex); if ((precision + recall) == 0) { return 0; } return 2 * precision * recall / (precision + recall); } /** * Sets the class prior probabilities * * @param train the training instances used to determine * the prior probabilities * @throws Exception if the class attribute of the instances is not * set */ public void setPriors(Instances train) throws Exception { m_NoPriors = false; if (!m_ClassIsNominal) { m_NumTrainClassVals = 0; m_TrainClassVals = null; m_TrainClassWeights = null; m_PriorErrorEstimator = null; m_ErrorEstimator = null; for (int i = 0; i < train.numInstances(); i++) { Instance currentInst = train.instance(i); if (!currentInst.classIsMissing()) { addNumericTrainClass(currentInst.classValue(), currentInst.weight()); } } } else { for (int i = 0; i < m_NumClasses; i++) { m_ClassPriors[i] = 1; } m_ClassPriorsSum = m_NumClasses; for (int i = 0; i < train.numInstances(); i++) { if (!train.instance(i).classIsMissing()) { m_ClassPriors[(int) train.instance(i).classValue()] += train.instance(i).weight(); m_ClassPriorsSum += train.instance(i).weight(); } } } } /** * Get the current weighted class counts * * @return the weighted class counts */ public double[] getClassPriors() { return m_ClassPriors; } /** * Updates the class prior probabilities (when incrementally * training) * * @param instance the new training instance seen * @throws Exception if the class of the instance is not * set */ public void updatePriors(Instance instance) throws Exception { if (!instance.classIsMissing()) { if (!m_ClassIsNominal) { if (!instance.classIsMissing()) { addNumericTrainClass(instance.classValue(), instance.weight()); } } else { m_ClassPriors[(int) instance.classValue()] += instance.weight(); m_ClassPriorsSum += instance.weight(); } } } /** * disables the use of priors, e.g., in case of de-serialized schemes * that have no access to the original training set, but are evaluated * on a set set. */ public void useNoPriors() { m_NoPriors = true; } /** * Tests whether the current evaluation object is equal to another * evaluation object * * @param obj the object to compare against * @return true if the two objects are equal */ public boolean equals(Object obj) { if ((obj == null) || !(obj.getClass().equals(this.getClass()))) { return false; } Evaluation cmp = (Evaluation) obj; if (m_ClassIsNominal != cmp.m_ClassIsNominal) { return false; } if (m_NumClasses != cmp.m_NumClasses) { return false; } if (m_Incorrect != cmp.m_Incorrect) { return false; } if (m_Correct != cmp.m_Correct) { return false; } if (m_Unclassified != cmp.m_Unclassified) { return false; } if (m_MissingClass != cmp.m_MissingClass) { return false; } if (m_WithClass != cmp.m_WithClass) { return false; } if (m_SumErr != cmp.m_SumErr) { return false; } if (m_SumAbsErr != cmp.m_SumAbsErr) { return false; } if (m_SumSqrErr != cmp.m_SumSqrErr) { return false; } if (m_SumClass != cmp.m_SumClass) { return false; } if (m_SumSqrClass != cmp.m_SumSqrClass) { return false; } if (m_SumPredicted != cmp.m_SumPredicted) { return false; } if (m_SumSqrPredicted != cmp.m_SumSqrPredicted) { return false; } if (m_SumClassPredicted != cmp.m_SumClassPredicted) { return false; } if (m_ClassIsNominal) { for (int i = 0; i < m_NumClasses; i++) { for (int j = 0; j < m_NumClasses; j++) { if (m_ConfusionMatrix[i][j] != cmp.m_ConfusionMatrix[i][j]) { return false; } } } } return true; } /** * Prints the predictions for the given dataset into a String variable. * * @param classifier the classifier to use * @param train the training data * @param testSource the test set * @param classIndex the class index (1-based), if -1 ot does not * override the class index is stored in the data * file (by using the last attribute) * @param attributesToOutput the indices of the attributes to output * @return the generated predictions for the attribute range * @throws Exception if test file cannot be opened */ public static void printClassifications(Classifier classifier, Instances train, DataSource testSource, int classIndex, Range attributesToOutput, StringBuffer predsText) throws Exception { printClassifications(classifier, train, testSource, classIndex, attributesToOutput, false, predsText); } /** * Prints the header for the predictions output into a supplied StringBuffer * * @param test structure of the test set to print predictions for * @param attributesToOutput indices of the attributes to output * @param printDistribution prints the complete distribution for nominal * attributes, not just the predicted value * @param text the StringBuffer to print to */ protected static void printClassificationsHeader(Instances test, Range attributesToOutput, boolean printDistribution, StringBuffer text) { // print header if (test.classAttribute().isNominal()) { if (printDistribution) { text.append(" inst# actual predicted error distribution"); } else { text.append(" inst# actual predicted error prediction"); } } else { text.append(" inst# actual predicted error"); } if (attributesToOutput != null) { attributesToOutput.setUpper(test.numAttributes() - 1); text.append(" ("); boolean first = true; for (int i = 0; i < test.numAttributes(); i++) { if (i == test.classIndex()) { continue; } if (attributesToOutput.isInRange(i)) { if (!first) { text.append(","); } text.append(test.attribute(i).name()); first = false; } } text.append(")"); } text.append("\n"); } /** * Prints the predictions for the given dataset into a supplied StringBuffer * * @param classifier the classifier to use * @param train the training data * @param testSource the test set * @param classIndex the class index (1-based), if -1 ot does not * override the class index is stored in the data * file (by using the last attribute) * @param attributesToOutput the indices of the attributes to output * @param printDistribution prints the complete distribution for nominal * classes, not just the predicted value * @param text StringBuffer to hold the printed predictions * @throws Exception if test file cannot be opened */ public static void printClassifications(Classifier classifier, Instances train, DataSource testSource, int classIndex, Range attributesToOutput, boolean printDistribution, StringBuffer text) throws Exception { if (testSource != null) { Instances test = testSource.getStructure(); if (classIndex != -1) { test.setClassIndex(classIndex - 1); } else { if (test.classIndex() == -1) { test.setClassIndex(test.numAttributes() - 1); } } // print the header // printClassificationsHeader(test, attributesToOutput, printDistribution, text); // print predictions int i = 0; testSource.reset(); test = testSource.getStructure(test.classIndex()); while (testSource.hasMoreElements(test)) { Instance inst = testSource.nextElement(test); double[] dist = classifier.distributionForInstance(inst); double prob = dist[1]; int actual = (int)inst.classValue(); System.out.format( "%9e %d\n", prob, actual ); //System.out.println(predictionText(classifier, inst, i, attributesToOutput, printDistribution)); //text.append(predictionText(classifier, inst, i, // attributesToOutput, printDistribution)); i++; } } // return text.toString(); } /** * store the prediction made by the classifier as a string * * @param classifier the classifier to use * @param inst the instance to generate text from * @param instNum the index in the dataset * @param attributesToOutput the indices of the attributes to output * @param printDistribution prints the complete distribution for nominal * classes, not just the predicted value * @return the prediction as a String * @throws Exception if something goes wrong * @see #printClassifications(Classifier, Instances, String, int, Range, boolean) */ protected static String predictionText(Classifier classifier, Instance inst, int instNum, Range attributesToOutput, boolean printDistribution) throws Exception { StringBuffer result = new StringBuffer(); int width = 10; int prec = 9; Instance withMissing = (Instance) inst.copy(); withMissing.setDataset(inst.dataset()); double predValue = ((Classifier) classifier).classifyInstance(withMissing); // index result.append(Utils.padLeft("" + (instNum + 1), 6)); if (inst.dataset().classAttribute().isNumeric()) { // actual if (inst.classIsMissing()) { result.append(" " + Utils.padLeft("?", width)); } else { result.append(" " + Utils.doubleToString(inst.classValue(), width, prec)); } // predicted if (Instance.isMissingValue(predValue)) { result.append(" " + Utils.padLeft("?", width)); } else { result.append(" " + Utils.doubleToString(predValue, width, prec)); } // error if (Instance.isMissingValue(predValue) || inst.classIsMissing()) { result.append(" " + Utils.padLeft("?", width)); } else { result.append(" " + Utils.doubleToString(predValue - inst.classValue(), width, prec)); } } else { // actual result.append(" " + Utils.padLeft(((int) inst.classValue() + 1) + ":" + inst.toString(inst.classIndex()), width)); // predicted if (Instance.isMissingValue(predValue)) { result.append(" " + Utils.padLeft("?", width)); } else { result.append(" " + Utils.padLeft(((int) predValue + 1) + ":" + inst.dataset().classAttribute().value((int) predValue), width)); } // error? if ((int) predValue + 1 != (int) inst.classValue() + 1) { result.append(" " + " + "); } else { result.append(" " + " "); } // prediction/distribution if (printDistribution) { if (Instance.isMissingValue(predValue)) { result.append(" " + "?"); } else { result.append(" "); double[] dist = classifier.distributionForInstance(withMissing); for (int n = 0; n < dist.length; n++) { if (n > 0) { result.append(","); } if (n == (int) predValue) { result.append("*"); } result.append(Utils.doubleToString(dist[n], prec)); } } } else { if (Instance.isMissingValue(predValue)) { result.append(" " + "?"); } else { result.append(" " + Utils.doubleToString(classifier.distributionForInstance(withMissing)[(int) predValue], prec)); } } } // attributes result.append(" " + attributeValuesString(withMissing, attributesToOutput) + "\n"); return result.toString(); } /** * Builds a string listing the attribute values in a specified range of indices, * separated by commas and enclosed in brackets. * * @param instance the instance to print the values from * @param attRange the range of the attributes to list * @return a string listing values of the attributes in the range */ protected static String attributeValuesString(Instance instance, Range attRange) { StringBuffer text = new StringBuffer(); if (attRange != null) { boolean firstOutput = true; attRange.setUpper(instance.numAttributes() - 1); for (int i = 0; i < instance.numAttributes(); i++) { if (attRange.isInRange(i) && i != instance.classIndex()) { if (firstOutput) { text.append("("); } else { text.append(","); } text.append(instance.toString(i)); firstOutput = false; } } if (!firstOutput) { text.append(")"); } } return text.toString(); } /** * Make up the help string giving all the command line options * * @param classifier the classifier to include options for * @return a string detailing the valid command line options */ protected static String makeOptionString(Classifier classifier) { StringBuffer optionsText = new StringBuffer(""); // General options optionsText.append("\n\nGeneral options:\n\n"); optionsText.append("-t \n"); optionsText.append("\tSets training file.\n"); optionsText.append("-T \n"); optionsText.append("\tSets test file. If missing, a cross-validation will be performed\n"); optionsText.append("\ton the training data.\n"); optionsText.append("-c \n"); optionsText.append("\tSets index of class attribute (default: last).\n"); optionsText.append("-x \n"); optionsText.append("\tSets number of folds for cross-validation (default: 10).\n"); optionsText.append("-no-cv\n"); optionsText.append("\tDo not perform any cross validation.\n"); optionsText.append("-split-percentage \n"); optionsText.append("\tSets the percentage for the train/test set split, e.g., 66.\n"); optionsText.append("-preserve-order\n"); optionsText.append("\tPreserves the order in the percentage split.\n"); optionsText.append("-s \n"); optionsText.append("\tSets random number seed for cross-validation or percentage split\n"); optionsText.append("\t(default: 1).\n"); optionsText.append("-m \n"); optionsText.append("\tSets file with cost matrix.\n"); optionsText.append("-l \n"); optionsText.append("\tSets model input file. In case the filename ends with '.xml',\n"); optionsText.append("\tthe options are loaded from the XML file.\n"); optionsText.append("-d \n"); optionsText.append("\tSets model output file. In case the filename ends with '.xml',\n"); optionsText.append("\tonly the options are saved to the XML file, not the model.\n"); optionsText.append("-v\n"); optionsText.append("\tOutputs no statistics for training data.\n"); optionsText.append("-o\n"); optionsText.append("\tOutputs statistics only, not the classifier.\n"); optionsText.append("-i\n"); optionsText.append("\tOutputs detailed information-retrieval"); optionsText.append(" statistics for each class.\n"); optionsText.append("-k\n"); optionsText.append("\tOutputs information-theoretic statistics.\n"); optionsText.append("-p \n"); optionsText.append("\tOnly outputs predictions for test instances (or the train\n" + "\tinstances if no test instances provided and -no-cv is used),\n" + "\talong with attributes (0 for none).\n"); optionsText.append("-distribution\n"); optionsText.append("\tOutputs the distribution instead of only the prediction\n"); optionsText.append("\tin conjunction with the '-p' option (only nominal classes).\n"); optionsText.append("-r\n"); optionsText.append("\tOnly outputs cumulative margin distribution.\n"); if (classifier instanceof Sourcable) { optionsText.append("-z \n"); optionsText.append("\tOnly outputs the source representation" + " of the classifier,\n\tgiving it the supplied" + " name.\n"); } if (classifier instanceof Drawable) { optionsText.append("-g\n"); optionsText.append("\tOnly outputs the graph representation" + " of the classifier.\n"); } optionsText.append("-xml filename | xml-string\n"); optionsText.append("\tRetrieves the options from the XML-data instead of the " + "command line.\n"); optionsText.append("-threshold-file \n"); optionsText.append("\tThe file to save the threshold data to.\n" + "\tThe format is determined by the extensions, e.g., '.arff' for ARFF \n" + "\tformat or '.csv' for CSV.\n"); optionsText.append("-threshold-label