python-weka-wrapper-0.3.6/ 0000755 0172670 0172670 00000000000 12677713207 017110 5 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.3.6/python/ 0000755 0172670 0172670 00000000000 12677713207 020431 5 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.3.6/python/python_weka_wrapper.egg-info/ 0000755 0172670 0172670 00000000000 12677713207 026213 5 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.3.6/python/python_weka_wrapper.egg-info/dependency_links.txt 0000644 0172670 0172670 00000000001 12677713207 032261 0 ustar fracpete fracpete 0000000 0000000
python-weka-wrapper-0.3.6/python/python_weka_wrapper.egg-info/requires.txt 0000644 0172670 0172670 00000000106 12677713207 030610 0 ustar fracpete fracpete 0000000 0000000 javabridge==1.0.11
numpy
[graphs]
pygraphviz
PIL
[plots]
matplotlib
python-weka-wrapper-0.3.6/python/python_weka_wrapper.egg-info/SOURCES.txt 0000644 0172670 0172670 00000002621 12677713207 030100 0 ustar fracpete fracpete 0000000 0000000 CHANGES.rst
DESCRIPTION.rst
MANIFEST.in
setup.cfg
setup.py
python/python_weka_wrapper.egg-info/PKG-INFO
python/python_weka_wrapper.egg-info/SOURCES.txt
python/python_weka_wrapper.egg-info/dependency_links.txt
python/python_weka_wrapper.egg-info/requires.txt
python/python_weka_wrapper.egg-info/top_level.txt
python/weka/__init__.py
python/weka/associations.py
python/weka/attribute_selection.py
python/weka/classifiers.py
python/weka/clusterers.py
python/weka/datagenerators.py
python/weka/experiments.py
python/weka/filters.py
python/weka/core/__init__.py
python/weka/core/capabilities.py
python/weka/core/classes.py
python/weka/core/converters.py
python/weka/core/database.py
python/weka/core/dataset.py
python/weka/core/jvm.py
python/weka/core/packages.py
python/weka/core/serialization.py
python/weka/core/stemmers.py
python/weka/core/stopwords.py
python/weka/core/tokenizers.py
python/weka/core/types.py
python/weka/core/version.py
python/weka/flow/__init__.py
python/weka/flow/base.py
python/weka/flow/container.py
python/weka/flow/control.py
python/weka/flow/conversion.py
python/weka/flow/sink.py
python/weka/flow/source.py
python/weka/flow/transformer.py
python/weka/lib/python-weka-wrapper-src.jar
python/weka/lib/python-weka-wrapper.jar
python/weka/plot/__init__.py
python/weka/plot/classifiers.py
python/weka/plot/clusterers.py
python/weka/plot/dataset.py
python/weka/plot/experiments.py
python/weka/plot/graph.py python-weka-wrapper-0.3.6/python/python_weka_wrapper.egg-info/top_level.txt 0000644 0172670 0172670 00000000005 12677713207 030740 0 ustar fracpete fracpete 0000000 0000000 weka
python-weka-wrapper-0.3.6/python/python_weka_wrapper.egg-info/PKG-INFO 0000644 0172670 0172670 00000024717 12677713207 027323 0 ustar fracpete fracpete 0000000 0000000 Metadata-Version: 1.1
Name: python-weka-wrapper
Version: 0.3.6
Summary: Python wrapper for the Weka Machine Learning Workbench
Home-page: https://github.com/fracpete/python-weka-wrapper
Author: Peter "fracpete" Reutemann
Author-email: pythonwekawrapper at gmail dot com
License: GNU General Public License version 3.0 (GPLv3)
Description: The **python-weka-wrapper** package makes it easy to run
Weka algorithms and filters from within Python. It offers access to Weka
API using thin wrappers around JNI calls using the **javabridge** package.
Changelog
=========
0.3.6 (2016-04-02)
------------------
- `Loader.load_file` method now checks whether the dataset file really exists, otherwise a previously loaded
file gets loaded again without an error message (seems to be a Weka issue)
- replaced `org.pentaho.packageManagement` with `weka.core.packageManagement` as the package management code
is now part of Weka rather than a third-party library
- `jvm.start()` no longer tries to load packages and therefore suppresses error message if `$HOME/wekafiles/packages`
should not yet exist
0.3.5 (2016-01-29)
------------------
- added support for `weka.core.BatchPredictor` to class `Classifier` in module `weka.classifiers`
- upgraded Weka to revision 12410 (post 3.7.13) to avoid performance bottleneck when using setOptions method
- fixed class `SetupGenerator` from module `weka.core.classes`
- added `load_any_file` method to the `weka.core.converters` module
- added `save_any_file` method to the `weka.core.converters` module
- if `GridSearch` instantiation (module `weka.classifiers`) fails, it now outputs message whether package
installed and JVM with package support started
0.3.4 (2016-01-15)
------------------
- added convenience method `create_instances_from_lists` to `weka.core.dataset` module to easily create
an `Instances` object from numeric lists (x and y)
- added `get_object_tags` method to `Tags` class from module `weka.core.classes`, to allow obtaining
`weka.core.Tag` array from the method of a `JavaObject` rather than a static field (MultiSearch)
- updated `MultiSearch` wrapper in module `weka.classifiers` to work with the `multi-search` package
version 2016.1.15 or later
0.3.3 (2015-09-26)
------------------
- updated to Weka 3.7.13
- documentation now covers the API as well
0.3.2 (2015-06-29)
------------------
- The `packages` parameter of the `weka.core.jvm.start()` function can be used for specifying an alternative
Weka home directory now as well
- added `train_test_split` method to `weka.core.Instances` class to easily create train/test splits
- `evaluate_train_test_split` method of `weka.classifiers.Evaluation` class now uses the `train_test_split` method
0.3.1 (2015-04-23)
------------------
- added `get_tags` class method to `Tags` method for easier instantiation of Tag arrays
- added `find` method to `Tags` class to locate `Tag` object that matches the string
- fixed `__getitem__` and `__setitem__` methods of the `Tags` class
- added `GridSearch` meta-classifier with convenience properties to module `weka.classifiers`
- added `SetupGenerator` and various parameter classes to `weka.core.classes`
- added `MultiSearch` meta-classifier with convenience properties to module `weka.classifiers`
- added `quote`/`unquote` and `backquote`/`unbackquote` methods to `weka.core.classes` module
- added `main` method to `weka.core.classes` for operations on options: join, split, code
- added support for option handling to `weka.core.classes` module
0.3.0 (2015-04-15)
------------------
- added method `ndarray_to_instances` to `weka.converters` module for converting Numpy 2-dimensional array into `Instances` object
- added method `plot_learning_curve` to `weka.plot.classifiers` module for creating learning curves for multiple classifiers for a specific metric
- added plotting of experiments with `plot_experiment` methid in `weka.plot.experiments` module
- `Instance.create_instance` method now takes list of tuples (index, internal float value) when generating sparse instances
- added `weka.core.database` module for loading data from a database
- added `make_copy` class method to `Clusterer` class
- added `make_copy` class method to `Associator` class
- added `make_copy` class method to `Filter` class
- added `make_copy` class method to `DataGenerator` class
- most classes (like Classifier and Filter) now have a default classname value in the constructor
- added `TextDirectoryLoader` class to `weka.core.converters`
- moved all methods from `weka.core.utils` to `weka.core.classes`
- fixed `Attribute.index_of` method for determining label index
- fixed `Attribute.add_string_value` method (used incorrect JNI parameter)
- `create_instance` and `create_sparse_instance` methods of class `Instance` now ensure that list values are float
- added `to_help` method to `OptionHandler` class which outputs a help string generated from the base class's
`globalInfo` and `listOptions` methods
- fixed `test_model` method of `Evaluation` class when supplying a `PredictionOutput` object (previously generated `No dataset structure provided!` exception)
- added `batch_finished` method to `Filter` class for incremental filtering
- added `line_plot` method to `weka.plot.dataset` module for plotting dataset using internal format (one line plot per instance)
- added `is_serializable` property to `JavaObject` class
- added `has_class` convenience property to `Instance` class
- added `__repr__` method to `JavaObject` classes (simply calls `toString()` method)
- added `Stemmer` class in module `weka.core.stemmers`
- added `Stopwords` class in module `weka.core.stopwords`
- added `Tokenizer` class in module `weka.core.tokenizers`
- added `StringToWordVector` filter class in module `weka.filters`
- added simple workflow engine (see documentation on *Flow*)
0.2.2 (2015-01-05)
------------------
- added convenience methods `no_class` (to unset class) and `has_class` (class set?) to `Instances` class
- switched to using faster method objects for methods `classify_instance`/`distribution_for_instance` in `Classifier` class
- switched to using faster method objects for methods `cluster_instance`/`distribution_for_instance` in `Clusterer` class
- switched to using faster method objects for methods `class_index`, `is_missing`, `get/set_value`, `get/set_string_value`, `weight` in `Instance` class
- switched to using faster method objects for methods `input`, `output`, `outputformat` in `Filter` class
- switched to using faster method objects for methods `attribute`, `attribute_by_name`, `num_attributes`, `num_instances`,
`class_index`, `class_attribute`, `set_instance`, `get_instance`, `add_instance` in `Instances` class
0.2.1 (2015-01-05)
------------------
- added unit testing framework
- added method `refresh_cache()` to `weka/core/packages.py` to allow user to refresh local cache
- method `get_classname` in `weka.core.utils` now handles Python objects and class objects as well
- added convenience method `get_jclass` to `weka.core.utils` to instantiate a Java class
- added a `JavaArray` wrapper for `arrays`, which allows getting/setting elements and iterating
- added property `classname` to class `JavaObject` for easy access to classname of underlying object
- added class method `parse_matlab` for parsing Matlab matrix strings to `CostMatrix` class
- `predictions` method of `Evaluation` class now return `None` if predictions are discarded
- `Associator.get_capabilities()` method is now a property: `Associator.capabilities`
- added wrapper classes for Java enums: `weka.core.classes.Enum`
- fixed retrieval of `sumSq` in `Stats` class (used by `AttributeStats`)
- fixed `cluster_instance` method in `Clusterer` class
- fixed `filter` and `clusterer` properties in clusterer classes (`SingleClustererEnhancer`, `FilteredClusterer`)
- added `crossvalidate_model` method to `ClusterEvaluation`
- added `get_prc` method to `plot/classifiers.py` for calculating the area under the precision-recall curve
- `Filter.filter` method now handles list of `Instances` objects as well, applying the filter sequentially
to all the datasets (allows generation of compatible train/test sets)
0.2.0 (2014-12-22)
------------------
NB: This release is not backwards compatible!
- requires `JavaBridge` 1.0.9 at least
- moved from Java-like get/set (`getIndex()` and `setIndex(int)`) to nicer Python properties
- using Python properties (also only read-only ones) wherevere possible
- added `weka.core.version` for accessing the Weka version currently in use
- added `jwrapper` and `jclasswrapper` methods to `JavaObject` class (the mother of all objects in python-weka-wrapper)
to allow generic access to an object's methods: http://pythonhosted.org//javabridge/highlevel.html#wrapping-java-objects-using-reflection
- added convenience methods `class_is_last()` and `class_is_first()` to `weka.core.Instances` class
- added convenience methods `delete_last_attribute()` and `delete_first_attribute()` to `weka.core.Instances` class
Older releases
--------------
https://github.com/fracpete/python-weka-wrapper/blob/7fd0bba3c74277313eb463e338c1a7e117a1ea22/CHANGES.rst
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python
python-weka-wrapper-0.3.6/python/weka/ 0000755 0172670 0172670 00000000000 12677713207 021360 5 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.3.6/python/weka/experiments.py 0000644 0172670 0172670 00000074600 12576426262 024304 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# experiments.py
# Copyright (C) 2014-2015 Fracpete (pythonwekawrapper at gmail dot com)
import logging
import javabridge
from weka.core.classes import OptionHandler, Range, from_commandline
from weka.core.dataset import Instances
from weka.classifiers import Classifier
# logging setup
logger = logging.getLogger("weka.experiments")
class Experiment(OptionHandler):
"""
Wrapper class for an experiment.
"""
def __init__(self, classname="weka.experiment.Experiment", jobject=None, options=None):
"""
Initializes the specified experiment using either the classname or the supplied JB_Object.
:param classname: the classname of the experiment
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to use
:type options: list
"""
if jobject is None:
jobject = Experiment.new_instance(classname)
super(Experiment, self).__init__(jobject=jobject, options=options)
class SimpleExperiment(OptionHandler):
"""
Ancestor for simple experiments.
See following URL for how to use the Experiment API:
http://weka.wikispaces.com/Using+the+Experiment+API
"""
def __init__(self, datasets, classifiers, jobject=None, classification=True, runs=10, result=None):
"""
Initializes the experiment.
:param datasets: the filenames of datasets to use in the experiment
:type datasets: list
:param classifiers: the Classifier objects or commandline strings to use in the experiment
:type classifiers: list
:param jobject: the Java Object to use
:type jobject: JB_Object
:param classification: whether to perform classification or regression
:type classification:bool
:param runs: the number of runs to perform
:type runs: int
:param result: the filename of the file to store the results in
:type result: str
"""
if not jobject is None:
self.enforce_type(jobject, "weka.experiment.Experiment")
if jobject is None:
jobject = javabridge.make_instance("weka/experiment/Experiment", "()V")
self.classification = classification
self.runs = runs
self.datasets = datasets[:]
self.classifiers = classifiers[:]
self.result = result
super(SimpleExperiment, self).__init__(jobject=jobject)
def configure_splitevaluator(self):
"""
Configures and returns the SplitEvaluator and Classifier instance as tuple.
:return: evaluator and classifier
:rtype: tuple
"""
if self.classification:
speval = javabridge.make_instance("weka/experiment/ClassifierSplitEvaluator", "()V")
else:
speval = javabridge.make_instance("weka/experiment/RegressionSplitEvaluator", "()V")
classifier = javabridge.call(speval, "getClassifier", "()Lweka/classifiers/Classifier;")
return speval, classifier
def configure_resultproducer(self):
"""
Configures and returns the ResultProducer and PropertyPath as tuple.
:return: producer and property path
:rtype: tuple
"""
raise Exception("Not implemented!")
def setup(self):
"""
Initializes the experiment.
"""
# basic options
javabridge.call(
self.jobject, "setPropertyArray", "(Ljava/lang/Object;)V",
javabridge.get_env().make_object_array(0, javabridge.get_env().find_class("weka/classifiers/Classifier")))
javabridge.call(
self.jobject, "setUsePropertyIterator", "(Z)V", True)
javabridge.call(
self.jobject, "setRunLower", "(I)V", 1)
javabridge.call(
self.jobject, "setRunUpper", "(I)V", self.runs)
# setup result producer
rproducer, prop_path = self.configure_resultproducer()
javabridge.call(
self.jobject, "setResultProducer", "(Lweka/experiment/ResultProducer;)V", rproducer)
javabridge.call(
self.jobject, "setPropertyPath", "([Lweka/experiment/PropertyNode;)V", prop_path)
# classifiers
classifiers = javabridge.get_env().make_object_array(
len(self.classifiers), javabridge.get_env().find_class("weka/classifiers/Classifier"))
for i, classifier in enumerate(self.classifiers):
if type(classifier) is Classifier:
javabridge.get_env().set_object_array_element(
classifiers, i, classifier.jobject)
else:
javabridge.get_env().set_object_array_element(
classifiers, i, from_commandline(classifier).jobject)
javabridge.call(
self.jobject, "setPropertyArray", "(Ljava/lang/Object;)V",
classifiers)
# datasets
datasets = javabridge.make_instance("javax/swing/DefaultListModel", "()V")
for dataset in self.datasets:
f = javabridge.make_instance("java/io/File", "(Ljava/lang/String;)V", dataset)
javabridge.call(datasets, "addElement", "(Ljava/lang/Object;)V", f)
javabridge.call(
self.jobject, "setDatasets", "(Ljavax/swing/DefaultListModel;)V", datasets)
# output file
if str(self.result).lower().endswith(".arff"):
rlistener = javabridge.make_instance("weka/experiment/InstancesResultListener", "()V")
elif str(self.result).lower().endswith(".csv"):
rlistener = javabridge.make_instance("weka/experiment/CSVResultListener", "()V")
else:
raise Exception("Unhandled output format for results: " + self.result)
rfile = javabridge.make_instance("java/io/File", "(Ljava/lang/String;)V", self.result)
javabridge.call(
rlistener, "setOutputFile", "(Ljava/io/File;)V", rfile)
javabridge.call(
self.jobject, "setResultListener", "(Lweka/experiment/ResultListener;)V", rlistener)
def run(self):
"""
Executes the experiment.
"""
logger.info("Initializing...")
javabridge.call(self.jobject, "initialize", "()V")
logger.info("Running...")
javabridge.call(self.jobject, "runExperiment", "()V")
logger.info("Finished...")
javabridge.call(self.jobject, "postProcess", "()V")
def experiment(self):
"""
Returns the internal experiment, if set up, otherwise None.
:return: the internal experiment
:rtype: Experiment
"""
if self.jobject is None:
return None
else:
return Experiment(jobject=self.jobject)
@classmethod
def load(cls, filename):
"""
Loads the experiment from disk.
:param filename: the filename of the experiment to load
:type filename: str
:return: the experiment
:rtype: Experiment
"""
jobject = javabridge.static_call(
"weka/experiment/Experiment", "read", "(Ljava/lang/String;)Lweka/experiment/Experiment;",
filename)
return Experiment(jobject=jobject)
@classmethod
def save(cls, filename, experiment):
"""
Saves the experiment to disk.
:param filename: the filename to save the experiment to
:type filename: str
:param experiment: the Experiment to save
:type experiment: Experiment
"""
javabridge.static_call(
"weka/experiment/Experiment", "write", "(Ljava/lang/String;Lweka/experiment/Experiment;)V",
filename, experiment.jobject)
class SimpleCrossValidationExperiment(SimpleExperiment):
"""
Performs a simple cross-validation experiment. Can output the results either in ARFF or CSV.
"""
def __init__(self, datasets, classifiers, classification=True, runs=10, folds=10, result=None):
"""
Initializes the experiment.
:param datasets: the filenames of datasets to use in the experiment
:type datasets: list
:param classifiers: the Classifier objects to use in the experiment
:type classifiers: list
:param classification: whether to perform classification
:type classification: bool
:param runs: the number of runs to perform
:type runs: int
:param folds: the number folds to use for CV
:type folds: int
:param result: the filename of the file to store the results in
:type result: str
"""
if runs < 1:
raise Exception("Number of runs must be at least 1!")
if folds < 2:
raise Exception("Number of folds must be at least 2!")
if len(datasets) == 0:
raise Exception("No datasets provided!")
if len(classifiers) == 0:
raise Exception("No classifiers provided!")
if result is None:
raise Exception("No filename for results provided!")
super(SimpleCrossValidationExperiment, self).__init__(
classification=classification, runs=runs, datasets=datasets,
classifiers=classifiers, result=result)
self.folds = folds
def configure_resultproducer(self):
"""
Configures and returns the ResultProducer and PropertyPath as tuple.
:return: producer and property path
:rtype: tuple
"""
rproducer = javabridge.make_instance("weka/experiment/CrossValidationResultProducer", "()V")
javabridge.call(rproducer, "setNumFolds", "(I)V", self.folds)
speval, classifier = self.configure_splitevaluator()
javabridge.call(rproducer, "setSplitEvaluator", "(Lweka/experiment/SplitEvaluator;)V", speval)
prop_path = javabridge.get_env().make_object_array(
2, javabridge.get_env().find_class("weka/experiment/PropertyNode"))
cls = javabridge.get_env().find_class("weka/experiment/CrossValidationResultProducer")
desc = javabridge.make_instance(
"java/beans/PropertyDescriptor", "(Ljava/lang/String;Ljava/lang/Class;)V", "splitEvaluator", cls)
node = javabridge.make_instance(
"weka/experiment/PropertyNode", "(Ljava/lang/Object;Ljava/beans/PropertyDescriptor;Ljava/lang/Class;)V",
speval, desc, cls)
javabridge.get_env().set_object_array_element(prop_path, 0, node)
cls = javabridge.get_env().get_object_class(speval)
desc = javabridge.make_instance(
"java/beans/PropertyDescriptor", "(Ljava/lang/String;Ljava/lang/Class;)V", "classifier", cls)
node = javabridge.make_instance(
"weka/experiment/PropertyNode", "(Ljava/lang/Object;Ljava/beans/PropertyDescriptor;Ljava/lang/Class;)V",
javabridge.get_env().get_object_class(speval), desc, cls)
javabridge.get_env().set_object_array_element(prop_path, 1, node)
return rproducer, prop_path
class SimpleRandomSplitExperiment(SimpleExperiment):
"""
Performs a simple random split experiment. Can output the results either in ARFF or CSV.
"""
def __init__(self, datasets, classifiers, classification=True, runs=10, percentage=66.6, preserve_order=False,
result=None):
"""
Initializes the experiment.
:param classification: whether to perform classification or regression
:type classification: bool
:param runs: the number of runs to perform
:type runs: int
:param percentage: the percentage to use for training
:type percentage: float
:param preserve_order: whether to preserve the order in the datasets
:type preserve_order: bool
:param datasets: the filenames of datasets to use in the experiment
:type datasets: list
:param classifiers: the Classifier objects to use in the experiment
:type classifiers: list
:param result: the filename of the file to store the results in
:type result: str
"""
if runs < 1:
raise Exception("Number of runs must be at least 1!")
if percentage <= 0:
raise Exception("Percentage for training must be >0!")
if percentage >= 100:
raise Exception("Percentage for training must be <100!")
if len(datasets) == 0:
raise Exception("No datasets provided!")
if len(classifiers) == 0:
raise Exception("No classifiers provided!")
if result is None:
raise Exception("No filename for results provided!")
super(SimpleRandomSplitExperiment, self).__init__(
classification=classification, runs=runs, datasets=datasets,
classifiers=classifiers, result=result)
self.percentage = percentage
self.preserve_order = preserve_order
def configure_resultproducer(self):
"""
Configures and returns the ResultProducer and PropertyPath as tuple.
:return: producer and property path
:rtype: tuple
"""
rproducer = javabridge.make_instance("weka/experiment/RandomSplitResultProducer", "()V")
javabridge.call(rproducer, "setRandomizeData", "(Z)V", not self.preserve_order)
javabridge.call(rproducer, "setTrainPercent", "(D)V", self.percentage)
speval, classifier = self.configure_splitevaluator()
javabridge.call(rproducer, "setSplitEvaluator", "(Lweka/experiment/SplitEvaluator;)V", speval)
prop_path = javabridge.get_env().make_object_array(
2, javabridge.get_env().find_class("weka/experiment/PropertyNode"))
cls = javabridge.get_env().find_class("weka/experiment/RandomSplitResultProducer")
desc = javabridge.make_instance(
"java/beans/PropertyDescriptor", "(Ljava/lang/String;Ljava/lang/Class;)V", "splitEvaluator", cls)
node = javabridge.make_instance(
"weka/experiment/PropertyNode", "(Ljava/lang/Object;Ljava/beans/PropertyDescriptor;Ljava/lang/Class;)V",
speval, desc, cls)
javabridge.get_env().set_object_array_element(prop_path, 0, node)
cls = javabridge.get_env().get_object_class(speval)
desc = javabridge.make_instance(
"java/beans/PropertyDescriptor", "(Ljava/lang/String;Ljava/lang/Class;)V", "classifier", cls)
node = javabridge.make_instance(
"weka/experiment/PropertyNode", "(Ljava/lang/Object;Ljava/beans/PropertyDescriptor;Ljava/lang/Class;)V",
javabridge.get_env().get_object_class(speval), desc, cls)
javabridge.get_env().set_object_array_element(prop_path, 1, node)
return rproducer, prop_path
class ResultMatrix(OptionHandler):
"""
For generating results from an Experiment run.
"""
def __init__(self, classname="weka.experiment.ResultMatrixPlainText", jobject=None, options=None):
"""
Initializes the specified ResultMatrix using either the classname or the supplied JB_Object.
:param classname: the classname of the ResultMatrix
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to use
:type options: list
"""
if jobject is None:
jobject = ResultMatrix.new_instance(classname)
self.enforce_type(jobject, "weka.experiment.ResultMatrix")
super(ResultMatrix, self).__init__(jobject=jobject, options=options)
@property
def rows(self):
"""
Returns the row count.
:return: the count
:rtype: int
"""
return javabridge.call(self.jobject, "getRowCount", "()I")
@property
def columns(self):
"""
Returns the column count.
:return: the count
:rtype: int
"""
return javabridge.call(self.jobject, "getColCount", "()I")
def is_col_hidden(self, index):
"""
Returns whether the column is hidden.
:param index: the 0-based column index
:type index: int
:return: true if hidden
:rtype: bool
"""
return javabridge.call(self.jobject, "getColHidden", "(I)Z", index)
def hide_col(self, index):
"""
Hides the column.
:param index: the 0-based column index
:type index: int
"""
javabridge.call(self.jobject, "setColHidden", "(IZ)V", index, True)
def show_col(self, index):
"""
Shows the column.
:param index: the 0-based column index
:type index: int
"""
javabridge.call(self.jobject, "setColHidden", "(IZ)V", index, False)
def is_row_hidden(self, index):
"""
Returns whether the row is hidden.
:param index: the 0-based row index
:type index: int
:return: true if hidden
:rtype: bool
"""
return javabridge.call(self.jobject, "getRowHidden", "(I)Z", index)
def hide_row(self, index):
"""
Hides the row.
:param index: the 0-based row index
:type index: int
"""
javabridge.call(self.jobject, "setRowHidden", "(IZ)V", index, True)
def show_row(self, index):
"""
Shows the row.
:param index: the 0-based row index
:type index: int
"""
javabridge.call(self.jobject, "setRowHidden", "(IZ)V", index, False)
def get_row_name(self, index):
"""
Returns the row name.
:param index: the 0-based row index
:type index: int
:return: the row name, None if invalid index
:rtype: str
"""
return javabridge.call(self.jobject, "getRowName", "(I)Ljava/lang/String;", index)
def set_row_name(self, index, name):
"""
Sets the row name.
:param index: the 0-based row index
:type index: int
:param name: the name of the row
:type name: str
"""
javabridge.call(self.jobject, "setRowName", "(ILjava/lang/String;)V", index, name)
def get_col_name(self, index):
"""
Returns the column name.
:param index: the 0-based row index
:type index: int
:return: the column name, None if invalid index
:rtype: str
"""
return javabridge.call(self.jobject, "getColName", "(I)Ljava/lang/String;", index)
def set_col_name(self, index, name):
"""
Sets the column name.
:param index: the 0-based row index
:type index: int
:param name: the name of the column
:type name: str
"""
javabridge.call(self.jobject, "setColName", "(ILjava/lang/String;)V", index, name)
def get_mean(self, col, row):
"""
Returns the mean at this location (if valid location).
:param col: the 0-based column index
:type col: int
:param row: the 0-based row index
:type row: int
:return: the mean
:rtype: float
"""
return javabridge.call(self.jobject, "getMean", "(II)D", col, row)
def set_mean(self, col, row, mean):
"""
Sets the mean at this location (if valid location).
:param col: the 0-based column index
:type col: int
:param row: the 0-based row index
:type row: int
:param mean: the mean to set
:type mean: float
"""
javabridge.call(self.jobject, "setMean", "(IID)V", col, row, mean)
def get_stdev(self, col, row):
"""
Returns the standard deviation at this location (if valid location).
:param col: the 0-based column index
:type col: int
:param row: the 0-based row index
:type row: int
:return: the standard deviation
:rtype: float
"""
return javabridge.call(self.jobject, "getStdDev", "(II)D", col, row)
def set_stdev(self, col, row, stdev):
"""
Sets the standard deviation at this location (if valid location).
:param col: the 0-based column index
:type col: int
:param row: the 0-based row index
:type row: int
:param stdev: the standard deviation to set
:type stdev: float
"""
javabridge.call(self.jobject, "setStdDev", "(IID)V", col, row, stdev)
def average(self, col):
"""
Returns the average mean at this location (if valid location).
:param col: the 0-based column index
:type col: int
:return: the mean
:rtype: float
"""
return javabridge.call(self.jobject, "getAverage", "(I)D", col)
def to_string_matrix(self):
"""
Returns the matrix as a string.
:return: the generated output
:rtype: str
"""
return javabridge.call(self.jobject, "toStringMatrix", "()V")
def to_string_key(self):
"""
Returns a key for all the col names, for better readability if the names got cut off.
:return: the key
:rtype: str
"""
return javabridge.call(self.jobject, "toStringKey", "()V")
def to_string_header(self):
"""
Returns the header of the matrix as a string.
:return: the header
:rtype: str
"""
return javabridge.call(self.jobject, "toStringHeader", "()V")
def to_string_summary(self):
"""
returns the summary as string.
:return: the summary
:rtype: str
"""
return javabridge.call(self.jobject, "toStringSummary", "()V")
def to_string_ranking(self):
"""
Returns the ranking in a string representation.
:return: the ranking
:rtype: str
"""
return javabridge.call(self.jobject, "toStringRanking", "()V")
class Tester(OptionHandler):
"""
For generating statistical results from an experiment.
"""
def __init__(self, classname="weka.experiment.PairedCorrectedTTester", jobject=None, options=None):
"""
Initializes the specified tester using either the classname or the supplied JB_Object.
:param classname: the classname of the tester
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
if jobject is None:
jobject = Tester.new_instance(classname)
self.enforce_type(jobject, "weka.experiment.Tester")
self.columns_determined = False
self._dataset_columns = ["Key_Dataset"]
self._run_column = "Key_Run"
self._fold_column = "Key_Fold"
self._result_columns = ["Key_Scheme", "Key_Scheme_options", "Key_Scheme_version_ID"]
super(Tester, self).__init__(jobject=jobject, options=options)
@property
def resultmatrix(self):
"""
Returns the ResultMatrix instance in use.
:return: the matrix in use
:rtype: ResultMatrix
"""
return ResultMatrix(
jobject=javabridge.call(self.jobject, "getResultMatrix", "()Lweka/experiment/ResultMatrix;"))
@resultmatrix.setter
def resultmatrix(self, matrix):
"""
Sets the ResultMatrix to use.
:param matrix: the ResultMatrix instance to use
:type matrix: ResultMatrix
"""
javabridge.call(self.jobject, "setResultMatrix", "(Lweka/experiment/ResultMatrix;)V", matrix.jobject)
@property
def instances(self):
"""
Returns the data used in the analysis.
:return: the data in use
:rtype: Instances
"""
inst = javabridge.call(self.jobject, "getInstances", "()Lweka/core/Instances;")
if inst is None:
return None
else:
return Instances(inst)
@instances.setter
def instances(self, data):
"""
Sets the data to use for analysis.
:param data: the Instances to analyze
:type data: Instances
"""
javabridge.call(self.jobject, "setInstances", "(Lweka/core/Instances;)V", data.jobject)
self.columns_determined = False
@property
def dataset_columns(self):
"""
Returns the list of column names that identify uniquely a dataset.
:return: the list of attributes names
:rtype: list
"""
return self._dataset_columns
@dataset_columns.setter
def dataset_columns(self, col_names):
"""
Sets the list of column names that identify uniquely a dataset.
:param col_names: the list of attribute names
:type col_names: list
"""
self._dataset_columns = col_names[:]
@property
def run_column(self):
"""
Returns the column name that holds the Run number.
:return: the attribute name
:rtype: str
"""
return self._run_column
@run_column.setter
def run_column(self, col_name):
"""
Sets the column name that holds the Run number.
:param col_name: the attribute name
:type col_name: str
"""
self._run_column = col_name
@property
def fold_column(self):
"""
Returns the column name that holds the Fold number.
:return: the attribute name
:rtype: str
"""
return self._fold_column
@fold_column.setter
def fold_column(self, col_name):
"""
Sets the column name that holds the Fold number.
:param col_name: the attribute name
:type col_name: str
"""
self._fold_column = col_name
@property
def result_columns(self):
"""
Returns the list of column names that identify uniquely a result (eg classifier + options + ID).
:return: the list of attribute names
:rtype: list
"""
return self._result_columns
@result_columns.setter
def result_columns(self, col_names):
"""
Sets the list of column names that identify uniquely a result (eg classifier + options + ID).
:param col_names: the list of attribute names
:type col_names: list
"""
self._result_columns = col_names[:]
def init_columns(self):
"""
Sets the column indices based on the supplied names if necessary.
"""
if self.columns_determined:
return
data = self.instances
if data is None:
print("No instances set, cannot determine columns!")
return
# dataset
if self.dataset_columns is None:
raise Exception("No dataset columns set!")
cols = ""
for name in self.dataset_columns:
att = data.attribute_by_name(name)
if att is None:
raise Exception("Dataset column not found: " + name)
if len(cols) > 0:
cols += ","
cols += str(att.index + 1)
javabridge.call(
self.jobject, "setDatasetKeyColumns", "(Lweka/core/Range;)V", Range(ranges=cols).jobject)
# run
if self.run_column is None:
raise Exception("No run columnn set!")
att = data.attribute_by_name(self.run_column)
if att is None:
raise Exception("Run column not found: " + self.run_column)
javabridge.call(
self.jobject, "setRunColumn", "(I)V", att.index)
# fold
if not self.fold_column is None:
att = data.attribute_by_name(self.fold_column)
if att is None:
index = -1
else:
index = att.index
javabridge.call(
self.jobject, "setFoldColumn", "(I)V", index)
# result
if self._result_columns is None:
raise Exception("No reset columns set!")
cols = ""
for name in self._result_columns:
att = data.attribute_by_name(name)
if att is None:
raise Exception("Result column not found: " + name)
if len(cols) > 0:
cols += ","
cols += str(att.index + 1)
javabridge.call(
self.jobject, "setResultsetKeyColumns", "(Lweka/core/Range;)V", Range(ranges=cols).jobject)
self.columns_determined = True
def header(self, comparison_column):
"""
Creates a "header" string describing the current resultsets.
:param comparison_column: the index of the column to compare against
:type comparison_column: int
:return: the header
:rtype: str
"""
self.init_columns()
return javabridge.call(
self.jobject, "header", "(I)Ljava/lang/String;", comparison_column)
def multi_resultset_full(self, base_resultset, comparison_column):
"""
Creates a comparison table where a base resultset is compared to the other resultsets.
:param base_resultset: the 0-based index of the base resultset (eg classifier to compare against)
:type base_resultset: int
:param comparison_column: the 0-based index of the column to compare against
:type comparison_column: int
:return: the comparison
:rtype: str
"""
return javabridge.call(
self.jobject, "multiResultsetFull", "(II)Ljava/lang/String;", base_resultset, comparison_column)
def multi_resultset_ranking(self, comparison_column):
"""
Creates a ranking.
:param comparison_column: the 0-based index of the column to compare against
:type comparison_column: int
:return: the ranking
:rtype: str
"""
return javabridge.call(
self.jobject, "multiResultsetRanking", "(I)Ljava/lang/String;", comparison_column)
def multi_resultset_summary(self, comparison_column):
"""
Carries out a comparison between all resultsets, counting the number of datsets where one resultset
outperforms the other.
:param comparison_column: the 0-based index of the column to compare against
:type comparison_column: int
:return: the summary
:rtype: str
"""
return javabridge.call(
self.jobject, "multiResultsetSummary", "(I)Ljava/lang/String;", comparison_column)
python-weka-wrapper-0.3.6/python/weka/core/ 0000755 0172670 0172670 00000000000 12677713207 022310 5 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.3.6/python/weka/core/classes.py 0000644 0172670 0172670 00000155631 12652560730 024324 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# classes.py
# Copyright (C) 2014-2016 Fracpete (pythonwekawrapper at gmail dot com)
import os
import inspect
import argparse
import json
import logging
import re
import types
import javabridge
from javabridge import JWrapper, JClassWrapper
from javabridge.jutil import JavaException
from weka.core import types as arrays
import weka.core.jvm as jvm
# logging setup
logger = logging.getLogger("weka.core.classes")
def get_class(classname):
"""
Returns the class object associated with the dot-notation classname.
Taken from here: http://stackoverflow.com/a/452981
:param classname: the classname
:type classname: str
:return: the class object
:rtype: object
"""
parts = classname.split('.')
module = ".".join(parts[:-1])
m = __import__(module)
for comp in parts[1:]:
m = getattr(m, comp)
return m
def get_jclass(classname):
"""
Returns the Java class object associated with the dot-notation classname.
:param classname: the classname
:type classname: str
:return: the class object
:rtype: JB_Object
"""
return javabridge.class_for_name(classname=classname)
def get_classname(obj):
"""
Returns the classname of the JB_Object, Python class or object.
:param obj: the java object or Python class/object to get the classname for
:type obj: object
:return: the classname
:rtype: str
"""
if isinstance(obj, javabridge.JB_Object):
cls = javabridge.call(obj, "getClass", "()Ljava/lang/Class;")
return javabridge.call(cls, "getName", "()Ljava/lang/String;")
elif inspect.isclass(obj):
return obj.__module__ + "." + obj.__name__
else:
return get_classname(obj.__class__)
class Stoppable(object):
"""
Classes that can be stopped.
"""
def is_stopped(self):
"""
Returns whether the object has been stopped.
:return: whether stopped
:rtype: bool
"""
raise Exception("Not implemented!")
def stop_execution(self):
"""
Triggers the stopping of the object.
"""
raise Exception("Not implemented!")
from_dict_handlers = {}
"""
The methods that handle the restoration from a JSON dictionary, stored under their 'type'.
"""
def register_dict_handler(typestr, handler):
"""
Registers a handler for restoring an object from a JSON dictionary.
:param typestr: the type of the object
:type typestr: str
:param handler: the method
"""
global from_dict_handlers
from_dict_handlers[typestr] = handler
def deregister_dict_handler(typestr):
"""
Deregisters a handler for restoring an object from a JSON dictionary.
:param typestr: the type of the object
:type typestr: str
"""
global from_dict_handlers
del from_dict_handlers[typestr]
def has_dict_handler(typestr):
"""
Returns the handler for restoring an object from a JSON dictionary.
:param typestr: the type of the object
:type typestr: str
:return: the handler, None if not available
"""
global from_dict_handlers
return typestr in from_dict_handlers
def get_dict_handler(typestr):
"""
Returns the handler for restoring an object from a JSON dictionary.
:param typestr: the type of the object
:type typestr: str
:return: the handler, None if not available
"""
global from_dict_handlers
return from_dict_handlers[str(typestr)]
class JSONObject(object):
"""
Ancestor for classes that can be represented as JSON and restored from JSON.
"""
def to_dict(self):
"""
Returns a dictionary that represents this object, to be used for JSONification.
:return: the object dictionary
:rtype: dict
"""
raise Exception("Not implemented!")
@classmethod
def from_dict(cls, d):
"""
Restores an object state from a dictionary, used in de-JSONification.
:param d: the object dictionary
:type d: dict
:return: the object
:rtype: object
"""
raise Exception("Not implemented!")
def to_json(self):
"""
Returns the options as JSON.
:return: the object as string
:rtype: str
"""
return json.dumps(self.to_dict(), sort_keys=True, indent=2, separators=(',', ': '))
@classmethod
def from_json(cls, s):
"""
Restores the object from the given JSON.
:param s: the JSON string to parse
:type s: str
:return: the
"""
d = json.loads(s)
return get_dict_handler(d["type"])(d)
def shallow_copy(self):
"""
Returns a shallow copy of itself.
:return: the copy
:rtype: object
"""
return self.from_json(self.to_json())
class Configurable(JSONObject):
"""
The ancestor for all actors.
"""
def __init__(self, config=None):
"""
Initializes the object.
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
self._logger = None
self._help = {}
self._config = self.fix_config({})
if config is not None:
self.config = config
if not has_dict_handler("Configurable"):
register_dict_handler("Configurable", Configurable.from_dict)
def __repr__(self):
"""
Returns Python code for instantiating the object.
:return: the representation
:rtype: str
"""
return \
self.__class__.__module__ + "." + self.__class__.__name__ \
+ "(config=" + str(self.config) + ")"
def description(self):
"""
Returns a description of the object.
:return: the description
:rtype: str
"""
raise Exception("Not implemented!")
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
return options
@property
def config(self):
"""
Obtains the currently set options of the actor.
:return: the options
:rtype: dict
"""
return self._config
@config.setter
def config(self, options):
"""
Sets the options of the actor.
:param options: the options
:type options: dict
"""
self._config = self.fix_config(options)
def to_dict(self):
"""
Returns a dictionary that represents this object, to be used for JSONification.
:return: the object dictionary
:rtype: dict
"""
result = {}
result["type"] = "Configurable"
result["class"] = get_classname(self)
result["config"] = {}
for k in self._config:
v = self._config[k]
if isinstance(v, JSONObject):
result["config"][k] = v.to_dict()
else:
result["config"][k] = v
return result
@classmethod
def from_dict(cls, d):
"""
Restores its state from a dictionary, used in de-JSONification.
:param d: the object dictionary
:type d: dict
"""
conf = {}
for k in d["config"]:
v = d["config"][k]
if isinstance(v, dict):
conf[str(k)] = get_dict_handler(d["config"]["type"])(v)
else:
conf[str(k)] = v
return get_class(str(d["class"]))(config=conf)
def new_logger(self):
"""
Returns a new logger instance.
:return: the logger instance
:rtype: logger
"""
return logging.getLogger(get_classname(self))
@property
def logger(self):
"""
Returns the logger object.
:return: the logger
:rtype: logger
"""
if self._logger is None:
self._logger = self.new_logger()
return self._logger
@property
def help(self):
"""
Obtains the help information per option for this actor.
:return: the help
:rtype: dict
"""
return self._help
def generate_help(self):
"""
Generates a help string for this actor.
:return: the help string
:rtype: str
"""
result = []
result.append(self.__class__.__name__)
result.append(re.sub(r'.', '=', self.__class__.__name__))
result.append("")
result.append("DESCRIPTION")
result.append(self.description())
result.append("")
result.append("OPTIONS")
opts = self.config.keys()
opts.sort()
for opt in opts:
result.append(opt)
helpstr = self.help[opt]
if helpstr is None:
helpstr = "-missing help-"
result.append("\t" + helpstr)
result.append("")
return '\n'.join(result)
def print_help(self):
"""
Prints a help string for this actor to stdout.
"""
print(self.generate_help())
class JavaObject(JSONObject):
"""
Basic Java object.
"""
def __init__(self, jobject):
"""
Initializes the wrapper with the specified Java object.
:param jobject: the Java object to wrap
:type jobject: JB_Object
"""
if jobject is None:
raise Exception("No Java object supplied!")
self.jobject = jobject
if not has_dict_handler("JavaObject"):
register_dict_handler("JavaObject", JavaObject.from_dict)
def __str__(self):
"""
Just calls the toString() method.
:rtype: str
"""
return javabridge.to_string(self.jobject)
def __repr__(self):
"""
Just calls the toString() method.
:rtype: str
"""
return javabridge.to_string(self.jobject)
def __unicode__(self):
"""
Just calls the toString() method.
:rtype: str
"""
return javabridge.to_string(self.jobject)
@property
def classname(self):
"""
Returns the Java classname in dot-notation.
:return: the Java classname
:rtype: str
"""
cls = javabridge.call(self.jobject, "getClass", "()Ljava/lang/Class;")
return javabridge.call(cls, "getName", "()Ljava/lang/String;")
def set_property(self, path, jobject):
"""
Attempts to set the value (jobject, a Java object) of the provided (bean) property path.
:param path: the property path, e.g., "filter" for a setFilter(...)/getFilter() method pair
:type path: str
:param jobject: the Java object to set; if instance of JavaObject class, the jobject member is automatically used
:type jobject: JB_Object
"""
# unwrap?
if isinstance(jobject, JavaObject):
jobject = jobject.jobject
javabridge.static_call(
"Lweka/core/PropertyPath;", "setValue",
"(Ljava/lang/Object;Ljava/lang/String;Ljava/lang/Object;)V",
self.jobject, path, jobject)
def get_property(self, path):
"""
Attempts to get the value (jobject, a Java object) of the provided (bean) property path.
:param path: the property path, e.g., "filter" for a setFilter(...)/getFilter() method pair
:type path: str
:return: the wrapped Java object
:rtype: JavaObject
"""
return JavaObject(javabridge.static_call(
"Lweka/core/PropertyPath;", "getValue",
"(Ljava/lang/Object;Ljava/lang/String;)Ljava/lang/Object;",
self.jobject, path))
@property
def jclass(self):
"""
Returns the Java class object of the underlying Java object.
:return: the Java class
:rtype: JB_Object
"""
return javabridge.call(self.jobject, "getClass", "()Ljava/lang/Class;")
@property
def jwrapper(self):
"""
Returns a JWrapper instance of the encapsulated Java object, giving access to methods
using dot notation.
http://pythonhosted.org//javabridge/highlevel.html#wrapping-java-objects-using-reflection
:return: the wrapper
:rtype: JWrapper
"""
return JWrapper(self.jobject)
@property
def jclasswrapper(self):
"""
Returns a JClassWrapper instance of the class for the encapsulated Java object, giving
access to the class methods using dot notation.
http://pythonhosted.org//javabridge/highlevel.html#wrapping-java-objects-using-reflection
:return: the wrapper
:rtype: JClassWrapper
"""
return JClassWrapper(javabridge.call(self.jobject, "getClass", "()Ljava/lang/Class;"))
@property
def is_serializable(self):
"""
Returns true if the object is serialiable.
:return: true if serializable
:rtype: bool
"""
return JavaObject.check_type(self.jobject, "java.io.Serializable")
def to_dict(self):
"""
Returns a dictionary that represents this object, to be used for JSONification.
:return: the object dictionary
:rtype: dict
"""
result = {}
result["type"] = "JavaObject"
result["class"] = self.classname
return result
@classmethod
def from_dict(cls, d):
"""
Restores an object state from a dictionary, used in de-JSONification.
:param d: the object dictionary
:type d: dict
:return: the object
:rtype: object
"""
return JavaObject(cls.new_instance(d["class"]))
@classmethod
def check_type(cls, jobject, intf_or_class, jni_intf_or_class=None):
"""
Returns whether the object implements the specified interface or is a subclass.
E.g.: self._check_type('weka.core.OptionHandler', 'Lweka/core/OptionHandler;')
or self._check_type('weka.core.converters.AbstractFileLoader')
:param jobject: the Java object to check
:type jobject: JB_Object
:param intf_or_class: the classname in Java notation (eg "weka.core.DenseInstance;")
:type jni_intf_or_class: str
:return: whether object implements interface or is subclass
:rtype: bool
"""
if jni_intf_or_class is None:
jni_intf_or_class = "L" + intf_or_class.replace(".", "/") + ";"
return javabridge.is_instance_of(jobject, jni_intf_or_class)
@classmethod
def enforce_type(cls, jobject, intf_or_class, jni_intf_or_class=None):
"""
Raises an exception if the object does not implement the specified interface or is not a subclass.
E.g.: self._enforce_type('weka.core.OptionHandler', 'Lweka/core/OptionHandler;')
or self._enforce_type('weka.core.converters.AbstractFileLoader')
:param jobject: the Java object to check
:type jobject: JB_Object
:param intf_or_class: the classname in Java notation (eg "weka.core.DenseInstance")
:type intf_or_class: str
:param jni_intf_or_class: the classname in JNI notation (eg "Lweka/core/DenseInstance;")
:type jni_intf_or_class: str
"""
if not cls.check_type(jobject, intf_or_class, jni_intf_or_class):
raise TypeError("Object does not implement or subclass " + intf_or_class + ": " + get_classname(jobject))
@classmethod
def new_instance(cls, classname, jni_classname=None):
"""
Creates a new object from the given classname using the default constructor, None in case of error.
:param classname: the classname in Java notation (eg "weka.core.DenseInstance")
:type classname: str
:param jni_classname: the classname in JNI notation (eg "Lweka/core/DenseInstance;")
:type jni_classname: str
:return: the Java object
:rtype: JB_Object
"""
if jni_classname is None:
jni_classname = classname.replace(".", "/")
try:
return javabridge.make_instance(jni_classname, "()V")
except JavaException, e:
print("Failed to instantiate " + classname + "/" + jni_classname + ": " + str(e))
return None
class JavaArrayIterator(object):
"""
Iterator for elements in a Java array.
"""
def __init__(self, data):
"""
:param data: the Java array to iterate over
:type data: JavaArray
"""
self.data = data
self.index = 0
self.length = len(data)
def __iter__(self):
"""
Returns itself.
"""
return self
def next(self):
"""
Returns the next element from the array.
:return: the next array element object, wrapped as JavaObject if not null
:rtype: JavaObject or None
"""
if self.index < self.length:
index = self.index
self.index += 1
return self.data[index]
else:
raise StopIteration()
class JavaArray(JavaObject):
"""
Convenience wrapper around Java arrays.
"""
def __init__(self, jobject):
"""
Initializes the wrapper with the specified Java object.
:param jobject: the java array object to wrap
:type jobject: JB_Object
"""
super(JavaArray, self).__init__(jobject)
c = self.jclass
if not javabridge.call(c, "isArray", "()Z"):
raise Exception("Not an array!")
def __len__(self):
"""
Returns the length of the array.
:return: the array length
:rtype: int
"""
return javabridge.get_env().get_array_length(self.jobject)
def __getitem__(self, key):
"""
Returns the specified element in the array wrapped in a JavaObject.
:param key: the index of the element to retrieve
:type key: int
:return: the element or None if element is null
:rtype; JavaObject
"""
if not isinstance(key, (int, long)):
raise Exception("Key must be an integer!")
element = javabridge.static_call(
"Ljava/lang/reflect/Array;", "get", "(Ljava/lang/Object;I)Ljava/lang/Object;",
self.jobject, key)
if element is None:
return None
return JavaObject(element)
def __setitem__(self, key, value):
"""
Sets the specified element in the array.
:param key: the index of the element to set
:type key: int
:param value: the object to set (JavaObject or JB_Object)
"""
if isinstance(value, JavaObject):
obj = value.jobject
else:
obj = value
if not isinstance(key, (int, long)):
raise Exception("Key must be an integer!")
javabridge.static_call(
"Ljava/lang/reflect/Array;", "set", "(Ljava/lang/Object;ILjava/lang/Object;)V",
self.jobject, key, obj)
def __delitem__(self, key):
"""
Not implemented, raises an exception.
"""
raise Exception("Cannot delete item from array!")
def __iter__(self):
"""
Returns an iterator over the elements.
:return: the iterator
:rtype: JavaArrayIterator
"""
return JavaArrayIterator(self)
@classmethod
def new_instance(cls, classname, length):
"""
Creates a new array with the given classname and length; initial values are null.
:param classname: the classname in Java notation (eg "weka.core.DenseInstance")
:type classname: str
:param length: the length of the array
:type length: int
:return: the Java array
:rtype: JB_Object
"""
return javabridge.static_call(
"Ljava/lang/reflect/Array;",
"newInstance",
"(Ljava/lang/Class;I)Ljava/lang/Object;",
javabridge.class_for_name(classname=classname), length)
class Enum(JavaObject):
"""
Wrapper for Java enums.
"""
def __init__(self, jobject=None, enum=None, member=None):
"""
Initializes the enum class.
:param jobject: the Java object to wrap
:type jobject: JB_Object
:param enum: the enum class to instantiate (dot notation)
:type enum: str
:param member: the member of the enum class to instantiate
:type member: str
"""
if jobject is None:
enumclass = javabridge.class_for_name(classname=enum)
jobject = javabridge.static_call(
"java/lang/Enum", "valueOf",
"(Ljava/lang/Class;Ljava/lang/String;)Ljava/lang/Enum;",
enumclass, member)
super(Enum, self).__init__(jobject)
@property
def name(self):
"""
Returns the name of the enum member.
:return: the name
:rtype: str
"""
return javabridge.call(self.jobject, "name", "()Ljava/lang/String;")
@property
def ordinal(self):
"""
Returns the ordinal of the enum member.
:return: the ordinal
:rtype: int
"""
return javabridge.call(self.jobject, "ordinal", "()I")
@property
def values(self):
"""
Returns list of all enum members.
:return: all enum members
:rtype: list
"""
cls = javabridge.call(self.jobject, "getClass", "()Ljava/lang/Class;")
clsname = javabridge.call(cls, "getName", "()Ljava/lang/String;")
l = javabridge.static_call(clsname.replace(".", "/"), "values", "()[L" + clsname.replace(".", "/") + ";")
l = javabridge.get_env().get_object_array_elements(l)
result = []
for item in l:
result.append(Enum(jobject=item))
return result
class Random(JavaObject):
"""
Wrapper for the java.util.Random class.
"""
def __init__(self, seed):
"""
The seed value.
:param seed: the seed value
:type seed: int
"""
super(Random, self).__init__(javabridge.make_instance("Ljava/util/Random;", "(J)V", seed))
def next_int(self, n=None):
"""
Next random integer. if n is provided, then between 0 and n-1.
:param n: the upper limit (minus 1) for the random integer
:type n: int
:return: the next random integer
:rtype: int
"""
if n is None:
return javabridge.call(self.jobject, "nextInt", "()I")
else:
return javabridge.call(self.jobject, "nextInt", "(I)I", n)
def next_double(self):
"""
Next random double.
:return: the next random double
:rtype: double
"""
return javabridge.call(self.jobject, "nextDouble", "()D")
class Option(JavaObject):
"""
Wrapper for the weka.core.Option class.
"""
def __init__(self, jobject):
"""
Initializes the wrapper with the specified option object.
:param jobject: the java object to wrap
:type jobject: JB_Object
"""
super(Option, self).__init__(jobject)
Option.enforce_type(jobject, "weka.core.Option")
@property
def name(self):
"""
Returns the name of the option.
:return: the name
:rtype: str
"""
return javabridge.call(self.jobject, "name", "()Ljava/lang/String;")
@property
def description(self):
"""
Returns the description of the option.
:return: the description
:rtype: str
"""
return javabridge.call(self.jobject, "description", "()Ljava/lang/String;")
@property
def synopsis(self):
"""
Returns the synopsis of the option.
:return: the synopsis
:rtype: str
"""
return javabridge.call(self.jobject, "synopsis", "()Ljava/lang/String;")
@property
def num_arguments(self):
"""
Returns the synopsis of the option.
:return: the synopsis
:rtype: str
"""
return javabridge.call(self.jobject, "numArguments", "()I")
class OptionHandler(JavaObject, Configurable):
"""
Ancestor for option-handling classes.
Classes should implement the weka.core.OptionHandler interface to have any effect.
"""
def __init__(self, jobject, options=None):
"""
Initializes the wrapper with the specified Java object.
:param jobject: the java object to wrap
:type jobject: JB_Object
:param options: the options to set
:type options: list
"""
super(OptionHandler, self).__init__(jobject)
self.is_optionhandler = OptionHandler.check_type(jobject, "weka.core.OptionHandler")
if (options is not None) and (len(options) > 0):
self.options = options
# we have to manually instantiate some objects, since multiple inheritance doesn't call
# Configurable constructor
self._logger = None
self._help = {}
self._config = self.fix_config({})
if not has_dict_handler("OptionHandler"):
register_dict_handler("OptionHandler", OptionHandler.from_dict)
def global_info(self):
"""
Returns the globalInfo() result, None if not available.
:rtypes: str
"""
try:
return javabridge.call(self.jobject, "globalInfo", "()Ljava/lang/String;")
except JavaException:
return None
def description(self):
"""
Returns a description of the object.
:return: the description
:rtype: str
"""
return self.global_info()
@property
def options(self):
"""
Obtains the currently set options as list.
:return: the list of options
:rtype: list
"""
if self.is_optionhandler:
return types.string_array_to_list(javabridge.call(self.jobject, "getOptions", "()[Ljava/lang/String;"))
else:
return []
@options.setter
def options(self, options):
"""
Sets the command-line options (as list).
:param options: the list of command-line options to set
:type options: list
"""
if self.is_optionhandler:
javabridge.call(self.jobject, "setOptions", "([Ljava/lang/String;)V", types.string_list_to_array(options))
def to_commandline(self):
"""
Generates a commandline string from the JavaObject instance.
:return: the commandline string
:rtype: str
"""
return javabridge.static_call(
"Lweka/core/Utils;", "toCommandLine",
"(Ljava/lang/Object;)Ljava/lang/String;",
self.jobject)
def to_help(self):
"""
Returns a string that contains the 'global_info' text and the options.
:return: the generated help string
:rtype: str
"""
result = []
result.append(self.classname)
result.append("=" * len(self.classname))
result.append("")
result.append("DESCRIPTION")
result.append("")
result.append(self.global_info())
result.append("")
result.append("OPTIONS")
result.append("")
options = javabridge.call(self.jobject, "listOptions", "()Ljava/util/Enumeration;")
enum = javabridge.get_enumeration_wrapper(options)
while enum.hasMoreElements():
opt = Option(enum.nextElement())
result.append(opt.synopsis)
result.append(opt.description)
result.append("")
return '\n'.join(result)
def __str__(self):
"""
Calls the toString() method of the java object.
:return: the result of the toString() method
:rtype: str
"""
return javabridge.to_string(self.jobject)
def to_dict(self):
"""
Returns a dictionary that represents this object, to be used for JSONification.
:return: the object dictionary
:rtype: dict
"""
result = super(OptionHandler, self).to_dict()
result["type"] = "OptionHandler"
result["options"] = join_options(self.options)
return result
@classmethod
def from_dict(cls, d):
"""
Restores an object state from a dictionary, used in de-JSONification.
:param d: the object dictionary
:type d: dict
:return: the object
:rtype: object
"""
result = OptionHandler(cls.new_instance(d["class"]))
result.options = split_options(d["options"])
return result
class SingleIndex(JavaObject):
"""
Wrapper for a Weka SingleIndex object.
"""
def __init__(self, jobject=None, index=None):
"""
Initializes the wrapper with the specified Java object or string index.
:param jobject: the java object to wrap
:type jobject: JB_Object
:param index: the string index to use
:type index: str
"""
if jobject is None:
if index is None:
jobject = javabridge.make_instance("weka/core/SingleIndex", "()V")
else:
jobject = javabridge.make_instance("weka/core/SingleIndex", "(Ljava/lang/String;)V", index)
else:
self.enforce_type(jobject, "weka.core.SingleIndex")
super(SingleIndex, self).__init__(jobject)
def upper(self, upper):
"""
Sets the upper limit.
:param upper: the upper limit
:type upper: int
"""
javabridge.call(self.jobject, "setUpper", "(I)V", upper)
def index(self):
"""
Returns the integer index.
:return: the 0-based integer index
:rtype: int
"""
return javabridge.call(self.jobject, "getIndex", "()I")
@property
def single_index(self):
"""
Returns the string index.
:return: the 1-based string index
:rtype: str
"""
return javabridge.call(self.jobject, "getSingleIndex", "()Ljava/lang/String;")
@single_index.setter
def single_index(self, index):
"""
Sets the string index.
:param index: the 1-based string index
::type index: str
"""
javabridge.call(self.jobject, "setSingleIndex", "(Ljava/lang/String;)V", index)
class Range(JavaObject):
"""
Wrapper for a Weka Range object.
"""
def __init__(self, jobject=None, ranges=None):
"""
Initializes the wrapper with the specified Java object or string range.
:param jobject: the java object to wrap
:type jobject: JB_Object
:param ranges: the string range to use
:type ranges: str
"""
if jobject is None:
if ranges is None:
jobject = javabridge.make_instance("weka/core/Range", "()V")
else:
jobject = javabridge.make_instance("weka/core/Range", "(Ljava/lang/String;)V", ranges)
else:
self.enforce_type(jobject, "weka.core.Range")
super(Range, self).__init__(jobject)
def upper(self, upper):
"""
Sets the upper limit.
:param upper: the upper limit
:type upper: int
"""
javabridge.call(self.jobject, "setUpper", "(I)V", upper)
def selection(self):
"""
Returns the selection list.
:return: the list of 0-based integer indices
:rtype: list
"""
return javabridge.get_env().get_int_array_elements(javabridge.call(self.jobject, "getSelection", "()[I"))
@property
def ranges(self):
"""
Returns the string range.
:return: the string range of 1-based indices
:rtype: str
"""
return javabridge.call(self.jobject, "getRanges", "()Ljava/lang/String;")
@ranges.setter
def ranges(self, rng):
"""
Sets the string range.
:param rng: the range to set
:type rng: str
"""
javabridge.call(self.jobject, "setRanges", "(Ljava/lang/String;)V", rng)
@property
def invert(self):
"""
Returns whether the range is inverted.
:return: true if inverted
:rtype: bool
"""
return javabridge.call(self.jobject, "getInvert", "()Z")
@invert.setter
def invert(self, invert):
"""
Sets the invert state.
:param invert: whether to invert or not
:type invert: bool
"""
javabridge.call(self.jobject, "setInvert", "(Z)V", invert)
class Tag(JavaObject):
"""
Wrapper for the weka.core.Tag class.
"""
def __init__(self, jobject=None, ident=None, ident_str="", readable="", uppercase=True):
"""
:param jobject: the java object to wrap
:type jobject: JB_Object
:param ident: the ID integer associated with the tag
:type ident: int
:param ident_str: the ID string associated with the tag (case-insensitive)
:type ident_str: str
:param readable: the text of the tag
:type readable: str
:param uppercase: whether to uppercase the id string
:type uppercase: bool
"""
if jobject is None:
jobject = javabridge.make_instance(
"weka/core/Tag", "(ILjava/lang/String;Ljava/lang/String;Z)V",
ident, ident_str, readable, uppercase)
else:
self.enforce_type(jobject, "weka.core.Tag")
super(Tag, self).__init__(jobject)
@property
def ident(self):
"""
Returns the current integer ID of the tag.
:return: the integer ID
:rtype: int
"""
return javabridge.call(self.jobject, "getID", "()I")
@ident.setter
def ident(self, value):
"""
Sets the integer ID of the tag.
:param value: the new ID
:type value: int
"""
javabridge.call(self.jobject, "setID", "(I)V", value)
@property
def identstr(self):
"""
Returns the current ID string.
:return: the ID string
:rtype: str
"""
return javabridge.call(self.jobject, "getIDStr", "()Ljava/lang/String;")
@identstr.setter
def identstr(self, value):
"""
Sets the ID string.
:param value: the new ID string
:type value: str
"""
javabridge.call(self.jobject, "setIDStr", "(Ljava/lang/String;)V", value)
@property
def readable(self):
"""
Returns the 'human readable' string.
:return: the readable string
:rtype: str
"""
return javabridge.call(self.jobject, "getReadable", "()Ljava/lang/String;")
@readable.setter
def readable(self, value):
"""
Sets the 'human readable' string.
:param value: the new readable string
:type value: str
"""
javabridge.call(self.jobject, "setReadable", "(Ljava/lang/String;)V", value)
class Tags(JavaObject):
"""
Wrapper for an array of weka.core.Tag objects.
"""
def __init__(self, jobject=None, tags=None):
"""
:param jobject: the Java Tag array to wrap.
:type jobject: JB_Object
:param tags: the list of Tag objects to use
:type tags: list
"""
if tags is not None:
jarray = JavaArray(JavaArray.new_instance("weka.core.Tag", len(tags)))
for i in range(len(tags)):
jarray[i] = tags[i]
jobject = jarray.jobject
self.enforce_type(jobject, "weka.core.Tag", jni_intf_or_class="[Lweka/core/Tag;")
super(Tags, self).__init__(jobject)
self.array = JavaArray(self.jobject)
def __len__(self):
"""
Returns the number of Tag objects in the array.
:return: the number of tag objects
:rtype: int
"""
return len(self.array)
def __getitem__(self, item):
"""
Returns the specified Tag from the array.
:param item: the 0-based index
:type item: int
:return: the tag
:rtype: Tag
"""
return Tag(self.array[item].jobject)
def __setitem__(self, key, value):
"""
Not implemented.
"""
raise Exception("Cannot set a Tag!")
def __delitem__(self, key):
"""
Not implemented.
"""
raise Exception("Cannot delete a Tag!")
def __str__(self):
"""
Just calls the toString() method.
:rtype: str
"""
result = ""
for i in xrange(len(self.array)):
if i > 0:
result += "|"
result += str(self.array[i])
return result
def find(self, name):
"""
Returns the Tag that matches the name.
:param name: the string representation of the tag
:type name: str
:return: the tag, None if not found
:rtype: Tag
"""
result = None
for t in self.array:
if str(t) == name:
result = Tag(t.jobject)
break
return result
@classmethod
def get_tags(cls, classname, field):
"""
Instantiates the Tag array located in the specified class with the given field name.
Example:
tags = Tags.get_tags("weka.classifiers.functions.SMO", "TAGS_FILTER")
:param classname: the classname in which the tags reside
:type classname: str
:param field: the field name of the Tag array
:type field: str
:return: the Tags objects
:rtype: Tags
"""
classname = "L" + classname.replace(".", "/") + ";"
return Tags(jobject=javabridge.get_static_field(classname, field, "[Lweka/core/Tag;"))
@classmethod
def get_object_tags(cls, javaobject, methodname):
"""
Instantiates the Tag array obtained from the object using the specified method name.
Example:
cls = Classifier(classname="weka.classifiers.meta.MultiSearch")
tags = Tags.get_object_tags(cls, "getMetricsTags")
:param javaobject: the javaobject to obtain the tags from
:type javaobject: JavaObject
:param methodname: the method name returning the Tag array
:type methodname: str
:return: the Tags objects
:rtype: Tags
"""
return Tags(jobject=javabridge.call(javaobject.jobject, methodname, "()[Lweka/core/Tag;"))
class SelectedTag(JavaObject):
"""
Wrapper for the weka.core.SelectedTag class.
"""
def __init__(self, jobject=None, tag_id=None, tag_text=None, tags=None):
"""
Initializes the wrapper with the specified Java object or tags and either tag_id or tag_text.
:param jobject: the java object to wrap
:type jobject: JB_Object
:param tag_id: the integer associated with the tag
:type tag_id: int
:param tag_text: the text associated with the tag
:type tag_text: str
:param tags: list of Tag objects or Tags wrapper object
:type tags: list or Tags
"""
if isinstance(tags, Tags):
tobj = tags.jobject
else:
tobj = tags
if jobject is None:
if tag_id is None:
jobject = javabridge.make_instance(
"weka/core/SelectedTag", "(Ljava/lang/String;[Lweka/core/Tag;)V", tag_text, tobj)
else:
jobject = javabridge.make_instance(
"weka/core/SelectedTag", "(I[Lweka/core/Tag;)V", tag_id, tobj)
else:
self.enforce_type(jobject, "weka.core.SelectedTag")
super(SelectedTag, self).__init__(jobject)
@property
def selected(self):
"""
Returns the selected tag.
:return: the tag
:rtype: Tag
"""
return Tag(javabridge.call(self.jobject, "getSelectedTag", "()Lweka/core/Tag;"))
@property
def tags(self):
"""
Returns the associated tags.
:return: the list of Tag objects
:rtype: list
"""
result = []
a = javabridge.call(self.jobject, "getTags", "()Lweka/core/Tag;]")
length = javabridge.get_env().get_array_length(a)
wrapped = javabridge.get_env().get_object_array_elements(a)
for i in xrange(length):
result.append(Tag(javabridge.get_env().get_string(wrapped[i])))
return result
def join_options(options):
"""
Turns the list of options back into a single commandline string.
:param options: the list of options to process
:type options: list
:return: the combined options
:rtype: str
"""
return javabridge.static_call(
"Lweka/core/Utils;", "joinOptions",
"([Ljava/lang/String;)Ljava/lang/String;",
options)
def split_options(cmdline):
"""
Splits the commandline into a list of options.
:param cmdline: the commandline string to split into individual options
:type cmdline: str
:return: the split list of commandline options
:rtype: list
"""
return arrays.string_array_to_list(
javabridge.static_call(
"Lweka/core/Utils;", "splitOptions",
"(Ljava/lang/String;)[Ljava/lang/String;",
cmdline))
def backquote(s):
"""
Backquotes the string.
:param s: the string to process
:type s: str
:return: the backquoted string
:rtype: str
"""
return javabridge.static_call(
"Lweka/core/Utils;", "backQuoteChars", "(Ljava/lang/String;)Ljava/lang/String;", s)
def unbackquote(s):
"""
Un-backquotes the string.
:param s: the string to process
:type s: str
:return: the un-backquoted string
:rtype: str
"""
return javabridge.static_call(
"Lweka/core/Utils;", "unbackQuoteChars", "(Ljava/lang/String;)Ljava/lang/String;", s)
def quote(s):
"""
Quotes the string if necessary.
:param s: the string to process
:type s: str
:return: the quoted string
:rtype: str
"""
return javabridge.static_call(
"Lweka/core/Utils;", "quote", "(Ljava/lang/String;)Ljava/lang/String;", s)
def unquote(s):
"""
Un-quotes the string.
:param s: the string to process
:type s: str
:return: the un-quoted string
:rtype: str
"""
return javabridge.static_call(
"Lweka/core/Utils;", "unquote", "(Ljava/lang/String;)Ljava/lang/String;", s)
def to_commandline(optionhandler):
"""
Generates a commandline string from the OptionHandler instance.
:param optionhandler: the OptionHandler instance to turn into a commandline
:type optionhandler: OptionHandler
:return: the commandline string
:rtype: str
"""
return javabridge.static_call(
"Lweka/core/Utils;", "toCommandLine",
"(Ljava/lang/Object;)Ljava/lang/String;",
optionhandler.jobject)
def from_commandline(cmdline, classname=None):
"""
Creates an OptionHandler based on the provided commandline string.
:param cmdline: the commandline string to use
:type cmdline: str
:param classname: the classname of the wrapper to return other than OptionHandler (in dot-notation)
:type classname: str
:return: the generated option handler instance
:rtype: object
"""
params = split_options(cmdline)
cls = params[0]
params = params[1:]
handler = OptionHandler(jobject=javabridge.make_instance(cls.replace(".", "/"), "()V"), options=params)
if classname is None:
return handler
else:
c = get_class(classname)
return c(jobject=handler.jobject)
class AbstractParameter(OptionHandler):
"""
Ancestor for all parameter classes used by SetupGenerator and MultiSearch.
"""
def __init__(self, classname=None, jobject=None, options=None):
"""
Initializes the specified parameter using either the classname or the supplied JB_Object.
:param classname: the classname of the search parameter
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
if jobject is None:
jobject = AbstractParameter.new_instance(classname)
self.enforce_type(jobject, "weka.core.setupgenerator.AbstractParameter")
super(AbstractParameter, self).__init__(jobject=jobject, options=options)
@property
def prop(self):
"""
Returns the currently set property to apply the parameter to.
:return: the property
:rtype: str
"""
return javabridge.call(self.jobject, "getProperty", "()Ljava/lang/String;")
@prop.setter
def prop(self, s):
"""
Sets the property to apply the parameter to.
:param s: the property
:type s: str
"""
javabridge.call(self.jobject, "setProperty", "(Ljava/lang/String;)V", s)
class ListParameter(AbstractParameter):
"""
Parameter using a predefined list of values, used by SetupGenerator and MultiSearch.
"""
def __init__(self, jobject=None, options=None):
"""
Initializes the specified parameter using either its classname or the supplied JB_Object.
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
classname = "weka.core.setupgenerator.ListParameter"
if jobject is None:
jobject = ListParameter.new_instance(classname)
self.enforce_type(jobject, "weka.core.setupgenerator.ListParameter")
super(ListParameter, self).__init__(jobject=jobject, options=options)
@property
def values(self):
"""
Returns the currently set values.
:return: the list of values (strings)
:rtype: list
"""
return split_options(javabridge.call(self.jobject, "getList", "()Ljava/lang/String;"))
@values.setter
def values(self, l):
"""
Sets the list of values to apply.
:param l: the list of values (strings)
:type l: list
"""
javabridge.call(self.jobject, "setList", "(Ljava/lang/String;)V", join_options(l))
class MathParameter(AbstractParameter):
"""
Parameter using a math expression for generating values, used by SetupGenerator and MultiSearch.
"""
def __init__(self, jobject=None, options=None):
"""
Initializes the specified parameter using either its classname or the supplied JB_Object.
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
classname = "weka.core.setupgenerator.MathParameter"
if jobject is None:
jobject = ListParameter.new_instance(classname)
self.enforce_type(jobject, "weka.core.setupgenerator.MathParameter")
super(MathParameter, self).__init__(jobject=jobject, options=options)
@property
def minimum(self):
"""
Returns the currently set minimum value.
:return: the minimum
:rtype: float
"""
return javabridge.call(self.jobject, "getMin", "()D")
@minimum.setter
def minimum(self, m):
"""
Sets the new minimum value.
:param m: the minimum
:type m: float
"""
javabridge.call(self.jobject, "setMin", "(D)V", m)
@property
def maximum(self):
"""
Returns the currently set maximum value.
:return: the maximum
:rtype: float
"""
return javabridge.call(self.jobject, "getMax", "()D")
@maximum.setter
def maximum(self, m):
"""
Sets the new maximum value.
:param m: the maximum
:type m: float
"""
javabridge.call(self.jobject, "setMax", "(D)V", m)
@property
def step(self):
"""
Returns the currently set step value.
:return: the step
:rtype: float
"""
return javabridge.call(self.jobject, "getStep", "()D")
@step.setter
def step(self, s):
"""
Sets the new step value.
:param s: the step
:type s: float
"""
javabridge.call(self.jobject, "setStep", "(D)V", s)
@property
def base(self):
"""
Returns the currently set base value.
:return: the base
:rtype: float
"""
return javabridge.call(self.jobject, "getBase", "()D")
@base.setter
def base(self, b):
"""
Sets the new base value.
:param b: the base
:type b: float
"""
javabridge.call(self.jobject, "setBase", "(D)V", b)
@property
def expression(self):
"""
Returns the currently set expression.
:return: the expression
:rtype: str
"""
return javabridge.call(self.jobject, "getExpression", "()Ljava/lang/String;")
@expression.setter
def expression(self, e):
"""
Sets the new expression.
:param e: the expression
:type e: str
"""
javabridge.call(self.jobject, "setExpression", "(Ljava/lang/String;)V", e)
class SetupGenerator(OptionHandler):
"""
Allows generation of large number of setups using parameter setups.
"""
def __init__(self, jobject=None, options=None):
"""
Initializes the specified classifier using its classname or the supplied JB_Object.
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
classname = "weka.core.SetupGenerator"
if jobject is None:
jobject = OptionHandler.new_instance(classname)
else:
self.enforce_type(jobject, classname)
super(SetupGenerator, self).__init__(jobject=jobject, options=options)
@property
def base_object(self):
"""
Returns the base object to apply the setups to.
:return: the base object
:rtype: JavaObject or OptionHandler
"""
jobj = javabridge.call(self.jobject, "getBaseObject", "()Ljava/io/Serializable;")
if OptionHandler.check_type(jobj, "weka.core.OptionHandler"):
return OptionHandler(jobj)
else:
return JavaObject(jobj)
@base_object.setter
def base_object(self, obj):
"""
Sets the base object to apply the setups to.
:param obj: the object to use (must be serializable!)
:type obj: JavaObject
"""
if not obj.is_serializable:
raise Exception("Base object must be serializable: " + obj.classname)
javabridge.call(self.jobject, "setBaseObject", "(Ljava/io/Serializable;)V", obj.jobject)
@property
def parameters(self):
"""
Returns the list of currently set search parameters.
:return: the list of AbstractSearchParameter objects
:rtype: list
"""
array = JavaArray(javabridge.call(self.jobject, "getParameters", "()[Lweka/core/setupgenerator/AbstractParameter;"))
result = []
for item in array:
result.append(AbstractParameter(jobject=item.jobject))
return result
@parameters.setter
def parameters(self, params):
"""
Sets the list of search parameters to use.
:param params: list of AbstractSearchParameter objects
:type params: list
"""
array = JavaArray(jobject=JavaArray.new_instance("weka.core.setupgenerator.AbstractParameter", len(params)))
for idx, obj in enumerate(params):
array[idx] = obj.jobject
javabridge.call(self.jobject, "setParameters", "([Lweka/core/setupgenerator/AbstractParameter;)V", array.jobject)
def setups(self):
"""
Generates and returns all the setups according to the parameter search space.
:return: the list of configured objects (of type JavaObject)
:rtype: list
"""
result = []
has_options = self.base_object.is_optionhandler
enm = javabridge.get_enumeration_wrapper(javabridge.call(self.jobject, "setups", "()Ljava/util/Enumeration;"))
while enm.hasMoreElements():
if has_options:
result.append(OptionHandler(enm.nextElement()))
else:
result.append(JavaObject(enm.nextElement()))
return result
def main():
"""
Runs a classifier from the command-line. Calls JVM start/stop automatically.
Use -h to see all options.
"""
parser = argparse.ArgumentParser(
description='Performs option handling operations from the command-line. Calls JVM start/stop automatically.')
parser.add_argument("-j", metavar="classpath", dest="classpath", help="additional classpath, jars/directories")
parser.add_argument("-action", metavar="action", dest="action", required=True,
help="The action to perform on the options: join=create single string, "\
"split=create array from quoted option, code=generate code from options")
parser.add_argument("option", nargs=argparse.REMAINDER, help="The option(s) to process")
parsed = parser.parse_args()
jars = []
if parsed.classpath is not None:
jars = parsed.classpath.split(os.pathsep)
jvm.start(jars, packages=True)
try:
if parsed.action == "join":
output = "cmdline = \"" + backquote(join_options(parsed.option)) + "\""
elif parsed.action == "split" and len(parsed.option) == 1:
output = "options = [\n"
opts = split_options(parsed.option[0])
for idx, opt in enumerate(opts):
if idx > 0:
output += ",\n"
output += " \"" + backquote(opt) + "\""
output += "]"
elif parsed.action == "code":
options = parsed.option[:]
cname = None
# classname + options?
if options[0].find(".") > -1:
cname = options[0]
options = options[1:]
output = "options = [ \n"
for idx, opt in enumerate(options):
if idx > 0:
output += ",\n"
output += " \"" + backquote(opt) + "\""
output += "]\n"
if cname is not None:
output += 'handler = OptionHandler(JavaObject.new_instance("' + cname + '"))\n'
output += 'handler.options = options\n'
else:
raise Exception("Unsupported action: " + parsed.action)
if output is not None:
print(output)
except Exception, e:
print(e)
finally:
jvm.stop()
if __name__ == "__main__":
try:
main()
except Exception, ex:
print(ex)
python-weka-wrapper-0.3.6/python/weka/core/database.py 0000644 0172670 0172670 00000013725 12576427176 024443 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# database.py
# Copyright (C) 2015 Fracpete (pythonwekawrapper at gmail dot com)
import javabridge
from weka.core.classes import OptionHandler
from weka.core.dataset import Instances
class DatabaseUtils(OptionHandler):
"""
Wrapper class for weka.experiment.DatabaseUtils.
"""
def __init__(self, jobject=None, options=None):
"""
Initializes a DatabaseUtils object from scratch or uses the provided JB_Object.
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to use
:type options: list
"""
if jobject is None:
jobject = DatabaseUtils.new_instance("weka.experiment.DatabaseUtils")
self.enforce_type(jobject, "weka.experiment.DatabaseUtils")
super(DatabaseUtils, self).__init__(jobject=jobject, options=options)
@property
def db_url(self):
"""
Obtains the currently set database URL.
:return: the database URL
:rtype: str
"""
return javabridge.call(self.jobject, "getDatabaseURL", "()Ljava/lang/String;")
@db_url.setter
def db_url(self, url):
"""
Sets the database URL.
:param url: the database URL
:type url: str
"""
javabridge.call(self.jobject, "setDatabaseURL", "(Ljava/lang/String;)V", url)
@property
def user(self):
"""
Obtains the currently set database user.
:return: the database user
:rtype: str
"""
return javabridge.call(self.jobject, "getUsername", "()Ljava/lang/String;")
@user.setter
def user(self, user):
"""
Sets the database user.
:param user: the database user
:type user: str
"""
javabridge.call(self.jobject, "setUsername", "(Ljava/lang/String;)V", user)
@property
def password(self):
"""
Obtains the currently set database password.
:return: the database password
:rtype: str
"""
return javabridge.call(self.jobject, "getPassword", "()Ljava/lang/String;")
@password.setter
def password(self, password):
"""
Sets the database password.
:param password: the database password
:type password: str
"""
javabridge.call(self.jobject, "setPassword", "(Ljava/lang/String;)V", password)
class InstanceQuery(DatabaseUtils):
"""
Wrapper class for weka.experiment.InstanceQuery.
"""
def __init__(self, jobject=None, options=None):
"""
Initializes an InstanceQuery object from scratch or uses the provided JB_Object.
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to use
:type options: list
"""
if jobject is None:
jobject = InstanceQuery.new_instance("weka.experiment.InstanceQuery")
self.enforce_type(jobject, "weka.experiment.InstanceQuery")
super(InstanceQuery, self).__init__(jobject=jobject, options=options)
@property
def custom_properties(self):
"""
Obtains the currently set custom properties file.
:return: the custom properties file
:rtype: str
"""
return javabridge.to_string(javabridge.call(self.jobject, "getCustomPropsFile", "()Ljava/io/File;"))
@custom_properties.setter
def custom_properties(self, props):
"""
Sets the custom properties file to use.
:param props: the props file
:type props: str
"""
fprops = javabridge.make_instance("java/io/File", "(Ljava/lang/String;)V", props)
javabridge.call(self.jobject, "setCustomPropsFile", "(Ljava/io/File;)V", fprops)
@property
def sparse_data(self):
"""
Obtains the whether sparse data is returned or not.
:return: whether sparse data is generated
:rtype: bool
"""
return javabridge.call(self.jobject, "getSparseData", "()Z")
@sparse_data.setter
def sparse_data(self, sparse):
"""
Sets whether to generate sparse data.
:param sparse: whether to generated sparse data
:type sparse: bool
"""
javabridge.call(self.jobject, "setSparseData", "(Z)V", sparse)
@property
def query(self):
"""
Obtains the current SQL query to execute.
:return: the SQL query
:rtype: str
"""
return javabridge.call(self.jobject, "getQuery", "()Ljava/lang/String;")
@query.setter
def query(self, query):
"""
Sets the SQL query to execute.
:param query: the SQL query
:type query: str
"""
javabridge.call(self.jobject, "setQuery", "(Ljava/lang/String;)V", query)
def retrieve_instances(self, query=None):
"""
Executes either the supplied query or the one set via options (or the 'query' property).
:param query: query to execute if not the currently set one
:type query: str
:return: the generated dataq
:rtype: Instances
"""
if query is None:
data = javabridge.call(self.jobject, "retrieveInstances", "()Lweka/core/Instances;")
else:
data = javabridge.call(self.jobject, "retrieveInstances", "(Ljava/lang/String;)Lweka/core/Instances;")
return Instances(data)
python-weka-wrapper-0.3.6/python/weka/core/dataset.py 0000644 0172670 0172670 00000127350 12652561011 024302 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# dataset.py
# Copyright (C) 2014-2016 Fracpete (pythonwekawrapper at gmail dot com)
import javabridge
import logging
import numpy
from weka.core.classes import JavaObject
import weka.core.types as types
# logging setup
logger = logging.getLogger(__name__)
class Instances(JavaObject):
"""
Wrapper class for weka.core.Instances.
"""
def __init__(self, jobject):
"""
Initializes the weka.core.Instances wrapper.
:param jobject: the weka.core.Instances object to wrap
:type jobject: JB_Object
"""
self.enforce_type(jobject, "weka.core.Instances")
super(Instances, self).__init__(jobject)
self.__attribute = javabridge.make_call(self.jobject, "attribute", "(I)Lweka/core/Attribute;")
self.__attribute_by_name = javabridge.make_call(self.jobject, "attribute", "(Ljava/lang/String;)Lweka/core/Attribute;")
self.__num_attributes = javabridge.make_call(self.jobject, "numAttributes", "()I")
self.__num_instances = javabridge.make_call(self.jobject, "numInstances", "()I")
self.__get_class_index = javabridge.make_call(self.jobject, "classIndex", "()I")
self.__set_class_index = javabridge.make_call(self.jobject, "setClassIndex", "(I)V")
self.__class_attribute = javabridge.make_call(self.jobject, "classAttribute", "()Lweka/core/Attribute;")
self.__get_instance = javabridge.make_call(self.jobject, "instance", "(I)Lweka/core/Instance;")
self.__set_instance = javabridge.make_call(self.jobject, "set", "(ILweka/core/Instance;)Lweka/core/Instance;")
self.__append_instance = javabridge.make_call(self.jobject, "add", "(Lweka/core/Instance;)Z")
self.__insert_instance = javabridge.make_call(self.jobject, "add", "(ILweka/core/Instance;)V")
def __iter__(self):
"""
Allows iterating over the rows.
:return: the iterator
:rtype: InstanceIterator
"""
return InstanceIterator(self)
@property
def relationname(self):
"""
Returns the name of the dataset.
:return: the name
:rtype: str
"""
return javabridge.call(self.jobject, "relationName", "()Ljava/lang/String;")
@relationname.setter
def relationname(self, value):
"""
Sets the name of the dataset.
:param value: the name
:type value: str
"""
javabridge.call(self.jobject, "setRelationName", "(Ljava/lang/String;)V", value)
@property
def num_attributes(self):
"""
Returns the number of attributes.
:return: the number of attributes
:rtype: int
"""
return self.__num_attributes()
def attributes(self):
"""
Returns an iterator over the attributes.
"""
return AttributeIterator(self)
def attribute(self, index):
"""
Returns the specified attribute.
:param index: the 0-based index of the attribute
:type index: int
:return: the attribute
:rtype: Attribute
"""
return Attribute(self.__attribute(index))
def attribute_by_name(self, name):
"""
Returns the specified attribute, None if not found.
:param name: the name of the attribute
:type name: str
:return: the attribute or None
:rtype: Attribute
"""
att = self.__attribute_by_name(javabridge.get_env().new_string(name))
if att is None:
return None
else:
return Attribute(att)
def attribute_stats(self, index):
"""
Returns the specified attribute statistics.
:param index: the 0-based index of the attribute
:type index: int
:return: the attribute statistics
:rtype: AttributeStats
"""
return AttributeStats(javabridge.call(self.jobject, "attributeStats", "(I)Lweka/core/AttributeStats;", index))
def values(self, index):
"""
Returns the internal values of this attribute from all the instance objects.
:return: the values as numpy array
:rtype: list
"""
values = []
for i in xrange(self.num_instances):
inst = self.get_instance(i)
values.append(inst.get_value(index))
return numpy.array(values)
@property
def num_instances(self):
"""
Returns the number of instances.
:return: the number of instances
:rtype: int
"""
return self.__num_instances()
@property
def class_attribute(self):
"""
Returns the currently set class attribute.
:return: the class attribute
:rtype: Attribute
"""
return Attribute(self.__class_attribute())
@property
def class_index(self):
"""
Returns the currently set class index (0-based).
:return: the class index, -1 if not set
:rtype: int
"""
return self.__get_class_index()
@class_index.setter
def class_index(self, index):
"""
Sets the class index (0-based).
:param index: the new index, use -1 to unset
:type index: int
"""
self.__set_class_index(index)
def has_class(self):
"""
Returns whether a class attribute is set (convenience method).
:return: whether a class attribute is currently set
:rtype: bool
"""
return self.class_index != -1
def no_class(self):
"""
Unsets the class attribute (convenience method).
"""
self.class_index = -1
def class_is_first(self):
"""
Sets the first attribute as class attribute (convenience method).
"""
self.class_index = 0
def class_is_last(self):
"""
Sets the last attribute as class attribute (convenience method).
"""
self.class_index = self.num_attributes - 1
def get_instance(self, index):
"""
Returns the Instance object at the specified location.
:param index: the 0-based index of the instance
:type index: int
:return: the instance
:rtype: Instance
"""
return Instance(self.__get_instance(index))
def add_instance(self, inst, index=None):
"""
Adds the specified instance to the dataset.
:param inst: the Instance to add
:type inst: Instance
:param index: the 0-based index where to add the Instance
:type index: int
"""
if index is None:
self.__append_instance(inst.jobject)
else:
self.__insert_instance(index, inst.jobject)
def set_instance(self, index, inst):
"""
Sets the Instance at the specified location in the dataset.
:param index: the 0-based index of the instance to replace
:type index: int
:param inst: the Instance to set
:type inst: Instance
:return: the instance
:rtype: Instance
"""
return Instance(
self.__set_instance(index, inst.jobject))
def delete(self, index=None):
"""
Removes either the specified Instance or all Instance objects.
:param index: the 0-based index of the instance to remove
:type index: int
"""
if index is None:
javabridge.call(self.jobject, "delete", "()V")
else:
javabridge.call(self.jobject, "delete", "(I)V", index)
def delete_attribute(self, index):
"""
Deletes an attribute at the given position.
:param index: the 0-based index of the attribute to remove
:type index: int
"""
javabridge.call(self.jobject, "deleteAttributeAt", "(I)V", index)
def delete_first_attribute(self):
"""
Deletes the first attribute.
"""
self.delete_attribute(0)
def delete_last_attribute(self):
"""
Deletes the last attribute.
"""
self.delete_attribute(self.num_attributes - 1)
def delete_attribute_type(self, typ):
"""
Deletes all attributes of the given type in the dataset.
:param typ: the attribute type to remove, see weka.core.Attribute Javadoc
:type typ: int
"""
javabridge.call(self.jobject, "deleteAttributeType", "(I)V", typ)
def delete_with_missing(self, index):
"""
Deletes all rows that have a missing value at the specified attribute index.
:param index: the attribute index to check for missing attributes
:type index: int
"""
javabridge.call(self.jobject, "deleteWithMissing", "(I)V", index)
def compactify(self):
"""
Compactifies the set of instances.
"""
javabridge.call(self.jobject, "compactify", "()V")
def sort(self, index):
"""
Sorts the dataset using the specified attribute index.
:param index: the index of the attribute
:type index: int
"""
javabridge.call(self.jobject, "sort", "(I)V", index)
def randomize(self, random):
"""
Randomizes the dataset using the random number generator.
:param random: the random number generator to use
:type random: Random
"""
javabridge.call(self.jobject, "randomize", "(Ljava/util/Random;)V", random.jobject)
def stratify(self, folds):
"""
Stratifies the data after randomization for nominal class attributes.
:param folds: the number of folds to perform the stratification for
:type folds: int
"""
javabridge.call(self.jobject, "stratify", "(I)V", folds)
def train_cv(self, num_folds, fold, random=None):
"""
Generates a training fold for cross-validation.
:param num_folds: the number of folds of cross-validation, eg 10
:type num_folds: int
:param fold: the current fold (0-based)
:type fold: int
:param random: the random number generator
:type random: Random
:return: the training fold
:rtype: Instances
"""
if random is None:
return Instances(
javabridge.call(self.jobject, "trainCV", "(II)Lweka/core/Instances;",
num_folds, fold))
else:
return Instances(
javabridge.call(self.jobject, "trainCV", "(IILjava/util/Random;)Lweka/core/Instances;",
num_folds, fold, random.jobject))
def test_cv(self, num_folds, fold):
"""
Generates a test fold for cross-validation.
:param num_folds: the number of folds of cross-validation, eg 10
:type num_folds: int
:param fold: the current fold (0-based)
:type fold: int
:return: the training fold
:rtype: Instances
"""
return Instances(
javabridge.call(self.jobject, "testCV", "(II)Lweka/core/Instances;",
num_folds, fold))
def equal_headers(self, inst):
"""
Compares this dataset against the given one in terms of attributes.
:param inst: the dataset to compare against
:type inst: Instances
:return: None if the same, otherwise an error message
:rtype: str
"""
return javabridge.call(
self.jobject, "equalHeadersMsg", "(Lweka/core/Instances;)Ljava/lang/String;", inst.jobject)
@classmethod
def copy_instances(cls, dataset, from_row=None, num_rows=None):
"""
Creates a copy of the Instances. If either from_row or num_rows are None, then all of
the data is being copied.
:param dataset: the original dataset
:type dataset: Instances
:param from_row: the 0-based start index of the rows to copy
:type from_row: int
:param num_rows: the number of rows to copy
:type num_rows: int
:return: the copy of the data
:rtype: Instances
"""
if from_row is None or num_rows is None:
return Instances(
javabridge.make_instance(
"weka/core/Instances", "(Lweka/core/Instances;)V",
dataset.jobject))
else:
dataset = cls.copy_instances(dataset)
return Instances(
javabridge.make_instance(
"weka/core/Instances", "(Lweka/core/Instances;II)V",
dataset.jobject, from_row, num_rows))
@classmethod
def template_instances(cls, dataset, capacity=0):
"""
Uses the Instances as template to create an empty dataset.
:param dataset: the original dataset
:type dataset: Instances
:param capacity: how many data rows to reserve initially (see compactify)
:type capacity: int
:return: the empty dataset
:rtype: Instances
"""
return Instances(
javabridge.make_instance(
"weka/core/Instances", "(Lweka/core/Instances;I)V", dataset.jobject, capacity))
@classmethod
def create_instances(cls, name, atts, capacity):
"""
Creates a new Instances.
:param name: the relation name
:type name: str
:param atts: the list of attributes to use for the dataset
:type atts: list of Attribute
:param capacity: how many data rows to reserve initially (see compactify)
:type capacity: int
:return: the dataset
:rtype: Instances
"""
attributes = []
for att in atts:
attributes.append(att.jobject)
return Instances(
javabridge.make_instance(
"weka/core/Instances", "(Ljava/lang/String;Ljava/util/ArrayList;I)V",
name, javabridge.make_list(attributes), capacity))
@classmethod
def merge_instances(cls, inst1, inst2):
"""
Merges the two datasets (side-by-side).
:param inst1: the first dataset
:type inst1: Instances or str
:param inst2: the first dataset
:type inst2: Instances
:return: the combined dataset
:rtype: Instances
"""
return Instances(javabridge.static_call(
"weka/core/Instances", "mergeInstances",
"(Lweka/core/Instances;Lweka/core/Instances;)Lweka/core/Instances;", inst1.jobject, inst2.jobject))
@classmethod
def append_instances(cls, inst1, inst2):
"""
Merges the two datasets (one-after-the-other). Throws an exception if the datasets aren't compatible.
:param inst1: the first dataset
:type inst1: Instances
:param inst2: the first dataset
:type inst2: Instances
:return: the combined dataset
:rtype: Instances
"""
msg = inst1.equal_headers(inst2)
if msg is not None:
raise Exception("Cannot appent instances: " + msg)
result = cls.copy_instances(inst1)
for i in xrange(inst2.num_instances):
result.add_instance(inst2.get_instance(i))
return result
def train_test_split(self, percentage, rnd=None):
"""
Generates a train/test split.
:param percentage: the percentage split to use (amount to use for training; 0-100)
:type percentage: double
:param rnd: the random number generator to use, if None the order gets preserved
:type rnd: Random
:return: the train/test splits
:rtype: tuple
"""
if percentage <= 0:
raise Exception("Split percentage must be > 0, provided: " + str(percentage))
if percentage >= 100:
raise Exception("Split percentage must be < 100, provided: " + str(percentage))
if rnd is not None:
self.randomize(rnd)
train_size = int(round(self.num_instances * percentage / 100))
test_size = self.num_instances - train_size
train_inst = Instances.copy_instances(self, 0, train_size)
test_inst = Instances.copy_instances(self, train_size, test_size)
return train_inst, test_inst
@classmethod
def summary(cls, inst):
"""
Generates a summary of the dataset.
:param inst: the dataset
:type inst: Instances
:return: the summary
:rtype: str
"""
return javabridge.call(inst.jobject, "toSummaryString", "()Ljava/lang/String;")
class Instance(JavaObject):
"""
Wrapper class for weka.core.Instance.
"""
def __init__(self, jobject):
"""
Initializes the weka.core.Instance wrapper.
:param jobject: the weka.core.Instance object to initialize with
:type jobject: JB_Object
"""
self.enforce_type(jobject, "weka.core.Instance")
super(Instance, self).__init__(jobject)
self.__set_value = javabridge.make_call(self.jobject, "setValue", "(ID)V")
self.__get_value = javabridge.make_call(self.jobject, "value", "(I)D")
self.__set_string_value = javabridge.make_call(self.jobject, "setValue", "(ILjava/lang/String;)V")
self.__get_string_value = javabridge.make_call(self.jobject, "stringValue", "(I)Ljava/lang/String;")
self.__set_weight = javabridge.make_call(self.jobject, "setWeight", "(D)V")
self.__get_weight = javabridge.make_call(self.jobject, "weight", "()D")
self.__is_missing = javabridge.make_call(self.jobject, "isMissing", "(I)Z")
self.__class_index = javabridge.make_call(self.jobject, "classIndex", "()I")
def __iter__(self):
"""
Returns an iterator over the values.
:return: a value iterator
:rtype: InstanceValueIterator
"""
return InstanceValueIterator(self)
@property
def dataset(self):
"""
Returns the dataset that this instance belongs to.
:return: the dataset or None if no dataset set
:rtype: Instances
"""
dataset = javabridge.call(self.jobject, "dataset", "()Lweka/core/Instances;")
if dataset is None:
return None
else:
return Instances(dataset)
@dataset.setter
def dataset(self, dataset):
"""
Sets the dataset that this instance belongs to (for attribute information).
:param dataset: the dataset this instance belongs to.
:type dataset: Instances
"""
javabridge.call(self.jobject, "setDataset", "(Lweka/core/Instances;)V", dataset.jobject)
@property
def num_attributes(self):
"""
Returns the number of attributes.
:return: the numer of attributes
:rtype: int
"""
return javabridge.call(self.jobject, "numAttributes", "()I")
@property
def num_classes(self):
"""
Returns the number of class labels.
:return: the numer of class labels
:rtype: int
"""
return javabridge.call(self.jobject, "numClasses", "()I")
@property
def class_attribute(self):
"""
Returns the currently set class attribute.
:return: the class attribute
:rtype: Attribute
"""
return Attribute(javabridge.call(self.jobject, "classAttribute", "()Lweka/core/Attribute;"))
@property
def class_index(self):
"""
Returns the currently set class index.
:return: the class index, -1 if not set
:rtype: int
"""
return self.__class_index()
def has_class(self):
"""
Returns whether a class attribute is set (convenience method).
:return: whether a class attribute is currently set
:rtype: bool
"""
return self.class_index != -1
def set_value(self, index, value):
"""
Sets the internal value at the specified position (0-based).
:param index: the 0-based index of the attribute
:type index: int
:param value: the internal float value to set
:type value: float
"""
self.__set_value(index, value)
def get_value(self, index):
"""
Returns the internal value at the specified position (0-based).
:param index: the 0-based index of the inernal value
:type index: int
:return: the internal value
:rtype: float
"""
return self.__get_value(index)
def set_string_value(self, index, s):
"""
Sets the string value at the specified position (0-based).
:param index: the 0-based index of the inernal value
:type index: int
:param s: the string value
:type s: str
"""
return self.__set_string_value(index, javabridge.get_env().new_string(s))
def get_string_value(self, index):
"""
Returns the string value at the specified position (0-based).
:param index: the 0-based index of the inernal value
:type index: int
:return: the string value
:rtype: str
"""
return javabridge.get_env().get_string(self.__get_string_value(index))
def get_relational_value(self, index):
"""
Returns the relational value at the specified position (0-based).
:param index: the 0-based index of the inernal value
:type index: int
:return: the relational value
:rtype: Instances
"""
return Instances(javabridge.call(self.jobject, "relationalValue", "(I)Lweka/core/Instances;", index))
def set_missing(self, index):
"""
Sets the attribute at the specified index to missing.
:param index: the 0-based index of the attribute
:type index: int
"""
javabridge.call(self.jobject, "setMissing", "(I)V", index)
def is_missing(self, index):
"""
Returns whether the attribute at the specified index is missing.
:param index: the 0-based index of the attribute
:type index: int
:return: whether the value is missing
:rtype: bool
"""
return self.__is_missing(index)
def has_missing(self):
"""
Returns whether at least one attribute has a missing value.
:return: whether at least one value is missing
:rtype: bool
"""
return javabridge.call(self.jobject, "hasMissingValue", "()Z")
@property
def weight(self):
"""
Returns the currently set weight.
:return: the weight
:rtype: float
"""
return self.__get_weight()
@weight.setter
def weight(self, weight):
"""
Sets the weight.
:param weight: the weight to set
:type weight: float
"""
self.__set_weight(weight)
@property
def values(self):
"""
Returns the internal values of this instance.
:return: the values as numpy array
:rtype: ndarray
"""
return javabridge.get_env().get_double_array_elements(javabridge.call(self.jobject, "toDoubleArray", "()[D"))
@classmethod
def create_instance(cls, values, classname="weka.core.DenseInstance", weight=1.0):
"""
Creates a new instance.
:param values: the float values (internal format) to use, numpy array or list.
:type values: ndarray or list
:param classname: the classname of the instance (eg weka.core.DenseInstance).
:type classname: str
:param weight: the weight of the instance
:type weight: float
"""
jni_classname = classname.replace(".", "/")
if type(values) is list:
for i in xrange(len(values)):
values[i] = float(values[i])
values = numpy.array(values)
return Instance(
javabridge.make_instance(
jni_classname, "(D[D)V",
weight, javabridge.get_env().make_double_array(values)))
@classmethod
def create_sparse_instance(cls, values, max_values, classname="weka.core.SparseInstance", weight=1.0):
"""
Creates a new sparse instance.
:param values: the list of tuples (0-based index and internal format float). The indices of the
tuples must be in ascending order and "max_values" must be set to the maximum
number of attributes in the dataset.
:type values: list
:param max_values: the maximum number of attributes
:type max_values: int
:param classname: the classname of the instance (eg weka.core.SparseInstance).
:type classname: str
:param weight: the weight of the instance
:type weight: float
"""
jni_classname = classname.replace(".", "/")
indices = []
vals = []
for (i, v) in values:
indices.append(i)
vals.append(float(v))
indices = numpy.array(indices, dtype=numpy.int32)
vals = numpy.array(vals)
return Instance(
javabridge.make_instance(
jni_classname, "(D[D[II)V",
weight, javabridge.get_env().make_double_array(vals),
javabridge.get_env().make_int_array(indices), max_values))
class Attribute(JavaObject):
"""
Wrapper class for weka.core.Attribute.
"""
def __init__(self, jobject):
"""
Initializes the weka.core.Attribute wrapper.
:param jobject: the JB_Object
:type jobject: JB_Object
"""
self.enforce_type(jobject, "weka.core.Attribute")
super(Attribute, self).__init__(jobject)
@property
def name(self):
"""
Returns the name of the attribute.
:return: the name
:rtype: str
"""
return javabridge.call(self.jobject, "name", "()Ljava/lang/String;")
@property
def index(self):
"""
Returns the index of this attribute.
:return: the index
:rtype: int
"""
return javabridge.call(self.jobject, "index", "()I")
@property
def weight(self):
"""
Returns the weight of the attribute.
:return: the weight
:rtype: float
"""
return javabridge.call(self.jobject, "weight", "()D")
@weight.setter
def weight(self, weight):
"""
Sets the weight of the attribute.
:param weight: the weight of the attribute
:type weight: float
"""
javabridge.call(self.jobject, "setWeight", "(D)V", weight)
def index_of(self, label):
"""
Returns the index of the label in this attribute.
:param label: the string label to get the index for
:type label: str
:return: the 0-based index
:rtype: int
"""
return javabridge.call(self.jobject, "indexOfValue", "(Ljava/lang/String;)I", label)
def value(self, index):
"""
Returns the label for the index.
:param index: the 0-based index of the label to return
:type index: int
:return: the label
:rtype: str
"""
return javabridge.call(self.jobject, "value", "(I)Ljava/lang/String;", index)
@property
def num_values(self):
"""
Returns the number of labels.
:return: the number of labels
:rtype: int
"""
return javabridge.call(self.jobject, "numValues", "()I")
@property
def values(self):
"""
Returns the labels, strings or relation-values.
:return: all the values, None if not NOMINAL, STRING, or RELATION
:rtype: list
"""
enm = javabridge.call(self.jobject, "enumerateValues", "()Ljava/util/Enumeration;")
if enm is None:
return None
else:
return types.enumeration_to_list(enm)
@property
def ordering(self):
"""
Returns the ordering of the attribute.
:return: the ordering (ORDERING_SYMBOLIC, ORDERING_ORDERED, ORDERING_MODULO)
:rtype: int
"""
return javabridge.call(self.jobject, "ordering", "()I")
@property
def type(self):
"""
Returns the type of the attribute. See weka.core.Attribute Javadoc.
:return: the type
:rtype: int
"""
return javabridge.call(self.jobject, "type", "()I")
def type_str(self, short=False):
"""
Returns the type of the attribute as string.
:return: the type
:rtype: str
"""
if short:
return javabridge.static_call(
"weka/core/Attribute", "typeToStringShort", "(Lweka/core/Attribute;)Ljava/lang/String;",
self.jobject)
else:
return javabridge.static_call(
"weka/core/Attribute", "typeToString", "(Lweka/core/Attribute;)Ljava/lang/String;",
self.jobject)
@property
def is_averagable(self):
"""
Returns whether the attribute is averagable.
:return: whether averagable
:rtype: bool
"""
return javabridge.call(self.jobject, "isAveragable", "()Z")
@property
def is_date(self):
"""
Returns whether the attribute is a date one.
:return: whether date attribute
:rtype: bool
"""
return javabridge.call(self.jobject, "isDate", "()Z")
@property
def is_nominal(self):
"""
Returns whether the attribute is a nominal one.
:return: whether nominal attribute
:rtype: bool
"""
return javabridge.call(self.jobject, "isNominal", "()Z")
@property
def is_numeric(self):
"""
Returns whether the attribute is a numeric one (date or numeric).
:return: whether numeric attribute
:rtype: bool
"""
return javabridge.call(self.jobject, "isNumeric", "()Z")
@property
def is_relation_valued(self):
"""
Returns whether the attribute is a relation valued one.
:return: whether relation valued attribute
:rtype: bool
"""
return javabridge.call(self.jobject, "isRelationValued", "()Z")
@property
def is_string(self):
"""
Returns whether the attribute is a string attribute.
:return: whether string attribute
:rtype: bool
"""
return javabridge.call(self.jobject, "isString", "()Z")
@property
def date_format(self):
"""
Returns the format of this data attribute. See java.text.SimpleDateFormat Javadoc.
:return: the format string
:rtype: str
"""
return javabridge.call(self.jobject, "getDateFormat", "()Ljava/lang/String;")
@property
def lower_numeric_bound(self):
"""
Returns the lower numeric bound of the numeric attribute.
:return: the lower bound
:rtype: float
"""
return javabridge.call(self.jobject, "getLowerNumericBound", "()D")
@property
def upper_numeric_bound(self):
"""
Returns the upper numeric bound of the numeric attribute.
:return: the upper bound
:rtype: float
"""
return javabridge.call(self.jobject, "getUpperNumericBound", "()D")
def is_in_range(self, value):
"""
Checks whether the value is within the bounds of the numeric attribute.
:param value: the numeric value to check
:type value: float
:return: whether between lower and upper bound
:rtype: bool
"""
return javabridge.call(self.jobject, "isInRange", "(D)Z", value)
def add_string_value(self, s):
"""
Adds the string value, returns the index.
:param s: the string to add
:type s: str
:return: the index
:rtype: int
"""
return javabridge.call(self.jobject, "addStringValue", "(Ljava/lang/String;)I", s)
def add_relation(self, instances):
"""
Adds the relation value, returns the index.
:param instances: the Instances object to add
:type instances: Instances
:return: the index
:rtype: int
"""
return javabridge.call(self.jobject, "addRelation", "(Lweka/core/Instances;)I", instances.jobject)
def parse_date(self, s):
"""
Parses the date string and returns the internal format value.
:param s: the date string
:type s: str
:return: the internal format
:rtype: float
"""
return javabridge.call(self.jobject, "parseDate", "(Ljava/lang/String;)D", s)
def equals(self, att):
"""
Checks whether this attributes is the same as the provided one.
:param att: the Attribute to check against
:type att: Attribute
:return: whether the same
:rtype: bool
"""
return javabridge.call(self.jobject, "equals", "(Lweka/core/Attribute;)Z", att.jobject)
def equals_msg(self, att):
"""
Checks whether this attributes is the same as the provided one.
Returns None if the same, otherwise error message.
:param att: the Attribute to check against
:type att: Attribute
:return: None if the same, otherwise error message
:rtype: str
"""
return javabridge.call(self.jobject, "equalsMsg", "(Lweka/core/Attribute;)Ljava/lang/String;", att.jobject)
def copy(self, name=None):
"""
Creates a copy of this attribute.
:param name: the new name, uses the old one if None
:type name: str
:return: the copy of the attribute
:rtype: Attribute
"""
if name is None:
return Attribute(
javabridge.call(self.jobject, "copy", "()Ljava/lang/Object;"))
else:
return Attribute(
javabridge.call(self.jobject, "copy", "(Ljava/lang/String;)Lweka/core/Attribute;", name))
@classmethod
def create_numeric(cls, name):
"""
Creates a numeric attribute.
:param name: the name of the attribute
:type name: str
"""
return Attribute(
javabridge.make_instance(
"weka/core/Attribute", "(Ljava/lang/String;)V", name))
@classmethod
def create_date(cls, name, formt="yyyy-MM-dd'T'HH:mm:ss"):
"""
Creates a date attribute.
:param name: the name of the attribute
:type name: str
:param formt: the date format, see Javadoc for java.text.SimpleDateFormat
:type formt: str
"""
return Attribute(
javabridge.make_instance(
"weka/core/Attribute", "(Ljava/lang/String;Ljava/lang/String;)V", name, formt))
@classmethod
def create_nominal(cls, name, labels):
"""
Creates a nominal attribute.
:param name: the name of the attribute
:type name: str
:param labels: the list of string labels to use
:type labels: list
"""
return Attribute(
javabridge.make_instance(
"weka/core/Attribute", "(Ljava/lang/String;Ljava/util/List;)V", name, javabridge.make_list(labels)))
@classmethod
def create_string(cls, name):
"""
Creates a string attribute.
:param name: the name of the attribute
:type name: str
"""
return Attribute(
javabridge.make_instance(
"weka/core/Attribute", "(Ljava/lang/String;Ljava/util/List;)V", name, None))
class AttributeStats(JavaObject):
"""
Container for attribute statistics.
"""
def __init__(self, jobject):
"""
Initializes the container.
:param jobject: The Java object to wrap
:type jobject: JB_Object
"""
self.enforce_type(jobject, "weka.core.AttributeStats")
super(AttributeStats, self).__init__(jobject)
@property
def distinct_count(self):
"""
The number of distinct values.
:return: The number of distinct values
:rtype: int
"""
return javabridge.get_field(self.jobject, "distinctCount", "I")
@property
def int_count(self):
"""
The number of int-like values.
:return: The number of int-like values
:rtype: int
"""
return javabridge.get_field(self.jobject, "intCount", "I")
@property
def missing_count(self):
"""
The number of missing values.
:return: The number of missing values
:rtype: int
"""
return javabridge.get_field(self.jobject, "missingCount", "I")
@property
def nominal_counts(self):
"""
Counts of each nominal value.
:return: Counts of each nominal value
:rtype: ndarray
"""
return javabridge.get_env().get_int_array_elements(
javabridge.get_field(self.jobject, "nominalCounts", "[I"))
@property
def nominal_weights(self):
"""
Weight mass for each nominal value.
:return: Weight mass for each nominal value
:rtype: ndarray
"""
return javabridge.get_env().get_double_array_elements(
javabridge.get_field(self.jobject, "nominalWeights", "[D"))
@property
def numeric_stats(self):
"""
Stats on numeric value distributions.
:return: Stats on numeric value distributions
:rtype: NumericStats
"""
return Stats(javabridge.get_field(self.jobject, "numericStats", "Lweka/experiment/Stats;"))
@property
def total_count(self):
"""
The total number of values.
:return: The total number of values
:rtype: int
"""
return javabridge.get_field(self.jobject, "totalCount", "I")
@property
def unique_count(self):
"""
The number of values that only appear once.
:return: The number of values that only appear once
:rtype: int
"""
return javabridge.get_field(self.jobject, "uniqueCount", "I")
class Stats(JavaObject):
"""
Container for numeric attribute stats.
"""
def __init__(self, jobject):
"""
Initializes the container.
:param jobject: The Java object to wrap
:type jobject: JB_Object
"""
self.enforce_type(jobject, "weka.experiment.Stats")
super(Stats, self).__init__(jobject)
@property
def count(self):
"""
The number of values seen.
:return: The number of values seen
:rtype: float
"""
return javabridge.get_field(self.jobject, "count", "D")
@property
def min(self):
"""
The minimum value seen, or Double.NaN if no values seen.
:return: The minimum value seen, or Double.NaN if no values seen
:rtype: float
"""
return javabridge.get_field(self.jobject, "min", "D")
@property
def max(self):
"""
The maximum value seen, or Double.NaN if no values seen.
:return: The maximum value seen, or Double.NaN if no values seen
:rtype: float
"""
return javabridge.get_field(self.jobject, "max", "D")
@property
def mean(self):
"""
The mean of values at the last calculateDerived() call.
:return: The mean of values at the last calculateDerived() call
:rtype: float
"""
return javabridge.get_field(self.jobject, "mean", "D")
@property
def stddev(self):
"""
The std deviation of values at the last calculateDerived() call.
:return: The std deviation of values at the last calculateDerived() call
:rtype: float
"""
return javabridge.get_field(self.jobject, "stdDev", "D")
@property
def sum(self):
"""
The sum of values seen.
:return: The sum of values seen
:rtype: float
"""
return javabridge.get_field(self.jobject, "sum", "D")
@property
def sumsq(self):
"""
The sum of values squared seen.
:return: The sum of values squared seen
:rtype: float
"""
return javabridge.get_field(self.jobject, "sumSq", "D")
class InstanceIterator(object):
"""
Iterator for rows in an Instances object.
"""
def __init__(self, data):
"""
Initializes the iterator.
:param data: the Instances object to iterate over
:type data: Instances
"""
self.data = data
self.row = 0
def __iter__(self):
"""
Returns itself.
"""
return self
def next(self):
"""
Returns the next row from the Instances object.
:return: the next Instance object
:rtype: Instance
"""
if self.row < self.data.num_instances:
index = self.row
self.row += 1
return self.data.get_instance(index)
else:
raise StopIteration()
class AttributeIterator(object):
"""
Iterator for attributes in an Instances object.
"""
def __init__(self, data):
"""
Initializes the iterator.
:param data: the Instances object to iterate over
:type data: Instances
"""
self.data = data
self.col = 0
def __iter__(self):
"""
Returns itself.
"""
return self
def next(self):
"""
Returns the next attribute from the Instances object.
:return: the next Attribute object
:rtype: Attribute
"""
if self.col < self.data.num_attributes:
index = self.col
self.col += 1
return self.data.attribute(index)
else:
raise StopIteration()
class InstanceValueIterator(object):
"""
Iterator for values in an Instance object.
"""
def __init__(self, data):
"""
Initializes the iterator.
:param data: the Instance object to iterate over
:type data: Instance
"""
self.data = data
self.col = 0
def __iter__(self):
"""
Returns itself.
"""
return self
def next(self):
"""
Returns the next value from the Instance object.
:return: the next value, depending on the attribute that can be either a number of a string
:rtype: str or float
"""
if self.col < self.data.num_attributes:
index = self.col
self.col += 1
if self.data.dataset.attribute(index).is_numeric:
return self.data.get_value(index)
else:
return self.data.get_string_value(index)
else:
raise StopIteration()
def create_instances_from_lists(x, y, name="data"):
"""
Allows the generation of an Instances object from a list of lists for X and a list for Y.
All data must be numerical. Attributes can be converted to nominal with the
weka.filters.unsupervised.attribute.NumericToNominal filter.
:param x: the input variables (row wise)
:type x: list of list
:param y: the output variable
:type y: list
:param name: the name of the dataset
:type name: str
:return: the generated dataset
:rtype: Instances
"""
if len(x) != len(y):
raise Exception("Dimensions of x and y differ: " + str(len(x)) + " != " + str(len(y)))
# create header
atts = []
for i in xrange(len(x[0])):
atts.append(Attribute.create_numeric("x" + str(i+1)))
atts.append(Attribute.create_numeric("y"))
result = Instances.create_instances(name, atts, len(y))
# add data
for i in xrange(len(x)):
values = x[i][:]
values.append(y[i])
result.add_instance(Instance.create_instance(values))
return result
python-weka-wrapper-0.3.6/python/weka/core/converters.py 0000644 0172670 0172670 00000030027 12677301027 025047 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# converters.py
# Copyright (C) 2014-2015 Fracpete (pythonwekawrapper at gmail dot com)
import javabridge
from weka.core.classes import OptionHandler
from weka.core.capabilities import Capabilities
from weka.core.dataset import Instances, Instance, Attribute
import numpy
import os
class Loader(OptionHandler):
"""
Wrapper class for Loaders.
"""
def __init__(self, classname="weka.core.converters.ArffLoader", jobject=None, options=None):
"""
Initializes the specified loader either using the classname or the JB_Object.
:param classname: the classname of the loader
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
if jobject is None:
jobject = Loader.new_instance(classname)
self.enforce_type(jobject, "weka.core.converters.Loader")
super(Loader, self).__init__(jobject=jobject, options=options)
self.incremental = False
self.structure = None
def __iter__(self):
"""
Returns an iterator in case the loader was instantiated in incremental mode, otherwise
an Exception is raised.
:return: the iterator
:rtype: IncrementalLoaderIterator
"""
if not self.incremental:
raise Exception("Not in incremental mode, cannot iterate!")
return IncrementalLoaderIterator(self, self.structure)
def load_file(self, dfile, incremental=False):
"""
Loads the specified file and returns the Instances object.
In case of incremental loading, only the structure.
:param dfile: the file to load
:type dfile: str
:param incremental: whether to load the dataset incrementally
:type incremental: bool
:return: the full dataset or the header (if incremental)
:rtype: Instances
:raises Exception: if the file does not exist
"""
self.enforce_type(self.jobject, "weka.core.converters.FileSourcedConverter")
self.incremental = incremental
if not javabridge.is_instance_of(dfile, "Ljava/io/File;"):
dfile = javabridge.make_instance(
"Ljava/io/File;", "(Ljava/lang/String;)V", javabridge.get_env().new_string_utf(str(dfile)))
javabridge.call(self.jobject, "reset", "()V")
# check whether file exists, otherwise previously set file gets loaded again
sfile = javabridge.to_string(dfile)
if not os.path.exists(sfile):
raise Exception("Dataset file does not exist: " + str(sfile))
javabridge.call(self.jobject, "setFile", "(Ljava/io/File;)V", dfile)
if incremental:
self.structure = Instances(javabridge.call(self.jobject, "getStructure", "()Lweka/core/Instances;"))
return self.structure
else:
return Instances(javabridge.call(self.jobject, "getDataSet", "()Lweka/core/Instances;"))
def load_url(self, url, incremental=False):
"""
Loads the specified URL and returns the Instances object.
In case of incremental loading, only the structure.
:param url: the URL to load the data from
:type url: str
:param incremental: whether to load the dataset incrementally
:type incremental: bool
:return: the full dataset or the header (if incremental)
:rtype: Instances
"""
self.enforce_type(self.jobject, "weka.core.converters.URLSourcedLoader")
self.incremental = incremental
javabridge.call(self.jobject, "reset", "()V")
javabridge.call(self.jobject, "setURL", "(Ljava/lang/String;)V", str(url))
if incremental:
self.structure = Instances(javabridge.call(self.jobject, "getStructure", "()Lweka/core/Instances;"))
return self.structure
else:
return Instances(javabridge.call(self.jobject, "getDataSet", "()Lweka/core/Instances;"))
class IncrementalLoaderIterator(object):
"""
Iterator for dataset rows when loarding incrementally.
"""
def __init__(self, loader, structure):
"""
:param loader: the loader instance to use for loading the data incrementally
:type loader: Loader
:param structure: the dataset structure
:type structure: Instances
"""
self.loader = loader
self.structure = structure
def __iter__(self):
"""
Returns itself.
"""
return self
def next(self):
"""
Reads the next dataset row.
:return: the next row
:rtype: Instance
"""
result = javabridge.call(
self.loader.jobject, "getNextInstance",
"(Lweka/core/Instances;)Lweka/core/Instance;", self.structure.jobject)
if result is None:
raise StopIteration()
else:
return Instance(result)
class TextDirectoryLoader(OptionHandler):
"""
Wrapper class for TextDirectoryLoader.
"""
def __init__(self, jobject=None, options=None):
"""
Initializes the text directory loader either using a new instance or the JB_Object.
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
if jobject is None:
jobject = TextDirectoryLoader.new_instance("weka.core.converters.TextDirectoryLoader")
self.enforce_type(jobject, "weka.core.converters.TextDirectoryLoader")
super(TextDirectoryLoader, self).__init__(jobject=jobject, options=options)
def load(self):
"""
Loads the text files from the specified directory and returns the Instances object.
In case of incremental loading, only the structure.
:return: the full dataset or the header (if incremental)
:rtype: Instances
"""
javabridge.call(self.jobject, "reset", "()V")
return Instances(javabridge.call(self.jobject, "getDataSet", "()Lweka/core/Instances;"))
class Saver(OptionHandler):
"""
Wrapper class for Savers.
"""
def __init__(self, classname="weka.core.converters.ArffSaver", jobject=None, options=None):
"""
Initializes the specified saver either using the classname or the provided JB_Object.
:param classname: the classname of the saver
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to use
:type options: list
"""
if jobject is None:
jobject = Saver.new_instance(classname)
self.enforce_type(jobject, "weka.core.converters.Saver")
super(Saver, self).__init__(jobject=jobject, options=options)
def capabilities(self):
"""
Returns the capabilities of the saver.
:return: the capabilities
:rtype: Capabilities
"""
return Capabilities(javabridge.call(self.jobject, "getCapabilities", "()Lweka/core/Capabilities;"))
def save_file(self, data, dfile):
"""
Saves the Instances object in the specified file.
:param data: the data to save
:type data: Instances
:param dfile: the file to save the data to
:type dfile: str
"""
self.enforce_type(self.jobject, "weka.core.converters.FileSourcedConverter")
if not javabridge.is_instance_of(dfile, "Ljava/io/File;"):
dfile = javabridge.make_instance(
"Ljava/io/File;", "(Ljava/lang/String;)V", javabridge.get_env().new_string_utf(str(dfile)))
javabridge.call(self.jobject, "setFile", "(Ljava/io/File;)V", dfile)
javabridge.call(self.jobject, "setInstances", "(Lweka/core/Instances;)V", data.jobject)
javabridge.call(self.jobject, "writeBatch", "()V")
def loader_for_file(filename):
"""
Returns a Loader that can load the specified file, based on the file extension. None if failed to determine.
:param filename: the filename to get the loader for
:type filename: str
:return: the assoicated loader instance or None if none found
:rtype: Loader
"""
loader = javabridge.static_call(
"weka/core/converters/ConverterUtils", "getLoaderForFile",
"(Ljava/lang/String;)Lweka/core/converters/AbstractFileLoader;", filename)
if loader is None:
return None
else:
return Loader(jobject=loader)
def load_any_file(filename):
"""
Determines a Loader based on the the file extension. If successful, loads the full dataset and returns it.
:param filename: the name of the file to load
:type filename: str
:return: the
:rtype: Instances
"""
loader = loader_for_file(filename)
if loader is None:
return None
else:
return loader.load_file(filename)
def saver_for_file(filename):
"""
Returns a Saver that can load the specified file, based on the file extension. None if failed to determine.
:param filename: the filename to get the saver for
:type filename: str
:return: the associated saver instance or None if none found
:rtype: Saver
"""
saver = javabridge.static_call(
"weka/core/converters/ConverterUtils", "getSaverForFile",
"(Ljava/lang/String;)Lweka/core/converters/AbstractFileSaver;", filename)
if saver is None:
return None
else:
return Saver(jobject=saver)
def save_any_file(data, filename):
"""
Determines a Saver based on the the file extension. Returns whether successfully saved.
:param filename: the name of the file to save
:type filename: str
:param data: the data to save
:type data: Instances
:return: whether successfully saved
:rtype: bool
"""
saver = saver_for_file(filename)
if saver is None:
return False
else:
saver.save_file(data, filename)
return True
def ndarray_to_instances(array, relation, att_template="Att-#", att_list=None):
"""
Converts the numpy matrix into an Instances object and returns it.
:param array: the numpy ndarray to convert
:type array: numpy.darray
:param relation: the name of the dataset
:type relation: str
:param att_template: the prefix to use for the attribute names, "#" is the 1-based index,
"!" is the 0-based index, "@" the relation name
:type att_template: str
:param att_list: the list of attribute names to use
:type att_list: list
:return: the generated instances object
:rtype: Instances
"""
if len(numpy.shape(array)) != 2:
raise Exception("Number of array dimensions must be 2!")
rows, cols = numpy.shape(array)
# header
atts = []
if att_list is not None:
if len(att_list) != cols:
raise Exception(
"Number columns and provided attribute names differ: " + str(cols) + " != " + len(att_list))
for name in att_list:
att = Attribute.create_numeric(name)
atts.append(att)
else:
for i in xrange(cols):
name = att_template.replace("#", str(i+1)).replace("!", str(i)).replace("@", relation)
att = Attribute.create_numeric(name)
atts.append(att)
result = Instances.create_instances(relation, atts, rows)
# data
for i in xrange(rows):
inst = Instance.create_instance(array[i])
result.add_instance(inst)
return result
python-weka-wrapper-0.3.6/python/weka/core/tokenizers.py 0000644 0172670 0172670 00000005327 12576427676 025100 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# tokenizers.py
# Copyright (C) 2015 Fracpete (pythonwekawrapper at gmail dot com)
import javabridge
from weka.core.classes import OptionHandler
class TokenIterator(object):
"""
Iterator for string tokens.
"""
def __init__(self, tokenizer):
"""
Initializes the iterator.
:param tokenizer: the tokenizer instance to use
:type tokenizer: Tokenizer
"""
self.tokenizer = tokenizer
self.__has_more = javabridge.make_call(self.tokenizer.jobject, "hasMoreElements", "()Z")
self.__next = javabridge.make_call(self.tokenizer.jobject, "nextElement", "()Ljava/lang/String;")
def __iter__(self):
"""
Returns itself.
"""
return self
def next(self):
"""
Reads the next dataset row.
:return: the next row
:rtype: Instance
"""
if not self.__has_more():
raise StopIteration()
else:
return javabridge.get_env().get_string(self.__next())
class Tokenizer(OptionHandler):
"""
Wrapper class for tokenizers.
"""
def __init__(self, classname="weka.core.tokenizers.AlphabeticTokenizer", jobject=None, options=None):
"""
Initializes the specified tokenizer using either the classname or the supplied JB_Object.
:param classname: the classname of the tokenizer
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
if jobject is None:
jobject = Tokenizer.new_instance(classname)
self.enforce_type(jobject, "weka.core.tokenizers.Tokenizer")
super(Tokenizer, self).__init__(jobject=jobject, options=options)
def tokenize(self, s):
"""
Tokenizes the string.
:param s: the string to tokenize
:type s: str
:return: the iterator
:rtype: TokenIterator
"""
javabridge.call(self.jobject, "tokenize", "(Ljava/lang/String;)V", s)
return TokenIterator(self)
python-weka-wrapper-0.3.6/python/weka/core/jvm.py 0000644 0172670 0172670 00000011056 12677673332 023465 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# jvm.py
# Copyright (C) 2014-2016 Fracpete (pythonwekawrapper at gmail dot com)
import javabridge
import os
import glob
import logging
started = None
# logging setup
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)
def add_bundled_jars():
"""
Adds the bundled jars to the JVM's classpath.
"""
# determine lib directory with jars
rootdir = os.path.split(os.path.dirname(__file__))[0]
libdir = rootdir + os.sep + "lib"
# add jars from lib directory
for l in glob.glob(libdir + os.sep + "*.jar"):
if l.lower().find("-src.") == -1:
javabridge.JARS.append(str(l))
def add_weka_packages(alt_path = None):
"""
Adds the jars from all Weka packages to the JVM's classpath.
:param alt_path: an alternative weka home path
:type alt_path: str
"""
if alt_path is None:
prefix = os.getenv("WEKA_HOME", "~" + os.sep + "wekafiles")
else:
prefix = alt_path
package_dir = os.path.expanduser(prefix + os.sep + "packages")
if not os.path.exists(package_dir):
logger.error("package_dir not found: " + package_dir)
return
else:
logger.debug("package_dir=" + package_dir)
# traverse packages
for p in os.listdir(package_dir):
if os.path.isdir(package_dir + os.sep + p):
directory = package_dir + os.sep + p
logger.debug(" directory=" + directory)
# inspect package directory
for l in os.listdir(directory):
if l.lower().endswith(".jar"):
javabridge.JARS.append(directory + os.sep + l)
if l == "lib":
for m in glob.glob(directory + os.sep + "lib" + os.sep + "*.jar"):
javabridge.JARS.append(m)
def add_system_classpath():
"""
Adds the system's classpath to the JVM's classpath.
"""
if os.environ['CLASSPATH'] is not None:
parts = os.environ['CLASSPATH'].split(os.pathsep)
for part in parts:
javabridge.JARS.append(part)
def start(class_path=None, bundled=True, packages=False, system_cp=False, max_heap_size=None):
"""
Initializes the javabridge connection (starts up the JVM).
:param class_path: the additional classpath elements to add
:type class_path: list
:param bundled: whether to add jars from the "lib" directory
:type bundled: bool
:param packages: whether to add jars from Weka packages as well (bool) or an alternative Weka home directory (str)
:type packages: bool or str
:param system_cp: whether to add the system classpath as well
:type system_cp: bool
:param max_heap_size: the maximum heap size (-Xmx parameter, eg 512m or 4g)
:type max_heap_size: str
"""
global started
if started is not None:
logger.info("JVM already running, call jvm.stop() first")
return
# add user-defined jars first
if class_path is not None:
for cp in class_path:
logger.debug("Adding user-supplied classpath=" + cp)
javabridge.JARS.append(cp)
if bundled:
logger.debug("Adding bundled jars")
add_bundled_jars()
if packages is not None:
if isinstance(packages, bool):
if packages:
logger.debug("Adding Weka packages")
add_weka_packages()
else:
logger.debug("Adding Weka packages, using: " + packages)
add_weka_packages(packages)
if system_cp:
logger.debug("Adding system classpath")
add_system_classpath()
logger.debug("Classpath=" + str(javabridge.JARS))
logger.debug("MaxHeapSize=" + ("default" if (max_heap_size is None) else max_heap_size))
javabridge.start_vm(run_headless=True, max_heap_size=max_heap_size)
javabridge.attach()
started = True
def stop():
"""
Kills the JVM.
"""
global started
if started is not None:
started = None
javabridge.kill_vm()
python-weka-wrapper-0.3.6/python/weka/core/serialization.py 0000644 0172670 0172670 00000007746 12576427646 025564 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# serialization.py
# Copyright (C) 2014-2015 Fracpete (pythonwekawrapper at gmail dot com)
import javabridge
import logging
import weka.core.classes as classes
from weka.core.classes import JavaObject
from javabridge.jutil import JavaException
# logging setup
logger = logging.getLogger(__name__)
def deepcopy(obj):
"""
Creates a deep copy of the JavaObject (or derived class) or JB_Object.
:param obj: the object to create a copy of
:type obj: object
:return: the copy, None if failed to copy
:rtype: object
"""
if isinstance(obj, JavaObject):
wrapped = True
jobject = obj.jobject
else:
wrapped = False
jobject = obj
try:
serialized = javabridge.make_instance("weka/core/SerializedObject", "(Ljava/lang/Object;)V", jobject)
jcopy = javabridge.call(serialized, "getObject", "()Ljava/lang/Object;")
if wrapped:
jcopy = obj.__class__(jobject=jcopy)
return jcopy
except JavaException, e:
print("Failed to create copy of " + classes.get_classname(obj) + ": " + str(e))
return None
def read(filename):
"""
Reads the serialized object from disk. Caller must wrap object in appropriate Python wrapper class.
:param filename: the file with the serialized object
:type filename: str
:return: the JB_Object
:rtype: JB_Object
"""
return javabridge.static_call(
"Lweka/core/SerializationHelper;", "read",
"(Ljava/lang/String;)Ljava/lang/Object;",
filename)
def read_all(filename):
"""
Reads the serialized objects from disk. Caller must wrap objects in appropriate Python wrapper classes.
:param filename: the file with the serialized objects
:type filename: str
:return: the list of JB_OBjects
:rtype: list
"""
array = javabridge.static_call(
"Lweka/core/SerializationHelper;", "readAll",
"(Ljava/lang/String;)[Ljava/lang/Object;",
filename)
if array is None:
return None
else:
return javabridge.get_env().get_object_array_elements(array)
def write(filename, jobject):
"""
Serializes the object to disk. JavaObject instances get automatically unwrapped.
:param filename: the file to serialize the object to
:type filename: str
:param jobject: the object to serialize
:type jobject: JB_Object or JavaObject
"""
if isinstance(jobject, JavaObject):
jobject = jobject.jobject
javabridge.static_call(
"Lweka/core/SerializationHelper;", "write",
"(Ljava/lang/String;Ljava/lang/Object;)V",
filename, jobject)
def write_all(filename, jobjects):
"""
Serializes the list of objects to disk. JavaObject instances get automatically unwrapped.
:param filename: the file to serialize the object to
:type filename: str
:param jobjects: the list of objects to serialize
:type jobjects: list
"""
array = javabridge.get_env().make_object_array(len(jobjects), javabridge.get_env().find_class("java/lang/Object"))
for i in xrange(len(jobjects)):
obj = jobjects[i]
if isinstance(obj, JavaObject):
obj = obj.jobject
javabridge.get_env().set_object_array_element(array, i, obj)
javabridge.static_call(
"Lweka/core/SerializationHelper;", "writeAll",
"(Ljava/lang/String;[Ljava/lang/Object;)V",
filename, array)
python-weka-wrapper-0.3.6/python/weka/core/packages.py 0000644 0172670 0172670 00000025221 12677303172 024436 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# packages.py
# Copyright (C) 2014-2015 Fracpete (pythonwekawrapper at gmail dot com)
import javabridge
import weka.core.jvm as jvm
from weka.core.classes import JavaObject
class Package(JavaObject):
"""
Wrapper for the weka.core.packageManagement.Package class.
"""
def __init__(self, jobject):
"""
Initializes the wrapper.
:param jobject: the java Package instance to wrap
:type jobject: JB_Object
"""
self.enforce_type(jobject, "weka.core.packageManagement.Package")
super(Package, self).__init__(jobject)
@property
def name(self):
"""
Returns the name of the package.
:return: the name
:rtype: str
"""
return javabridge.call(self.jobject, "getName", "()Ljava/lang/String;")
@property
def url(self):
"""
Returns the URL of the package.
:return: the url
:rtype: str
"""
return str(JavaObject(javabridge.call(self.jobject, "getPackageURL", "()Ljava/net/URL;")))
@property
def dependencies(self):
"""
Returns the dependencies of the package.
:return: the list of Dependency objects
:rtype: list of Dependency
"""
result = []
dependencies = javabridge.get_collection_wrapper(
javabridge.call(self.jobject, "getDependencies", "()Ljava/util/List;"))
for dependency in dependencies:
result.append(Dependency(dependency))
return result
@property
def metadata(self):
"""
Returns the meta-data.
:return: the meta-data dictionary
:rtype: dict
"""
return javabridge.get_dictionary_wrapper(
javabridge.call(self.jobject, "getPackageMetaData", "()Ljava/util/Map;"))
@property
def is_installed(self):
"""
Returns whether the package is installed.
:return: whether installed
:rtype: bool
"""
return javabridge.call(self.jobject, "isInstalled", "()Z")
def install(self):
"""
Installs the package.
"""
return javabridge.call(self.jobject, "install", "()V")
class PackageConstraint(JavaObject):
"""
Wrapper for the weka.core.packageManagement.PackageConstraint class.
"""
def __init__(self, jobject):
"""
Initializes the wrapper.
:param jobject: the java PackageConstraint instance to wrap
:type jobject: JB_Object
"""
self.enforce_type(jobject, "weka.core.packageManagement.PackageConstraint")
super(PackageConstraint, self).__init__(jobject)
def set_package(self, pkge):
"""
Sets the package.
:param pkge: the package
:type pkge: Package
"""
javabridge.call(self.jobject, "setPackage", "(Lweka/core/packageManagement/Package;)V", pkge.jobject)
def get_package(self):
"""
Returns the package.
:return: the package
:rtype: Package
"""
return Package(javabridge.call(self.jobject, "getPackage", "()Lweka/core/packageManagement/Package;"))
def check_constraint(self, pkge=None, constr=None):
"""
Checks the constraints.
:param pkge: the package to check
:type pkge: Package
:param constr: the package constraint to check
:type constr: PackageConstraint
"""
if not pkge is None:
return javabridge.call(
self.jobject, "checkConstraint", "(Lweka/core/packageManagement/Package;)Z", pkge.jobject)
if not constr is None:
return javabridge.call(
self.jobject, "checkConstraint", "(Lweka/core/packageManagement/PackageConstraint;)Z", pkge.jobject)
raise Exception("Either package or package constraing must be provided!")
class Dependency(JavaObject):
"""
Wrapper for the weka.core.packageManagement.Dependency class.
"""
def __init__(self, jobject):
"""
Initializes the wrapper.
:param jobject: the java Dependency instance to wrap
:type jobject: JB_Object
"""
self.enforce_type(jobject, "weka.core.packageManagement.Dependency")
super(Dependency, self).__init__(jobject)
@property
def source(self):
"""
Returns the source package.
:return: the package
:rtype: Package
"""
return Package(
javabridge.call(self.jobject, "getSource", "()Lweka/core/packageManagement/Package;"))
@source.setter
def source(self, pkge):
"""
Sets the source package.
:param pkge: the package
:type pkge: Package
"""
javabridge.call(
self.jobject, "setSource", "(Lweka/core/packageManagement/Package;)V", pkge.jobject)
@property
def target(self):
"""
Returns the target package constraint.
:return: the package constraint
:rtype: PackageConstraint
"""
return PackageConstraint(
javabridge.call(self.jobject, "getTarget", "()Lweka/core/packageManagement/PackageConstraint;"))
@target.setter
def target(self, constr):
"""
Sets the target package constraint.
:param constr: the package constraint
:type constr: Package
"""
javabridge.call(
self.jobject, "setTarget", "(Lweka/core/packageManagement/PackageConstraint;)V", constr.jobject)
def establish_cache():
"""
Establishes the package cache if necessary.
"""
javabridge.static_call(
"weka/core/WekaPackageManager", "establishCacheIfNeeded", "([Ljava/io/PrintStream;)Ljava/lang/Exception;", [])
def refresh_cache():
"""
Refreshes the cache.
"""
establish_cache()
javabridge.static_call(
"weka/core/WekaPackageManager", "refreshCache", "([Ljava/io/PrintStream;)Ljava/lang/Exception;", [])
def all_packages():
"""
Returns a list of all packages.
:return: the list of packages
:rtype: list
"""
establish_cache()
result = []
pkgs = javabridge.get_collection_wrapper(
javabridge.static_call(
"weka/core/WekaPackageManager", "getAllPackages", "()Ljava/util/List;"))
for pkge in pkgs:
result.append(Package(pkge))
return result
def available_packages():
"""
Returns a list of all packages that aren't installed yet.
:return: the list of packages
:rtype: list
"""
establish_cache()
result = []
pkgs = javabridge.get_collection_wrapper(
javabridge.static_call(
"weka/core/WekaPackageManager", "getAvailablePackages", "()Ljava/util/List;"))
for pkge in pkgs:
result.append(Package(pkge))
return result
def installed_packages():
"""
Returns a list of the installed packages.
:return: the list of packages
:rtype: list
"""
establish_cache()
result = []
pkgs = javabridge.get_collection_wrapper(
javabridge.static_call(
"weka/core/WekaPackageManager", "getInstalledPackages", "()Ljava/util/List;"))
for pkge in pkgs:
result.append(Package(pkge))
return result
def install_package(pkge, version="Latest"):
"""
The list of packages to install.
:param pkge: the name of the repository package, a URL (http/https) or a zip file
:type pkge: str
:param version: in case of the repository packages, the version
:type version: str
:return: whether successfully installed
:rtype: bool
"""
establish_cache()
if pkge.startswith("http://") or pkge.startswith("https://"):
url = javabridge.make_instance(
"java/net/URL", "(Ljava/lang/String;)V", javabridge.get_env().new_string_utf(pkge))
return not javabridge.static_call(
"weka/core/WekaPackageManager", "installPackageFromURL",
"(Ljava/net/URL;[Ljava/io/PrintStream;)Ljava/lang/String;", url, []) is None
elif pkge.lower().endswith(".zip"):
return not javabridge.static_call(
"weka/core/WekaPackageManager", "installPackageFromArchive",
"(Ljava/lang/String;[Ljava/io/PrintStream;)Ljava/lang/String;", pkge, []) is None
else:
return javabridge.static_call(
"weka/core/WekaPackageManager", "installPackageFromRepository",
"(Ljava/lang/String;Ljava/lang/String;[Ljava/io/PrintStream;)Z", pkge, version, [])
def uninstall_package(name):
"""
Uninstalls a package.
:param name: the name of the package
:type name: str
:return: whether successfully uninstalled
:rtype: bool
"""
establish_cache()
javabridge.static_call(
"weka/core/WekaPackageManager", "uninstallPackage",
"(Ljava/lang/String;Z[Ljava/io/PrintStream;)V", name, True, [])
def is_installed(name):
"""
Checks whether a package with the name is already installed.
:param name: the name of the package
:type name: str
:return: whether the package is installed
:rtype: bool
"""
pkgs = installed_packages()
for pkge in pkgs:
if pkge.name == name:
return True
return False
if __name__ == "__main__":
jvm.start()
try:
print("Establish cache")
print("===============")
establish_cache()
print("Refresh cache")
print("=============")
refresh_cache()
print("All packages")
print("============")
packages = all_packages()
for pkg in packages:
print(pkg.name)
print(" url: " + pkg.url)
print("")
print("Available packages")
print("==================")
packages = available_packages()
p = packages[0]
for pkg in packages:
print(pkg.name)
print(" url: " + pkg.url)
print("")
print("Installed packages")
print("==================")
packages = installed_packages()
for pkg in packages:
print(pkg.name)
print(" url: " + pkg.url)
print("")
except Exception, e:
print(e)
finally:
jvm.stop()
python-weka-wrapper-0.3.6/python/weka/core/stopwords.py 0000644 0172670 0172670 00000003705 12576427671 024740 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# stopwords.py
# Copyright (C) 2015 Fracpete (pythonwekawrapper at gmail dot com)
import javabridge
from weka.core.classes import OptionHandler
class Stopwords(OptionHandler):
"""
Wrapper class for stopwords handlers.
"""
def __init__(self, classname="weka.core.stopwords.Null", jobject=None, options=None):
"""
Initializes the specified stopwords handler using either the classname or the supplied JB_Object.
:param classname: the classname of the stopwords handler
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
if jobject is None:
jobject = Stopwords.new_instance(classname)
self.enforce_type(jobject, "weka.core.stopwords.StopwordsHandler")
super(Stopwords, self).__init__(jobject=jobject, options=options)
self.__is_stopword = javabridge.make_call(self.jobject, "isStopword", "(Ljava/lang/String;)Z")
def is_stopword(self, s):
"""
Checks a string whether it is a stopword.
:param s: the string to check
:type s: str
:return: True if a stopword
:rtype: bool
"""
return self.__is_stopword(javabridge.get_env().new_string_utf(s))
python-weka-wrapper-0.3.6/python/weka/core/stemmers.py 0000644 0172670 0172670 00000003650 12576427652 024531 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# stemmers.py
# Copyright (C) 2015 Fracpete (pythonwekawrapper at gmail dot com)
import javabridge
from weka.core.classes import OptionHandler
class Stemmer(OptionHandler):
"""
Wrapper class for stemmers.
"""
def __init__(self, classname="weka.core.stemmers.NullStemmer", jobject=None, options=None):
"""
Initializes the specified stemmer using either the classname or the supplied JB_Object.
:param classname: the classname of the stemmer
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
if jobject is None:
jobject = Stemmer.new_instance(classname)
self.enforce_type(jobject, "weka.core.stemmers.Stemmer")
super(Stemmer, self).__init__(jobject=jobject, options=options)
self.__stem = javabridge.make_call(self.jobject, "stem", "(Ljava/lang/String;)Ljava/lang/String;")
def stem(self, s):
"""
Performs stemming on the string.
:param s: the string to stem
:type s: str
:return: the stemmed string
:rtype: str
"""
return javabridge.get_env().get_string(self.__stem(javabridge.get_env().new_string_utf(s)))
python-weka-wrapper-0.3.6/python/weka/core/types.py 0000644 0172670 0172670 00000005767 12576427711 024045 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# types.py
# Copyright (C) 2014-2015 Fracpete (pythonwekawrapper at gmail dot com)
import javabridge
import logging
import numpy
# logging setup
logger = logging.getLogger(__name__)
def string_array_to_list(a):
"""
Turns the Java string array into Python unicode string list.
:param a: the string array to convert
:type a: JB_Object
:return: the string list
:rtype: list
"""
result = []
length = javabridge.get_env().get_array_length(a)
wrapped = javabridge.get_env().get_object_array_elements(a)
for i in xrange(length):
result.append(javabridge.get_env().get_string(wrapped[i]))
return result
def string_list_to_array(l):
"""
Turns a Python unicode string list into a Java String array.
:param l: the string list
:type: list
:rtype: java string array
:return: JB_Object
"""
result = javabridge.get_env().make_object_array(len(l), javabridge.get_env().find_class("java/lang/String"))
for i in xrange(len(l)):
javabridge.get_env().set_object_array_element(result, i, javabridge.get_env().new_string_utf(l[i]))
return result
def double_matrix_to_ndarray(m):
"""
Turns the Java matrix (2-dim array) of doubles into a numpy 2-dim array.
:param m: the double matrix
:type: JB_Object
:return: Numpy array
:rtype: numpy.darray
"""
rows = javabridge.get_env().get_object_array_elements(m)
num = javabridge.get_env().get_array_length(m)
result = numpy.zeros(num * num).reshape((num, num))
i = 0
for row in rows:
elements = javabridge.get_env().get_double_array_elements(row)
n = 0
for element in elements:
result[i][n] = element
n += 1
i += 1
return result
def enumeration_to_list(enm):
"""
Turns the java.util.Enumeration into a list.
:param enm: the enumeration to convert
:type enm: JB_Object
:return: the list
:rtype: list
"""
result = []
while javabridge.call(enm, "hasMoreElements", "()Z"):
result.append(javabridge.call(enm, "nextElement", "()Ljava/lang/Object;"))
return result
def double_to_float(d):
"""
Turns the Python float into a Java java.lang.Float object.
:param d: the Python float
:type d: float
:return: the Float object
:rtype: JB_Object
"""
return javabridge.make_instance("java/lang/Float", "(D)V", d)
python-weka-wrapper-0.3.6/python/weka/core/capabilities.py 0000644 0172670 0172670 00000035674 12576426714 025334 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# capabilities.py
# Copyright (C) 2014-2015 Fracpete (pythonwekawrapper at gmail dot com)
import javabridge
from weka.core.classes import JavaObject, Enum
from weka.core.dataset import Attribute, Instances
class Capability(Enum):
"""
Wrapper for a Capability.
"""
def __init__(self, jobject=None, member=None):
"""
Initializes the wrapper with the specified Capability object.
:param jobject: the Capability object to wrap
:type jobject: JB_Object
:param member: the capability string to parse
:type member: str
"""
super(Capability, self).__init__(\
jobject=jobject, enum="weka.core.Capabilities$Capability", member=member)
@property
def is_attribute(self):
"""
Returns whether this capability is an attribute.
:return: whether it is an attribute
:rtype: bool
"""
return javabridge.call(self.jobject, "isAttribute", "()Z")
@property
def is_attribute_capability(self):
"""
Returns whether this capability is an attribute capability.
:return: whether it is an attribute capability
:rtype: bool
"""
return javabridge.call(self.jobject, "isAttributeCapability", "()Z")
@property
def is_class(self):
"""
Returns whether this capability is a class.
:return: whether it is a class
:rtype: bool
"""
return javabridge.call(self.jobject, "isClass", "()Z")
@property
def is_class_capability(self):
"""
Returns whether this capability is a class capability.
:return: whether it is a class capability
:rtype: bool
"""
return javabridge.call(self.jobject, "isClassCapability", "()Z")
@property
def is_other_capability(self):
"""
Returns whether this capability is an other capability.
:return: whether it is an other capability
:rtype: bool
"""
return javabridge.call(self.jobject, "isOtherCapability", "()Z")
class Capabilities(JavaObject):
"""
Wrapper for Capabilities.
"""
def __init__(self, jobject=None, owner=None):
"""
Initializes the wrapper with the specified Capabilities object.
:param jobject: the Capabilities object to wrap, instantiates a new one if None
:type jobject: JB_Object
:param owner: the weka.core.CapabilitiesHandler object to use as owner, can be None
:type owner: JB_Object or JavaObject
"""
if jobject is None:
if isinstance(owner, JavaObject):
owner = owner.jobject
jobject = javabridge.make_instance(
"weka/core/Capabilities", "(Lweka/core/CapabilitiesHandler;)V", owner)
Capabilities.enforce_type(jobject, "weka.core.Capabilities")
super(Capabilities, self).__init__(jobject)
@property
def owner(self):
"""
Returns the owner of these capabilities, if any.
:return: the owner, can be None
:rtype: JavaObject
"""
obj = javabridge.call(self.jobject, "getOwner", "()Lweka/core/CapabilitiesHandler;")
if obj is None:
return None
else:
return JavaObject(jobject=obj)
@owner.setter
def owner(self, obj):
"""
Sets the new owner.
:param obj: the new owner
:type obj: JavaObject or JB_Object
"""
if isinstance(obj, JavaObject):
obj = obj.jobject
javabridge.call(self.jobject, "setOwner", "(Lweka/core/CapabilitiesHandler;)V", obj)
def capabilities(self):
"""
Returns all the capabilities.
:return: all capabilities
:rtype: list
"""
result = []
iterator = javabridge.iterate_java(javabridge.call(self.jobject, "capabilities", "()Ljava/util/Iterator;"))
for c in iterator:
result.append(Capability(c))
return result
def attribute_capabilities(self):
"""
Returns all the attribute capabilities.
:return: attribute capabilities
:rtype: Capabilities
"""
return Capabilities(
javabridge.call(self.jobject, "getAttributeCapabilities", "()Lweka/core/Capabilities;"))
def class_capabilities(self):
"""
Returns all the class capabilities.
:return: class capabilities
:rtype: Capabilities
"""
return Capabilities(
javabridge.call(self.jobject, "getClassCapabilities", "()Lweka/core/Capabilities;"))
def other_capabilities(self):
"""
Returns all other capabilities.
:return: all other capabilities
:rtype: Capabilities
"""
return Capabilities(
javabridge.call(self.jobject, "getOtherCapabilities", "()Lweka/core/Capabilities;"))
def dependencies(self):
"""
Returns all the dependencies.
:return: the dependency list
:rtype: list
"""
result = []
iterator = javabridge.iterate_java(javabridge.call(self.jobject, "dependencies", "()Ljava/util/Iterator;"))
for c in iterator:
result.append(Capability(c))
return result
def handles(self, capability):
"""
Returns whether the specified capability is set.
:param capability: the capability to check
:type capability: Capability
:return: whether the capability is set
:rtype: bool
"""
return javabridge.call(
self.jobject, "handles", "(Lweka/core/Capabilities$Capability;)Z", capability.jobject)
def enable_all(self):
"""
enables all capabilities.
"""
javabridge.call(self.jobject, "enableAll", "()V")
def enable_all_attributes(self):
"""
enables all attributes.
"""
javabridge.call(self.jobject, "enableAllAttributes", "()V")
def enable_all_classes(self):
"""
enables all classes.
"""
javabridge.call(self.jobject, "enableAllClasses", "()V")
def enable(self, capability):
"""
enables the specified capability.
:param capability: the capability to enable
:type capability: Capability
"""
javabridge.call(self.jobject, "enable", "(Lweka/core/Capabilities$Capability;)V", capability.jobject)
def enable_all_attribute_dependencies(self):
"""
enables all attribute dependencies.
"""
javabridge.call(self.jobject, "enableAllAttributeDependencies", "()V")
def enable_all_class_dependencies(self):
"""
enables all class dependencies.
"""
javabridge.call(self.jobject, "enableAllClassDependencies", "()V")
def enable_dependency(self, capability):
"""
enables the dependency of the given capability enabling NOMINAL_ATTRIBUTES also enables
BINARY_ATTRIBUTES, UNARY_ATTRIBUTES and EMPTY_NOMINAL_ATTRIBUTES.
:param capability: the dependency to enable
:type capability: Capability
"""
javabridge.call(
self.jobject, "enableDependency", "(Lweka/core/Capabilities$Capability;)V", capability.jobject)
def disable_all(self):
"""
Disables all capabilities.
"""
javabridge.call(self.jobject, "disableAll", "()V")
def disable_all_attributes(self):
"""
Disables all attributes.
"""
javabridge.call(self.jobject, "disableAllAttributes", "()V")
def disable_all_classes(self):
"""
Disables all classes.
"""
javabridge.call(self.jobject, "disableAllClasses", "()V")
def disable(self, capability):
"""
Disables the specified capability.
:param capability: the capability to disable
:type capability: Capability
"""
javabridge.call(self.jobject, "disable", "(Lweka/core/Capabilities$Capability;)V", capability.jobject)
def disable_all_attribute_dependencies(self):
"""
Disables all attribute dependencies.
"""
javabridge.call(self.jobject, "disableAllAttributeDependencies", "()V")
def disable_all_class_dependencies(self):
"""
Disables all class dependencies.
"""
javabridge.call(self.jobject, "disableAllClassDependencies", "()V")
def disable_dependency(self, capability):
"""
Disables the dependency of the given capability Disabling NOMINAL_ATTRIBUTES also disables
BINARY_ATTRIBUTES, UNARY_ATTRIBUTES and EMPTY_NOMINAL_ATTRIBUTES.
:param capability: the dependency to disable
:type capability: Capability
"""
javabridge.call(
self.jobject, "disableDependency", "(Lweka/core/Capabilities$Capability;)V", capability.jobject)
def has_dependencies(self):
"""
Returns whether any dependencies are set.
:return: whether any dependecies are set
:rtype: bool
"""
return javabridge.call(self.jobject, "hasDependencies", "()Z")
def has_dependency(self, capability):
"""
Returns whether the specified dependency is set.
:param capability: the capability to check
:type capability: Capability
:return: whether the dependency is set
:rtype: bool
"""
return javabridge.call(
self.jobject, "hasDependency", "(Lweka/core/Capabilities$Capability;)Z", capability.jobject)
def supports(self, capabilities):
"""
Returns true if the currently set capabilities support at least all of the capabiliites of the given
Capabilities object (checks only the enum!)
:param capabilities: the capabilities to check
:type capabilities: Capabilities
:return: whether the current capabilities support at least the specified ones
:rtype: bool
"""
return javabridge.call(self.jobject, "supports", "(Lweka/core/Capabilities;)Z", capabilities.jobject)
def supports_maybe(self, capabilities):
"""
Returns true if the currently set capabilities support (or have a dependency) at least all of the
capabilities of the given Capabilities object (checks only the enum!)
:param capabilities: the capabilities to check
:type capabilities: Capabilities
:return: whether the current capabilities (potentially) support the specified ones
:rtype: bool
"""
return javabridge.call(self.jobject, "supportsMaybe", "(Lweka/core/Capabilities;)Z", capabilities.jobject)
@property
def min_instances(self):
"""
Returns the minimum number of instances that must be supported.
:return: the minimum number
:rtype: int
"""
return javabridge.call(self.jobject, "getMinimumNumberInstances", "()I")
@min_instances.setter
def min_instances(self, minimum):
"""
Sets the minimum number of instances that must be supported.
:param minimum: the minimum number
:type minimum: int
"""
javabridge.call(self.jobject, "setMinimumNumberInstances", "(I)V", minimum)
def test_attribute(self, att, is_class=None, fail=False):
"""
Tests whether the attribute meets the conditions.
:param att: the Attribute to test
:type att: Attribute
:param is_class: whether this attribute is the class attribute
:type is_class: bool
:param fail: whether to fail with an exception in case the test fails
:type fail: bool
:return: whether the attribute meets the conditions
:rtype: bool
"""
if fail:
if is_class is None:
return javabridge.call(
self.jobject, "test", "(Lweka/core/Attribute;)Z", att.jobject)
else:
return javabridge.call(
self.jobject, "test", "(Lweka/core/Attribute;Z)Z", att.jobject, is_class)
else:
if is_class is None:
return javabridge.call(
self.jobject, "testWithFail", "(Lweka/core/Attribute;)Z", att.jobject)
else:
return javabridge.call(
self.jobject, "testWithFail", "(Lweka/core/Attribute;Z)Z", att.jobject, is_class)
def test_instances(self, data, from_index=None, to_index=None, fail=False):
"""
Tests whether the dataset meets the conditions.
:param data: the Instances to test
:type data: Instances
:param from_index: the first attribute to include
:type from_index: int
:param to_index: the last attribute to include
:type to_index: int
:return: wether the dataset meets the requirements
:rtype: bool
"""
if fail:
if (from_index is None) or (to_index is None):
return javabridge.call(
self.jobject, "test", "(Lweka/core/Instances;)Z", data.jobject)
else:
return javabridge.call(
self.jobject, "test", "(Lweka/core/Instances;II)Z", data.jobject, from_index, to_index)
else:
if (from_index is None) or (to_index is None):
return javabridge.call(
self.jobject, "testWithFail", "(Lweka/core/Instances;)Z", data.jobject)
else:
return javabridge.call(
self.jobject, "testWithFail", "(Lweka/core/Instances;II)Z", data.jobject, from_index, to_index)
@classmethod
def for_instances(cls, data, multi=None):
"""
returns a Capabilities object specific for this data. The minimum number of instances is not set, the check
for multi-instance data is optional.
:param data: the data to generate the capabilities for
:type data: Instances
:param multi: whether to check the structure, too
:type multi: bool
:return: the generated capabilities
:rtype: Capabilities
"""
if multi is None:
return Capabilities(javabridge.static_call(
"weka/core/Capabilities", "forInstances",
"(Lweka/core/Instances;)Lweka/core/Capabilities;", data.jobject))
else:
return Capabilities(javabridge.static_call(
"weka/core/Capabilities", "forInstances",
"(Lweka/core/Instances;Z)Lweka/core/Capabilities;", data.jobject, multi))
python-weka-wrapper-0.3.6/python/weka/core/__init__.py 0000644 0172670 0172670 00000001522 12456015042 024404 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# __init__.py
# Copyright (C) 2015 Fracpete (pythonwekawrapper at gmail dot com)
# check whether scipy is there
scipy_available = False
try:
import scipy
scipy_available = True
except ImportError:
pass
python-weka-wrapper-0.3.6/python/weka/core/version.py 0000644 0172670 0172670 00000001732 12576427713 024354 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# version.py
# Copyright (C) 2014-2015 Fracpete (pythonwekawrapper at gmail dot com)
from weka.core.classes import JavaObject
def weka_version():
"""
Determines the version of Weka in use.
:return: the version
:rtype: str
"""
jobj = JavaObject(JavaObject.new_instance(classname="weka.core.Version"))
return str(jobj)
python-weka-wrapper-0.3.6/python/weka/classifiers.py 0000644 0172670 0172670 00000212523 12652561707 024245 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# classifiers.py
# Copyright (C) 2014-2016 Fracpete (pythonwekawrapper at gmail dot com)
import sys
import os
import javabridge
import logging
import argparse
import weka.core.jvm as jvm
import weka.core.types as arrays
import weka.core.classes as classes
from numpy import *
from weka.core.classes import JavaObject, join_options, OptionHandler, Random, SelectedTag, Tags, Tag, JavaArray
from weka.core.classes import AbstractParameter
from weka.core.capabilities import Capabilities
from weka.core.dataset import Instances, Instance, Attribute
from weka.filters import Filter
# logging setup
logger = logging.getLogger("weka.classifiers")
class Classifier(OptionHandler):
"""
Wrapper class for classifiers.
"""
def __init__(self, classname="weka.classifiers.rules.ZeroR", jobject=None, options=None):
"""
Initializes the specified classifier using either the classname or the supplied JB_Object.
:param classname: the classname of the classifier
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
if jobject is None:
jobject = Classifier.new_instance(classname)
self.enforce_type(jobject, "weka.classifiers.Classifier")
self.is_updateable = self.check_type(jobject, "weka.classifiers.UpdateableClassifier")
self.is_drawable = self.check_type(jobject, "weka.core.Drawable")
self.is_batchpredictor = self.check_type(jobject, "weka.core.BatchPredictor")
super(Classifier, self).__init__(jobject=jobject, options=options)
self.__classify = javabridge.make_call(self.jobject, "classifyInstance", "(Lweka/core/Instance;)D")
self.__distribution = javabridge.make_call(self.jobject, "distributionForInstance", "(Lweka/core/Instance;)[D")
if self.is_batchpredictor:
self.__distributions = javabridge.make_call(
self.jobject, "distributionsForInstances", "(Lweka/core/Instances;)[[D")
@property
def capabilities(self):
"""
Returns the capabilities of the classifier.
:return: the capabilities
:rtype: Capabilities
"""
return Capabilities(javabridge.call(self.jobject, "getCapabilities", "()Lweka/core/Capabilities;"))
def build_classifier(self, data):
"""
Builds the classifier with the data.
:param data: the data to train the classifier with
:type data: Instances
"""
javabridge.call(self.jobject, "buildClassifier", "(Lweka/core/Instances;)V", data.jobject)
def update_classifier(self, inst):
"""
Updates the classifier with the instance.
:param inst: the Instance to update the classifier with
:type inst: Instance
"""
if self.is_updateable:
javabridge.call(self.jobject, "updateClassifier", "(Lweka/core/Instance;)V", inst.jobject)
else:
logger.critical(classes.get_classname(self.jobject) + " is not updateable!")
def classify_instance(self, inst):
"""
Peforms a prediction.
:param inst: the Instance to get a prediction for
:type inst: Instance
:return: the classification (either regression value or 0-based label index)
:rtype: float
"""
return self.__classify(inst.jobject)
def distribution_for_instance(self, inst):
"""
Peforms a prediction, returning the class distribution.
:param inst: the Instance to get the class distribution for
:type inst: Instance
:return: the class distribution array
:rtype: ndarray
"""
pred = self.__distribution(inst.jobject)
return javabridge.get_env().get_double_array_elements(pred)
def distributions_for_instances(self, data):
"""
Peforms predictions, returning the class distributions.
:param data: the Instances to get the class distributions for
:type data: Instances
:return: the class distribution matrix, None if not a batch predictor
:rtype: ndarray
"""
if self.is_batchpredictor:
return arrays.double_matrix_to_ndarray(self.__distributions(data.jobject))
else:
return None
@property
def batch_size(self):
"""
Returns the batch size, in case this classifier is a batch predictor.
:return: the batch size, None if not a batch predictor
:rtype: str
"""
if self.is_batchpredictor:
return javabridge.call(self.jobject, "getBatchSize", "()Ljava/lang/String;")
else:
return None
@batch_size.setter
def batch_size(self, size):
"""
Sets the batch size, in case this classifier is a batch predictor.
:param size: the size of the batch
:type size: str
"""
if self.is_batchpredictor:
javabridge.call(self.jobject, "setBatchSize", "(Ljava/lang/String;)V", size)
def has_efficient_batch_prediction(self):
"""
Returns whether the classifier implements a more efficient batch prediction.
:return: True if a more efficient batch prediction is implemented, always False if not batch predictor
:rtype: bool
"""
if self.is_batchpredictor:
return javabridge.call(self.jobject, "implementsMoreEfficientBatchPrediction", "()Z")
else:
return False
@property
def graph_type(self):
"""
Returns the graph type if classifier implements weka.core.Drawable, otherwise -1.
:return: the type
:rtype: int
"""
if self.is_drawable:
return javabridge.call(self.jobject, "graphType", "()I")
else:
return -1
@property
def graph(self):
"""
Returns the graph if classifier implements weka.core.Drawable, otherwise None.
:return: the generated graph string
:rtype: str
"""
if self.is_drawable:
return javabridge.call(self.jobject, "graph", "()Ljava/lang/String;")
else:
return None
@classmethod
def make_copy(cls, classifier):
"""
Creates a copy of the classifier.
:param classifier: the classifier to copy
:type classifier: Classifier
:return: the copy of the classifier
:rtype: Classifier
"""
return Classifier(
jobject=javabridge.static_call(
"weka/classifiers/AbstractClassifier", "makeCopy",
"(Lweka/classifiers/Classifier;)Lweka/classifiers/Classifier;", classifier.jobject))
class SingleClassifierEnhancer(Classifier):
"""
Wrapper class for classifiers that use a single base classifier.
"""
def __init__(self, classname=None, jobject=None, options=None):
"""
Initializes the specified classifier using either the classname or the supplied JB_Object.
:param classname: the classname of the classifier
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
if jobject is None:
jobject = Classifier.new_instance(classname)
self.enforce_type(jobject, "weka.classifiers.SingleClassifierEnhancer")
super(SingleClassifierEnhancer, self).__init__(classname=classname, jobject=jobject, options=options)
@property
def classifier(self):
"""
Returns the base classifier.
;return: the base classifier
:rtype: Classifier
"""
return Classifier(jobject=javabridge.call(self.jobject, "getClassifier", "()Lweka/classifiers/Classifier;"))
@classifier.setter
def classifier(self, classifier):
"""
Sets the base classifier.
:param classifier: the base classifier to use
:type classifier: Classifier
"""
javabridge.call(self.jobject, "setClassifier", "(Lweka/classifiers/Classifier;)V", classifier.jobject)
class FilteredClassifier(SingleClassifierEnhancer):
"""
Wrapper class for the filtered classifier.
"""
def __init__(self, jobject=None, options=None):
"""
Initializes the specified classifier using its classname or the supplied JB_Object.
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
classname = "weka.classifiers.meta.FilteredClassifier"
if jobject is None:
jobject = Classifier.new_instance(classname)
else:
self.enforce_type(jobject, classname)
super(FilteredClassifier, self).__init__(jobject=jobject, options=options)
@property
def filter(self):
"""
Returns the filter.
:return: the filter in use
:rtype: Filter
"""
return Filter(jobject=javabridge.call(self.jobject, "getFilter", "()Lweka/filters/Filter;"))
@filter.setter
def filter(self, filtr):
"""
Sets the filter.
:param filtr: the filter to use
:type filtr: Filter
"""
javabridge.call(self.jobject, "setFilter", "(Lweka/filters/Filter;)V", filtr.jobject)
class GridSearch(SingleClassifierEnhancer):
"""
Wrapper class for the GridSearch meta-classifier.
"""
def __init__(self, jobject=None, options=None):
"""
Initializes the specified classifier using its classname or the supplied JB_Object.
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
classname = "weka.classifiers.meta.GridSearch"
if jobject is None:
jobject = GridSearch.new_instance(classname)
if jobject is None:
raise Exception(
"Failed to instantiate GridSearch - package installed and jvm started with package support?")
else:
self.enforce_type(jobject, classname)
super(GridSearch, self).__init__(jobject=jobject, options=options)
self.tags_evaluation = Tags.get_tags("weka.classifiers.meta.GridSearch", "TAGS_EVALUATION")
@property
def evaluation(self):
"""
Returns the currently set statistic used for evaluation.
:return: the statistic
:rtype: SelectedTag
"""
return SelectedTag(
javabridge.call(self.jobject, "getEvaluation", "()Lweka/core/SelectedTag;"))
@evaluation.setter
def evaluation(self, evl):
"""
Sets the statistic to use for evaluation.
:param evl: the statistic
:type evl: SelectedTag, Tag or str
"""
if isinstance(evl, str):
evl = self.tags_evaluation.find(evl)
if isinstance(evl, Tag):
evl = SelectedTag(tag_id=evl.ident, tags=self.tags_evaluation)
javabridge.call(self.jobject, "setEvaluation", "(Lweka/core/SelectedTag;)V", evl.jobject)
@property
def x(self):
"""
Returns a dictionary with all the current values for the X of the grid.
Keys for the dictionary: property, min, max, step, base, expression
Types: property=str, min=float, max=float, step=float, base=float, expression=str
:return: the dictionary with the parameters
:rtype: dict
"""
result = {}
result["property"] = javabridge.call(self.jobject, "getXProperty", "()Ljava/lang/String;")
result["min"] = javabridge.call(self.jobject, "getXMin", "()D")
result["max"] = javabridge.call(self.jobject, "getXMax", "()D")
result["step"] = javabridge.call(self.jobject, "getXStep", "()D")
result["base"] = javabridge.call(self.jobject, "getXBase", "()D")
result["expression"] = javabridge.call(self.jobject, "getXExpression", "()Ljava/lang/String;")
return result
@x.setter
def x(self, d):
"""
Allows to configure the X of the grid with one method call.
Keys for the dictionary: property, min, max, step, base, expression
Types: property=str, min=float, max=float, step=float, base=float, expression=str
:param d: the dictionary with the parameters
:type d: dict
"""
if "property" in d:
javabridge.call(self.jobject, "setXProperty", "(Ljava/lang/String;)V", d["property"])
if "min" in d:
javabridge.call(self.jobject, "setXMin", "(D)V", d["min"])
if "max" in d:
javabridge.call(self.jobject, "setXMax", "(D)V", d["max"])
if "step" in d:
javabridge.call(self.jobject, "setXStep", "(D)V", d["step"])
if "base" in d:
javabridge.call(self.jobject, "setXBase", "(D)V", d["base"])
if "expression" in d:
javabridge.call(self.jobject, "setXExpression", "(Ljava/lang/String;)V", d["expression"])
@property
def y(self):
"""
Returns a dictionary with all the current values for the Y of the grid.
Keys for the dictionary: property, min, max, step, base, expression
Types: property=str, min=float, max=float, step=float, base=float, expression=str
:return: the dictionary with the parameters
:rtype: dict
"""
result = {}
result["property"] = javabridge.call(self.jobject, "getYProperty", "()Ljava/lang/String;")
result["min"] = javabridge.call(self.jobject, "getYMin", "()D")
result["max"] = javabridge.call(self.jobject, "getYMax", "()D")
result["step"] = javabridge.call(self.jobject, "getYStep", "()D")
result["base"] = javabridge.call(self.jobject, "getYBase", "()D")
result["expression"] = javabridge.call(self.jobject, "getYExpression", "()Ljava/lang/String;")
return result
@y.setter
def y(self, d):
"""
Allows to configure the Y of the grid with one method call.
Keys for the dictionary: property, min, max, step, base, expression
Types: property=str, min=float, max=float, step=float, base=float, expression=str
:param d: the dictionary with the parameters
:type d: dict
"""
if "property" in d:
javabridge.call(self.jobject, "setYProperty", "(Ljava/lang/String;)V", d["property"])
if "min" in d:
javabridge.call(self.jobject, "setYMin", "(D)V", d["min"])
if "max" in d:
javabridge.call(self.jobject, "setYMax", "(D)V", d["max"])
if "step" in d:
javabridge.call(self.jobject, "setYStep", "(D)V", d["step"])
if "base" in d:
javabridge.call(self.jobject, "setYBase", "(D)V", d["base"])
if "expression" in d:
javabridge.call(self.jobject, "setYExpression", "(Ljava/lang/String;)V", d["expression"])
@property
def best(self):
"""
Returns the best classifier setup found during the th search.
:return: the best classifier setup
:rtype: Classifier
"""
return Classifier(jobject=javabridge.call(self.jobject, "getBestClassifier", "()Lweka/classifiers/Classifier;"))
class MultiSearch(SingleClassifierEnhancer):
"""
Wrapper class for the MultiSearch meta-classifier.
NB: 'multi-search-weka-package' must be installed (https://github.com/fracpete/multisearch-weka-package),
version 2016.1.15 or later.
"""
def __init__(self, jobject=None, options=None):
"""
Initializes the specified classifier using its classname or the supplied JB_Object.
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
classname = "weka.classifiers.meta.MultiSearch"
if jobject is None:
jobject = MultiSearch.new_instance(classname)
else:
self.enforce_type(jobject, classname)
super(MultiSearch, self).__init__(jobject=jobject, options=options)
self.tags_evaluation = Tags.get_object_tags(self, "getMetricsTags")
@property
def evaluation(self):
"""
Returns the currently set statistic used for evaluation.
:return: the statistic
:rtype: SelectedTag
"""
return SelectedTag(
javabridge.call(self.jobject, "getEvaluation", "()Lweka/core/SelectedTag;"))
@evaluation.setter
def evaluation(self, evl):
"""
Sets the statistic to use for evaluation.
:param evl: the statistic
:type evl: SelectedTag, Tag or str
"""
if isinstance(evl, str):
evl = self.tags_evaluation.find(evl)
if isinstance(evl, Tag):
evl = SelectedTag(tag_id=evl.ident, tags=self.tags_evaluation)
javabridge.call(self.jobject, "setEvaluation", "(Lweka/core/SelectedTag;)V", evl.jobject)
@property
def parameters(self):
"""
Returns the list of currently set search parameters.
:return: the list of AbstractSearchParameter objects
:rtype: list
"""
array = JavaArray(
javabridge.call(self.jobject, "getSearchParameters", "()[Lweka/core/setupgenerator/AbstractParameter;"))
result = []
for item in array:
result.append(AbstractParameter(jobject=item.jobject))
return result
@parameters.setter
def parameters(self, params):
"""
Sets the list of search parameters to use.
:param params: list of AbstractSearchParameter objects
:type params: list
"""
array = JavaArray(JavaArray.new_instance("weka.core.setupgenerator.AbstractParameter", len(params)))
for idx, obj in enumerate(params):
array[idx] = obj.jobject
javabridge.call(self.jobject, "setSearchParameters", "([Lweka/core/setupgenerator/AbstractParameter;)V", array.jobject)
@property
def best(self):
"""
Returns the best classifier setup found during the th search.
:return: the best classifier setup
:rtype: Classifier
"""
return Classifier(jobject=javabridge.call(self.jobject, "getBestClassifier", "()Lweka/classifiers/Classifier;"))
class MultipleClassifiersCombiner(Classifier):
"""
Wrapper class for classifiers that use a multiple base classifiers.
"""
def __init__(self, classname=None, jobject=None, options=None):
"""
Initializes the specified classifier using either the classname or the supplied JB_Object.
:param classname: the classname of the classifier
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: list of commandline options
:type options: list
"""
if jobject is None:
jobject = Classifier.new_instance(classname)
self.enforce_type(jobject, "weka.classifiers.MultipleClassifiersCombiner")
super(MultipleClassifiersCombiner, self).__init__(classname=classname, jobject=jobject, options=options)
@property
def classifiers(self):
"""
Returns the list of base classifiers.
:return: the classifier list
:rtype: list
"""
objects = javabridge.get_env().get_object_array_elements(
javabridge.call(self.jobject, "getClassifiers", "()[Lweka/classifiers/Classifier;"))
result = []
for obj in objects:
result.append(Classifier(jobject=obj))
return result
@classifiers.setter
def classifiers(self, classifiers):
"""
Sets the base classifiers.
:param classifiers: the list of base classifiers to use
:type classifiers: list
"""
obj = []
for classifier in classifiers:
obj.append(classifier.jobject)
javabridge.call(self.jobject, "setClassifiers", "([Lweka/classifiers/Classifier;)V", obj)
class Kernel(OptionHandler):
"""
Wrapper class for kernels.
"""
def __init__(self, classname=None, jobject=None, options=None):
"""
Initializes the specified kernel using either the classname or the supplied JB_Object.
:param classname: the classname of the kernel
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
if jobject is None:
jobject = Classifier.new_instance(classname)
self.enforce_type(jobject, "weka.classifiers.functions.supportVector.Kernel")
super(Kernel, self).__init__(jobject=jobject, options=options)
def capabilities(self):
"""
Returns the capabilities of the classifier.
:return: the capabilities
:rtype: Capabilities
"""
return Capabilities(javabridge.call(self.jobject, "getCapabilities", "()Lweka/core/Capabilities;"))
@property
def checks_turned_off(self):
"""
Returns whether checks are turned off.
:return: True if checks turned off
:rtype: bool
"""
return javabridge.call(self.jobject, "getChecksTurnedOff", "()Z")
@checks_turned_off.setter
def checks_turned_off(self, off):
"""
Turns any checks on/off.
:param off: True to turn off checks
:type off: bool
"""
javabridge.call(self.jobject, "setChecksTurnedOff", "(Z)V", off)
def clean(self):
"""
Frees the memory used by the kernel.
"""
javabridge.call(self.jobject, "clean", "()V")
def build_kernel(self, data):
"""
Builds the classifier with the data.
:param data: the data to train the classifier with
:type data: Instances
"""
javabridge.call(self.jobject, "buildKernel", "(Lweka/core/Instances;)V", data.jobject)
def eval(self, id1, id2, inst1):
"""
Computes the result of the kernel function for two instances. If id1 == -1, eval use inst1 instead of an
instance in the dataset.
:param id1: the index of the first instance in the dataset
:type id1: int
:param id2: the index of the second instance in the dataset
:type id2: int
:param inst1: the instance corresponding to id1 (used if id1 == -1)
:type inst1: Instance
"""
jinst1 = None
if inst1 is not None:
jinst1 = inst1.jobject
return javabridge.call(self.jobject, "eval", "(IILweka/core/Instance;)D", id1, id2, jinst1)
@classmethod
def make_copy(cls, kernel):
"""
Creates a copy of the kernel.
:param kernel: the kernel to copy
:type kernel: Kernel
:return: the copy of the kernel
:rtype: Kernel
"""
return Kernel(
jobject=javabridge.static_call(
"weka/classifiers/functions/supportVector/Kernel", "makeCopy",
"(Lweka/classifiers/functions/supportVector/Kernel;)Lweka/classifiers/functions/supportVector/Kernel;",
kernel.jobject))
class KernelClassifier(Classifier):
"""
Wrapper class for classifiers that have a kernel property, like SMO.
"""
def __init__(self, classname=None, jobject=None, options=None):
"""
Initializes the specified classifier using either the classname or the supplied JB_Object.
:param classname: the classname of the classifier
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: list of commandline options
:type options: list
"""
if jobject is None:
jobject = Classifier.new_instance(classname)
self.enforce_type(jobject, "weka.classifiers.Classifier")
if not javabridge.static_call(
"weka/classifiers/KernelHelper", "hasKernelProperty",
"(Ljava/lang/Object;)Z",
jobject):
raise Exception("Does not handle a kernel: " + classes.get_classname(jobject))
super(KernelClassifier, self).__init__(classname=classname, jobject=jobject, options=options)
@property
def kernel(self):
"""
Returns the current kernel.
:return: the kernel or None if none found
:rtype: Kernel
"""
result = javabridge.static_call(
"weka/classifiers/KernelHelper", "getKernel",
"(Ljava/lang/Object;)Lweka/classifiers/functions/supportVector/Kernel;",
self.jobject)
if result is None:
return None
else:
return Kernel(jobject=result)
@kernel.setter
def kernel(self, kernel):
"""
Sets the kernel.
:param kernel: the kernel to set
:type kernel: Kernel
"""
result = javabridge.static_call(
"weka/classifiers/KernelHelper", "setKernel",
"(Ljava/lang/Object;Lweka/classifiers/functions/supportVector/Kernel;)Z",
self.jobject, kernel.jobject)
if not result:
raise Exception("Failed to set kernel!")
class Prediction(JavaObject):
"""
Wrapper class for a prediction.
"""
def __init__(self, jobject):
"""
Initializes the wrapper.
:param jobject: the prediction to wrap
:type jobject: JB_Object
"""
self.enforce_type(jobject, "weka.classifiers.evaluation.Prediction")
super(Prediction, self).__init__(jobject)
@property
def actual(self):
"""
Returns the actual value.
:return: the actual value (internal representation)
:rtype: float
"""
return javabridge.call(self.jobject, "actual", "()D")
@property
def predicted(self):
"""
Returns the predicted value.
:return: the predicted value (internal representation)
:rtype: float
"""
return javabridge.call(self.jobject, "predicted", "()D")
@property
def weight(self):
"""
Returns the weight.
:return: the weight of the Instance that was used
:rtype: float
"""
return javabridge.call(self.jobject, "weight", "()D")
class NominalPrediction(Prediction):
"""
Wrapper class for a nominal prediction.
"""
def __init__(self, jobject):
"""
Initializes the wrapper.
:param jobject: the prediction to wrap
:type jobject: JB_Object
"""
self.enforce_type(jobject, "weka.classifiers.evaluation.NominalPrediction")
super(NominalPrediction, self).__init__(jobject)
@property
def distribution(self):
"""
Returns the class distribution.
:return: the class distribution list
:rtype: ndarray
"""
return javabridge.get_env().get_double_array_elements(javabridge.call(self.jobject, "distribution", "()[D"))
@property
def margin(self):
"""
Returns the margin.
:return: the margin
:rtype: float
"""
return javabridge.call(self.jobject, "margin", "()D")
class NumericPrediction(Prediction):
"""
Wrapper class for a numeric prediction.
"""
def __init__(self, jobject):
"""
Initializes the wrapper.
:param jobject: the prediction to wrap
:type jobject: JB_Object
"""
self.enforce_type(jobject, "weka.classifiers.evaluation.NumericPrediction")
super(NumericPrediction, self).__init__(jobject)
@property
def error(self):
"""
Returns the error.
:return: the error
:rtype: float
"""
return javabridge.call(self.jobject, "error", "()D")
@property
def prediction_intervals(self):
"""
Returns the prediction intervals.
:return: the intervals
:rtype: ndarray
"""
return arrays.double_matrix_to_ndarray(javabridge.call(self.jobject, "predictionIntervals", "()[[D"))
class CostMatrix(JavaObject):
"""
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).
"""
def __init__(self, matrx=None, num_classes=None):
"""
Initializes the matrix object.
:param matrx: the matrix to copy
:type matrx: CostMatrix or ndarray or JB_Object
:param num_classes: the number of classes
:type num_classes: int
"""
if matrx is not None:
if isinstance(matrx, CostMatrix):
jobject = javabridge.make_instance(
"weka/classifiers/CostMatrix", "(Lweka/classifiers/CostMatrix;)V", matrx.jobject)
super(CostMatrix, self).__init__(jobject)
elif isinstance(matrx, ndarray):
shp = matrx.shape
if len(shp) != 2:
raise Exception("Numpy array must be a 2-dimensional array!")
rows, cols = shp
if rows == cols:
cmatrix = CostMatrix(num_classes=rows)
for r in xrange(rows):
for c in xrange(cols):
cmatrix.set_element(r, c, matrx[r][c])
super(CostMatrix, self).__init__(cmatrix.jobject)
else:
raise Exception("Numpy array must be a square matrix!")
elif isinstance(matrx, javabridge.JB_Object):
super(CostMatrix, self).__init__(matrx)
else:
raise Exception(
"Matrix must be either a CostMatrix or a 2-dimensional numpy array: " + str(type(matrx)))
elif not num_classes is None:
jobject = javabridge.make_instance(
"weka/classifiers/CostMatrix", "(I)V", num_classes)
super(CostMatrix, self).__init__(jobject)
else:
raise Exception("Either matrix or number of classes must be provided!")
def apply_cost_matrix(self, data, rnd):
"""
Applies the cost matrix to the data.
:param data: the data to apply to
:type data: Instances
:param rnd: the random number generator
:type rnd: Random
"""
return Instances(
javabridge.call(
self.jobject, "applyCostMatrix", "(Lweka/core/Instances;Ljava/util/Random;)Lweka/core/Instances;",
data.jobject, rnd.jobject))
def expected_costs(self, class_probs, inst=None):
"""
Calculates the expected misclassification cost for each possible class value, given class probability
estimates.
:param class_probs: the class probabilities
:type class_probs: ndarray
:return: the calculated costs
:rtype: ndarray
"""
if inst is None:
costs = javabridge.call(
self.jobject, "expectedCosts", "([D)[D", javabridge.get_env().make_double_array(class_probs))
return javabridge.get_env().get_double_array_elements(costs)
else:
costs = javabridge.call(
self.jobject, "expectedCosts", "([DLweka/core/Instance;)[D",
javabridge.get_env().make_double_array(class_probs), inst.jobject)
return javabridge.get_env().get_double_array_elements(costs)
def get_cell(self, row, col):
"""
Returns the JB_Object at the specified location.
:param row: the 0-based index of the row
:type row: int
:param col: the 0-based index of the column
:type col: int
:return: the object in that cell
:rtype: JB_Object
"""
return javabridge.call(
self.jobject, "getCell", "(II)Ljava/lang/Object;", row, col)
def set_cell(self, row, col, obj):
"""
Sets the JB_Object at the specified location. Automatically unwraps JavaObject.
:param row: the 0-based index of the row
:type row: int
:param col: the 0-based index of the column
:type col: int
:param obj: the object for that cell
:type obj: object
"""
if isinstance(obj, JavaObject):
obj = obj.jobject
javabridge.call(
self.jobject, "setCell", "(IILjava/lang/Object;)V", row, col, obj)
def get_element(self, row, col, inst=None):
"""
Returns the value at the specified location.
:param row: the 0-based index of the row
:type row: int
:param col: the 0-based index of the column
:type col: int
:param inst: the Instace
:type inst: Instance
:return: the value in that cell
:rtype: float
"""
if inst is None:
return javabridge.call(
self.jobject, "getElement", "(II)D", row, col)
else:
return javabridge.call(
self.jobject, "getElement", "(IILweka/core/Instance;)D", row, col, inst.jobject)
def set_element(self, row, col, value):
"""
Sets the float value at the specified location.
:param row: the 0-based index of the row
:type row: int
:param col: the 0-based index of the column
:type col: int
:param value: the float value for that cell
:type value: float
"""
javabridge.call(
self.jobject, "setElement", "(IID)V", row, col, value)
def get_max_cost(self, class_value, inst=None):
"""
Gets the maximum cost for a particular class value.
:param class_value: the class value to get the maximum cost for
:type class_value: int
:param inst: the Instance
:type inst: Instance
:return: the cost
:rtype: float
"""
if inst is None:
return javabridge.call(
self.jobject, "getMaxCost", "(I)D", class_value)
else:
return javabridge.call(
self.jobject, "getElement", "(ILweka/core/Instance;)D", class_value, inst.jobject)
def initialize(self):
"""
Initializes the matrix.
"""
javabridge.call(self.jobject, "initialize", "()V")
def normalize(self):
"""
Normalizes the matrix.
"""
javabridge.call(self.jobject, "normalize", "()V")
@property
def num_columns(self):
"""
Returns the number of columns.
:return: the number of columns
:rtype: int
"""
return javabridge.call(self.jobject, "numColumns", "()I")
@property
def num_rows(self):
"""
Returns the number of rows.
:return: the number of rows
:rtype: int
"""
return javabridge.call(self.jobject, "numRows", "()I")
@property
def size(self):
"""
Returns the number of rows/columns.
:return: the number of rows/columns
:rtype: int
"""
return javabridge.call(self.jobject, "size", "()I")
def to_matlab(self):
"""
Returns the matrix in Matlab format.
:return: the matrix as Matlab formatted string
:rtype: str
"""
return javabridge.call(self.jobject, "toMatlab", "()Ljava/lang/String;")
@classmethod
def parse_matlab(cls, matlab):
"""
Parses the costmatrix definition in matlab format and returns a matrix.
:param matlab: the matlab matrix string, eg [1 2; 3 4].
:type matlab: str
:return: the generated matrix
:rtype: CostMatrix
"""
return CostMatrix(
matrx=javabridge.static_call(
"weka/classifiers/CostMatrix", "parseMatlab",
"(Ljava/lang/String;)Lweka/classifiers/CostMatrix;", matlab))
class Evaluation(JavaObject):
"""
Evaluation class for classifiers.
"""
def __init__(self, data, cost_matrix=None):
"""
Initializes an Evaluation object.
:param data: the data to use to initialize the priors with
:type data: Instances
:param cost_matrix: the cost matrix to use for initializing
:type cost_matrix: CostMatrix
"""
if cost_matrix is None:
jobject = javabridge.make_instance(
"weka/classifiers/EvaluationWrapper", "(Lweka/core/Instances;)V",
data.jobject)
else:
jobject = javabridge.make_instance(
"weka/classifiers/EvaluationWrapper", "(Lweka/core/Instances;Lweka/classifiers/CostMatrix;)V",
data.jobject, cost_matrix.jobject)
self.wrapper = jobject
jobject = javabridge.call(jobject, "getEvaluation", "()Lweka/classifiers/Evaluation;")
super(Evaluation, self).__init__(jobject)
def crossvalidate_model(self, classifier, data, num_folds, rnd, output=None):
"""
Crossvalidates the model using the specified data, number of folds and random number generator wrapper.
:param classifier: the classifier to cross-validate
:type classifier: Classifier
:param data: the data to evaluate on
:type data: Instances
:param num_folds: the number of folds
:type num_folds: int
:param rnd: the random number generator to use
:type rnd: Random
:param output: the output generator to use
:type output: PredictionOutput
"""
if output is None:
generator = []
else:
generator = [output.jobject]
javabridge.call(
self.jobject, "crossValidateModel",
"(Lweka/classifiers/Classifier;Lweka/core/Instances;ILjava/util/Random;[Ljava/lang/Object;)V",
classifier.jobject, data.jobject, num_folds, rnd.jobject, generator)
def evaluate_train_test_split(self, classifier, data, percentage, rnd=None, output=None):
"""
Splits the data into train and test, builds the classifier with the training data and
evaluates it against the test set.
:param classifier: the classifier to cross-validate
:type classifier: Classifier
:param data: the data to evaluate on
:type data: Instances
:param percentage: the percentage split to use (amount to use for training)
:type percentage: double
:param rnd: the random number generator to use, if None the order gets preserved
:type rnd: Random
:param output: the output generator to use
:type output: PredictionOutput
"""
train_inst, test_inst = data.train_test_split(percentage, rnd=rnd)
cls = Classifier.make_copy(classifier)
cls.build_classifier(train_inst)
self.test_model(cls, test_inst, output=output)
def test_model(self, classifier, data, output=None):
"""
Evaluates the built model using the specified test data and returns the classifications.
:param classifier: the trained classifier to evaluate
:type classifier: Classifier
:param data: the data to evaluate on
:type data: Instances
:param output: the output generator to use
:type output: PredictionOutput
:return: the classifications
:rtype: ndarray
"""
if output is None:
generator = []
else:
output.header = data
generator = [output.jobject]
cls = javabridge.call(
self.jobject, "evaluateModel",
"(Lweka/classifiers/Classifier;Lweka/core/Instances;[Ljava/lang/Object;)[D",
classifier.jobject, data.jobject, generator)
if cls is None:
return None
else:
return javabridge.get_env().get_double_array_elements(cls)
def test_model_once(self, classifier, inst):
"""
Evaluates the built model using the specified test instance and returns the classification.
:param classifier: the classifier to cross-validate
:type classifier: Classifier
:param inst: the Instance to evaluate on
:type inst: Instances
:return: the classification
:rtype: float
"""
return javabridge.call(
self.jobject, "evaluateModelOnce",
"(Lweka/classifiers/Classifier;Lweka/core/Instance;)D",
classifier.jobject, inst.jobject)
def summary(self, title=None, complexity=False):
"""
Generates a summary.
:param title: optional title
:type title: str
:param complexity: whether to print the complexity information as well
:type complexity: bool
:return: the summary
:rtype: str
"""
if title is None:
return javabridge.call(
self.jobject, "toSummaryString", "()Ljava/lang/String;")
else:
return javabridge.call(
self.jobject, "toSummaryString", "(Ljava/lang/String;Z)Ljava/lang/String;", title, complexity)
def class_details(self, title=None):
"""
Generates the class details.
:param title: optional title
:type title: str
:return: the details
:rtype: str
"""
if title is None:
return javabridge.call(
self.jobject, "toClassDetailsString", "()Ljava/lang/String;")
else:
return javabridge.call(
self.jobject, "toClassDetailsString", "(Ljava/lang/String;)Ljava/lang/String;", title)
def matrix(self, title=None):
"""
Generates the confusion matrix.
:param title: optional title
:type title: str
:return: the matrix
:rtype: str
"""
if title is None:
return javabridge.call(self.jobject, "toMatrixString", "()Ljava/lang/String;")
else:
return javabridge.call(self.jobject, "toMatrixString", "(Ljava/lang/String;)Ljava/lang/String;", title)
def area_under_prc(self, class_index):
"""
Returns the area under precision recall curve.
:param class_index: the 0-based index of the class label
:type class_index: int
:return: the area
:rtype: float
"""
return javabridge.call(self.jobject, "areaUnderPRC", "(I)D", class_index)
@property
def weighted_area_under_prc(self):
"""
Returns the weighted area under precision recall curve.
:return: the weighted area
:rtype: float
"""
return javabridge.call(self.jobject, "weightedAreaUnderPRC", "()D")
def area_under_roc(self, class_index):
"""
Returns the area under receiver operators characteristics curve.
:param class_index: the 0-based index of the class label
:type class_index: int
:return: the area
:rtype: float
"""
return javabridge.call(self.jobject, "areaUnderROC", "(I)D", class_index)
@property
def weighted_area_under_roc(self):
"""
Returns the weighted area under receiver operator characteristic curve.
:return: the weighted area
:rtype: float
"""
return javabridge.call(self.jobject, "weightedAreaUnderROC", "()D")
@property
def avg_cost(self):
"""
Returns the average cost.
:return: the cost
:rtype: float
"""
return javabridge.call(self.jobject, "avgCost", "()D")
@property
def total_cost(self):
"""
Returns the total cost.
:return: the cost
:rtype: float
"""
return javabridge.call(self.jobject, "totalCost", "()D")
@property
def confusion_matrix(self):
"""
Returns the confusion matrix.
:return: the matrix
:rtype: ndarray
"""
return arrays.double_matrix_to_ndarray(javabridge.call(self.jobject, "confusionMatrix", "()[[D"))
@property
def correct(self):
"""
Returns the correct count (nominal classes).
:return: the count
:rtype: float
"""
return javabridge.call(self.jobject, "correct", "()D")
@property
def incorrect(self):
"""
Returns the incorrect count (nominal classes).
:return: the count
:rtype: float
"""
return javabridge.call(self.jobject, "incorrect", "()D")
@property
def unclassified(self):
"""
Returns the unclassified count.
:return: the count
:rtype: float
"""
return javabridge.call(self.jobject, "unclassified", "()D")
@property
def num_instances(self):
"""
Returns the number of instances that had a known class value.
:return: the number of instances
:rtype: float
"""
return javabridge.call(self.jobject, "numInstances", "()D")
@property
def percent_correct(self):
"""
Returns the percent correct (nominal classes).
:return: the percentage
:rtype: float
"""
return javabridge.call(self.jobject, "pctCorrect", "()D")
@property
def percent_incorrect(self):
"""
Returns the percent incorrect (nominal classes).
:return: the percentage
:rtype: float
"""
return javabridge.call(self.jobject, "pctIncorrect", "()D")
@property
def percent_unclassified(self):
"""
Returns the percent unclassified.
:return: the percentage
:rtype: float
"""
return javabridge.call(self.jobject, "pctUnclassified", "()D")
@property
def correlation_coefficient(self):
"""
Returns the correlation coefficient (numeric classes).
:return: the coefficient
:rtype: float
"""
return javabridge.call(self.jobject, "correlationCoefficient", "()D")
def matthews_correlation_coefficient(self, class_index):
"""
Returns the Matthews correlation coefficient (nominal classes).
:param class_index: the 0-based index of the class label
:type class_index: int
:return: the coefficient
:rtype: float
"""
return javabridge.call(self.jobject, "matthewsCorrelationCoefficient", "(I)D", class_index)
@property
def weighted_matthews_correlation(self):
"""
Returns the weighted Matthews correlation (nominal classes).
:return: the correlation
:rtype: float
"""
return javabridge.call(self.jobject, "weightedMatthewsCorrelation", "()D")
@property
def coverage_of_test_cases_by_predicted_regions(self):
"""
Returns the coverage of the test cases by the predicted regions at the confidence level
specified when evaluation was performed.
:return: the coverage
:rtype: float
"""
return javabridge.call(self.jobject, "coverageOfTestCasesByPredictedRegions", "()D")
@property
def size_of_predicted_regions(self):
"""
Returns the average size of the predicted regions, relative to the range of the target in the
training data, at the confidence level specified when evaluation was performed.
:return:the size of the regions
:rtype: float
"""
return javabridge.call(self.jobject, "sizeOfPredictedRegions", "()D")
@property
def error_rate(self):
"""
Returns the error rate (numeric classes).
:return: the rate
:rtype: float
"""
return javabridge.call(self.jobject, "errorRate", "()D")
@property
def mean_absolute_error(self):
"""
Returns the mean absolute error.
:return: the error
:rtype: float
"""
return javabridge.call(self.jobject, "meanAbsoluteError", "()D")
@property
def relative_absolute_error(self):
"""
Returns the relative absolute error.
:return: the error
:rtype: float
"""
return javabridge.call(self.jobject, "relativeAbsoluteError", "()D")
@property
def root_mean_squared_error(self):
"""
Returns the root mean squared error.
:return: the error
:rtype: float
"""
return javabridge.call(self.jobject, "rootMeanSquaredError", "()D")
@property
def root_relative_squared_error(self):
"""
Returns the root relative squared error.
:return: the error
:rtype: float
"""
return javabridge.call(self.jobject, "rootRelativeSquaredError", "()D")
@property
def root_mean_prior_squared_error(self):
"""
Returns the root mean prior squared error.
:return: the error
:rtype: float
"""
return javabridge.call(self.jobject, "rootMeanPriorSquaredError", "()D")
@property
def mean_prior_absolute_error(self):
"""
Returns the mean prior absolute error.
:return: the error
:rtype: float
"""
return javabridge.call(self.jobject, "meanPriorAbsoluteError", "()D")
def false_negative_rate(self, class_index):
"""
Returns the false negative rate.
:param class_index: the 0-based index of the class label
:type class_index: int
:return: the rate
:rtype: float
"""
return javabridge.call(self.jobject, "falseNegativeRate", "(I)D", class_index)
@property
def weighted_false_negative_rate(self):
"""
Returns the weighted false negative rate.
:return: the rate
:rtype: float
"""
return javabridge.call(self.jobject, "weightedFalseNegativeRate", "()D")
def false_positive_rate(self, class_index):
"""
Returns the false positive rate.
:param class_index: the 0-based index of the class label
:type class_index: int
:return: the rate
:rtype: float
"""
return javabridge.call(self.jobject, "falsePositiveRate", "(I)D", class_index)
@property
def weighted_false_positive_rate(self):
"""
Returns the weighted false positive rate.
:return: the rate
:rtype: float
"""
return javabridge.call(self.jobject, "weightedFalsePositiveRate", "()D")
def num_false_negatives(self, class_index):
"""
Returns the number of false negatives.
:param class_index: the 0-based index of the class label
:type class_index: int
:return: the count
:rtype: float
"""
return javabridge.call(self.jobject, "numFalseNegatives", "(I)D", class_index)
def true_negative_rate(self, class_index):
"""
Returns the true negative rate.
:param class_index: the 0-based index of the class label
:type class_index: int
:return: the rate
:rtype: float
"""
return javabridge.call(self.jobject, "trueNegativeRate", "(I)D", class_index)
@property
def weighted_true_negative_rate(self):
"""
Returns the weighted true negative rate.
:return: the rate
:rtype: float
"""
return javabridge.call(self.jobject, "weightedTrueNegativeRate", "()D")
def num_true_negatives(self, class_index):
"""
Returns the number of true negatives.
:param class_index: the 0-based index of the class label
:type class_index: int
:return: the count
:rtype: float
"""
return javabridge.call(self.jobject, "numTrueNegatives", "(I)D", class_index)
def num_false_positives(self, class_index):
"""
Returns the number of false positives.
:param class_index: the 0-based index of the class label
:type class_index: int
:return: the count
:rtype: float
"""
return javabridge.call(self.jobject, "numFalsePositives", "(I)D", class_index)
def true_positive_rate(self, class_index):
"""
Returns the true positive rate.
:param class_index: the 0-based index of the class label
:type class_index: int
:return: the rate
:rtype: float
"""
return javabridge.call(self.jobject, "truePositiveRate", "(I)D", class_index)
@property
def weighted_true_positive_rate(self):
"""
Returns the weighted true positive rate.
:return: the rate
:rtype: float
"""
return javabridge.call(self.jobject, "weightedTruePositiveRate", "()D")
def num_true_positives(self, class_index):
"""
Returns the number of true positives.
:param class_index: the 0-based index of the class label
:type class_index: int
:return: the count
:rtype: float
"""
return javabridge.call(self.jobject, "numTruePositives", "(I)D", class_index)
def f_measure(self, class_index):
"""
Returns the f measure.
:param class_index: the 0-based index of the class label
:type class_index: int
:return: the measure
:rtype: float
"""
return javabridge.call(self.jobject, "fMeasure", "(I)D", class_index)
@property
def weighted_f_measure(self):
"""
Returns the weighted f measure.
:return: the measure
:rtype: float
"""
return javabridge.call(self.jobject, "weightedFMeasure", "()D")
@property
def unweighted_macro_f_measure(self):
"""
Returns the unweighted macro-averaged F-measure.
:return: the measure
:rtype: float
"""
return javabridge.call(self.jobject, "unweightedMacroFmeasure", "()D")
@property
def unweighted_micro_f_measure(self):
"""
Returns the unweighted micro-averaged F-measure.
:return: the measure
:rtype: float
"""
return javabridge.call(self.jobject, "unweightedMicroFmeasure", "()D")
def precision(self, class_index):
"""
Returns the precision.
:param class_index: the 0-based index of the class label
:type class_index: int
:return: the precision
:rtype: float
"""
return javabridge.call(self.jobject, "precision", "(I)D", class_index)
@property
def weighted_precision(self):
"""
Returns the weighted precision.
:return: the precision
:rtype: float
"""
return javabridge.call(self.jobject, "weightedPrecision", "()D")
def recall(self, class_index):
"""
Returns the recall.
:param class_index: the 0-based index of the class label
:type class_index: int
:return: the recall
:rtype: float
"""
return javabridge.call(self.jobject, "recall", "(I)D", class_index)
@property
def weighted_recall(self):
"""
Returns the weighted recall.
:return: the recall
:rtype: float
"""
return javabridge.call(self.jobject, "weightedRecall", "()D")
@property
def kappa(self):
"""
Returns kappa.
:return: kappa
:rtype: float
"""
return javabridge.call(self.jobject, "kappa", "()D")
@property
def kb_information(self):
"""
Returns KB information.
:return: the information
:rtype: float
"""
return javabridge.call(self.jobject, "KBInformation", "()D")
@property
def kb_mean_information(self):
"""
Returns KB mean information.
:return: the information
:rtype: float
"""
return javabridge.call(self.jobject, "KBMeanInformation", "()D")
@property
def kb_relative_information(self):
"""
Returns KB relative information.
:return: the information
:rtype: float
"""
return javabridge.call(self.jobject, "KBRelativeInformation", "()D")
@property
def sf_entropy_gain(self):
"""
Returns the total SF, which is the null model entropy minus the scheme entropy.
:return: the gain
:rtype: float
"""
return javabridge.call(self.jobject, "SFEntropyGain", "()D")
@property
def sf_mean_entropy_gain(self):
"""
Returns the SF per instance, which is the null model entropy minus the scheme entropy, per instance.
:return: the gain
:rtype: float
"""
return javabridge.call(self.jobject, "SFMeanEntropyGain", "()D")
@property
def sf_mean_prior_entropy(self):
"""
Returns the entropy per instance for the null model.
:return: the entropy
:rtype: float
"""
return javabridge.call(self.jobject, "SFMeanPriorEntropy", "()D")
@property
def sf_mean_scheme_entropy(self):
"""
Returns the entropy per instance for the scheme.
:return: the entropy
:rtype: float
"""
return javabridge.call(self.jobject, "SFMeanSchemeEntropy", "()D")
@property
def class_priors(self):
"""
Returns the class priors.
:return: the priors
:rtype: ndarray
"""
return javabridge.get_env().get_double_array_elements(javabridge.call(self.jobject, "getClassPriors", "()[D"))
@class_priors.setter
def class_priors(self, data):
"""
Sets the class priors derived from the dataset.
:param data: the dataset to derive the priors from
:type data: Instances
"""
javabridge.call(self.jobject, "setClassPriors", "(Lweka/core/Instances;)V", data)
@property
def header(self):
"""
Returns the header format.
:return: the header format
:rtype: Instances
"""
return Instances(javabridge.call(self.jobject, "getHeader", "()Lweka/core/Instances;"))
@property
def discard_predictions(self):
"""
Returns whether to discard predictions (saves memory).
:return: True if to discard
:rtype: bool
"""
return javabridge.call(self.jobject, "getDiscardPredictions", "()Z")
@discard_predictions.setter
def discard_predictions(self, discard):
"""
Sets whether to discard predictions (saves memory).
:param discard: True if to discard predictions
:type discard: bool
"""
javabridge.call(self.jobject, "setDiscardPredictions", "(Z)V", discard)
@property
def predictions(self):
"""
Returns the predictions.
:return: the predictions. None if not available
:rtype: list
"""
preds = javabridge.get_collection_wrapper(
javabridge.call(self.jobject, "predictions", "()Ljava/util/ArrayList;"))
if self.discard_predictions:
result = None
else:
result = []
for pred in preds:
if javabridge.is_instance_of(pred, "weka/classifiers/evaluation/NominalPrediction"):
result.append(NominalPrediction(pred))
elif javabridge.is_instance_of(pred, "weka/classifiers/evaluation/NumericPrediction"):
result.append(NumericPrediction(pred))
else:
result.append(Prediction(pred))
return result
@classmethod
def evaluate_model(cls, classifier, args):
"""
Evaluates the classifier with the given options.
:param classifier: the classifier instance to use
:type classifier: Classifier
:param args: the command-line arguments to use
:type args: list
:return: the evaluation string
:rtype: str
"""
return javabridge.static_call(
"Lweka/classifiers/Evaluation;", "evaluateModel",
"(Lweka/classifiers/Classifier;[Ljava/lang/String;)Ljava/lang/String;",
classifier.jobject, args)
class PredictionOutput(OptionHandler):
"""
For collecting predictions and generating output from.
Must be derived from weka.classifiers.evaluation.output.prediction.AbstractOutput
"""
def __init__(self, classname="weka.classifiers.evaluation.output.prediction.PlainText", jobject=None, options=None):
"""
Initializes the specified output generator using either the classname or the supplied JB_Object.
:param classname: the classname of the generator
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
if jobject is None:
jobject = PredictionOutput.new_instance(classname)
self.enforce_type(jobject, "weka.classifiers.evaluation.output.prediction.AbstractOutput")
super(PredictionOutput, self).__init__(jobject=jobject, options=options)
buf = javabridge.make_instance("java/lang/StringBuffer", "()V")
javabridge.call(self.jobject, "setBuffer", "(Ljava/lang/StringBuffer;)V", buf)
@property
def header(self):
"""
Returns the header format.
:return: The dataset format
:rtype: Instances
"""
return Instances(javabridge.call(self.jobject, "getHeader", "()Lweka/core/Instances;"))
@header.setter
def header(self, data):
"""
Sets the header format.
:param data: The dataset format
:type data: Instances
"""
javabridge.call(self.jobject, "setHeader", "(Lweka/core/Instances;)V", data.jobject)
def print_header(self):
"""
Prints the header to the buffer.
"""
javabridge.call(self.jobject, "printHeader", "()V")
def print_footer(self):
"""
Prints the footer to the buffer.
"""
javabridge.call(self.jobject, "printFooter", "()V")
def print_all(self, cls, data):
"""
Prints the header, classifications and footer to the buffer.
:param cls: the classifier
:type cls: Classifier
:param data: the test data
:type data: Instances
"""
javabridge.call(
self.jobject, "print", "(Lweka/classifiers/Classifier;Lweka/core/Instances;)V",
cls.jobject, data.jobject)
def print_classifications(self, cls, data):
"""
Prints the classifications to the buffer.
:param cls: the classifier
:type cls: Classifier
:param data: the test data
:type data: Instances
"""
javabridge.call(
self.jobject, "printClassifications", "(Lweka/classifiers/Classifier;Lweka/core/Instances;)V",
cls.jobject, data.jobject)
def print_classification(self, cls, inst, index):
"""
Prints the classification to the buffer.
:param cls: the classifier
:type cls: Classifier
:param inst: the test instance
:type inst: Instance
:param index: the 0-based index of the test instance
:type index: int
"""
javabridge.call(
self.jobject, "printClassification", "(Lweka/classifiers/Classifier;Lweka/core/Instance;I)V",
cls.jobject, inst.jobject, index)
def buffer_content(self):
"""
Returns the content of the buffer as string.
:return: The buffer content
:rtype: str
"""
return javabridge.to_string(javabridge.call(self.jobject, "getBuffer", "()Ljava/lang/StringBuffer;"))
def __str__(self):
"""
Returns the content of the buffer.
:return: the current buffer content
:rtype: str
"""
return self.buffer_content()
def predictions_to_instances(data, preds):
"""
Turns the predictions turned into an Instances object.
:param data: the original dataset format
:type data: Instances
:param preds: the predictions to convert
:type preds: list
:return: the predictions, None if no predictions present
:rtype: Instances
"""
if len(preds) == 0:
return None
is_numeric = isinstance(preds[0], NumericPrediction)
# create header
atts = []
if is_numeric:
atts.append(Attribute.create_numeric("index"))
atts.append(Attribute.create_numeric("weight"))
atts.append(Attribute.create_numeric("actual"))
atts.append(Attribute.create_numeric("predicted"))
atts.append(Attribute.create_numeric("error"))
else:
atts.append(Attribute.create_numeric("index"))
atts.append(Attribute.create_numeric("weight"))
atts.append(data.class_attribute.copy(name="actual"))
atts.append(data.class_attribute.copy(name="predicted"))
atts.append(Attribute.create_nominal("error", ["no", "yes"]))
atts.append(Attribute.create_numeric("classification"))
for i in xrange(data.class_attribute.num_values):
atts.append(Attribute.create_numeric("distribution-" + data.class_attribute.value(i)))
result = Instances.create_instances("Predictions", atts, len(preds))
count = 0
for pred in preds:
count += 1
if is_numeric:
values = array([count, pred.weight, pred.actual, pred.predicted, pred.error])
else:
if pred.actual == pred.predicted:
error = 0.0
else:
error = 1.0
l = [count, pred.weight, pred.actual, pred.predicted, error, max(pred.distribution)]
for i in xrange(data.class_attribute.num_values):
l.append(pred.distribution[i])
values = array(l)
inst = Instance.create_instance(values)
result.add_instance(inst)
return result
def main():
"""
Runs a classifier from the command-line. Calls JVM start/stop automatically.
Use -h to see all options.
"""
parser = argparse.ArgumentParser(
description='Performs classification/regression from the command-line. Calls JVM start/stop automatically.')
parser.add_argument("-j", metavar="classpath", dest="classpath", help="additional classpath, jars/directories")
parser.add_argument("-X", metavar="heap", dest="heap", help="max heap size for jvm, e.g., 512m")
parser.add_argument("-t", metavar="train", dest="train", required=True, help="Training set file")
parser.add_argument("-T", metavar="test", dest="test", help="Test set file")
parser.add_argument("-c", metavar="class index", dest="classindex", help="1-based class attribute index")
parser.add_argument("-d", metavar="outmodel", dest="outmodel", help="model output file name")
parser.add_argument("-l", metavar="inmodel", dest="inmodel", help="model input file name")
parser.add_argument("-x", metavar="num folds", dest="numfolds", help="number of folds for cross-validation")
parser.add_argument("-s", metavar="seed", dest="seed", help="seed value for randomization")
parser.add_argument("-v", action="store_true", dest="notrainstats", help="no statistics for training")
parser.add_argument("-o", action="store_true", dest="onlystats", help="only statistics, don't output model")
parser.add_argument("-i", action="store_true", dest="irstats", help="output information retrieval statistics")
parser.add_argument("-k", action="store_true", dest="itstats", help="output information theoretic statistics")
parser.add_argument("-m", metavar="costmatrix", dest="costmatrix", help="cost matrix file")
parser.add_argument("-g", metavar="graph", dest="graph", help="output file for graph (if supported)")
parser.add_argument("classifier", help="classifier classname, e.g., weka.classifiers.trees.J48")
parser.add_argument("option", nargs=argparse.REMAINDER, help="additional classifier options")
parsed = parser.parse_args()
jars = []
if parsed.classpath is not None:
jars = parsed.classpath.split(os.pathsep)
params = []
if parsed.train is not None:
params.extend(["-t", parsed.train])
if parsed.test is not None:
params.extend(["-T", parsed.test])
if parsed.classindex is not None:
params.extend(["-c", parsed.classindex])
if parsed.outmodel is not None:
params.extend(["-d", parsed.outmodel])
if parsed.inmodel is not None:
params.extend(["-l", parsed.inmodel])
if parsed.numfolds is not None:
params.extend(["-x", parsed.numfolds])
if parsed.seed is not None:
params.extend(["-s", parsed.seed])
if parsed.notrainstats:
params.append("-v")
if parsed.onlystats:
params.append("-o")
if parsed.irstats:
params.append("-i")
if parsed.itstats:
params.append("-k")
if parsed.costmatrix is not None:
params.extend(["-m", parsed.costmatrix])
if parsed.graph is not None:
params.extend(["-g", parsed.graph])
jvm.start(jars, max_heap_size=parsed.heap, packages=True)
logger.debug("Commandline: " + join_options(sys.argv[1:]))
try:
classifier = Classifier(classname=parsed.classifier)
if len(parsed.option) > 0:
classifier.options = parsed.option
print(Evaluation.evaluate_model(classifier, params))
except Exception, e:
print(e)
finally:
jvm.stop()
if __name__ == "__main__":
try:
main()
except Exception, ex:
print(ex)
python-weka-wrapper-0.3.6/python/weka/associations.py 0000644 0172670 0172670 00000010575 12576425365 024444 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# associations.py
# Copyright (C) 2014-2015 Fracpete (pythonwekawrapper at gmail dot com)
import javabridge
import logging
import os
import sys
import argparse
import weka.core.jvm as jvm
import weka.core.converters as converters
from weka.core.classes import OptionHandler, join_options
from weka.core.capabilities import Capabilities
# logging setup
logger = logging.getLogger("weka.associations")
class Associator(OptionHandler):
"""
Wrapper class for associators.
"""
def __init__(self, classname=None, jobject=None, options=None):
"""
Initializes the specified associator using either the classname or the supplied JB_Object.
:param classname: the classname of the associator
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
if jobject is None:
jobject = Associator.new_instance(classname)
self.enforce_type(jobject, "weka.associations.Associator")
super(Associator, self).__init__(jobject=jobject, options=options)
@property
def capabilities(self):
"""
Returns the capabilities of the associator.
:return: the capabilities
:rtype: Capabilities
"""
return Capabilities(javabridge.call(self.jobject, "getCapabilities", "()Lweka/core/Capabilities;"))
def build_associations(self, data):
"""
Builds the associator with the data.
:param data: the data to train the associator with
:type data: Instances
"""
javabridge.call(self.jobject, "buildAssociations", "(Lweka/core/Instances;)V", data.jobject)
@classmethod
def make_copy(cls, associator):
"""
Creates a copy of the clusterer.
:param associator: the associator to copy
:type associator: Associator
:return: the copy of the associator
:rtype: Associator
"""
return Associator(
jobject=javabridge.static_call(
"weka/associations/AbstractAssociator", "makeCopy",
"(Lweka/associations/Associator;)Lweka/associations/Associator;", associator.jobject))
def main():
"""
Runs a associator from the command-line. Calls JVM start/stop automatically.
Use -h to see all options.
"""
parser = argparse.ArgumentParser(
description='Executes an associator from the command-line. Calls JVM start/stop automatically.')
parser.add_argument("-j", metavar="classpath", dest="classpath", help="additional classpath, jars/directories")
parser.add_argument("-X", metavar="heap", dest="heap", help="max heap size for jvm, e.g., 512m")
parser.add_argument("-t", metavar="train", dest="train", required=True, help="training set file")
parser.add_argument("associator", help="associator classname, e.g., weka.associations.Apriori")
parser.add_argument("option", nargs=argparse.REMAINDER, help="additional associator options")
parsed = parser.parse_args()
jars = []
if parsed.classpath is not None:
jars = parsed.classpath.split(os.pathsep)
jvm.start(jars, max_heap_size=parsed.heap, packages=True)
logger.debug("Commandline: " + join_options(sys.argv[1:]))
try:
associator = Associator(classname=parsed.associator)
if len(parsed.option) > 0:
associator.options = parsed.option
loader = converters.loader_for_file(parsed.train)
data = loader.load_file(parsed.train)
associator.build_associations(data)
print(str(associator))
except Exception, e:
print(e)
finally:
jvm.stop()
if __name__ == "__main__":
try:
main()
except Exception, ex:
print(ex)
python-weka-wrapper-0.3.6/python/weka/clusterers.py 0000644 0172670 0172670 00000036205 12576426050 024126 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# clusterers.py
# Copyright (C) 2014-2015 Fracpete (pythonwekawrapper at gmail dot com)
import javabridge
import logging
import os
import sys
import argparse
import weka.core.jvm as jvm
import weka.core.classes as classes
from weka.core.classes import JavaObject, join_options
from weka.core.classes import OptionHandler
from weka.core.classes import Random
from weka.core.capabilities import Capabilities
from weka.filters import Filter
# logging setup
logger = logging.getLogger("weka.clusterers")
class Clusterer(OptionHandler):
"""
Wrapper class for clusterers.
"""
def __init__(self, classname="weka.clusterers.SimpleKMeans", jobject=None, options=None):
"""
Initializes the specified clusterer using either the classname or the supplied JB_Object.
:param classname: the classname of the clusterer
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to use
:type options: list
"""
if jobject is None:
jobject = Clusterer.new_instance(classname)
self.is_updateable = self.check_type(jobject, "weka.clusterers.UpdateableClusterer")
self.is_drawable = self.check_type(jobject, "weka.core.Drawable")
self.enforce_type(jobject, "weka.clusterers.Clusterer")
super(Clusterer, self).__init__(jobject=jobject, options=options)
self.__cluster = javabridge.make_call(self.jobject, "clusterInstance", "(Lweka/core/Instance;)I")
self.__distribution = javabridge.make_call(self.jobject, "distributionForInstance", "(Lweka/core/Instance;)[D")
@property
def capabilities(self):
"""
Returns the capabilities of the clusterer.
:return: the capabilities
:rtype: Capabilities
"""
return Capabilities(javabridge.call(self.jobject, "getCapabilities", "()Lweka/core/Capabilities;"))
def build_clusterer(self, data):
"""
Builds the clusterer with the data.
:param data: the data to use for training the clusterer
:type data: Instances
"""
javabridge.call(self.jobject, "buildClusterer", "(Lweka/core/Instances;)V", data.jobject)
def update_clusterer(self, inst):
"""
Updates the clusterer with the instance.
:param inst: the Instance to update the clusterer with
:type inst: Instance
"""
if self.is_updateable:
javabridge.call(self.jobject, "updateClusterer", "(Lweka/core/Instance;)V", inst.jobject)
else:
logger.critical(classes.get_classname(self.jobject) + " is not updateable!")
def update_finished(self):
"""
Signals the clusterer that updating with new data has finished.
"""
if self.is_updateable:
javabridge.call(self.jobject, "updateFinished", "()V")
else:
logger.critical(classes.get_classname(self.jobject) + " is not updateable!")
def cluster_instance(self, inst):
"""
Peforms a prediction.
:param inst: the instance to determine the cluster for
:type inst: Instance
:return: the clustering result
:rtype: float
"""
return self.__cluster(inst.jobject)
def distribution_for_instance(self, inst):
"""
Peforms a prediction, returning the cluster distribution.
:param inst: the Instance to get the cluster distribution for
:type inst: Instance
:return: the cluster distribution
:rtype: float[]
"""
pred = self.__distribution(inst.jobject)
return javabridge.get_env().get_double_array_elements(pred)
@property
def number_of_clusters(self):
"""
Returns the number of clusters found.
:return: the number fo clusters
:rtype: int
"""
return javabridge.call(self.jobject, "numberOfClusters", "()I")
@property
def graph_type(self):
"""
Returns the graph type if classifier implements weka.core.Drawable, otherwise -1.
:return: the type
:rtype: int
"""
if self.is_drawable:
return javabridge.call(self.jobject, "graphType", "()I")
else:
return -1
@property
def graph(self):
"""
Returns the graph if classifier implements weka.core.Drawable, otherwise None.
:return: the graph or None if not available
:rtype: str
"""
if self.is_drawable:
return javabridge.call(self.jobject, "graph", "()Ljava/lang/String;")
else:
return None
@classmethod
def make_copy(cls, clusterer):
"""
Creates a copy of the clusterer.
:param clusterer: the clustererto copy
:type clusterer: Clusterer
:return: the copy of the clusterer
:rtype: Clusterer
"""
return Clusterer(
jobject=javabridge.static_call(
"weka/clusterers/AbstractClusterer", "makeCopy",
"(Lweka/clusterers/Clusterer;)Lweka/clusterers/Clusterer;", clusterer.jobject))
class SingleClustererEnhancer(Clusterer):
"""
Wrapper class for clusterers that use a single base clusterer.
"""
def __init__(self, classname=None, jobject=None, options=None):
"""
Initializes the specified clusterer using either the classname or the supplied JB_Object.
:param classname: the classname of the clusterer
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to use
:type options: list
"""
if jobject is None:
jobject = Clusterer.new_instance(classname)
self.enforce_type(jobject, "weka.clusterers.SingleClustererEnhancer")
super(SingleClustererEnhancer, self).__init__(classname=classname, jobject=jobject, options=options)
@property
def clusterer(self):
"""
Returns the base clusterer.
:return: the clusterer
:rtype: Clusterer
"""
return Clusterer(jobject=javabridge.call(self.jobject, "getClusterer", "()Lweka/clusterers/Clusterer;"))
@clusterer.setter
def clusterer(self, clusterer):
"""
Sets the base clusterer.
:param clusterer: the base clusterer to use
:type clusterer: Clusterer
"""
javabridge.call(self.jobject, "setClusterer", "(Lweka/clusterers/Clusterer;)V", clusterer.jobject)
class FilteredClusterer(SingleClustererEnhancer):
"""
Wrapper class for the filtered clusterer.
"""
def __init__(self, jobject=None, options=None):
"""
Initializes the specified clusterer using either the classname or the supplied JB_Object.
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to use
:type options: list
"""
classname = "weka.clusterers.FilteredClusterer"
if jobject is None:
jobject = Clusterer.new_instance(classname)
self.enforce_type(jobject, classname)
super(FilteredClusterer, self).__init__(classname=classname, jobject=jobject, options=options)
@property
def filter(self):
"""
Returns the filter.
:return: the filter
:rtype: Filter
"""
return Filter(jobject=javabridge.call(self.jobject, "getFilter", "()Lweka/filters/Filter;"))
@filter.setter
def filter(self, filtr):
"""
Sets the filter.
:param filtr: the filter to use
:type filtr: Filter
"""
javabridge.call(self.jobject, "setFilter", "(Lweka/filters/Filter;)V", filtr.jobject)
class ClusterEvaluation(JavaObject):
"""
Evaluation class for clusterers.
"""
def __init__(self):
"""
Initializes a ClusterEvaluation object.
"""
super(ClusterEvaluation, self).__init__(ClusterEvaluation.new_instance("weka.clusterers.ClusterEvaluation"))
def set_model(self, clusterer):
"""
Sets the built clusterer to evaluate.
:param clusterer: the clusterer to evaluate
:type clusterer: Clusterer
"""
javabridge.call(self.jobject, "setClusterer", "(Lweka/clusterers/Clusterer;)V", clusterer.jobject)
def test_model(self, test):
"""
Evaluates the currently set clusterer on the test set.
:param test: the test set to use for evaluating
:type test: Instances
"""
javabridge.call(self.jobject, "evaluateClusterer", "(Lweka/core/Instances;)V", test.jobject)
@property
def cluster_results(self):
"""
The cluster results as string.
:return: the results string
:rtype: str
"""
return javabridge.call(self.jobject, "clusterResultsToString", "()Ljava/lang/String;")
@property
def cluster_assignments(self):
"""
Return an array of cluster assignments corresponding to the most recent set of instances clustered.
:return: the cluster assignments
:rtype: ndarray
"""
array = javabridge.call(self.jobject, "getClusterAssignments", "()[D")
if array is None:
return None
else:
return javabridge.get_env().get_double_array_elements(array)
@property
def num_clusters(self):
"""
Returns the number of clusters.
:return: the number of clusters
:rtype: int
"""
return javabridge.call(self.jobject, "getNumClusters", "()I")
@property
def log_likelihood(self):
"""
Returns the log likelihood.
:return: the log likelihood
:rtype: float
"""
return javabridge.call(self.jobject, "getLogLikelihood", "()D")
@property
def classes_to_clusters(self):
"""
Return the array (ordered by cluster number) of minimum error class to cluster mappings.
:return: the mappings
:rtype: ndarray
"""
array = javabridge.call(self.jobject, "getClassesToClusters", "()[I")
if array is None:
return None
else:
return javabridge.get_env().get_int_array_elements(array)
@classmethod
def evaluate_clusterer(cls, clusterer, args):
"""
Evaluates the clusterer with the given options.
:param clusterer: the clusterer instance to evaluate
:type clusterer: Clusterer
:param args: the command-line arguments
:type args: list
:return: the evaluation result
:rtype: str
"""
return javabridge.static_call(
"Lweka/clusterers/ClusterEvaluation;", "evaluateClusterer",
"(Lweka/clusterers/Clusterer;[Ljava/lang/String;)Ljava/lang/String;",
clusterer.jobject, args)
@classmethod
def crossvalidate_model(cls, clusterer, data, num_folds, rnd):
"""
Cross-validates the clusterer and returns the loglikelihood.
:param clusterer: the clusterer instance to evaluate
:type clusterer: Clusterer
:param data: the data to evaluate on
:type data: Instances
:param num_folds: the number of folds
:type num_folds: int
:param rnd: the random number generator to use
:type rnd: Random
:return: the cross-validated loglikelihood
:rtype: float
"""
return javabridge.static_call(
"Lweka/clusterers/ClusterEvaluation;", "crossValidateModel",
"(Lweka/clusterers/DensityBasedClusterer;Lweka/core/Instances;ILjava/util/Random;)D",
clusterer.jobject, data.jobject, num_folds, rnd.jobject)
def main():
"""
Runs a clusterer from the command-line. Calls JVM start/stop automatically.
Use -h to see all options.
"""
parser = argparse.ArgumentParser(
description='Performs clustering from the command-line. Calls JVM start/stop automatically.')
parser.add_argument("-j", metavar="classpath", dest="classpath", help="additional classpath, jars/directories")
parser.add_argument("-X", metavar="heap", dest="heap", help="max heap size for jvm, e.g., 512m")
parser.add_argument("-t", metavar="train", dest="train", required=True, help="training set file")
parser.add_argument("-T", metavar="test", dest="test", help="test set file")
parser.add_argument("-d", metavar="outmodel", dest="outmodel", help="model output file name")
parser.add_argument("-l", metavar="inmodel", dest="inmodel", help="model input file name")
parser.add_argument("-p", metavar="attributes", dest="attributes", help="attribute range")
parser.add_argument("-x", metavar="num folds", dest="numfolds", help="number of folds")
parser.add_argument("-s", metavar="seed", dest="seed", help="seed value for randomization")
parser.add_argument("-c", metavar="class index", dest="classindex", help="1-based class attribute index")
parser.add_argument("-g", metavar="graph", dest="graph", help="graph output file (if supported)")
parser.add_argument("clusterer", help="clusterer classname, e.g., weka.clusterers.SimpleKMeans")
parser.add_argument("option", nargs=argparse.REMAINDER, help="additional clusterer options")
parsed = parser.parse_args()
jars = []
if parsed.classpath is not None:
jars = parsed.classpath.split(os.pathsep)
params = []
if parsed.train is not None:
params.extend(["-t", parsed.train])
if parsed.test is not None:
params.extend(["-T", parsed.test])
if parsed.outmodel is not None:
params.extend(["-d", parsed.outmodel])
if parsed.inmodel is not None:
params.extend(["-l", parsed.inmodel])
if parsed.attributes is not None:
params.extend(["-p", parsed.attributes])
if parsed.numfolds is not None:
params.extend(["-x", parsed.numfolds])
if parsed.seed is not None:
params.extend(["-s", parsed.seed])
if parsed.classindex is not None:
params.extend(["-c", parsed.classindex])
if parsed.graph is not None:
params.extend(["-g", parsed.graph])
jvm.start(jars, max_heap_size=parsed.heap, packages=True)
logger.debug("Commandline: " + join_options(sys.argv[1:]))
try:
clusterer = Clusterer(classname=parsed.clusterer)
if len(parsed.option) > 0:
clusterer.options = parsed.option
print(ClusterEvaluation.evaluate_clusterer(clusterer, params))
except Exception, e:
print(e)
finally:
jvm.stop()
if __name__ == "__main__":
try:
main()
except Exception, ex:
print(ex)
python-weka-wrapper-0.3.6/python/weka/attribute_selection.py 0000644 0172670 0172670 00000030514 12576431570 026003 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# attribute_selection.py
# Copyright (C) 2014-2015 Fracpete (pythonwekawrapper at gmail dot com)
import javabridge
import logging
import argparse
import os
import sys
import weka.core.jvm as jvm
import weka.core.types as arrays
from weka.core.classes import JavaObject, join_options
from weka.core.classes import OptionHandler
from weka.core.capabilities import Capabilities
from weka.core.dataset import Instances, Instance
# logging setup
logger = logging.getLogger("weka.attribute_selection")
class ASSearch(OptionHandler):
"""
Wrapper class for attribute selection search algorithm.
"""
def __init__(self, classname="weka.attributeSelection.BestFirst", jobject=None, options=None):
"""
Initializes the specified search algorithm using either the classname or the supplied JB_Object.
:param classname: the classname of the search algorithms
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the commandline options to use
:type options: list
"""
if jobject is None:
jobject = ASSearch.new_instance(classname)
self.enforce_type(jobject, "weka.attributeSelection.ASSearch")
super(ASSearch, self).__init__(jobject=jobject, options=options)
def search(self, evaluation, data):
"""
Performs the search and returns the indices of the selected attributes.
:param evaluation: the evaluation algorithm to use
:type evaluation: ASEvaluation
:param data: the data to use
:type data: Instances
:return: the selected attributes (0-based indices)
:rtype: ndarray
"""
array = javabridge.call(
self.jobject, "search", "(Lweka/attributeSelection/ASEvaluation;Lweka/core/Instances;)[I",
evaluation.jobject, data.jobject)
if array is None:
return None
else:
javabridge.get_env().get_int_array_elements(array)
class ASEvaluation(OptionHandler):
"""
Wrapper class for attribute selection evaluation algorithm.
"""
def __init__(self, classname="weka.attributeSelection.CfsSubsetEval", jobject=None, options=None):
"""
Initializes the specified search algorithm using either the classname or the supplied JB_Object.
:param classname: the classname of the search algorithms
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
if jobject is None:
jobject = ASEvaluation.new_instance(classname)
self.enforce_type(jobject, "weka.attributeSelection.ASEvaluation")
super(ASEvaluation, self).__init__(jobject=jobject, options=options)
@property
def capabilities(self):
"""
Returns the capabilities of the classifier.
:return: the capabilities
:rtype: Capabilities
"""
return Capabilities(javabridge.call(self.jobject, "getCapabilities", "()Lweka/core/Capabilities;"))
def build_evaluator(self, data):
"""
Builds the evaluator with the data.
:param data: the data to use
:type data: Instances
"""
javabridge.call(self.jobject, "buildEvaluator", "(Lweka/core/Instances;)V", data.jobject)
def post_process(self, indices):
"""
Post-processes the evaluator with the selected attribute indices.
:param indices: the attribute indices list to use
:type indices: ndarray
:return: the processed indices
:rtype: ndarray
"""
array = javabridge.call(self.jobject, "postProcess", "([I)[I", indices)
if array is None:
return None
else:
return javabridge.get_env().get_int_array_elements(array)
class AttributeSelection(JavaObject):
"""
Performs attribute selection using search and evaluation algorithms.
"""
def __init__(self):
"""
Initializes the attribute selection.
"""
jobject = AttributeSelection.new_instance("weka.attributeSelection.AttributeSelection")
super(AttributeSelection, self).__init__(jobject)
def evaluator(self, evaluator):
"""
Sets the evaluator to use.
:param evaluator: the evaluator to use.
:type evaluator: ASEvaluation
"""
javabridge.call(self.jobject, "setEvaluator", "(Lweka/attributeSelection/ASEvaluation;)V", evaluator.jobject)
def search(self, search):
"""
Sets the search algorithm to use.
:param search: the search algorithm
:type search: ASSearch
"""
javabridge.call(self.jobject, "setSearch", "(Lweka/attributeSelection/ASSearch;)V", search.jobject)
def folds(self, folds):
"""
Sets the number of folds to use for cross-validation.
:param folds: the number of folds
:type folds: int
"""
javabridge.call(self.jobject, "setFolds", "(I)V", folds)
def ranking(self, ranking):
"""
Sets whether to perform a ranking, if possible.
:param ranking: whether to perform a ranking
:type ranking: bool
"""
javabridge.call(self.jobject, "setRanking", "(Z)V", ranking)
def seed(self, seed):
"""
Sets the seed for cross-validation.
:param seed: the seed value
:type seed: int
"""
javabridge.call(self.jobject, "setSeed", "(I)V", seed)
def crossvalidation(self, crossvalidation):
"""
Sets whether to perform cross-validation.
:param crossvalidation: whether to perform cross-validation
:type crossvalidation: bool
"""
javabridge.call(self.jobject, "setXval", "(Z)V", crossvalidation)
def select_attributes(self, instances):
"""
Performs attribute selection on the given dataset.
:param instances: the data to process
:type instances: Instances
"""
javabridge.call(self.jobject, "SelectAttributes", "(Lweka/core/Instances;)V", instances.jobject)
def select_attributes_cv_split(self, instances):
"""
Performs attribute selection on the given cross-validation split.
:param instances: the data to process
:type instances: Instances
"""
javabridge.call(self.jobject, "selectAttributesCVSplit", "(Lweka/core/Instances;)V", instances.jobject)
@property
def selected_attributes(self):
"""
Returns the selected attributes from the last run.
:return: the Numpy array of 0-based indices
:rtype: ndarray
"""
array = javabridge.call(self.jobject, "selectedAttributes", "()[I")
if array is None:
return None
else:
return javabridge.get_env().get_int_array_elements(array)
@property
def results_string(self):
"""
Generates a results string from the last attribute selection.
:return: the results string
:rtype: str
"""
return javabridge.call(self.jobject, "toResultsString", "()Ljava/lang/String;")
@property
def cv_results(self):
"""
Generates a results string from the last cross-validation attribute selection.
:return: the results string
:rtype: str
"""
return javabridge.call(self.jobject, "CVResultsString", "()Ljava/lang/String;")
@property
def number_attributes_selected(self):
"""
Returns the number of attributes that were selected.
:return: the number of attributes
:rtype: int
"""
return javabridge.call(self.jobject, "numberAttributesSelected", "()I")
@property
def ranked_attributes(self):
"""
Returns the matrix of ranked attributes from the last run.
:return: the Numpy matrix
:rtype: ndarray
"""
matrix = javabridge.call(self.jobject, "rankedAttributes", "()[[D")
if matrix is None:
return None
else:
return arrays.double_matrix_to_ndarray(matrix)
def reduce_dimensionality(self, data):
"""
Reduces the dimensionality of the provided Instance or Instances object.
:param data: the data to process
:type data: Instances
:return: the reduced dataset
:rtype: Instances
"""
if type(data) is Instance:
return Instance(
javabridge.call(
self.jobject, "reduceDimensionality",
"(Lweka/core/Instance;)Lweka/core/Instance;", data.jobject))
else:
return Instances(
javabridge.call(
self.jobject, "reduceDimensionality",
"(Lweka/core/Instances;)Lweka/core/Instances;", data.jobject))
@classmethod
def attribute_selection(cls, evaluator, args):
"""
Performs attribute selection using the given attribute evaluator and options.
:param evaluator: the evaluator to use
:type evaluator: ASEvaluation
:param args: the command-line args for the attribute selection
:type args: list
:return: the results string
:rtype: str
"""
return javabridge.static_call(
"Lweka/attributeSelection/AttributeSelection;", "SelectAttributes",
"(Lweka/attributeSelection/ASEvaluation;[Ljava/lang/String;)Ljava/lang/String;",
evaluator.jobject, args)
def main():
"""
Runs attribute selection from the command-line. Calls JVM start/stop automatically.
Use -h to see all options.
"""
parser = argparse.ArgumentParser(
description='Performs attribute selection from the command-line. Calls JVM start/stop automatically.')
parser.add_argument("-j", metavar="classpath", dest="classpath", help="additional classpath, jars/directories")
parser.add_argument("-X", metavar="heap", dest="heap", help="max heap size for jvm, e.g., 512m")
parser.add_argument("-i", metavar="input", dest="input", required=True, help="input file")
parser.add_argument("-c", metavar="class index", dest="classindex", help="1-based class attribute index")
parser.add_argument("-s", metavar="search", dest="search", help="search method, classname and options")
parser.add_argument("-x", metavar="num folds", dest="numfolds", help="number of folds")
parser.add_argument("-n", metavar="seed", dest="seed", help="the seed value for randomization")
parser.add_argument("evaluator", help="evaluator classname, e.g., weka.attributeSelection.CfsSubsetEval")
parser.add_argument("option", nargs=argparse.REMAINDER, help="additional evaluator options")
parsed = parser.parse_args()
jars = []
if parsed.classpath is not None:
jars = parsed.classpath.split(os.pathsep)
params = []
if parsed.input is not None:
params.extend(["-i", parsed.input])
if parsed.classindex is not None:
params.extend(["-c", parsed.classindex])
if parsed.search is not None:
params.extend(["-s", parsed.search])
if parsed.numfolds is not None:
params.extend(["-x", parsed.numfolds])
if parsed.seed is not None:
params.extend(["-n", parsed.seed])
jvm.start(jars, max_heap_size=parsed.heap, packages=True)
logger.debug("Commandline: " + join_options(sys.argv[1:]))
try:
evaluation = ASEvaluation(classname=parsed.evaluator)
if len(parsed.option) > 0:
evaluation.options = parsed.option
print(AttributeSelection.attribute_selection(evaluation, params))
except Exception, e:
print(e)
finally:
jvm.stop()
if __name__ == "__main__":
try:
main()
except Exception, ex:
print(ex)
python-weka-wrapper-0.3.6/python/weka/lib/ 0000755 0172670 0172670 00000000000 12677713207 022126 5 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.3.6/python/weka/lib/python-weka-wrapper.jar 0000644 0172670 0172670 00000005734 12424564746 026563 0 ustar fracpete fracpete 0000000 0000000 PK Çu_E META-INF/þÊ PK PK Çu_E META-INF/MANIFEST.MFóMÌËLK-.Ñ
K-*ÎÌϳR0Ô3àår.JM,IMÑuª ˜ëÄ›˜*hø%&ç¤*8çä%– •kòrñr PKXEC D PK
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# filters.py
# Copyright (C) 2014-2015 Fracpete (pythonwekawrapper at gmail dot com)
import javabridge
import logging
import os
import sys
import argparse
import weka.core.jvm as jvm
from weka.core.classes import OptionHandler, join_options
from weka.core.capabilities import Capabilities
from weka.core.converters import Loader
from weka.core.converters import Saver
from weka.core.dataset import Instances
from weka.core.dataset import Instance
from weka.core.stemmers import Stemmer
from weka.core.stopwords import Stopwords
from weka.core.tokenizers import Tokenizer
# logging setup
logger = logging.getLogger("weka.filters")
class Filter(OptionHandler):
"""
Wrapper class for filters.
"""
def __init__(self, classname="weka.filters.AllFilter", jobject=None, options=None):
"""
Initializes the specified filter using either the classname or the supplied JB_Object.
:param classname: the classname of the filter
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: the list of commandline options to set
:type options: list
"""
if jobject is None:
jobject = Filter.new_instance(classname)
self.enforce_type(jobject, "weka.filters.Filter")
super(Filter, self).__init__(jobject=jobject, options=options)
self.__input = javabridge.make_call(self.jobject, "input", "(Lweka/core/Instance;)Z")
self.__batchfinished = javabridge.make_call(self.jobject, "batchFinished", "()Z")
self.__output = javabridge.make_call(self.jobject, "output", "()Lweka/core/Instance;")
self.__outputformat = javabridge.make_call(self.jobject, "getOutputFormat", "()Lweka/core/Instances;")
def capabilities(self):
"""
Returns the capabilities of the filter.
:return: the capabilities
:rtype: Capabilities
"""
return Capabilities(javabridge.call(self.jobject, "getCapabilities", "()Lweka/core/Capabilities;"))
def inputformat(self, data):
"""
Sets the input format.
:param data: the data to use as input
:type data: Instances
"""
return javabridge.call(self.jobject, "setInputFormat", "(Lweka/core/Instances;)Z", data.jobject)
def input(self, inst):
"""
Inputs the Instance.
:param inst: the instance to filter
:type inst: Instance
:return: True if filtered can be collected from output
:rtype: bool
"""
return self.__input(inst.jobject)
def batch_finished(self):
"""
Signals the filter that the batch of data has finished.
:return: True if instances can be collected from the output
:rtype: bool
"""
return self.__batchfinished()
def outputformat(self):
"""
Returns the output format.
:return: the output format
:rtype: Instances
"""
inst = self.__outputformat()
if inst is None:
return None
else:
return Instances(inst)
def output(self):
"""
Outputs the filtered Instance.
:return: the filtered instance
:rtype: an Instance object
"""
return Instance(jobject=self.__output())
def filter(self, data):
"""
Filters the dataset(s). When providing a list, this can be used to create compatible train/test sets,
since the filter only gets initialized with the first dataset and all subsequent datasets get transformed
using the same setup.
NB: inputformat(Instances) must have been called beforehand.
:param data: the Instances to filter
:type data: Instances or list of Instances
:return: the filtered Instances object(s)
:rtype: Instances or list of Instances
"""
if isinstance(data, list):
result = []
for d in data:
result.append(Instances(javabridge.static_call(
"Lweka/filters/Filter;", "useFilter",
"(Lweka/core/Instances;Lweka/filters/Filter;)Lweka/core/Instances;",
d.jobject, self.jobject)))
return result
else:
return Instances(javabridge.static_call(
"Lweka/filters/Filter;", "useFilter",
"(Lweka/core/Instances;Lweka/filters/Filter;)Lweka/core/Instances;",
data.jobject, self.jobject))
@classmethod
def make_copy(cls, flter):
"""
Creates a copy of the filter.
:param flter: the filter to copy
:type flter: Filter
:return: the copy of the filter
:rtype: Filter
"""
return Filter(
jobject=javabridge.static_call(
"weka/filters/Filter", "makeCopy",
"(Lweka/filters/Filter;)Lweka/filters/Filter;", flter.jobject))
class MultiFilter(Filter):
"""
Wrapper class for weka.filters.MultiFilter.
"""
def __init__(self, jobject=None, options=None):
"""
Initializes the MultiFilter instance using either creating new instance or using the supplied JB_Object.
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: list of commandline options
:type options: list
"""
if jobject is None:
classname = "weka.filters.MultiFilter"
jobject = MultiFilter.new_instance(classname)
self.enforce_type(jobject, "weka.filters.MultiFilter")
super(MultiFilter, self).__init__(jobject=jobject, options=options)
@property
def filters(self):
"""
Returns the list of base filters.
:return: the filter list
:rtype: list
"""
objects = javabridge.get_env().get_object_array_elements(
javabridge.call(self.jobject, "getFilters", "()[Lweka/filters/Filter;"))
result = []
for obj in objects:
result.append(Filter(jobject=obj))
return result
@filters.setter
def filters(self, filters):
"""
Sets the base filters.
:param filters: the list of base filters to use
:type filters: list
"""
obj = []
for fltr in filters:
obj.append(fltr.jobject)
javabridge.call(self.jobject, "setFilters", "([Lweka/filters/Filter;)V", obj)
class StringToWordVector(Filter):
"""
Wrapper class for weka.filters.unsupervised.attribute.StringToWordVector.
"""
def __init__(self, jobject=None, options=None):
"""
Initializes the StringToWordVector instance using either creating new instance or using the supplied JB_Object.
:param jobject: the JB_Object to use
:type jobject: JB_Object
:param options: list of commandline options
:type options: list
"""
if jobject is None:
classname = "weka.filters.unsupervised.attribute.StringToWordVector"
jobject = StringToWordVector.new_instance(classname)
self.enforce_type(jobject, "weka.filters.unsupervised.attribute.StringToWordVector")
super(StringToWordVector, self).__init__(jobject=jobject, options=options)
@property
def stemmer(self):
"""
Returns the stemmer.
:return: the stemmer
:rtype: Stemmer
"""
return Stemmer(
jobject=javabridge.call(self.jobject, "getStemmer", "()Lweka/core/stemmers/Stemmer;"))
@stemmer.setter
def stemmer(self, stemmer):
"""
Sets the stemmer.
:param stemmer: the stemmer to use
:type stemmer: Stemmer
"""
javabridge.call(
self.jobject, "setStemmer", "(Lweka/core/stemmers/Stemmer;)V", stemmer.jobject)
@property
def stopwords(self):
"""
Returns the stopwords handler.
:return: the stopwords handler
:rtype: Stopwords
"""
return Stopwords(
jobject=javabridge.call(self.jobject, "getStopwordsHandler", "()Lweka/core/stopwords/StopwordsHandler;"))
@stopwords.setter
def stopwords(self, stopwords):
"""
Sets the stopwords handler.
:param stopwords: the stopwords handler to use
:type stopwords: Stopwords
"""
javabridge.call(
self.jobject, "setStopwordsHandler", "(Lweka/core/stopwords/StopwordsHandler;)V", stopwords.jobject)
@property
def tokenizer(self):
"""
Returns the tokenizer.
:return: the tokenizer
:rtype: Tokenizer
"""
return Tokenizer(
jobject=javabridge.call(self.jobject, "getTokenizer", "()Lweka/core/tokenizers/Tokenizer;"))
@tokenizer.setter
def tokenizer(self, tokenizer):
"""
Sets the tokenizer.
:param tokenizer: the tokenizer to use
:type tokenizer: Tokenizer
"""
javabridge.call(
self.jobject, "setTokenizer", "(Lweka/core/tokenizers/Tokenizer;)V", tokenizer.jobject)
def main():
"""
Runs a filter from the command-line. Calls JVM start/stop automatically.
Use -h to see all options.
"""
parser = argparse.ArgumentParser(
description='Executes a filter from the command-line. Calls JVM start/stop automatically.')
parser.add_argument("-j", metavar="classpath", dest="classpath", help="additional classpath, jars/directories")
parser.add_argument("-X", metavar="heap", dest="heap", help="max heap size for jvm, e.g., 512m")
parser.add_argument("-i", metavar="input1", dest="input1", required=True, help="input file 1")
parser.add_argument("-o", metavar="output1", dest="output1", required=True, help="output file 1")
parser.add_argument("-r", metavar="input2", dest="input2", help="input file 2")
parser.add_argument("-s", metavar="output2", dest="output2", help="output file 2")
parser.add_argument("-c", metavar="classindex", default="-1", dest="classindex",
help="1-based class attribute index")
parser.add_argument("filter", help="filter classname, e.g., weka.filters.AllFilter")
parser.add_argument("option", nargs=argparse.REMAINDER, help="additional filter options")
parsed = parser.parse_args()
if parsed.input2 is None and parsed.output2 is not None:
raise Exception("No second input file provided ('-r ...')!")
jars = []
if parsed.classpath is not None:
jars = parsed.classpath.split(os.pathsep)
params = []
if parsed.input1 is not None:
params.extend(["-i", parsed.input1])
if parsed.output1 is not None:
params.extend(["-o", parsed.output1])
if parsed.input2 is not None:
params.extend(["-r", parsed.input2])
if parsed.output2 is not None:
params.extend(["-s", parsed.output2])
if parsed.classindex is not None:
params.extend(["-c", parsed.classindex])
jvm.start(jars, max_heap_size=parsed.heap, packages=True)
logger.debug("Commandline: " + join_options(sys.argv[1:]))
try:
flter = Filter(parsed.filter)
if len(parsed.option) > 0:
flter.options = parsed.option
loader = Loader(classname="weka.core.converters.ArffLoader")
in1 = loader.load_file(parsed.input1)
cls = parsed.classindex
if str(parsed.classindex) == "first":
cls = "0"
if str(parsed.classindex) == "last":
cls = str(in1.num_attributes - 1)
in1.class_index = int(cls)
flter.inputformat(in1)
out1 = flter.filter(in1)
saver = Saver(classname="weka.core.converters.ArffSaver")
saver.save_file(out1, parsed.output1)
if parsed.input2 is not None:
in2 = loader.load_file(parsed.input2)
in2.class_index = int(cls)
out2 = flter.filter(in2)
saver.save_file(out2, parsed.output2)
except Exception, e:
print(e)
finally:
jvm.stop()
if __name__ == "__main__":
try:
main()
except Exception, ex:
print(ex)
python-weka-wrapper-0.3.6/python/weka/plot/ 0000755 0172670 0172670 00000000000 12677713207 022336 5 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.3.6/python/weka/plot/dataset.py 0000644 0172670 0172670 00000016407 12576431303 024335 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# dataset.py
# Copyright (C) 2014-2015 Fracpete (pythonwekawrapper at gmail dot com)
import logging
import weka.plot as plot
if plot.matplotlib_available:
import matplotlib.pyplot as plt
from weka.core.dataset import Instances
# logging setup
logger = logging.getLogger(__name__)
def scatter_plot(data, index_x, index_y, percent=100.0, seed=1, size=50, title=None, outfile=None, wait=True):
"""
Plots two attributes against each other.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
:param data: the dataset
:type data: Instances
:param index_x: the 0-based index of the attribute on the x axis
:type index_x: int
:param index_y: the 0-based index of the attribute on the y axis
:type index_y: int
:param percent: the percentage of the dataset to use for plotting
:type percent: float
:param seed: the seed value to use for subsampling
:type seed: int
:param size: the size of the circles in point
:type size: int
:param title: an optional title
:type title: str
:param outfile: the (optional) file to save the generated plot to. The extension determines the file format.
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
# create subsample
data = plot.create_subsample(data, percent=percent, seed=seed)
# collect data
x = []
y = []
if data.class_index == -1:
c = None
else:
c = []
for i in xrange(data.num_instances):
inst = data.get_instance(i)
x.append(inst.get_value(index_x))
y.append(inst.get_value(index_y))
if c is not None:
c.append(inst.get_value(inst.class_index))
# plot data
fig, ax = plt.subplots()
if c is None:
ax.scatter(x, y, s=size, alpha=0.5)
else:
ax.scatter(x, y, c=c, s=size, alpha=0.5)
ax.set_xlabel(data.attribute(index_x).name)
ax.set_ylabel(data.attribute(index_y).name)
if title is None:
title = "Attribute scatter plot"
if percent != 100:
title += " (%0.1f%%)" % percent
ax.set_title(title)
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c="0.3")
ax.grid(True)
fig.canvas.set_window_title(data.relationname)
plt.draw()
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show()
def matrix_plot(data, percent=100.0, seed=1, size=10, title=None, outfile=None, wait=True):
"""
Plots all attributes against each other.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
:param data: the dataset
:type data: Instances
:param percent: the percentage of the dataset to use for plotting
:type percent: float
:param seed: the seed value to use for subsampling
:type seed: int
:param size: the size of the circles in point
:type size: int
:param title: an optional title
:type title: str
:param outfile: the (optional) file to save the generated plot to. The extension determines the file format.
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
# create subsample
data = plot.create_subsample(data, percent=percent, seed=seed)
fig = plt.figure()
if data.class_index == -1:
c = None
else:
c = []
for i in xrange(data.num_instances):
inst = data.get_instance(i)
c.append(inst.get_value(inst.class_index))
for index_x in xrange(data.num_attributes):
x = []
for i in xrange(data.num_instances):
inst = data.get_instance(i)
x.append(inst.get_value(index_x))
for index_y in xrange(data.num_attributes):
y = []
for i in xrange(data.num_instances):
inst = data.get_instance(i)
y.append(inst.get_value(index_y))
ax = fig.add_subplot(
data.num_attributes, data.num_attributes, index_x * data.num_attributes + index_y + 1)
if c is None:
ax.scatter(x, y, s=size, alpha=0.5)
else:
ax.scatter(x, y, c=c, s=size, alpha=0.5)
ax.set_xlabel(data.attribute(index_x).name)
ax.set_ylabel(data.attribute(index_y).name)
ax.get_yaxis().set_ticklabels([])
ax.get_xaxis().set_ticklabels([])
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c="0.3")
ax.grid(True)
if title is None:
title = data.relationname
if percent != 100:
title += " (%0.1f%%)" % percent
fig.canvas.set_window_title(title)
plt.draw()
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show()
def line_plot(data, atts=None, percent=100.0, seed=1, title=None, outfile=None, wait=True):
"""
Uses the internal format to plot the dataset, one line per instance.
:param data: the dataset
:type data: Instances
:param atts: the list of 0-based attribute indices of attributes to plot
:type atts: list
:param percent: the percentage of the dataset to use for plotting
:type percent: float
:param seed: the seed value to use for subsampling
:type seed: int
:param title: an optional title
:type title: str
:param outfile: the (optional) file to save the generated plot to. The extension determines the file format.
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
# create subsample
data = plot.create_subsample(data, percent=percent, seed=seed)
fig = plt.figure()
if atts is None:
x = []
for i in xrange(data.num_attributes):
x.append(i)
else:
x = atts
ax = fig.add_subplot(111)
ax.set_xlabel("attributes")
ax.set_ylabel("value")
ax.grid(True)
for index_y in xrange(data.num_instances):
y = []
for index_x in x:
y.append(data.get_instance(index_y).get_value(index_x))
ax.plot(x, y, "o-", alpha=0.5)
if title is None:
title = data.relationname
if percent != 100:
title += " (%0.1f%%)" % percent
fig.canvas.set_window_title(title)
plt.draw()
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show()
python-weka-wrapper-0.3.6/python/weka/plot/experiments.py 0000644 0172670 0172670 00000006432 12576431307 025254 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# experiments.py
# Copyright (C) 2015 Fracpete (pythonwekawrapper at gmail dot com)
import logging
import math
import weka.plot as plot
if plot.matplotlib_available:
import matplotlib.pyplot as plt
from weka.experiments import ResultMatrix
# logging setup
logger = logging.getLogger(__name__)
def plot_experiment(mat, title="Experiment", axes_swapped=False, measure="Statistic", show_stdev=False,
key_loc="lower right", outfile=None, wait=True):
"""
Plots the results from an experiment.
:param mat: the result matrix to plot
:type mat: ResultMatrix
:param title: the title for the experiment
:type title: str
:param axes_swapped: whether the axes whether swapped ("sets x cls" or "cls x sets")
:type axes_swapped: bool
:param measure: the measure that is being displayed
:type measure: str
:param show_stdev: whether to show the standard deviation as error bar
:type show_stdev: bool
:param key_loc: the location string for the key
:type key_loc: str
:param outfile: the output file, ignored if None
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
if not isinstance(mat, ResultMatrix):
logger.error("Need to supply a result matrix!")
return
fig, ax = plt.subplots()
if axes_swapped:
ax.set_xlabel(measure)
ax.set_ylabel("Classifiers")
else:
ax.set_xlabel("Classifiers")
ax.set_ylabel(measure)
ax.set_title(title)
fig.canvas.set_window_title(title)
ax.grid(True)
ticksx = []
ticks = []
inc = 1.0 / float(mat.columns)
for i in range(mat.columns):
ticksx.append((i + 0.5) * inc)
ticks.append("[" + str(i+1) + "]")
plt.xticks(ticksx, ticks)
plt.xlim([0.0, 1.0])
for r in range(mat.rows):
x = []
means = []
stdevs = []
for c in range(mat.columns):
mean = mat.get_mean(c, r)
stdev = mat.get_stdev(c, r)
if not math.isnan(mean):
x.append((c + 0.5) * inc)
means.append(mean)
if not math.isnan(stdev):
stdevs.append(stdev)
plot_label = mat.get_row_name(r)
if show_stdev:
ax.errorbar(x, means, yerr=stdevs, fmt='-o', ls="-", label=plot_label)
else:
ax.plot(x, means, "o-", label=plot_label)
plt.draw()
plt.legend(loc=key_loc, shadow=True)
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show()
python-weka-wrapper-0.3.6/python/weka/plot/classifiers.py 0000644 0172670 0172670 00000031735 12576431241 025221 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# classifiers.py
# Copyright (C) 2014-2015 Fracpete (pythonwekawrapper at gmail dot com)
import javabridge
import logging
import weka.plot as plot
if plot.matplotlib_available:
import matplotlib.pyplot as plt
from weka.core.classes import JavaObject, join_options
from weka.core.dataset import Instances
from weka.classifiers import Classifier, Evaluation, NumericPrediction, NominalPrediction
# logging setup
logger = logging.getLogger(__name__)
def plot_classifier_errors(predictions, absolute=True, max_relative_size=50, absolute_size=50, title=None,
outfile=None, wait=True):
"""
Plots the classifers for the given list of predictions.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
:param predictions: the predictions to plot
:type predictions: list
:param absolute: whether to use absolute errors as size or relative ones
:type absolute: bool
:param max_relative_size: the maximum size in point in case of relative mode
:type max_relative_size: int
:param absolute_size: the size in point in case of absolute mode
:type absolute_size: int
:param title: an optional title
:type title: str
:param outfile: the output file, ignored if None
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
actual = []
predicted = []
error = None
cls = None
for pred in predictions:
actual.append(pred.actual)
predicted.append(pred.predicted)
if isinstance(pred, NumericPrediction):
if error is None:
error = []
error.append(abs(pred.error))
elif isinstance(pred, NominalPrediction):
if cls is None:
cls = []
if pred.actual != pred.predicted:
cls.append(1)
else:
cls.append(0)
fig, ax = plt.subplots()
if error is None and cls is None:
ax.scatter(actual, predicted, s=absolute_size, alpha=0.5)
elif cls is not None:
ax.scatter(actual, predicted, c=cls, s=absolute_size, alpha=0.5)
elif error is not None:
if not absolute:
min_err = min(error)
max_err = max(error)
factor = (max_err - min_err) / max_relative_size
for i in xrange(len(error)):
error[i] = error[i] / factor * max_relative_size
ax.scatter(actual, predicted, s=error, alpha=0.5)
ax.set_xlabel("actual")
ax.set_ylabel("predicted")
if title is None:
title = "Classifier errors"
ax.set_title(title)
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c="0.3")
ax.grid(True)
fig.canvas.set_window_title(title)
plt.draw()
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show()
def generate_thresholdcurve_data(evaluation, class_index):
"""
Generates the threshold curve data from the evaluation object's predictions.
:param evaluation: the evaluation to obtain the predictions from
:type evaluation: Evaluation
:param class_index: the 0-based index of the class-label to create the plot for
:type class_index: int
:return: the generated threshold curve data
:rtype: Instances
"""
jtc = JavaObject.new_instance("weka.classifiers.evaluation.ThresholdCurve")
pred = javabridge.call(evaluation.jobject, "predictions", "()Ljava/util/ArrayList;")
result = Instances(
javabridge.call(jtc, "getCurve", "(Ljava/util/ArrayList;I)Lweka/core/Instances;", pred, class_index))
return result
def get_thresholdcurve_data(data, xname, yname):
"""
Retrieves x and y columns from of the data generated by the weka.classifiers.evaluation.ThresholdCurve
class.
:param data: the threshold curve data
:type data: Instances
:param xname: the name of the X column
:type xname: str
:param yname: the name of the Y column
:type yname: str
:return: tuple of x and y arrays
:rtype: tuple
"""
xi = data.attribute_by_name(xname).index
yi = data.attribute_by_name(yname).index
x = []
y = []
for i in xrange(data.num_instances):
inst = data.get_instance(i)
x.append(inst.get_value(xi))
y.append(inst.get_value(yi))
return x, y
def get_auc(data):
"""
Calculates the area under the ROC curve (AUC).
:param data: the threshold curve data
:type data: Instances
:return: the area
:rtype: float
"""
return javabridge.static_call(
"weka/classifiers/evaluation/ThresholdCurve", "getROCArea", "(Lweka/core/Instances;)D", data.jobject)
def get_prc(data):
"""
Calculates the area under the precision recall curve (PRC).
:param data: the threshold curve data
:type data: Instances
:return: the area
:rtype: float
"""
return javabridge.static_call(
"weka/classifiers/evaluation/ThresholdCurve", "getPRCArea", "(Lweka/core/Instances;)D", data.jobject)
def plot_roc(evaluation, class_index=None, title=None, key_loc="lower right", outfile=None, wait=True):
"""
Plots the ROC (receiver operator characteristics) curve for the given predictions.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
:param evaluation: the evaluation to obtain the predictions from
:type evaluation: Evaluation
:param class_index: the list of 0-based indices of the class-labels to create the plot for
:type class_index: list
:param title: an optional title
:type title: str
:param key_loc: the position string for the key
:type key_loc: str
:param outfile: the output file, ignored if None
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
if class_index is None:
class_index = [0]
ax = None
for cindex in class_index:
data = generate_thresholdcurve_data(evaluation, cindex)
head = evaluation.header
area = get_auc(data)
x, y = get_thresholdcurve_data(data, "False Positive Rate", "True Positive Rate")
if ax is None:
fig, ax = plt.subplots()
ax.set_xlabel("False Positive Rate")
ax.set_ylabel("True Positive Rate")
if title is None:
title = "ROC"
ax.set_title(title)
ax.grid(True)
fig.canvas.set_window_title(title)
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plot_label = head.class_attribute.value(cindex) + " (AUC: %0.4f)" % area
ax.plot(x, y, label=plot_label)
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c="0.3")
plt.draw()
plt.legend(loc=key_loc, shadow=True)
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show()
def plot_prc(evaluation, class_index=None, title=None, key_loc="lower center", outfile=None, wait=True):
"""
Plots the PRC (precision recall) curve for the given predictions.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
:param evaluation: the evaluation to obtain the predictions from
:type evaluation: Evaluation
:param class_index: the list of 0-based indices of the class-labels to create the plot for
:type class_index: list
:param title: an optional title
:type title: str
:param key_loc: the location string for the key
:type key_loc: str
:param outfile: the output file, ignored if None
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
if class_index is None:
class_index = [0]
ax = None
for cindex in class_index:
data = generate_thresholdcurve_data(evaluation, cindex)
head = evaluation.header
area = get_prc(data)
x, y = get_thresholdcurve_data(data, "Recall", "Precision")
if ax is None:
fig, ax = plt.subplots()
ax.set_xlabel("Recall")
ax.set_ylabel("Precision")
if title is None:
title = "PRC"
ax.set_title(title)
fig.canvas.set_window_title(title)
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
ax.grid(True)
plot_label = head.class_attribute.value(cindex) + " (PRC: %0.4f)" % area
ax.plot(x, y, label=plot_label)
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c="0.3")
plt.draw()
plt.legend(loc=key_loc, shadow=True)
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show()
def plot_learning_curve(classifiers, train, test=None, increments=100, metric="percent_correct",
title="Learning curve", label_template="[#] @ $", key_loc="lower right",
outfile=None, wait=True):
"""
Plots a learning curve.
:param classifiers: list of Classifier template objects
:type classifiers: list of Classifier
:param train: dataset to use for the building the classifier, used for evaluating it test set None
:type train: Instances
:param test: optional dataset to use for the testing the built classifiers
:type test: Instances
:param increments: the increments (>= 1: # of instances, <1: percentage of dataset)
:type increments: float
:param metric: the name of the numeric metric to plot (Evaluation.)
:type metric: str
:param title: the title for the plot
:type title: str
:param label_template: the template for the label in the plot
(#: 1-based index, @: full classname, !: simple classname, $: options)
:type label_template: str
:param key_loc: the location string for the key
:type key_loc: str
:param outfile: the output file, ignored if None
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
if not train.has_class():
logger.error("Training set has no class attribute set!")
return
if (test is not None) and (train.equal_headers(test) is not None):
logger.error("Training and test set are not compatible: " + train.equal_headers(test))
return
if increments >= 1:
inc = increments
else:
inc = round(train.num_instances * increments)
steps = []
cls = []
evls = {}
for classifier in classifiers:
cl = Classifier.make_copy(classifier)
cls.append(cl)
evls[cl] = []
if test is None:
tst = train
else:
tst = test
for i in xrange(train.num_instances):
if (i > 0) and (i % inc == 0):
steps.append(i+1)
for cl in cls:
# train
if cl.is_updateable:
if i == 0:
tr = Instances.copy_instances(train, 0, 1)
cl.build_classifier(tr)
else:
cl.update_classifier(train.get_instance(i))
else:
if (i > 0) and (i % inc == 0):
tr = Instances.copy_instances(train, 0, i + 1)
cl.build_classifier(tr)
# evaluate
if (i > 0) and (i % inc == 0):
evl = Evaluation(tst)
evl.test_model(cl, tst)
evls[cl].append(getattr(evl, metric))
fig, ax = plt.subplots()
ax.set_xlabel("# of instances")
ax.set_ylabel(metric)
ax.set_title(title)
fig.canvas.set_window_title(title)
ax.grid(True)
i = 0
for cl in cls:
evl = evls[cl]
i += 1
plot_label = label_template.\
replace("#", str(i)).\
replace("@", cl.classname).\
replace("!", cl.classname[cl.classname.rfind(".") + 1:]).\
replace("$", join_options(cl.config))
ax.plot(steps, evl, label=plot_label)
plt.draw()
plt.legend(loc=key_loc, shadow=True)
if outfile is not None:
plt.savefig(outfile)
if wait:
plt.show()
python-weka-wrapper-0.3.6/python/weka/plot/clusterers.py 0000644 0172670 0172670 00000007167 12576431255 025114 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# clusterers.py
# Copyright (C) 2014-2015 Fracpete (pythonwekawrapper at gmail dot com)
import logging
import weka.plot as plot
if plot.matplotlib_available:
import matplotlib.pyplot as plt
from weka.core.dataset import Instances
from weka.clusterers import ClusterEvaluation
# logging setup
logger = logging.getLogger(__name__)
def plot_cluster_assignments(evl, data, atts=None, inst_no=False, size=10, title=None, outfile=None, wait=True):
"""
Plots the cluster assignments against the specified attributes.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
:param evl: the cluster evaluation to obtain the cluster assignments from
:type evl: ClusterEvaluation
:param data: the dataset the clusterer was evaluated against
:type data: Instances
:param atts: the list of attribute indices to plot, None for all
:type atts: list
:param inst_no: whether to include a fake attribute with the instance number
:type inst_no: bool
:param size: the size of the circles in point
:type size: int
:param title: an optional title
:type title: str
:param outfile: the (optional) file to save the generated plot to. The extension determines the file format.
:type outfile: str
:param wait: whether to wait for the user to close the plot
:type wait: bool
"""
if not plot.matplotlib_available:
logger.error("Matplotlib is not installed, plotting unavailable!")
return
fig = plt.figure()
if data.class_index == -1:
c = None
else:
c = []
for i in xrange(data.num_instances):
inst = data.get_instance(i)
c.append(inst.get_value(inst.class_index))
if atts is None:
atts = []
for i in xrange(data.num_attributes):
atts.append(i)
num_plots = len(atts)
if inst_no:
num_plots += 1
clusters = evl.cluster_assignments
for index, att in enumerate(atts):
x = data.values(att)
ax = fig.add_subplot(
1, num_plots, index + 1)
if c is None:
ax.scatter(clusters, x, s=size, alpha=0.5)
else:
ax.scatter(clusters, x, c=c, s=size, alpha=0.5)
ax.set_xlabel("Clusters")
ax.set_title(data.attribute(att).name)
ax.get_xaxis().set_ticks(list(set(clusters)))
ax.grid(True)
if inst_no:
x = []
for i in xrange(data.num_instances):
x.append(i+1)
ax = fig.add_subplot(
1, num_plots, num_plots)
if c is None:
ax.scatter(clusters, x, s=size, alpha=0.5)
else:
ax.scatter(clusters, x, c=c, s=size, alpha=0.5)
ax.set_xlabel("Clusters")
ax.set_title("Instance number")
ax.get_xaxis().set_ticks(list(set(clusters)))
ax.grid(True)
if title is None:
title = data.relationname
fig.canvas.set_window_title(title)
plt.draw()
if not outfile is None:
plt.savefig(outfile)
if wait:
plt.show()
python-weka-wrapper-0.3.6/python/weka/plot/graph.py 0000644 0172670 0172670 00000003264 12576431323 024010 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# graph.py
# Copyright (C) 2014-2015 Fracpete (pythonwekawrapper at gmail dot com)
import logging
import tempfile
import weka.plot as plot
if plot.pygraphviz_available:
from pygraphviz.agraph import AGraph
if plot.PIL_available:
from PIL import Image
# logging setup
logger = logging.getLogger(__name__)
def plot_dot_graph(graph, filename=None):
"""
Plots a graph in graphviz dot notation.
:param graph: the dot notation graph
:type graph: str
:param filename: the (optional) file to save the generated plot to. The extension determines the file format.
:type filename: str
"""
if not plot.pygraphviz_available:
logger.error("Pygraphviz is not installed, cannot generate graph plot!")
return
if not plot.PIL_available:
logger.error("PIL is not installed, cannot display graph plot!")
return
agraph = AGraph(graph)
agraph.layout(prog='dot')
if filename is None:
filename = tempfile.mktemp(suffix=".png")
agraph.draw(filename)
image = Image.open(filename)
image.show()
python-weka-wrapper-0.3.6/python/weka/plot/__init__.py 0000644 0172670 0172670 00000003372 12446346264 024453 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# __init__.py
# Copyright (C) 2014 Fracpete (pythonwekawrapper at gmail dot com)
from weka.core.dataset import Instances
from weka.core.classes import Random
# check whether pygraphviz is there
pygraphviz_available = False
try:
import pygraphviz
pygraphviz_available = True
except ImportError:
pass
# check whether PIL is there
PIL_available = False
try:
import PIL
PIL_available = True
except ImportError:
pass
# check whether matplotlib is there
matplotlib_available = False
try:
import matplotlib
matplotlib_available = True
except ImportError:
pass
def create_subsample(data, percent, seed=1):
"""
Generates a subsample of the dataset.
:param data: the data to create the subsample from
:type data: Instances
:param percent: the percentage (0-100)
:type percent: float
:param seed: the seed value to use
:type seed: int
"""
if percent <= 0 or percent >= 100:
return data
data = Instances.copy_instances(data)
data.randomize(Random(seed))
data = Instances.copy_instances(data, 0, int(round(data.num_instances * percent / 100.0)))
return data
python-weka-wrapper-0.3.6/python/weka/__init__.py 0000644 0172670 0172670 00000000000 12322574641 023451 0 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.3.6/python/weka/datagenerators.py 0000644 0172670 0172670 00000015350 12576426115 024736 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# datagenerators.py
# Copyright (C) 2014-2015 Fracpete (pythonwekawrapper at gmail dot com)
import javabridge
import logging
import os
import sys
import argparse
import weka.core.jvm as jvm
import weka.core.classes as classes
from weka.core.classes import OptionHandler, join_options, to_commandline, from_commandline
from weka.core.dataset import Instances, Instance
# logging setup
logger = logging.getLogger("weka.datagenerators")
class DataGenerator(OptionHandler):
"""
Wrapper class for datagenerators.
"""
def __init__(self, classname="weka.datagenerators.classifiers.classification.Agrawal", jobject=None, options=None):
"""
Initializes the specified datagenerator using either the classname or the supplied JB_Object.
:param classname: the classname of the datagenerator
:type classname: str
:param jobject: the JB_Object to use
:type jobject: JB_Object
"""
if jobject is None:
jobject = DataGenerator.new_instance(classname)
self.enforce_type(jobject, "weka.datagenerators.DataGenerator")
super(DataGenerator, self).__init__(jobject=jobject, options=options)
def define_data_format(self):
"""
Returns the data format.
:return: the data format
:rtype: Instances
"""
data = javabridge.call(self.jobject, "defineDataFormat", "()Lweka/core/Instances;")
if data is None:
return None
else:
return Instances(data)
@property
def single_mode_flag(self):
"""
Returns whether data is generated row by row (True) or in one go (False).
:return: whether incremental
:rtype: bool
"""
return javabridge.call(self.jobject, "getSingleModeFlag", "()Z")
@property
def dataset_format(self):
"""
Returns the dataset format.
:return: the format
:rtype: Instances
"""
data = javabridge.call(self.jobject, "getDatasetFormat", "()Lweka/core/Instances;")
if data is None:
return None
else:
return Instances(data)
@dataset_format.setter
def dataset_format(self, inst):
"""
Sets the dataset format.
:param inst: the Instances to use as dataset format
:type inst: Instances
"""
javabridge.call(self.jobject, "setDatasetFormat", "(Lweka/core/Instances;)V", inst.jobject)
def generate_start(self):
"""
Returns a "start" string.
:return: the start comment
:rtype: str
"""
return javabridge.call(self.jobject, "generateStart", "()Ljava/lang/String;")
@property
def num_examples_act(self):
"""
Returns a actual number of examples to generate.
:return: the number of examples
:rtype: int
"""
return javabridge.call(self.jobject, "getNumExamplesAct", "()I")
def generate_example(self):
"""
Returns a single Instance.
:return: the next example
:rtype: Instance
"""
data = javabridge.call(self.jobject, "generateExample", "()Lweka/core/Instance;")
if data is None:
return None
else:
return Instance(data)
def generate_examples(self):
"""
Returns complete dataset.
:return: the generated dataset
:rtype: Instances
"""
data = javabridge.call(self.jobject, "generateExamples", "()Lweka/core/Instances;")
if data is None:
return None
else:
return Instances(data)
def generate_finish(self):
"""
Returns a "finish" string.
:return: a finish comment
:rtype: str
"""
return javabridge.call(self.jobject, "generateFinish", "()Ljava/lang/String;")
@classmethod
def make_data(cls, generator, args):
"""
Generates data using the generator and commandline arguments.
:param generator: the generator instance to use
:type generator: DataGenerator
:param args: the command-line arguments
:type args: list
"""
javabridge.static_call(
"Lweka/datagenerators/DataGenerator;", "makeData",
"(Lweka/datagenerators/DataGenerator;[Ljava/lang/String;)V",
generator.jobject, args)
@classmethod
def make_copy(cls, generator):
"""
Creates a copy of the generator.
:param generator: the generator to copy
:type generator: DataGenerator
:return: the copy of the generator
:rtype: DataGenerator
"""
return from_commandline(
to_commandline(generator), classname=classes.get_classname(DataGenerator()))
def main():
"""
Runs a datagenerator from the command-line. Calls JVM start/stop automatically.
Use -h to see all options.
"""
parser = argparse.ArgumentParser(
description='Executes a data generator from the command-line. Calls JVM start/stop automatically.')
parser.add_argument("-j", metavar="classpath", dest="classpath", help="additional classpath, jars/directories")
parser.add_argument("-X", metavar="heap", dest="heap", help="max heap size for jvm, e.g., 512m")
parser.add_argument("datagenerator", help="data generator classname, e.g., "
+ "weka.datagenerators.classifiers.classification.LED24")
parser.add_argument("option", nargs=argparse.REMAINDER, help="additional data generator options")
parsed = parser.parse_args()
jars = []
if parsed.classpath is not None:
jars = parsed.classpath.split(os.pathsep)
jvm.start(jars, max_heap_size=parsed.heap, packages=True)
logger.debug("Commandline: " + join_options(sys.argv[1:]))
try:
generator = DataGenerator(classname=parsed.datagenerator)
if len(parsed.option) > 0:
generator.options = parsed.option
DataGenerator.make_data(generator, parsed.option)
except Exception, e:
print(e)
finally:
jvm.stop()
if __name__ == "__main__":
try:
main()
except Exception, ex:
print(ex)
python-weka-wrapper-0.3.6/python/weka/flow/ 0000755 0172670 0172670 00000000000 12677713207 022327 5 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.3.6/python/weka/flow/control.py 0000644 0172670 0172670 00000107552 12576430305 024364 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# control.py
# Copyright (C) 2015 Fracpete (pythonwekawrapper at gmail dot com)
import weka.flow.base as base
from weka.flow.base import Actor, InputConsumer, OutputProducer, Stoppable, StorageHandler, Token
from weka.flow.transformer import Transformer
import weka.core.classes as classes
class ActorHandler(Actor):
"""
The ancestor for all actors that handle other actors.
"""
def __init__(self, name=None, config=None):
"""
Initializes the actor handler.
:param name: the name of the actor handler
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(ActorHandler, self).__init__(name=name, config=config)
self._director = self.new_director()
if not classes.has_dict_handler("ActorHandler"):
classes.register_dict_handler("ActorHandler", ActorHandler.from_dict)
def new_director(self):
"""
Creates the director to use for handling the sub-actors.
:return: the director instance
:rtype: Director
"""
raise Exception("Not implemented!")
def default_actors(self):
"""
Returns the default actors to use.
:return: the default actors, if any
:rtype: list
"""
return []
def check_actors(self, actors):
"""
Performs checks on the actors that are to be used. Raises an exception if invalid setup.
:param actors: the actors to check
:type actors: list
"""
pass
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(ActorHandler, self).fix_config(options)
opt = "actors"
if opt not in options:
options[opt] = self.default_actors()
if opt not in self.help:
self.help[opt] = "The list of sub-actors that this actor manages."
return options
def to_dict(self):
"""
Returns a dictionary that represents this object, to be used for JSONification.
:return: the object dictionary
:rtype: dict
"""
result = super(ActorHandler, self).to_dict()
result["type"] = "ActorHandler"
del result["config"]["actors"]
result["actors"] = []
for actor in self.actors:
result["actors"].append(actor.to_dict())
return result
@classmethod
def from_dict(cls, d):
"""
Restores an object state from a dictionary, used in de-JSONification.
:param d: the object dictionary
:type d: dict
:return: the object
:rtype: object
"""
result = super(ActorHandler, cls).from_dict(d)
if "actors" in d:
l = d["actors"]
for e in l:
if u"type" in e:
typestr = e[u"type"]
else:
typestr = e["type"]
result.actors.append(classes.get_dict_handler(typestr)(e))
return result
@property
def actors(self):
"""
Obtains the currently set sub-actors.
:return: the sub-actors
:rtype: list
"""
result = self.config["actors"]
if result is None:
result = []
return result
@actors.setter
def actors(self, actors):
"""
Sets the sub-actors of the actor.
:param actors: the sub-actors
:type actors: list
"""
if actors is None:
actors = self.default_actors()
self.check_actors(actors)
self.config["actors"] = actors
@property
def active(self):
"""
Returns the count of non-skipped actors.
:return: the count
:rtype: int
"""
result = 0
for actor in self.actors:
if not actor.skip:
result += 1
return result
@property
def first_active(self):
"""
Returns the first non-skipped actor.
:return: the first active actor, None if not available
:rtype: Actor
"""
result = None
for actor in self.actors:
if not actor.skip:
result = actor
break
return result
@property
def last_active(self):
"""
Returns the last non-skipped actor.
:return: the last active actor, None if not available
:rtype: Actor
"""
result = None
for actor in reversed(self.actors):
if not actor.skip:
result = actor
break
return result
def index_of(self, name):
"""
Returns the index of the actor with the given name.
:param name: the name of the Actor to find
:type name: str
:return: the index, -1 if not found
:rtype: int
"""
result = -1
for index, actor in enumerate(self.actors):
if actor.name == name:
result = index
break
return result
def update_parent(self):
"""
Updates the parent in its sub-actors.
"""
for actor in self.actors:
actor.parent = self
def setup(self):
"""
Configures the actor before execution.
:return: None if successful, otherwise error message
:rtype: str
"""
result = super(ActorHandler, self).setup()
if result is None:
self.update_parent()
try:
self.check_actors(self.actors)
except Exception, e:
result = str(e)
if result is None:
for actor in self.actors:
name = actor.name
newname = actor.unique_name(actor.name)
if name != newname:
actor.name = newname
if result is None:
for actor in self.actors:
if actor.skip:
continue
result = actor.setup()
if result is not None:
break
if result is None:
result = self._director.setup()
return result
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
return self._director.execute()
def stop_execution(self):
"""
Triggers the stopping of the actor.
"""
self._director.stop_execution()
def wrapup(self):
"""
Finishes up after execution finishes, does not remove any graphical output.
"""
for actor in self.actors:
if actor.skip:
continue
actor.wrapup()
super(ActorHandler, self).wrapup()
def cleanup(self):
"""
Destructive finishing up after execution stopped.
"""
for actor in self.actors:
if actor.skip:
continue
actor.cleanup()
super(ActorHandler, self).cleanup()
def _build_tree(self, actor, content):
"""
Builds the tree for the given actor.
:param actor: the actor to process
:type actor: Actor
:param content: the rows of the tree collected so far
:type content: list
"""
depth = actor.depth
row = ""
for i in xrange(depth - 1):
row += "| "
if depth > 0:
row += "|-"
name = actor.name
if name != actor.__class__.__name__:
name = actor.__class__.__name__ + " '" + name + "'"
row += name
quickinfo = actor.quickinfo
if quickinfo is not None:
row += " [" + quickinfo + "]"
content.append(row)
if isinstance(actor, ActorHandler):
for sub in actor.actors:
self._build_tree(sub, content)
@property
def tree(self):
"""
Returns a tree representation of this sub-flow.
:return: the tree
:rtype: str
"""
content = []
self._build_tree(self, content)
return '\n'.join(content)
class Director(object):
"""
Ancestor for classes that "direct" the flow of tokens.
"""
def __init__(self, owner):
"""
Initializes the director.
:param owner: the owning actor
:type owner: Actor
"""
self.check_owner(owner)
self._owner = owner
def __str__(self):
"""
Returns a short description about the director.
:return: the description
:rtype: str
"""
result = self.__name__
if self.owner is not None:
result = result + ", owner=" + self.owner.name
return result
@property
def owner(self):
"""
Obtains the currently set owner.
:return: the owner
:rtype: Actor
"""
return self._owner
def check_owner(self, owner):
"""
Checks the owner. Raises an exception if invalid.
:param owner: the owner to check
:type owner: Actor
"""
pass
def setup(self):
"""
Performs some checks.
:return: None if successful, otherwise error message.
:rtype: str
"""
return None
def do_execute(self):
"""
Actual execution of the director.
:return: None if successful, otherwise error message
:rtype: str
"""
raise Exception("Not implemented!")
def execute(self):
"""
Executes the director.
:return: None if successful, otherwise error message
:rtype: str
"""
if self.owner is None:
return "No actor set as owner!"
if self.owner.skip:
return None
return self.do_execute()
class SequentialDirector(Director, Stoppable):
"""
Director for sequential execution of actors.
"""
def __init__(self, owner):
"""
Initializes the director.
:param owner: the owning actor
:type owner: Actor
"""
super(SequentialDirector, self).__init__(owner)
self._stopping = False
self._stopped = False
self._allow_source = False
self._record_output = True
self._recorded_output = []
@property
def allow_source(self):
"""
Obtains whether to allow a source actor.
:return: true if to allow
:rtype: bool
"""
return self._allow_source
@allow_source.setter
def allow_source(self, allow):
"""
Sets whether to allow a source actor.
:param allow: true if to allow
:type allow: bool
"""
self._allow_source = allow
@property
def record_output(self):
"""
Obtains whether to record the output of the last actor.
:return: true if to record
:rtype: bool
"""
return self._record_output
@record_output.setter
def record_output(self, record):
"""
Sets whether to record the output of the last actor.
:param record: true if to record
:type record: bool
"""
self._record_output = record
@property
def recorded_output(self):
"""
Obtains the recorded output.
:return: the output
:rtype: list
"""
return self._recorded_output
def stop_execution(self):
"""
Triggers the stopping of the object.
"""
if not (self._stopping or self._stopped):
for actor in self.owner.actors:
actor.stop_execution()
self._stopping = True
def is_stopping(self):
"""
Returns whether the director is in the process of stopping.
:return:
"""
return self._stopping
def is_stopped(self):
"""
Returns whether the object has been stopped.
:return: whether stopped
:rtype: bool
"""
return self._stopped
def check_owner(self, owner):
"""
Checks the owner. Raises an exception if invalid.
:param owner: the owner to check
:type owner: Actor
"""
if not isinstance(owner, ActorHandler):
raise Exception("Owner is not an ActorHandler: " + owner.__name__)
def check_actors(self):
"""
Checks the actors of the owner. Raises an exception if invalid.
"""
actors = []
for actor in self.owner.actors:
if actor.skip:
continue
actors.append(actor)
if len(actors) == 0:
return
if not self.allow_source and base.is_source(actors[0]):
raise Exception("Actor '" + actors[0].full_name + "' is a source, but no sources allowed!")
for i in xrange(1, len(actors)):
if not isinstance(actors[i], InputConsumer):
raise Exception("Actor does not accept any input: " + actors[i].full_name)
def setup(self):
"""
Performs some checks.
:return: None if successful, otherwise error message.
:rtype: str
"""
result = super(SequentialDirector, self).setup()
if result is None:
try:
self.check_actors()
except Exception, e:
result = str(e)
return result
def do_execute(self):
"""
Actual execution of the director.
:return: None if successful, otherwise error message
:rtype: str
"""
self._stopped = False
self._stopping = False
not_finished_actor = self.owner.first_active
pending_actors = []
finished = False
actor_result = None
while not (self.is_stopping() or self.is_stopped()) and not finished:
# determing starting point of next iteration
if len(pending_actors) > 0:
start_index = self.owner.index_of(pending_actors[-1].name)
else:
start_index = self.owner.index_of(not_finished_actor.name)
not_finished_actor = None
# iterate over actors
token = None
last_active = -1
if self.owner.active > 0:
last_active = self.owner.last_active.index
for i in xrange(start_index, last_active + 1):
# do we have to stop the execution?
if self.is_stopped() or self.is_stopping():
break
curr = self.owner.actors[i]
if curr.skip:
continue
# no token? get pending one or produce new one
if token is None:
if isinstance(curr, OutputProducer) and curr.has_output():
pending_actors.pop()
else:
actor_result = curr.execute()
if actor_result is not None:
self.owner.logger.error(
curr.full_name + " generated following error output:\n" + actor_result)
break
if isinstance(curr, OutputProducer) and curr.has_output():
token = curr.output()
else:
token = None
# still more to come?
if isinstance(curr, OutputProducer) and curr.has_output():
pending_actors.append(curr)
else:
# process token
curr.input = token
actor_result = curr.execute()
if actor_result is not None:
self.owner.logger.error(
curr.full_name + " generated following error output:\n" + actor_result)
break
# was a new token produced?
if isinstance(curr, OutputProducer):
if curr.has_output():
token = curr.output()
else:
token = None
# still more to come?
if curr.has_output():
pending_actors.append(curr)
else:
token = None
# token from last actor generated? -> store
if (i == self.owner.last_active.index) and (token is not None):
if self._record_output:
self._recorded_output.append(token)
# no token produced, ignore rest of actors
if isinstance(curr, OutputProducer) and (token is None):
break
# all actors finished?
finished = (not_finished_actor is None) and (len(pending_actors) == 0)
return actor_result
class Flow(ActorHandler, StorageHandler):
"""
Root actor for defining and executing flows.
"""
def __init__(self, name=None, config=None):
"""
Initializes the sequence.
:param name: the name of the sequence
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(Flow, self).__init__(name=name, config=config)
self._storage = {}
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Root actor for defining and executing flows."
def new_director(self):
"""
Creates the director to use for handling the sub-actors.
:return: the director instance
:rtype: Director
"""
result = SequentialDirector(self)
result.record_output = False
result.allow_source = True
return result
def check_actors(self, actors):
"""
Performs checks on the actors that are to be used. Raises an exception if invalid setup.
:param actors: the actors to check
:type actors: list
"""
super(Flow, self).check_actors(actors)
actor = self.first_active
if (actor is not None) and not base.is_source(actor):
raise Exception("First active actor is not a source: " + actor.full_name)
@property
def storage(self):
"""
Returns the internal storage.
:return: the internal storage
:rtype: dict
"""
return self._storage
@classmethod
def load(cls, fname):
"""
Loads the flow from a JSON file.
:param fname: the file to load
:type fname: str
:return: the flow
:rtype: Flow
"""
with open(fname) as f:
content = f.readlines()
return Flow.from_json(''.join(content))
@classmethod
def save(cls, flow, fname):
"""
Saves the flow to a JSON file.
:param flow: the flow to save
:type flow: Flow
:param fname: the file to load
:type fname: str
:return: None if successful, otherwise error message
:rtype: str
"""
result = None
try:
f = open(fname, 'w')
f.write(flow.to_json())
f.close()
except Exception, e:
result = str(e)
return result
class Sequence(ActorHandler, InputConsumer):
"""
Simple sequence of actors that get executed one after the other. Accepts input.
"""
def __init__(self, name=None, config=None):
"""
Initializes the sequence.
:param name: the name of the sequence
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(Sequence, self).__init__(name=name, config=config)
self._output = []
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Simple sequence of actors that get executed one after the other. Accepts input."
def new_director(self):
"""
Creates the director to use for handling the sub-actors.
:return: the director instance
:rtype: Director
"""
result = SequentialDirector(self)
result.record_output = False
result.allow_source = False
return result
def check_actors(self, actors):
"""
Performs checks on the actors that are to be used. Raises an exception if invalid setup.
:param actors: the actors to check
:type actors: list
"""
super(Sequence, self).check_actors(actors)
actor = self.first_active
if (actor is not None) and not isinstance(actor, InputConsumer):
raise Exception("First active actor does not accept input: " + actor.full_name)
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
self.first_active.input = self.input
result = self._director.execute()
if result is None:
self._output.append(self.input)
return result
class Tee(ActorHandler, Transformer):
"""
'Tees off' the current token to be processed in the sub-tree before passing it on.
"""
def __init__(self, name=None, config=None):
"""
Initializes the sequence.
:param name: the name of the sequence
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(Tee, self).__init__(name=name, config=config)
self._requires_active_actors = True
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "'Tees off' the current token to be processed in the sub-tree before passing it on."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
cond = str(self.config["condition"])
if len(cond) > 0:
return "condition: " + cond
else:
return None
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(Tee, self).fix_config(options)
opt = "condition"
if opt not in options:
options[opt] = "True"
if opt not in self.help:
self.help[opt] = "The (optional) condition for teeing off the tokens; uses the 'eval' method, "\
"ie the expression must evaluate to a boolean value; storage values placeholders "\
"'@{...}' get replaced with their string representations before evaluating the "\
"expression (string)."
return options
def new_director(self):
"""
Creates the director to use for handling the sub-actors.
:return: the director instance
:rtype: Director
"""
result = SequentialDirector(self)
result.record_output = False
result.allow_source = False
return result
def check_actors(self, actors):
"""
Performs checks on the actors that are to be used. Raises an exception if invalid setup.
:param actors: the actors to check
:type actors: list
"""
super(Tee, self).check_actors(actors)
actor = self.first_active
if actor is None:
if self._requires_active_actors:
raise Exception("No active actor!")
elif not isinstance(actor, InputConsumer):
raise Exception("First active actor does not accept input: " + actor.full_name)
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
result = None
teeoff = True
cond = self.storagehandler.expand(str(self.resolve_option("condition")))
if len(cond) > 0:
teeoff = bool(eval(cond))
if teeoff:
self.first_active.input = self.input
result = self._director.execute()
if result is None:
self._output.append(self.input)
return result
class Trigger(ActorHandler, Transformer):
"""
'Triggers' the sub-tree with each token passing through and then passes the token on.
"""
def __init__(self, name=None, config=None):
"""
Initializes the sequence.
:param name: the name of the sequence
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(Trigger, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "'Triggers' the sub-tree with each token passing through and then passes the token on."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
cond = str(self.resolve_option("condition"))
if len(cond) > 0:
return "condition: " + cond
else:
return None
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(Trigger, self).fix_config(options)
opt = "condition"
if opt not in options:
options[opt] = "True"
if opt not in self.help:
self.help[opt] = "The (optional) condition for teeing off the tokens; uses the 'eval' method, "\
"ie the expression must evaluate to a boolean value; storage values placeholders "\
"'@{...}' get replaced with their string representations before evaluating the "\
"expression (string)."
return options
def new_director(self):
"""
Creates the director to use for handling the sub-actors.
:return: the director instance
:rtype: Director
"""
result = SequentialDirector(self)
result.record_output = False
result.allow_source = True
return result
def check_actors(self, actors):
"""
Performs checks on the actors that are to be used. Raises an exception if invalid setup.
:param actors: the actors to check
:type actors: list
"""
super(Trigger, self).check_actors(actors)
actor = self.first_active
if actor is None:
raise Exception("No active actor!")
if not base.is_source(actor):
raise Exception("First active actor is not a source: " + actor.full_name)
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
result = None
teeoff = True
cond = self.storagehandler.expand(str(self.resolve_option("condition")))
if len(cond) > 0:
teeoff = bool(eval(cond))
if teeoff:
result = self._director.execute()
if result is None:
self._output.append(self.input)
return result
class BranchDirector(Director, Stoppable):
"""
Director for the Branch actor.
"""
def __init__(self, owner):
"""
Initializes the director.
:param owner: the owning actor
:type owner: Actor
"""
super(BranchDirector, self).__init__(owner)
self._stopping = False
self._stopped = False
def stop_execution(self):
"""
Triggers the stopping of the object.
"""
if not (self._stopping or self._stopped):
for actor in self.owner.actors:
actor.stop_execution()
self._stopping = True
def is_stopping(self):
"""
Returns whether the director is in the process of stopping.
:return:
"""
return self._stopping
def is_stopped(self):
"""
Returns whether the object has been stopped.
:return: whether stopped
:rtype: bool
"""
return self._stopped
def check_owner(self, owner):
"""
Checks the owner. Raises an exception if invalid.
:param owner: the owner to check
:type owner: Actor
"""
if not isinstance(owner, Branch):
raise Exception("Owner is not a Branch: " + owner.__name__)
def check_actors(self):
"""
Checks the actors of the owner. Raises an exception if invalid.
"""
actors = []
for actor in self.owner.actors:
if actor.skip:
continue
actors.append(actor)
if len(actors) == 0:
return
for actor in actors:
if not isinstance(actor, InputConsumer):
raise Exception("Actor does not accept any input: " + actor.full_name)
def setup(self):
"""
Performs some checks.
:return: None if successful, otherwise error message.
:rtype: str
"""
result = super(BranchDirector, self).setup()
if result is None:
try:
self.check_actors()
except Exception, e:
result = str(e)
return result
def do_execute(self):
"""
Actual execution of the director.
:return: None if successful, otherwise error message
:rtype: str
"""
result = None
self._stopped = False
self._stopping = False
for actor in self.owner.actors:
if self.is_stopping() or self.is_stopped():
break
actor.input = self.owner.input
result = actor.execute()
if result is not None:
break
return result
class Branch(ActorHandler, InputConsumer):
"""
Passes on the input token to all of its sub-actors, one after the other.
"""
def __init__(self, name=None, config=None):
"""
Initializes the branch.
:param name: the name of the branch
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(Branch, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Passes on the input token to all of its sub-actors, one after the other."
def new_director(self):
"""
Creates the director to use for handling the sub-actors.
:return: the director instance
:rtype: Director
"""
result = BranchDirector(self)
return result
class ContainerValuePicker(Tee):
"""
Picks the specified value from the container and 'tees' it off.
"""
def __init__(self, name=None, config=None):
"""
Initializes the actor.
:param name: the name of the actor
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(ContainerValuePicker, self).__init__(name=name, config=config)
self._requires_active_actors = False
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Picks the specified value from the container and 'tees' it off."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "value: " + str(self.config["value"]) + ", switch: " + str(self.config["switch"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(ContainerValuePicker, self).fix_config(options)
opt = "value"
if opt not in options:
options[opt] = "Model"
if opt not in self.help:
self.help[opt] = "The name of the container value to pick from the container (string)."
opt = "switch"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Whether to switch the ouputs, i.e., forward the container to the sub-flow and the " \
+ "container value to the following actor instead (bool)."
return options
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
result = None
cont = self.input.payload
name = str(self.resolve_option("value"))
value = cont.get(name)
switch = bool(self.resolve_option("switch"))
if switch:
if self.first_active is not None:
self.first_active.input = self.input
result = self._director.execute()
if result is None:
self._output.append(Token(value))
else:
if self.first_active is not None:
self.first_active.input = Token(value)
result = self._director.execute()
if result is None:
self._output.append(self.input)
return result
class Stop(InputConsumer):
"""
Stops the execution of the flow.
"""
def __init__(self, name=None, config=None):
"""
Initializes the branch.
:param name: the name of the branch
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(Stop, self).__init__(name=name, config=config)
super(InputConsumer, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Stops the execution of the flow."
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
self.root.stop_execution()
return None
python-weka-wrapper-0.3.6/python/weka/flow/source.py 0000644 0172670 0172670 00000055772 12576430655 024222 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# source.py
# Copyright (C) 2015 Fracpete (pythonwekawrapper at gmail dot com)
import os
import re
from weka.core.classes import to_commandline, from_commandline
import weka.flow.base as base
from weka.flow.base import Actor, OutputProducer, Token
from weka.core.database import InstanceQuery
import weka.datagenerators as datagen
class Source(OutputProducer, Actor):
"""
The ancestor for all sources.
"""
def __init__(self, name=None, config=None):
"""
Initializes the source.
:param name: the name of the source
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(Source, self).__init__(name=name, config=config)
super(OutputProducer, self).__init__(name=name, config=config)
class Start(Source):
"""
Outputs a None token for triggering other actors.
"""
def __init__(self, name=None, config=None):
"""
Initializes the source.
:param name: the name of the source
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(Start, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Outputs a None token for triggering other actors."
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
self._output.append(Token(None))
return None
class FileSupplier(Source):
"""
Outputs a fixed list of files.
"""
def __init__(self, name=None, config=None):
"""
Initializes the source.
:param name: the name of the source
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(FileSupplier, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Outputs a fixed list of files."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "files: " + str(len(self.config["files"]))
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(FileSupplier, self).fix_config(options)
opt = "files"
if opt not in options:
options[opt] = []
if opt not in self.help:
self.help[opt] = "The files to output (list of string)."
return options
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
for f in self.resolve_option("files"):
self._output.append(Token(f))
return None
class ListFiles(Source):
"""
Source that list files in a directory.
"""
def __init__(self, name=None, config=None):
"""
Initializes the source.
:param name: the name of the source
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(ListFiles, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Source that list files in a directory."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "dir: " + str(self.config["dir"]) \
+ ", files: " + str(self.config["list_files"]) \
+ ", dirs: " + str(self.resolve_option("list_dirs")) \
+ ", recursive: " + str(self.config["recursive"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(ListFiles, self).fix_config(options)
opt = "dir"
if opt not in options:
options[opt] = "."
if opt not in self.help:
self.help[opt] = "The directory to search (string)."
opt = "recursive"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Whether to search recursively (bool)."
opt = "list_files"
if opt not in options:
options[opt] = True
if opt not in self.help:
self.help[opt] = "Whether to include files (bool)."
opt = "list_dirs"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Whether to include directories (bool)."
opt = "regexp"
if opt not in options:
options[opt] = ".*"
if opt not in self.help:
self.help[opt] = "The regular expression that files/dirs must match (string)."
return options
def _list(self, path, collected):
"""
Lists all the files/dirs in directory that match the pattern.
:param path: the directory to search
:type path: str
:param collected: the files/dirs collected so far (full path)
:type collected: list
:return: None if successful, error otherwise
:rtype: str
"""
list_files = self.resolve_option("list_files")
list_dirs = self.resolve_option("list_dirs")
recursive = self.resolve_option("recursive")
spattern = str(self.resolve_option("regexp"))
pattern = None
if (spattern is not None) and (spattern != ".*"):
pattern = re.compile(spattern)
try:
items = os.listdir(path)
for item in items:
fp = path + os.sep + item
if list_files and os.path.isfile(fp):
if (pattern is None) or pattern.match(item):
collected.append(fp)
if list_dirs and os.path.isdir(fp):
if (pattern is None) or pattern.match(item):
collected.append(fp)
if recursive and os.path.isdir(fp):
self._list(fp, collected)
except Exception, e:
return "Error listing '" + path + "': " + str(e)
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
directory = str(self.resolve_option("dir"))
if not os.path.exists(directory):
return "Directory '" + directory + "' does not exist!"
if not os.path.isdir(directory):
return "Location '" + directory + "' is not a directory!"
collected = []
result = self._list(directory, collected)
if result is None:
for c in collected:
self._output.append(Token(c))
return result
class GetStorageValue(Source):
"""
Outputs the specified value from storage.
"""
def __init__(self, name=None, config=None):
"""
Initializes the source.
:param name: the name of the source
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(GetStorageValue, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Outputs the specified value from storage."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "name: " + str(self.config["storage_name"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(GetStorageValue, self).fix_config(options)
opt = "storage_name"
if opt not in options:
options[opt] = "unknown"
if opt not in self.help:
self.help[opt] = "The name of the storage value to retrieve (string)."
return options
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
if self.storagehandler is None:
return "No storage handler available!"
sname = str(self.resolve_option("storage_name"))
if sname not in self.storagehandler.storage:
return "No storage item called '" + sname + "' present!"
self._output.append(Token(self.storagehandler.storage[sname]))
return None
class ForLoop(Source):
"""
Outputs integers using the specified min, max and step.
"""
def __init__(self, name=None, config=None):
"""
Initializes the source.
:param name: the name of the source
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(ForLoop, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Outputs integers using the specified min, max and step."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "min: " + str(self.config["min"]) \
+ ", max: " + str(self.config["max"]) \
+ ", step: " + str(self.config["step"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(ForLoop, self).fix_config(options)
opt = "min"
if opt not in options:
options[opt] = 1
if opt not in self.help:
self.help[opt] = "The minimum for the loop (included, int)."
opt = "max"
if opt not in options:
options[opt] = 10
if opt not in self.help:
self.help[opt] = "The maximum for the loop (included, int)."
opt = "step"
if opt not in options:
options[opt] = 1
if opt not in self.help:
self.help[opt] = "The step size (int)."
return options
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
for i in xrange(
int(self.resolve_option("min")),
int(self.resolve_option("max")) + 1,
int(self.resolve_option("step"))):
self._output.append(Token(i))
return None
class LoadDatabase(Source):
"""
Loads a dataset from the database.
"""
def __init__(self, name=None, config=None):
"""
Initializes the source.
:param name: the name of the source
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(LoadDatabase, self).__init__(name=name, config=config)
self._loader = None
self._iterator = None
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Loads a dataset from a database using a supplied SQL query."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "url: " + str(self.config["db_url"]) \
+ ", query: " + str(self.config["query"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
opt = "db_url"
if opt not in options:
options[opt] = "jdbc:mysql://somehost:3306/somedatabase"
if opt not in self.help:
self.help[opt] = "The JDBC database URL to connect to (str)."
opt = "user"
if opt not in options:
options[opt] = "user"
if opt not in self.help:
self.help[opt] = "The database user to use for connecting (str)."
opt = "password"
if opt not in options:
options[opt] = "secret"
if opt not in self.help:
self.help[opt] = "The password for the database user (str)."
opt = "query"
if opt not in options:
options[opt] = "SELECT * FROM table"
if opt not in self.help:
self.help[opt] = "The SQL query for generating the dataset (str)."
opt = "sparse"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Whether to return the data in sparse format (bool)."
opt = "custom_props"
if opt not in options:
options[opt] = ""
if opt not in self.help:
self.help[opt] = "Custom properties filename (str)."
return super(LoadDatabase, self).fix_config(options)
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
iquery = InstanceQuery()
iquery.db_url = str(self.resolve_option("db_url"))
iquery.user = str(self.resolve_option("user"))
iquery.password = str(self.resolve_option("password"))
props = str(self.resolve_option("custom_props"))
if (len(props) > 0) and os.path.isfile(props):
iquery.custom_properties = props
iquery.query = str(self.resolve_option("query"))
data = iquery.retrieve_instances()
self._output.append(Token(data))
return None
class DataGenerator(Source):
"""
Generates artificial data using the specified data generator.
"""
def __init__(self, name=None, config=None):
"""
Initializes the source.
:param name: the name of the source
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(DataGenerator, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Generates artificial data using the specified data generator."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "setup: " + base.to_commandline(self.config["setup"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
opt = "setup"
if opt not in options:
options[opt] = datagen.DataGenerator(classname="weka.datagenerators.classifiers.classification.Agrawal")
if opt not in self.help:
self.help[opt] = "The data generator to use (DataGenerator)."
opt = "incremental"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Whether to output the data incrementally, in case the generator supports that (bool)."
return super(DataGenerator, self).fix_config(options)
def to_config(self, k, v):
"""
Hook method that allows conversion of individual options.
:param k: the key of the option
:type k: str
:param v: the value
:type v: object
:return: the potentially processed value
:rtype: object
"""
if k == "setup":
return base.to_commandline(v)
return super(DataGenerator, self).to_config(k, v)
def from_config(self, k, v):
"""
Hook method that allows converting values from the dictionary.
:param k: the key in the dictionary
:type k: str
:param v: the value
:type v: object
:return: the potentially parsed value
:rtype: object
"""
if k == "setup":
return from_commandline(v, classname=to_commandline(datagen.DataGenerator()))
return super(DataGenerator, self).from_config(k, v)
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
generator = datagen.DataGenerator.make_copy(self.resolve_option("setup"))
generator.dataset_format = generator.define_data_format()
if bool(self.resolve_option("incremental")) and generator.single_mode_flag:
for i in xrange(generator.num_examples_act):
self._output.append(Token(generator.generate_example()))
else:
data = generator.generate_examples()
self._output.append(Token(data))
return None
class CombineStorage(Source):
"""
Expands the storage items specified in format string and forwards the generated string.
"""
def __init__(self, name=None, config=None):
"""
Initializes the source.
:param name: the name of the source
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(CombineStorage, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Expands the storage items specified in format string and forwards the generated string."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "format: " + str(self.config["format"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(CombineStorage, self).fix_config(options)
opt = "format"
if opt not in options:
options[opt] = ""
if opt not in self.help:
self.help[opt] = "The format to use for generating the combined string; use '@{blah}' for accessing "\
"storage item 'blah' (string)."
return options
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
formatstr = str(self.resolve_option("format"))
expanded = self.storagehandler.expand(formatstr)
self._output.append(Token(expanded))
return None
class StringConstants(Source):
"""
Outputs a fixed list of strings.
"""
def __init__(self, name=None, config=None):
"""
Initializes the source.
:param name: the name of the source
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(StringConstants, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Outputs a fixed list of strings."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "strings: " + str(len(self.config["strings"]))
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(StringConstants, self).fix_config(options)
opt = "strings"
if opt not in options:
options[opt] = []
if opt not in self.help:
self.help[opt] = "The strings to output (list of string)."
return options
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
for s in self.resolve_option("strings"):
self._output.append(Token(s))
return None
python-weka-wrapper-0.3.6/python/weka/flow/base.py 0000644 0172670 0172670 00000041660 12576430071 023613 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# base.py
# Copyright (C) 2015 Fracpete (pythonwekawrapper at gmail dot com)
import logging
import re
import traceback
import uuid
from weka.core.classes import Configurable, Stoppable
import weka.core.classes as classes
class Actor(Configurable, Stoppable):
"""
The ancestor for all actors.
"""
def __init__(self, name=None, config=None):
"""
Initializes the actor.
:param name: the name of the actor
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(Actor, self).__init__(config=config)
self._name = self.__class__.__name__
self._parent = None
self._full_name = None
self._stopped = False
if name is not None:
self.name = name
if not classes.has_dict_handler("Actor"):
classes.register_dict_handler("Actor", Actor.from_dict)
def __str__(self):
"""
Returns a short representation of the actor's setup.
:return: the setup
:rtype: str
"""
return self.full_name + ": " + str(self._config)
def __repr__(self):
"""
Returns Python code for instantiating the object.
:return: the representation
:rtype: str
"""
return \
self.__class__.__module__ + "." + self.__class__.__name__ \
+ "(name=" + self.name + ", config=" + str(self.config) + ")"
def new_logger(self):
"""
Returns a new logger instance.
:return: the logger instance
:rtype: logger
"""
return logging.getLogger(self.full_name)
@property
def name(self):
"""
Obtains the currently set name of the actor.
:return: the name
:rtype: str
"""
return self._name
@name.setter
def name(self, name):
"""
Sets the name of the actor.
:param name: the name
:type name: str
"""
self._name = name
def unique_name(self, name):
"""
Generates a unique name.
:param name: the name to check
:type name: str
:return: the unique name
:rtype: str
"""
result = name
if self.parent is not None:
index = self.index
bname = re.sub(r'-[0-9]+$', '', name)
names = []
for idx, actor in enumerate(self.parent.actors):
if idx != index:
names.append(actor.name)
result = bname
count = 0
while result in names:
count += 1
result = bname + "-" + str(count)
return result
@property
def parent(self):
"""
Obtains the currently set parent of the actor.
:return: the name
:rtype: str
"""
return self._parent
@parent.setter
def parent(self, parent):
"""
Sets the parent of the actor.
:param parent: the parent
:type parent: Actor
"""
self._name = self.unique_name(self._name)
self._full_name = None
self._logger = None
self._parent = parent
@property
def index(self):
"""
Returns the index of this actor in its parent's list of actors.
:return: the index, -1 if not available
:rtype: int
"""
if self.parent is None:
return -1
else:
return self.parent.index_of(self.name)
@property
def full_name(self):
"""
Obtains the full name of the actor.
:return: the full name
:rtype: str
"""
if self._full_name is None:
fn = self.name.replace(".", "\\.")
parent = self._parent
if parent is not None:
fn = parent.full_name + "." + fn
self._full_name = fn
return self._full_name
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
opt = "annotation"
if opt not in options:
options[opt] = None
if opt not in self.help:
self.help[opt] = "The (optional) annotation for this actor (string)."
opt = "skip"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Whether to skip (disable) this actor (bool)."
return super(Actor, self).fix_config(options)
def to_dict(self):
"""
Returns a dictionary that represents this object, to be used for JSONification.
:return: the object dictionary
:rtype: dict
"""
result = super(Actor, self).to_dict()
result["type"] = "Actor"
result["name"] = self.name
return result
@classmethod
def from_dict(cls, d):
"""
Restores an object state from a dictionary, used in de-JSONification.
:param d: the object dictionary
:type d: dict
:return: the object
:rtype: object
"""
conf = {}
for k in d["config"]:
v = d["config"][k]
if isinstance(v, dict):
if u"type" in v:
typestr = v[u"type"]
else:
typestr = v["type"]
conf[str(k)] = classes.get_dict_handler(typestr)(v)
else:
conf[str(k)] = v
return classes.get_class(d["class"])(name=d["name"], config=conf)
def resolve_option(self, name, default=None):
"""
Resolves the option, i.e., interprets "@{...}" values and retrievs them instead from internal
storage.
:param name: the name of the option
:type name: str
:param default: the optional default value
:type default: object
:return: the resolved value
:rtype: object
"""
value = self.config[name]
if value is None:
return default
elif isinstance(value, str) \
and value.startswith("@{") \
and value.endswith("}") \
and (value.find("@{", 1) == -1):
stname = value[2:len(value)-1]
if (self.storagehandler is not None) and (stname in self.storagehandler.storage):
return self.storagehandler.storage[stname]
else:
return default
else:
return value
@property
def skip(self):
"""
Obtains whether the actor is disabled (skipped).
:return: True if skipped
:rtype: bool
"""
return self.resolve_option("skip")
@skip.setter
def skip(self, skip):
"""
Sets whether the actor is skipped.
:param skip: True if skipped
:type skip: bool
"""
self.config["skip"] = skip
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return None
@property
def storagehandler(self):
"""
Returns the storage handler available to thise actor.
:return: the storage handler, None if not available
"""
if isinstance(self, StorageHandler):
return self
elif self.parent is not None:
return self.parent.storagehandler
else:
return None
@property
def root(self):
"""
Returns the top-level actor.
:return: the top-level actor
:rtype: Actor
"""
if self.parent is None:
return self
else:
return self.parent.root
@property
def depth(self):
"""
Returns the depth of this actor inside the overall flow.
:return: the depth
:rtype: int
"""
if self.parent is None:
return 0
else:
return self.parent.depth + 1
def is_stopped(self):
"""
Returns whether the object has been stopped.
:return: whether stopped
:rtype: bool
"""
return self._stopped
def stop_execution(self):
"""
Triggers the stopping of the actor.
"""
self._stopped = True
def setup(self):
"""
Configures the actor before execution.
:return: None if successful, otherwise error message
:rtype: str
"""
return None
def pre_execute(self):
"""
Gets executed before the actual execution.
:return: None if successful, otherwise error message
:rtype: str
"""
return None
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
raise Exception("Not implemented!")
def post_execute(self):
"""
Gets executed after the actual execution.
:return: None if successful, otherwise error message
:rtype: str
"""
return None
def execute(self):
"""
Executes the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
if self.skip:
return None
result = self.pre_execute()
if result is None:
try:
result = self.do_execute()
except Exception, e:
result = traceback.format_exc()
print(self.full_name + "\n" + result)
if result is None:
result = self.post_execute()
return result
def wrapup(self):
"""
Finishes up after execution finishes, does not remove any graphical output.
"""
pass
def cleanup(self):
"""
Destructive finishing up after execution stopped.
"""
pass
class Token(object):
"""
Container for transporting data through the flow.
"""
def __init__(self, payload):
"""
Initializes the token with the given payload.
:param payload: the payload for the token.
:type payload: object
"""
self._id = str(uuid.uuid4())
self._payload = payload
@property
def id(self):
"""
Obtains the ID of the token.
:return: the ID
:rtype: str
"""
return self._id
@property
def payload(self):
"""
Obtains the currently set payload.
:return: the payload
:rtype: object
"""
return self._payload
def __str__(self):
"""
Returns a short representation of the token and its payload.
"""
return self._id + ": " + str(self._payload)
class InputConsumer(Actor):
"""
Actors that consume tokens inherit this class.
"""
def __init__(self, name=None, config=None):
"""
Initializes the actor.
:param name: the name of the actor
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(InputConsumer, self).__init__(name=name, config=config)
self._input = None
def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
pass
@property
def input(self):
"""
Returns the current input token, None if not available.
:return: the input token
:rtype: Token
"""
return self._input
@input.setter
def input(self, token):
"""
Accepts the data for processing.
:param token: the token to process
:type token: Token
"""
self.check_input(token)
self._input = token
class OutputProducer(Actor):
"""
Actors that generate output tokens inherit this class.
"""
def __init__(self, name=None, config=None):
"""
Initializes the actor.
:param name: the name of the actor
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(OutputProducer, self).__init__(name=name, config=config)
self._output = None
def pre_execute(self):
"""
Gets executed before the actual execution.
:return: None if successful, otherwise error message
:rtype: str
"""
self._output = []
return None
def has_output(self):
"""
Checks whether any output tokens are present.
:return: true if at least one output token present
:rtype: bool
"""
return (self._output is not None) and (len(self._output) > 0)
def output(self):
"""
Returns the next available output token.
:return: the next token, None if none available
:rtype: Token
"""
if (self._output is None) or (len(self._output) == 0):
result = None
else:
result = self._output.pop(0)
return result
class StorageHandler(object):
"""
For classes that support internal storage (= dictionary).
"""
@property
def storage(self):
"""
Returns the internal storage.
:return: the internal storage
:rtype: dict
"""
raise Exception("Not implemented!")
def expand(self, s):
"""
Expands all occurrences of "@{...}" within the string with the actual values currently stored
in internal storage.
:param s: the string to expand
:type s: str
:return: the expanded string
:rtype: str
"""
result = s
while result.find("@{") > -1:
start = result.index("@{")
end = result.index("}", start)
name = result[start + 2:end]
value = self.storage[name]
if value is None:
raise("Storage value '" + name + "' not present, failed to expand string: " + s)
else:
result = result[0:start] + str(value) + result[end + 1:]
return result
@classmethod
def pad(cls, name):
"""
Pads the name with "@{...}".
:param name: the name to pad
:type name: str
:return: the padded name
:rtype: str
"""
if name.startswith("@{"):
return name
else:
return "@{" + name + "}"
@classmethod
def extract(cls, padded):
"""
Removes the surrounding "@{...}" from the name.
:param padded: the padded string
:type padded: str
:return: the extracted name
:rtype: str
"""
if padded.startswith("@{") and padded.endswith("}"):
return padded[2:len(padded)-1]
else:
return padded
def is_source(actor):
"""
Checks whether the actor is a source.
:param actor: the actor to check
:type actor: Actor
:return: True if the actor is a source
:rtype: bool
"""
return not isinstance(actor, InputConsumer) and isinstance(actor, OutputProducer)
def is_transformer(actor):
"""
Checks whether the actor is a transformer.
:param actor: the actor to check
:type actor: Actor
:return: True if the actor is a transformer
:rtype: bool
"""
return isinstance(actor, InputConsumer) and isinstance(actor, OutputProducer)
def is_sink(actor):
"""
Checks whether the actor is a sink.
:param actor: the actor to check
:type actor: Actor
:return: True if the actor is a sink
:rtype: bool
"""
return isinstance(actor, InputConsumer) and not isinstance(actor, OutputProducer)
def to_commandline(o):
"""
Turns the object into a commandline string. However, first checks whether a string represents
a internal value placeholder (@{...}).
:param o: the object to turn into commandline
:type o: object
:return: the commandline
:rtype: str
"""
if isinstance(o, str) and o.startswith("@{") and o.endswith("}"):
return o
else:
return classes.to_commandline(o)
python-weka-wrapper-0.3.6/python/weka/flow/container.py 0000644 0172670 0172670 00000017365 12576430302 024665 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# container.py
# Copyright (C) 2015 Fracpete (pythonwekawrapper at gmail dot com)
import re
from weka.core.dataset import Instances
class Container(object):
"""
Container for storing multiple objects and passing them around together in the flow.
"""
def __init__(self):
"""
Initializes the container.
"""
self._data = {}
self._allowed = []
def get(self, name):
"""
Returns the stored data.
:param name: the name of the item to return
:type name: str
:return: the data
:rtype: object
"""
return self._data[name]
def set(self, name, value):
"""
Stores the given data (if not None).
:param name: the name of the item to store
:type name: str
:param value: the value to store
:type value: object
"""
if value is not None:
self._data[name] = value
@property
def allowed(self):
"""
Returns the all the allowed keys.
:return: the list of allowed keys.
:rtype: list
"""
return self._allowed
def is_valid(self):
"""
Checks whether the container is valid.
:return: True if the container is valid
:rtype: bool
"""
return True
def __str__(self):
"""
Returns the content of the container as string.
:return: the content
:rtype: str
"""
return str(self._data)
def generate_help(self):
"""
Generates a help string for this container.
:return: the help string
:rtype: str
"""
result = []
result.append(self.__class__.__name__)
result.append(re.sub(r'.', '=', self.__class__.__name__))
result.append("")
result.append("Supported value names:")
for a in self.allowed:
result.append(a)
return '\n'.join(result)
def print_help(self):
"""
Prints a help string for this actor to stdout.
"""
print(self.generate_help())
class ModelContainer(Container):
"""
Container for models.
"""
def __init__(self, model=None, header=None):
"""
Initializes the container.
:param model: the model to store (eg Classifier or Clusterer)
:type model: object
:param header: the header instances
:type header: Instances
"""
super(ModelContainer, self).__init__()
self.set("Model", model)
if header is not None:
header = Instances.template_instances(header)
self.set("Header", header)
self._allowed = ["Model", "Header"]
def is_valid(self):
"""
Checks whether the container is valid.
:return: True if the container is valid
:rtype: bool
"""
return ("Model" in self._data) or ("Model" in self._data and "Header" in self._data)
class AttributeSelectionContainer(Container):
"""
Container for models.
"""
def __init__(self, original=None, reduced=None, num_atts=None, selected=None, results=None):
"""
Initializes the container.
:param original: the original dataset
:type original: Instances
:param reduced: the reduced dataset
:type reduced: Instances
:param num_atts: the number of attributes
:type num_atts: int
:param selected: the list of selected attribute indices (0-based)
:type selected: list
:param results: the generated results string
:type results: str
"""
super(AttributeSelectionContainer, self).__init__()
self.set("Original", original)
self.set("Reduced", reduced)
self.set("NumAttributes", num_atts)
self.set("Selected", selected)
self.set("Results", results)
self._allowed = ["Original", "Reduced", "NumAttributes", "Selected", "Results"]
def is_valid(self):
"""
Checks whether the container is valid.
:return: True if the container is valid
:rtype: bool
"""
return ("Reduced" in self._data) and ("NumAttributes" in self._data) and ("Selected" in self._data)
class ModelContainer(Container):
"""
Container for models.
"""
def __init__(self, model=None, header=None):
"""
Initializes the container.
:param model: the model to store (eg Classifier or Clusterer)
:type model: object
:param header: the header instances
:type header: Instances
"""
super(ModelContainer, self).__init__()
self.set("Model", model)
if header is not None:
header = Instances.template_instances(header)
self.set("Header", header)
self._allowed = ["Model", "Header"]
def is_valid(self):
"""
Checks whether the container is valid.
:return: True if the container is valid
:rtype: bool
"""
return ("Model" in self._data) or ("Model" in self._data and "Header" in self._data)
class ClassificationContainer(Container):
"""
Container for predictions (classifiers).
"""
def __init__(self, inst=None, classification=None, label=None, distribution=None):
"""
Initializes the container.
:param inst: the instance used for making the prediction
:type inst: Instance
:param classification: the classification (numeric value or 0-based label index)
:type classification: float
:param label: classification label (for nominal classes)
:type label: str
:param distribution: the class distribution
:type distribution: ndarray
"""
super(ClassificationContainer, self).__init__()
self.set("Instance", inst)
self.set("Classification", classification)
self.set("Label", label)
self.set("Distribution", distribution)
self._allowed = ["Instance", "Classification", "Label", "Distribution"]
def is_valid(self):
"""
Checks whether the container is valid.
:return: True if the container is valid
:rtype: bool
"""
return ("Instance" in self._data) and ("Classification" in self._data)
class ClusteringContainer(Container):
"""
Container for predictions (clusterers).
"""
def __init__(self, inst=None, cluster=None, distribution=None):
"""
Initializes the container.
:param inst: the instance used for making the prediction
:type inst: Instance
:param cluster: the cluster
:type cluster: int
:param distribution: the class distribution
:type distribution: ndarray
"""
super(ClusteringContainer, self).__init__()
self.set("Instance", inst)
self.set("Cluster", cluster)
self.set("Distribution", distribution)
self._allowed = ["Instance", "Cluster", "Distribution"]
def is_valid(self):
"""
Checks whether the container is valid.
:return: True if the container is valid
:rtype: bool
"""
return ("Instance" in self._data) and ("Cluster" in self._data)
python-weka-wrapper-0.3.6/python/weka/flow/__init__.py 0000644 0172670 0172670 00000001437 12477706504 024445 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# __init__.py
# Copyright (C) 2015 Fracpete (pythonwekawrapper at gmail dot com)
# Simple workflow components, inspired by ADAMS (https://adams.cms.waikato.ac.nz/) python-weka-wrapper-0.3.6/python/weka/flow/conversion.py 0000644 0172670 0172670 00000013717 12576430520 025067 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# conversion.py
# Copyright (C) 2015 Fracpete (pythonwekawrapper at gmail dot com)
from weka.flow.base import Configurable
import weka.core.classes as classes
class Conversion(Configurable):
"""
Ancestor for conversions used by the 'Convert' transformer.
"""
def __init__(self, config=None):
"""
Initializes the conversion.
:param config: dictionary of options to use
:type config: dict
"""
super(Conversion, self).__init__(config=config)
self._input = None
self._output = None
def __str__(self):
"""
Returns a short representation of the actor's setup.
:return: the setup
:rtype: str
"""
return classes.get_classname(self) + ": " + str(self._config)
def check_input(self, obj):
"""
Performs checks on the input object. Raises an exception if unsupported.
:param obj: the object to check
:type obj: object
"""
pass
@property
def input(self):
"""
Returns the current input object, None if not available.
:return: the input object
:rtype: object
"""
return self._input
@input.setter
def input(self, obj):
"""
Accepts the data for processing.
:param obj: the object to process
:type obj: object
"""
self.check_input(obj)
self._input = obj
@property
def output(self):
"""
Returns the generated output object, None if not available.
:return: the output object
:rtype: object
"""
return self._output
def convert(self):
"""
Performs the actual conversion.
:return: None if successful, otherwise errors message
:rtype: str
"""
raise Exception("Not implemented!")
class PassThrough(Conversion):
"""
Dummy conversion, just passes through the data.
"""
def __init__(self, config=None):
"""
Initializes the conversion.
:param config: dictionary of options to use
:type config: dict
"""
super(PassThrough, self).__init__(config=config)
def description(self):
"""
Returns the description for the conversion.
:return: the description
:rtype: str
"""
return "Dummy conversion, just passes through the data."
def convert(self):
"""
Performs the actual conversion.
:return: None if successful, otherwise errors message
:rtype: str
"""
self._output = self._input
return None
class AnyToCommandline(Conversion):
"""
Generates a commandline string, e.g., from a classifier.
"""
def __init__(self, config=None):
"""
Initializes the conversion.
:param config: dictionary of options to use
:type config: dict
"""
super(AnyToCommandline, self).__init__(config=config)
def description(self):
"""
Returns the description for the conversion.
:return: the description
:rtype: str
"""
return "Generates a commandline string, e.g., from a classifier."
def convert(self):
"""
Performs the actual conversion.
:return: None if successful, otherwise errors message
:rtype: str
"""
self._output = classes.to_commandline(self._input)
return None
class CommandlineToAny(Conversion):
"""
Generates an object from a commandline string, e.g., from a classifier setup.
"""
def __init__(self, config=None):
"""
Initializes the conversion.
:param config: dictionary of options to use
:type config: dict
"""
super(CommandlineToAny, self).__init__(config=config)
def description(self):
"""
Returns the description for the conversion.
:return: the description
:rtype: str
"""
return "Generates an object from a commandline string, e.g., from a classifier setup."
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(CommandlineToAny, self).fix_config(options)
opt = "wrapper"
if opt not in options:
options[opt] = "weka.core.classes.OptionHandler"
if opt not in self.help:
self.help[opt] = "The name of the wrapper class to use (string)."
return options
def check_input(self, obj):
"""
Performs checks on the input object. Raises an exception if unsupported.
:param obj: the object to check
:type obj: object
"""
if isinstance(obj, str):
return
if isinstance(obj, unicode):
return
raise Exception("Unsupported class: " + self._input.__class__.__name__)
def convert(self):
"""
Performs the actual conversion.
:return: None if successful, otherwise errors message
:rtype: str
"""
cname = str(self.config["wrapper"])
self._output = classes.from_commandline(self._input, classname=cname)
return None
python-weka-wrapper-0.3.6/python/weka/flow/transformer.py 0000644 0172670 0172670 00000166414 12576431230 025246 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# transformer.py
# Copyright (C) 2015 Fracpete (pythonwekawrapper at gmail dot com)
import os
import re
import javabridge
import math # required for MathExpression
from weka.associations import Associator
import weka.core.classes as classes
import weka.core.serialization as serialization
import weka.attribute_selection as attsel
import weka.filters as filters
import weka.flow.base as base
from weka.flow.base import InputConsumer, OutputProducer, Token
from weka.flow.container import ModelContainer, AttributeSelectionContainer, ClassificationContainer, ClusteringContainer
import weka.core.converters as converters
from weka.core.dataset import Instances, Instance
from weka.classifiers import Classifier, Evaluation
from weka.clusterers import Clusterer, ClusterEvaluation
from weka.core.classes import Random
import weka.flow.conversion as conversion
class Transformer(InputConsumer, OutputProducer):
"""
The ancestor for all sources.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(InputConsumer, self).__init__(name=name, config=config)
super(OutputProducer, self).__init__(name=name, config=config)
def post_execute(self):
"""
Gets executed after the actual execution.
:return: None if successful, otherwise error message
:rtype: str
"""
result = super(Transformer, self).post_execute()
if result is None:
self._input = None
return result
class PassThrough(Transformer):
"""
Dummy actor that just passes through the data.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(PassThrough, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Dummy actor that just passes through the data."
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
self._output.append(self.input)
class LoadDataset(Transformer):
"""
Loads a dataset from a file.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(LoadDataset, self).__init__(name=name, config=config)
self._loader = None
self._iterator = None
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Loads a dataset from a file. Either all at once or incrementally."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "incremental: " + str(self.config["incremental"]) \
+ ", custom: " + str(self.config["use_custom_loader"]) \
+ ", loader: " + base.to_commandline(self.config["custom_loader"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
opt = "incremental"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Whether to load the dataset incrementally (bool)."
opt = "use_custom_loader"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Whether to use a custom loader."
opt = "custom_loader"
if opt not in options:
options[opt] = converters.Loader(classname="weka.core.converters.ArffLoader")
if opt not in self.help:
self.help[opt] = "The custom loader to use (Loader)."
return super(LoadDataset, self).fix_config(options)
def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if token is None:
raise Exception(self.full_name + ": No token provided!")
if isinstance(token.payload, str):
return
raise Exception(self.full_name + ": Unhandled class: " + classes.get_classname(token.payload))
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
fname = str(self.input.payload)
if not os.path.exists(fname):
return "File '" + fname + "' does not exist!"
if not os.path.isfile(fname):
return "Location '" + fname + "' is not a file!"
if self.resolve_option("use_custom_loader"):
self._loader = self.resolve_option("custom_loader")
else:
self._loader = converters.loader_for_file(fname)
dataset = self._loader.load_file(fname, incremental=bool(self.resolve_option("incremental")))
if not self.resolve_option("incremental"):
self._output.append(Token(dataset))
else:
self._iterator = self._loader.__iter__()
return None
def has_output(self):
"""
Checks whether any output tokens are present.
:return: true if at least one output token present
:rtype: bool
"""
return super(LoadDataset, self).has_output() or (self._iterator is not None)
def output(self):
"""
Returns the next available output token.
:return: the next token, None if none available
:rtype: Token
"""
if self._iterator is not None:
try:
inst = self._iterator.next()
result = Token(inst)
except Exception, e:
self._iterator = None
result = None
else:
result = super(LoadDataset, self).output()
return result
def stop_execution(self):
"""
Triggers the stopping of the object.
"""
super(LoadDataset, self).stop_execution()
self._loader = None
self._iterator = None
def wrapup(self):
"""
Finishes up after execution finishes, does not remove any graphical output.
"""
self._loader = None
self._iterator = None
super(LoadDataset, self).wrapup()
class SetStorageValue(Transformer):
"""
Store the payload of the current token in internal storage using the specified name.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(SetStorageValue, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Store the payload of the current token in internal storage using the specified name."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "name: " + str(self.config["storage_name"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(SetStorageValue, self).fix_config(options)
opt = "storage_name"
if opt not in options:
options[opt] = "unknown"
if opt not in self.help:
self.help[opt] = "The storage value name for storing the payload under (string)."
return options
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
if self.storagehandler is None:
return "No storage handler available!"
self.storagehandler.storage[self.resolve_option("storage_name")] = self.input.payload
self._output.append(self.input)
return None
class DeleteStorageValue(Transformer):
"""
Deletes the specified value from internal storage.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(DeleteStorageValue, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Deletes the specified value from internal storage."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "name: " + str(self.config["storage_name"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(DeleteStorageValue, self).fix_config(options)
opt = "storage_name"
if opt not in options:
options[opt] = "unknown"
if opt not in self.help:
self.help[opt] = "The name of the storage value to delete (string)."
return options
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
if self.storagehandler is None:
return "No storage handler available!"
self.storagehandler.storage.pop(self.resolve_option("storage_name"), None)
self._output.append(self.input)
return None
class InitStorageValue(Transformer):
"""
Initializes the storage value with the provided value (interpreted by 'eval' method).
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(InitStorageValue, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Initializes the storage value with the provided value (interpreted by 'eval' method)."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "name: " + str(self.config["storage_name"]) + ", value: " + str(self.config["value"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(InitStorageValue, self).fix_config(options)
opt = "storage_name"
if opt not in options:
options[opt] = "unknown"
if opt not in self.help:
self.help[opt] = "The name of the storage value to delete (string)."
opt = "value"
if opt not in options:
options[opt] = "1"
if opt not in self.help:
self.help[opt] = "The initial value (string)."
return options
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
if self.storagehandler is None:
return "No storage handler available!"
self.storagehandler.storage[self.resolve_option("storage_name")] = eval(str(self.resolve_option("value")))
self._output.append(self.input)
return None
class UpdateStorageValue(Transformer):
"""
Updates the specified storage value using the epxression interpreted by 'eval' method.
The current value is available through the variable {X} in the expression.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(UpdateStorageValue, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Updates the specified storage value using the epxression interpreted by 'eval' method.\n"\
"The current value is available through the variable {X} in the expression.\n"\
"Any storage value can be referenced using @{name} with 'name' being the name of the storage value."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "name: " + str(self.config["storage_name"]) + ", expression: " + str(self.config["expression"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(UpdateStorageValue, self).fix_config(options)
opt = "storage_name"
if opt not in options:
options[opt] = "unknown"
if opt not in self.help:
self.help[opt] = "The name of the storage value to update (string)."
opt = "expression"
if opt not in options:
options[opt] = "int({X} + 1)"
if opt not in self.help:
self.help[opt] = "The expression for updating the storage value; use {X} for current value (string)."
return options
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
if self.storagehandler is None:
return "No storage handler available!"
expr = str(self.resolve_option("expression")).replace(
"{X}", str(self.storagehandler.storage[str(self.resolve_option("storage_name"))]))
expr = self.storagehandler.expand(expr)
self.storagehandler.storage[self.resolve_option("storage_name")] = eval(expr)
self._output.append(self.input)
return None
class MathExpression(Transformer):
"""
Calculates a mathematical expression. The placeholder {X} in the expression gets replaced by
the value of the current token passing through. Uses the 'eval(str)' method for the calculation,
therefore mathematical functions can be accessed using the 'math' library, e.g., '1 + math.sin({X})'.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(MathExpression, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return \
"Calculates a mathematical expression. The placeholder {X} in the expression gets replaced by "\
+ "the value of the current token passing through. Uses the 'eval(str)' method for the calculation, "\
+ "therefore mathematical functions can be accessed using the 'math' library, e.g., '1 + math.sin({X})'."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "expression: " + str(self.config["expression"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(MathExpression, self).fix_config(options)
opt = "expression"
if opt not in options:
options[opt] = "{X}"
if opt not in self.help:
self.help[opt] = "The mathematical expression to evaluate (string)."
return options
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
expr = str(self.resolve_option("expression"))
expr = expr.replace("{X}", str(self.input.payload))
self._output.append(Token(eval(expr)))
return None
class ClassSelector(Transformer):
"""
Sets/unsets the class index of a dataset.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(ClassSelector, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Sets/unsets the class index of a dataset."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "index: " + str(self.config["index"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(ClassSelector, self).fix_config(options)
opt = "index"
if opt not in options:
options[opt] = "last"
if opt not in self.help:
self.help[opt] = "The class index (1-based number); 'first' and 'last' are accepted as well (string)."
opt = "unset"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Whether to unset the class index (bool)."
return options
def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if isinstance(token.payload, Instances):
return
if isinstance(token.payload, Instance):
return
raise Exception(self.full_name + ": Unhandled data type: " + str(token.payload.__class__.__name__))
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
if isinstance(self.input.payload, Instances):
inst = None
data = self.input.payload
elif isinstance(self.input.payload, Instance):
inst = self.input.payload
data = inst.dataset
index = str(self.resolve_option("index"))
unset = bool(self.resolve_option("unset"))
if unset:
data.no_class()
else:
if index == "first":
data.class_is_first()
elif index == "last":
data.class_is_last()
else:
data.class_index = int(index) - 1
if inst is None:
self._output.append(Token(data))
else:
self._output.append(Token(inst))
return None
class Train(Transformer):
"""
Trains the classifier/clusterer/associator on the incoming dataset and forwards a ModelContainer with the trained
model and the dataset header.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(Train, self).__init__(name=name, config=config)
self._model = None
self._header = None
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return \
"Trains the classifier/clusterer/associator on the incoming dataset and forwards a ModelContainer with the trained " \
+ "model and the dataset header."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "setup: " + base.to_commandline(self.config["setup"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(Train, self).fix_config(options)
opt = "setup"
if opt not in options:
options[opt] = Classifier(classname="weka.classifiers.rules.ZeroR")
if opt not in self.help:
self.help[opt] = "The classifier/clusterer/associator to train (Classifier/Clusterer/Associator)."
return options
def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if isinstance(token.payload, Instances):
return
if isinstance(token.payload, Instance):
return
raise Exception(self.full_name + ": Unhandled data type: " + str(token.payload.__class__.__name__))
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
if isinstance(self.input.payload, Instances):
inst = None
data = self.input.payload
else:
inst = self.input.payload
data = inst.dataset
retrain = False
if (self._header is None) or (self._header.equal_headers(data) is not None) or (inst is None):
retrain = True
self._header = Instances.template_instances(data, 0)
if retrain or (self._model is None):
cls = self.resolve_option("setup")
if isinstance(cls, Classifier):
self._model = Classifier.make_copy(cls)
elif isinstance(cls, Clusterer):
self._model = Clusterer.make_copy(cls)
elif isinstance(cls, Associator):
self._model = Associator.make_copy(cls)
else:
return "Unhandled class: " + classes.get_classname(cls)
if retrain:
if inst is not None:
data = Instances.template_instances(data, 1)
data.add_instance(inst)
if isinstance(self._model, Classifier):
self._model.build_classifier(data)
elif isinstance(self._model, Clusterer):
self._model.build_clusterer(data)
elif isinstance(self._model, Associator):
self._model.build_associations(data)
else:
if isinstance(self._model, Classifier):
self._model.update_classifier(inst)
elif isinstance(self._model, Clusterer):
self._model.update_clusterer(inst)
else:
return "Cannot train incrementally: " + classes.get_classname(self._model)
cont = ModelContainer(model=self._model, header=self._header)
self._output.append(Token(cont))
return None
class Filter(Transformer):
"""
Filters a dataset with the specified filter setup.
Automatically resets the filter if the dataset differs.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(Filter, self).__init__(name=name, config=config)
self._filter = None
self._header = None
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Filters a dataset with the specified filter setup.\n"\
"Automatically resets the filter if the dataset differs."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "setup: " + base.to_commandline(self.config["setup"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
opt = "setup"
if opt not in options:
options[opt] = filters.Filter(classname="weka.filters.AllFilter")
if opt not in self.help:
self.help[opt] = "The filter to apply to the dataset (Filter)."
opt = "keep_relationname"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Whether to keep the original relation name (bool)."
return super(Filter, self).fix_config(options)
def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if token is None:
raise Exception(self.full_name + ": No token provided!")
if isinstance(token.payload, Instances):
return
if isinstance(token.payload, Instance):
return
raise Exception(self.full_name + ": Unhandled class: " + classes.get_classname(token.payload))
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
if isinstance(self.input.payload, Instance):
inst = self.input.payload
data = Instances.template_instances(inst.dataset, 1)
data.add_instance(inst)
else:
inst = None
data = self.input.payload
relname = data.relationname
keep = self.resolve_option("keep_relationname")
if inst is None:
if (self._filter is None) or self._header.equal_headers(data) is not None:
self._header = Instances.template_instances(data)
self._filter = filters.Filter.make_copy(self.resolve_option("setup"))
self._filter.inputformat(data)
filtered = self._filter.filter(data)
if keep:
filtered.relationname = relname
self._output.append(Token(filtered))
else:
if (self._filter is None) or self._header.equal_headers(data) is not None:
self._header = Instances.template_instances(data)
self._filter = filters.Filter.make_copy(self.resolve_option("setup"))
self._filter.inputformat(data)
filtered = self._filter.filter(data)
if keep:
filtered.relationname = relname
self._output.append(Token(filtered.get_instance(0)))
else:
self._filter.input(inst)
self._filter.batch_finished()
filtered = self._filter.output()
if keep:
filtered.dataset.relationname = relname
self._output.append(Token(filtered))
return None
class DeleteFile(Transformer):
"""
Deletes the incoming files that match the regular expression.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(DeleteFile, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Deletes the incoming files that match the regular expression."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "regexp: " + str(self.config["regexp"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(DeleteFile, self).fix_config(options)
opt = "regexp"
if opt not in options:
options[opt] = ".*"
if opt not in self.help:
self.help[opt] = "The regular expression that the files must match (string)."
return options
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
fname = str(self.input.payload)
spattern = str(self.resolve_option("regexp"))
pattern = None
if (spattern is not None) and (spattern != ".*"):
pattern = re.compile(spattern)
if (pattern is None) or (pattern.match(fname)):
os.remove(fname)
self._output.append(self.input)
return None
class CrossValidate(Transformer):
"""
Cross-validates the classifier/clusterer on the incoming dataset. In case of a classifier, the Evaluation object
is forwarded. For clusterers the loglikelihood.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(CrossValidate, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Cross-validates the classifier/clusterer on the incoming dataset. In case of a classifier, the "\
"Evaluation object is forwarded. For clusterers the loglikelihood."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "setup: " + base.to_commandline(self.config["setup"]) + ", folds: " + str(self.config["folds"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(CrossValidate, self).fix_config(options)
opt = "setup"
if opt not in options:
options[opt] = Classifier(classname="weka.classifiers.rules.ZeroR")
if opt not in self.help:
self.help[opt] = "The classifier/clusterer to train (Classifier/Clusterer)."
opt = "folds"
if opt not in options:
options[opt] = 10
if opt not in self.help:
self.help[opt] = "The number of folds for CV (int)."
opt = "seed"
if opt not in options:
options[opt] = 1
if opt not in self.help:
self.help[opt] = "The seed value for randomizing the data (int)."
opt = "discard_predictions"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Discard classifier predictions to save memory (bool)."
opt = "output"
if opt not in options:
options[opt] = None
if opt not in self.help:
self.help[opt] = "For capturing the classifier's prediction output (PredictionOutput)."
return options
def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if isinstance(token.payload, Instances):
return
raise Exception(self.full_name + ": Unhandled data type: " + str(token.payload.__class__.__name__))
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
data = self.input.payload
cls = self.resolve_option("setup")
if isinstance(cls, Classifier):
cls = Classifier.make_copy(cls)
evl = Evaluation(data)
evl.discard_predictions = bool(self.resolve_option("discard_predictions"))
evl.crossvalidate_model(
cls,
data,
int(self.resolve_option("folds")),
Random(int(self.resolve_option("seed"))),
self.resolve_option("output"))
self._output.append(Token(evl))
elif isinstance(cls, Clusterer):
cls = Clusterer.make_copy(cls)
evl = ClusterEvaluation()
llh = evl.crossvalidate_model(
cls,
data,
int(self.resolve_option("folds")),
Random(int(self.resolve_option("seed"))))
self._output.append(Token(llh))
else:
return "Unhandled class: " + classes.get_classname(cls)
return None
class Evaluate(Transformer):
"""
Evaluates a trained classifier obtained from internal storage on the dataset passing through.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(Evaluate, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Evaluates a trained classifier obtained from internal storage on the dataset passing through."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "storage: " + self.config["storage_name"]
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(Evaluate, self).fix_config(options)
opt = "storage_name"
if opt not in options:
options[opt] = "unknown"
if opt not in self.help:
self.help[opt] = "The name of the classifier model in storage (string)."
opt = "discard_predictions"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Discard classifier predictions to save memory (bool)."
opt = "output"
if opt not in options:
options[opt] = None
if opt not in self.help:
self.help[opt] = "For capturing the classifier's prediction output (PredictionOutput)."
return options
def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if isinstance(token.payload, Instances):
return
raise Exception(self.full_name + ": Unhandled data type: " + str(token.payload.__class__.__name__))
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
data = self.input.payload
if self.storagehandler is None:
return "No storage handler available!"
sname = str(self.resolve_option("storage_name"))
if sname not in self.storagehandler.storage:
return "No storage item called '" + sname + "' present!"
cls = self.storagehandler.storage[sname]
if isinstance(cls, Classifier):
evl = Evaluation(data)
evl.discard_predictions = bool(self.resolve_option("discard_predictions"))
evl.test_model(
cls,
data,
self.resolve_option("output"))
elif isinstance(cls, Clusterer):
evl = ClusterEvaluation()
evl.set_model(cls)
evl.test_model(data)
else:
return "Unhandled class: " + classes.get_classname(cls)
self._output.append(Token(evl))
return None
class EvaluationSummary(Transformer):
"""
Generates a summary string from an Evaluation/ClusterEvaluation object.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(EvaluationSummary, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Generates a summary string from an Evaluation object."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "title: " + str(self.config["title"]) \
+ ", complexity: " + str(self.config["complexity"]) \
+ ", matrix: " + str(self.config["matrix"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(EvaluationSummary, self).fix_config(options)
opt = "title"
if opt not in options:
options[opt] = None
if opt not in self.help:
self.help[opt] = "The title for the output (string)."
opt = "complexity"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Whether to output classifier complexity information (bool)."
opt = "matrix"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Whether to output the classifier confusion matrix (bool)."
return options
def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if isinstance(token.payload, Evaluation):
return None
if isinstance(token.payload, ClusterEvaluation):
return None
raise Exception(
self.full_name + ": Input token is not a supported Evaluation object - "
+ classes.get_classname(token.payload))
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
evl = self.input.payload
if isinstance(evl, Evaluation):
summary = evl.summary(title=self.resolve_option("title"), complexity=bool(self.resolve_option("complexity")))
if bool(self.resolve_option("matrix")):
summary += "\n" + evl.matrix(title=self.resolve_option("title"))
else:
summary = evl.cluster_results
self._output.append(Token(summary))
return None
class ModelReader(Transformer):
"""
Reads the serialized model (Classifier/Clusterer) from disk and forwards a ModelContainer.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(ModelReader, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Reads the serialized model from disk and forwards a ModelContainer."
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
fname = self.input.payload
data = serialization.read_all(fname)
if len(data) == 1:
if javabridge.is_instance_of(data[0], "weka/classifiers/Classifier"):
cont = ModelContainer(model=Classifier(jobject=data[0]))
elif javabridge.is_instance_of(data[0], "weka/clusterers/Clusterer"):
cont = ModelContainer(model=Clusterer(jobject=data[0]))
else:
return "Unhandled class: " + classes.get_classname(data[0])
elif len(data) == 2:
if javabridge.is_instance_of(data[0], "weka/classifiers/Classifier"):
cont = ModelContainer(model=Classifier(jobject=data[0]), header=Instances(data[1]))
elif javabridge.is_instance_of(data[0], "weka/clusterers/Clusterer"):
cont = ModelContainer(model=Clusterer(jobject=data[0]), header=Instances(data[1]))
else:
return "Unhandled class: " + classes.get_classname(data[0])
else:
return "Expected 1 or 2 objects, but got " + str(len(data)) + " instead reading: " + fname
self._output.append(Token(cont))
return None
class Convert(Transformer):
"""
Converts the input data with the given conversion setup.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(Convert, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Converts the input data with the given conversion setup."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "setup: " + str(self.config["setup"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
opt = "setup"
if opt not in options:
options[opt] = conversion.PassThrough()
if opt not in self.help:
self.help[opt] = "The conversion to apply to the input data (Conversion)."
return super(Convert, self).fix_config(options)
def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if token is None:
raise Exception(self.full_name + ": No token provided!")
self.config["setup"].check_input(token.payload)
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
conv = self.config["setup"].shallow_copy()
conv.input = self._input.payload
result = conv.convert()
if result is None:
if conv.output is not None:
self._output.append(Token(conv.output))
return None
class AttributeSelection(Transformer):
"""
Performs attribute selection on the incoming dataset using the specified search and evaluation scheme.
Outputs a AttributeSelectionContainer with the results string, reduced dataset and seleted attributes.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(AttributeSelection, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return \
"Performs attribute selection on the incoming dataset using the specified search and evaluation scheme.\n" \
"Outputs a AttributeSelectionContainer with the results string, reduced dataset and seleted attributes."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "search: " + base.to_commandline(self.config["search"]) + ", eval: " \
+ base.to_commandline(self.config["eval"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
opt = "search"
if opt not in options:
options[opt] = attsel.ASSearch(classname="weka.attributeSelection.BestFirst")
if opt not in self.help:
self.help[opt] = "The search algorithm to use (ASSearch)."
opt = "eval"
if opt not in options:
options[opt] = attsel.ASEvaluation(classname="weka.attributeSelection.CfsSubsetEval")
if opt not in self.help:
self.help[opt] = "The evaluation algorithm to use (ASEvaluation)."
return super(AttributeSelection, self).fix_config(options)
def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if token is None:
raise Exception(self.full_name + ": No token provided!")
if not isinstance(token.payload, Instances):
raise Exception(self.full_name + ": Not an Instances object!")
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
data = self.input.payload
search = self.config["search"].shallow_copy()
evl = self.config["eval"].shallow_copy()
asel = attsel.AttributeSelection()
asel.search(search)
asel.evaluator(evl)
asel.select_attributes(data)
cont = AttributeSelectionContainer(
original=data,
reduced=asel.reduce_dimensionality(data),
num_atts=asel.number_attributes_selected,
selected=asel.selected_attributes,
results=asel.results_string)
self._output.append(Token(cont))
return None
class RenameRelation(Transformer):
"""
Updates the relation name of Instance/Instances objects passing through.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(RenameRelation, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Updates the relation name of Instance/Instances objects passing through."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "name: " + str(self.config["name"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(RenameRelation, self).fix_config(options)
opt = "name"
if opt not in options:
options[opt] = "newname"
if opt not in self.help:
self.help[opt] = "The new relation name to use (string)."
return options
def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if isinstance(token.payload, Instance):
return
if isinstance(token.payload, Instances):
return
raise Exception(self.full_name + ": Input token is neither an Instance nor Instances object!")
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
relname = self.resolve_option("name")
if isinstance(self.input.payload, Instance):
self.input.payload.dataset.relationname = relname
else:
self.input.payload.relationname = relname
self._output.append(self.input)
return None
class Copy(Transformer):
"""
Creates a deep copy of the token passing through (must be a serializable JavaObject),
other objects just get passed on.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(Copy, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Creates a deep copy of the token passing through (must be a serializable JavaObject), "\
"other objects just get passed on."
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
if isinstance(self.input.payload, classes.JavaObject) and self.input.payload.is_serializable:
copy = serialization.deepcopy(self.input.payload)
if copy is not None:
self._output.append(Token(copy))
else:
self._output.append(self.input)
return None
class Predict(Transformer):
"""
Uses the serialized model or, if pointing to a directory, the specified model from storage for
making a prediction on the incoming Instance object. The model can be either a Classifier or Clusterer.
Outputs either a ClassificationContainer or ClusteringContainer.
"""
def __init__(self, name=None, config=None):
"""
Initializes the transformer.
:param name: the name of the transformer
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(Predict, self).__init__(name=name, config=config)
self._model = None
self._is_classifier = None
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return \
"Uses the serialized model or, if pointing to a directory, the specified model from storage for "\
"making a prediction on the incoming Instance object. The model can be either a Classifier or Clusterer.\n"\
"Outputs either a ClassificationContainer or ClusteringContainer."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "model: " + self.config["model"] + ", storage: " + self.config["storage_name"]
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(Predict, self).fix_config(options)
opt = "model"
if opt not in options:
options[opt] = "."
if opt not in self.help:
self.help[opt] = "The serialized model to use for making predictions (string)."
opt = "storage_name"
if opt not in options:
options[opt] = "unknown"
if opt not in self.help:
self.help[opt] = "The name of the model (or ModelContainer) in storage to use (string)."
return options
def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if isinstance(token.payload, Instance):
return
raise Exception(self.full_name + ": Unhandled data type: " + str(token.payload.__class__.__name__))
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
inst = self.input.payload
if not inst.has_class:
return "No class set!"
# load model?
if self._model is None:
model = None
fname = str(self.resolve_option("model"))
if os.path.isfile(fname):
model = serialization.read(fname)
else:
name = self.resolve_option("storage_name")
if name in self.storagehandler.storage:
model = self.storagehandler.storage.get(name)
if isinstance(model, ModelContainer):
model = model.get("Model").jobject
if model is None:
return "No model available from storage or serialized file!"
self._is_classifier = javabridge.is_instance_of(model, "weka/classifiers/Classifier")
if self._is_classifier:
self._model = Classifier(jobject=model)
else:
self._model = Clusterer(jobject=model)
if self._is_classifier:
cls = self._model.classify_instance(inst)
dist = self._model.distribution_for_instance(inst)
label = inst.class_attribute.value(int(cls))
cont = ClassificationContainer(inst=inst, classification=cls, distribution=dist, label=label)
else:
cls = self._model.cluster_instance(inst)
dist = self._model.distribution_for_instance(inst)
cont = ClusteringContainer(inst=inst, cluster=int(cls), distribution=dist)
self._output.append(Token(cont))
return None
python-weka-wrapper-0.3.6/python/weka/flow/sink.py 0000644 0172670 0172670 00000072023 12576430667 023655 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# sink.py
# Copyright (C) 2015 Fracpete (pythonwekawrapper at gmail dot com)
import traceback
import weka.core.serialization as serialization
from weka.core.dataset import Instances, Instance
from weka.flow.base import InputConsumer
from weka.flow.container import ModelContainer
from weka.classifiers import Evaluation
import weka.plot.classifiers as pltclassifier
import weka.plot.clusterers as pltclusterer
import weka.plot.dataset as pltdataset
class Sink(InputConsumer):
"""
The ancestor for all sinks.
"""
def __init__(self, name=None, config=None):
"""
Initializes the sink.
:param name: the name of the sink
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(Sink, self).__init__(name=name, config=config)
super(InputConsumer, self).__init__(name=name, config=config)
def post_execute(self):
"""
Gets executed after the actual execution.
:return: None if successful, otherwise error message
:rtype: str
"""
result = super(Sink, self).post_execute()
if result is None:
self._input = None
return result
class Null(Sink):
"""
Sink that just gobbles up all the data.
"""
def __init__(self, name=None, config=None):
"""
Initializes the sink.
:param name: the name of the sink
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(Null, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Sink that just gobbles up all the data."
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
return None
class Console(Sink):
"""
Sink that outputs the payloads of the data on stdout.
"""
def __init__(self, name=None, config=None):
"""
Initializes the sink.
:param name: the name of the sink
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(Console, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Sink that outputs the payloads of the data on stdout."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "prefix: '" + str(self.config["prefix"]) + "'"
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(Console, self).fix_config(options)
opt = "prefix"
if opt not in options:
options[opt] = ""
if opt not in self.help:
self.help[opt] = "The prefix for the output (string)."
return options
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
print(self.resolve_option("prefix") + str(self.input.payload))
return None
class FileOutputSink(Sink):
"""
Ancestor for sinks that output data to a file.
"""
def __init__(self, name=None, config=None):
"""
Initializes the sink.
:param name: the name of the sink
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(FileOutputSink, self).__init__(name=name, config=config)
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "output: '" + str(self.config["output"]) + "'"
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(FileOutputSink, self).fix_config(options)
opt = "output"
if opt not in options:
options[opt] = "."
if opt not in self.help:
self.help[opt] = "The file to write to (string)."
return options
class DumpFile(FileOutputSink):
"""
Sink that outputs the payloads of the data to a file.
"""
def __init__(self, name=None, config=None):
"""
Initializes the sink.
:param name: the name of the sink
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(DumpFile, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Sink that outputs the payloads of the data to a file."
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return super(DumpFile, self).quickinfo + ", append: " + str(self.config["append"])
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(DumpFile, self).fix_config(options)
opt = "append"
if opt not in options:
options[opt] = False
if opt not in self.help:
self.help[opt] = "Whether to append to the file or overwrite (bool)."
return options
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
result = None
f = None
try:
if bool(self.resolve_option("append")):
f = open(str(self.resolve_option("output")), "a")
else:
f = open(str(self.resolve_option("output")), "w")
f.write(str(self.input.payload))
f.write("\n")
except Exception, e:
result = self.full_name + "\n" + traceback.format_exc()
finally:
if f is not None:
f.close()
return result
class ModelWriter(FileOutputSink):
"""
Writes a model to disk.
"""
def __init__(self, name=None, config=None):
"""
Initializes the sink.
:param name: the name of the sink
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(ModelWriter, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Writes a model to disk."
def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if not isinstance(token.payload, ModelContainer):
raise Exception(self.full_name + ": Input token is not a ModelContainer!")
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
result = None
cont = self.input.payload
serialization.write_all(
str(self.resolve_option("output")),
[cont.get("Model").jobject, cont.get("Header").jobject])
return result
class MatrixPlot(Sink):
"""
Displays the Instances object as matrix plot.
"""
def __init__(self, name=None, config=None):
"""
Initializes the sink.
:param name: the name of the sink
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(MatrixPlot, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Displays the Instances object as matrix plot."
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(MatrixPlot, self).fix_config(options)
opt = "percent"
if opt not in options:
options[opt] = 100.0
if opt not in self.help:
self.help[opt] = "The percentage of the data to display (0-100, float)."
opt = "seed"
if opt not in options:
options[opt] = 1
if opt not in self.help:
self.help[opt] = "The seed value for randomizing the plot when viewing a subset (int)."
opt = "size"
if opt not in options:
options[opt] = 10
if opt not in self.help:
self.help[opt] = "The size of the circles in the plot (int)."
opt = "title"
if opt not in options:
options[opt] = None
if opt not in self.help:
self.help[opt] = "The title for the plot (str)."
opt = "outfile"
if opt not in options:
options[opt] = None
if opt not in self.help:
self.help[opt] = "The file to store the plot in (str)."
opt = "wait"
if opt not in options:
options[opt] = True
if opt not in self.help:
self.help[opt] = "Whether to wait for user to close the plot window (bool)."
return options
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "percent: " + str(self.config["percent"]) \
+ ", title: " + str(self.config["title"]) \
+ ", outfile: " + str(self.config["outfile"]) \
+ ", wait: " + str(self.config["wait"])
def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if not isinstance(token.payload, Instances):
raise Exception(self.full_name + ": Input token is not an Instances object!")
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
result = None
data = self.input.payload
pltdataset.matrix_plot(
data,
percent=float(self.resolve_option("percent")),
seed=int(self.resolve_option("seed")),
size=int(self.resolve_option("size")),
title=self.resolve_option("title"),
outfile=self.resolve_option("outfile"),
wait=bool(self.resolve_option("wait")))
return result
class LinePlot(Sink):
"""
Displays the Instances object as line plot using the internal format, one line per Instance.
"""
def __init__(self, name=None, config=None):
"""
Initializes the sink.
:param name: the name of the sink
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(LinePlot, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Displays the Instances object as line plot using the internal format, one line per Instance."
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(LinePlot, self).fix_config(options)
opt = "attributes"
if opt not in options:
options[opt] = None
if opt not in self.help:
self.help[opt] = "The list of 0-based attribute indices to print; None for all (int)."
opt = "percent"
if opt not in options:
options[opt] = 100.0
if opt not in self.help:
self.help[opt] = "The percentage of the data to display (0-100, float)."
opt = "seed"
if opt not in options:
options[opt] = 1
if opt not in self.help:
self.help[opt] = "The seed value for randomizing the plot when viewing a subset (int)."
opt = "title"
if opt not in options:
options[opt] = None
if opt not in self.help:
self.help[opt] = "The title for the plot (str)."
opt = "outfile"
if opt not in options:
options[opt] = None
if opt not in self.help:
self.help[opt] = "The file to store the plot in (str)."
opt = "wait"
if opt not in options:
options[opt] = True
if opt not in self.help:
self.help[opt] = "Whether to wait for user to close the plot window (bool)."
return options
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "percent: " + str(self.config["percent"]) \
+ ", title: " + str(self.config["title"]) \
+ ", outfile: " + str(self.config["outfile"]) \
+ ", wait: " + str(self.config["wait"])
def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if not isinstance(token.payload, Instances):
raise Exception(self.full_name + ": Input token is not an Instances object!")
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
result = None
data = self.input.payload
pltdataset.line_plot(
data,
atts=self.resolve_option("attributes"),
percent=float(self.resolve_option("percent")),
seed=int(self.resolve_option("seed")),
title=self.resolve_option("title"),
outfile=self.resolve_option("outfile"),
wait=bool(self.resolve_option("wait")))
return result
class ClassifierErrors(Sink):
"""
Displays the errors obtained through a classifier evaluation.
"""
def __init__(self, name=None, config=None):
"""
Initializes the sink.
:param name: the name of the sink
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(ClassifierErrors, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Displays the errors obtained through a classifier evaluation."
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(ClassifierErrors, self).fix_config(options)
opt = "absolute"
if opt not in options:
options[opt] = True
if opt not in self.help:
self.help[opt] = "Whether to use absolute errors as size or relative ones (bool)."
opt = "max_relative_size"
if opt not in options:
options[opt] = 50
if opt not in self.help:
self.help[opt] = "The maximum size in point in case of relative mode (int)."
opt = "absolute_size"
if opt not in options:
options[opt] = 50
if opt not in self.help:
self.help[opt] = "The size in point in case of absolute mode (int)."
opt = "title"
if opt not in options:
options[opt] = None
if opt not in self.help:
self.help[opt] = "The title for the plot (str)."
opt = "outfile"
if opt not in options:
options[opt] = None
if opt not in self.help:
self.help[opt] = "The file to store the plot in (str)."
opt = "wait"
if opt not in options:
options[opt] = True
if opt not in self.help:
self.help[opt] = "Whether to wait for user to close the plot window (bool)."
return options
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "absolute: " + str(self.config["absolute"]) \
+ ", title: " + str(self.config["title"]) \
+ ", outfile: " + str(self.config["outfile"]) \
+ ", wait: " + str(self.config["wait"])
def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if not isinstance(token.payload, Evaluation):
raise Exception(self.full_name + ": Input token is not an Evaluation object!")
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
result = None
evl = self.input.payload
pltclassifier.plot_classifier_errors(
evl.predictions,
absolute=bool(self.resolve_option("absolute")),
max_relative_size=int(self.resolve_option("max_relative_size")),
absolute_size=int(self.resolve_option("absolute_size")),
title=self.resolve_option("title"),
outfile=self.resolve_option("outfile"),
wait=bool(self.resolve_option("wait")))
return result
class ROC(Sink):
"""
Displays the ROC curve obtained from a classifier evaluation.
"""
def __init__(self, name=None, config=None):
"""
Initializes the sink.
:param name: the name of the sink
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(ROC, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Displays the ROC curve obtained from a classifier evaluation."
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(ROC, self).fix_config(options)
opt = "class_index"
if opt not in options:
options[opt] = [0]
if opt not in self.help:
self.help[opt] = "The list of 0-based class-label indices to display (list)."
opt = "key_loc"
if opt not in options:
options[opt] = "lower right"
if opt not in self.help:
self.help[opt] = "The location of the key in the plot (str)."
opt = "title"
if opt not in options:
options[opt] = None
if opt not in self.help:
self.help[opt] = "The title for the plot (str)."
opt = "outfile"
if opt not in options:
options[opt] = None
if opt not in self.help:
self.help[opt] = "The file to store the plot in (str)."
opt = "wait"
if opt not in options:
options[opt] = True
if opt not in self.help:
self.help[opt] = "Whether to wait for user to close the plot window (bool)."
return options
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "classes: " + str(self.config["class_index"]) \
+ ", title: " + str(self.config["title"]) \
+ ", outfile: " + str(self.config["outfile"]) \
+ ", wait: " + str(self.config["wait"])
def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if not isinstance(token.payload, Evaluation):
raise Exception(self.full_name + ": Input token is not an Evaluation object!")
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
result = None
evl = self.input.payload
pltclassifier.plot_roc(
evl,
class_index=self.resolve_option("class_index"),
title=self.resolve_option("title"),
key_loc=self.resolve_option("key_loc"),
outfile=self.resolve_option("outfile"),
wait=bool(self.resolve_option("wait")))
return result
class PRC(Sink):
"""
Displays the PRC curve obtained from a classifier evaluation.
"""
def __init__(self, name=None, config=None):
"""
Initializes the sink.
:param name: the name of the sink
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(PRC, self).__init__(name=name, config=config)
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Displays the PRC curve obtained from a classifier evaluation."
def fix_config(self, options):
"""
Fixes the options, if necessary. I.e., it adds all required elements to the dictionary.
:param options: the options to fix
:type options: dict
:return: the (potentially) fixed options
:rtype: dict
"""
options = super(PRC, self).fix_config(options)
opt = "class_index"
if opt not in options:
options[opt] = [0]
if opt not in self.help:
self.help[opt] = "The list of 0-based class-label indices to display (list)."
opt = "key_loc"
if opt not in options:
options[opt] = "lower center"
if opt not in self.help:
self.help[opt] = "The location of the key in the plot (str)."
opt = "title"
if opt not in options:
options[opt] = None
if opt not in self.help:
self.help[opt] = "The title for the plot (str)."
opt = "outfile"
if opt not in options:
options[opt] = None
if opt not in self.help:
self.help[opt] = "The file to store the plot in (str)."
opt = "wait"
if opt not in options:
options[opt] = True
if opt not in self.help:
self.help[opt] = "Whether to wait for user to close the plot window (bool)."
return options
@property
def quickinfo(self):
"""
Returns a short string describing some of the options of the actor.
:return: the info, None if not available
:rtype: str
"""
return "classes: " + str(self.config["class_index"]) \
+ ", title: " + str(self.config["title"]) \
+ ", outfile: " + str(self.config["outfile"]) \
+ ", wait: " + str(self.config["wait"])
def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if not isinstance(token.payload, Evaluation):
raise Exception(self.full_name + ": Input token is not an Evaluation object!")
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
result = None
evl = self.input.payload
pltclassifier.plot_prc(
evl,
class_index=self.resolve_option("class_index"),
title=self.resolve_option("title"),
key_loc=self.resolve_option("key_loc"),
outfile=self.resolve_option("outfile"),
wait=bool(self.resolve_option("wait")))
return result
class InstanceDumper(FileOutputSink):
"""
Sink that dumps the incoming Instance/Instances into a file.
"""
def __init__(self, name=None, config=None):
"""
Initializes the sink.
:param name: the name of the sink
:type name: str
:param config: the dictionary with the options (str -> object).
:type config: dict
"""
super(InstanceDumper, self).__init__(name=name, config=config)
self._header = None
def description(self):
"""
Returns a description of the actor.
:return: the description
:rtype: str
"""
return "Sink that dumps the incoming Instance/Instances objects in a file."
def check_input(self, token):
"""
Performs checks on the input token. Raises an exception if unsupported.
:param token: the token to check
:type token: Token
"""
if isinstance(token.payload, Instance):
return
if isinstance(token.payload, Instances):
return
raise Exception(self.full_name + ": Input token is neither an Instance nor Instances object!")
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
result = None
data = self.input.payload
if isinstance(self._input.payload, Instance):
inst = self.input.payload
data = inst.dataset
elif isinstance(self.input.payload, Instances):
data = self.input.payload
inst = None
append = True
if self._header is None or (self._header.equal_headers(data) is not None):
self._header = Instances.template_instances(data, 0)
outstr = str(data)
append = False
elif inst is not None:
outstr = str(inst)
else:
outstr = str(data)
f = None
try:
if append:
f = open(str(self.resolve_option("output")), "a")
else:
f = open(str(self.resolve_option("output")), "w")
f.write(outstr)
f.write("\n")
except Exception, e:
result = self.full_name + "\n" + traceback.format_exc()
finally:
if f is not None:
f.close()
return result
python-weka-wrapper-0.3.6/CHANGES.rst 0000644 0172670 0172670 00000020716 12677712755 020727 0 ustar fracpete fracpete 0000000 0000000 Changelog
=========
0.3.6 (2016-04-02)
------------------
- `Loader.load_file` method now checks whether the dataset file really exists, otherwise a previously loaded
file gets loaded again without an error message (seems to be a Weka issue)
- replaced `org.pentaho.packageManagement` with `weka.core.packageManagement` as the package management code
is now part of Weka rather than a third-party library
- `jvm.start()` no longer tries to load packages and therefore suppresses error message if `$HOME/wekafiles/packages`
should not yet exist
0.3.5 (2016-01-29)
------------------
- added support for `weka.core.BatchPredictor` to class `Classifier` in module `weka.classifiers`
- upgraded Weka to revision 12410 (post 3.7.13) to avoid performance bottleneck when using setOptions method
- fixed class `SetupGenerator` from module `weka.core.classes`
- added `load_any_file` method to the `weka.core.converters` module
- added `save_any_file` method to the `weka.core.converters` module
- if `GridSearch` instantiation (module `weka.classifiers`) fails, it now outputs message whether package
installed and JVM with package support started
0.3.4 (2016-01-15)
------------------
- added convenience method `create_instances_from_lists` to `weka.core.dataset` module to easily create
an `Instances` object from numeric lists (x and y)
- added `get_object_tags` method to `Tags` class from module `weka.core.classes`, to allow obtaining
`weka.core.Tag` array from the method of a `JavaObject` rather than a static field (MultiSearch)
- updated `MultiSearch` wrapper in module `weka.classifiers` to work with the `multi-search` package
version 2016.1.15 or later
0.3.3 (2015-09-26)
------------------
- updated to Weka 3.7.13
- documentation now covers the API as well
0.3.2 (2015-06-29)
------------------
- The `packages` parameter of the `weka.core.jvm.start()` function can be used for specifying an alternative
Weka home directory now as well
- added `train_test_split` method to `weka.core.Instances` class to easily create train/test splits
- `evaluate_train_test_split` method of `weka.classifiers.Evaluation` class now uses the `train_test_split` method
0.3.1 (2015-04-23)
------------------
- added `get_tags` class method to `Tags` method for easier instantiation of Tag arrays
- added `find` method to `Tags` class to locate `Tag` object that matches the string
- fixed `__getitem__` and `__setitem__` methods of the `Tags` class
- added `GridSearch` meta-classifier with convenience properties to module `weka.classifiers`
- added `SetupGenerator` and various parameter classes to `weka.core.classes`
- added `MultiSearch` meta-classifier with convenience properties to module `weka.classifiers`
- added `quote`/`unquote` and `backquote`/`unbackquote` methods to `weka.core.classes` module
- added `main` method to `weka.core.classes` for operations on options: join, split, code
- added support for option handling to `weka.core.classes` module
0.3.0 (2015-04-15)
------------------
- added method `ndarray_to_instances` to `weka.converters` module for converting Numpy 2-dimensional array into `Instances` object
- added method `plot_learning_curve` to `weka.plot.classifiers` module for creating learning curves for multiple classifiers for a specific metric
- added plotting of experiments with `plot_experiment` methid in `weka.plot.experiments` module
- `Instance.create_instance` method now takes list of tuples (index, internal float value) when generating sparse instances
- added `weka.core.database` module for loading data from a database
- added `make_copy` class method to `Clusterer` class
- added `make_copy` class method to `Associator` class
- added `make_copy` class method to `Filter` class
- added `make_copy` class method to `DataGenerator` class
- most classes (like Classifier and Filter) now have a default classname value in the constructor
- added `TextDirectoryLoader` class to `weka.core.converters`
- moved all methods from `weka.core.utils` to `weka.core.classes`
- fixed `Attribute.index_of` method for determining label index
- fixed `Attribute.add_string_value` method (used incorrect JNI parameter)
- `create_instance` and `create_sparse_instance` methods of class `Instance` now ensure that list values are float
- added `to_help` method to `OptionHandler` class which outputs a help string generated from the base class's
`globalInfo` and `listOptions` methods
- fixed `test_model` method of `Evaluation` class when supplying a `PredictionOutput` object (previously generated `No dataset structure provided!` exception)
- added `batch_finished` method to `Filter` class for incremental filtering
- added `line_plot` method to `weka.plot.dataset` module for plotting dataset using internal format (one line plot per instance)
- added `is_serializable` property to `JavaObject` class
- added `has_class` convenience property to `Instance` class
- added `__repr__` method to `JavaObject` classes (simply calls `toString()` method)
- added `Stemmer` class in module `weka.core.stemmers`
- added `Stopwords` class in module `weka.core.stopwords`
- added `Tokenizer` class in module `weka.core.tokenizers`
- added `StringToWordVector` filter class in module `weka.filters`
- added simple workflow engine (see documentation on *Flow*)
0.2.2 (2015-01-05)
------------------
- added convenience methods `no_class` (to unset class) and `has_class` (class set?) to `Instances` class
- switched to using faster method objects for methods `classify_instance`/`distribution_for_instance` in `Classifier` class
- switched to using faster method objects for methods `cluster_instance`/`distribution_for_instance` in `Clusterer` class
- switched to using faster method objects for methods `class_index`, `is_missing`, `get/set_value`, `get/set_string_value`, `weight` in `Instance` class
- switched to using faster method objects for methods `input`, `output`, `outputformat` in `Filter` class
- switched to using faster method objects for methods `attribute`, `attribute_by_name`, `num_attributes`, `num_instances`,
`class_index`, `class_attribute`, `set_instance`, `get_instance`, `add_instance` in `Instances` class
0.2.1 (2015-01-05)
------------------
- added unit testing framework
- added method `refresh_cache()` to `weka/core/packages.py` to allow user to refresh local cache
- method `get_classname` in `weka.core.utils` now handles Python objects and class objects as well
- added convenience method `get_jclass` to `weka.core.utils` to instantiate a Java class
- added a `JavaArray` wrapper for `arrays`, which allows getting/setting elements and iterating
- added property `classname` to class `JavaObject` for easy access to classname of underlying object
- added class method `parse_matlab` for parsing Matlab matrix strings to `CostMatrix` class
- `predictions` method of `Evaluation` class now return `None` if predictions are discarded
- `Associator.get_capabilities()` method is now a property: `Associator.capabilities`
- added wrapper classes for Java enums: `weka.core.classes.Enum`
- fixed retrieval of `sumSq` in `Stats` class (used by `AttributeStats`)
- fixed `cluster_instance` method in `Clusterer` class
- fixed `filter` and `clusterer` properties in clusterer classes (`SingleClustererEnhancer`, `FilteredClusterer`)
- added `crossvalidate_model` method to `ClusterEvaluation`
- added `get_prc` method to `plot/classifiers.py` for calculating the area under the precision-recall curve
- `Filter.filter` method now handles list of `Instances` objects as well, applying the filter sequentially
to all the datasets (allows generation of compatible train/test sets)
0.2.0 (2014-12-22)
------------------
NB: This release is not backwards compatible!
- requires `JavaBridge` 1.0.9 at least
- moved from Java-like get/set (`getIndex()` and `setIndex(int)`) to nicer Python properties
- using Python properties (also only read-only ones) wherevere possible
- added `weka.core.version` for accessing the Weka version currently in use
- added `jwrapper` and `jclasswrapper` methods to `JavaObject` class (the mother of all objects in python-weka-wrapper)
to allow generic access to an object's methods: http://pythonhosted.org//javabridge/highlevel.html#wrapping-java-objects-using-reflection
- added convenience methods `class_is_last()` and `class_is_first()` to `weka.core.Instances` class
- added convenience methods `delete_last_attribute()` and `delete_first_attribute()` to `weka.core.Instances` class
Older releases
--------------
https://github.com/fracpete/python-weka-wrapper/blob/7fd0bba3c74277313eb463e338c1a7e117a1ea22/CHANGES.rst
python-weka-wrapper-0.3.6/MANIFEST.in 0000644 0172670 0172670 00000000263 12352664672 020650 0 ustar fracpete fracpete 0000000 0000000 recursive-include python *.arff
recursive-include python *.jar
recursive-exclude python weka-src.jar
recursive-exclude python weka.jar
include DESCRIPTION.rst
include CHANGES.rst
python-weka-wrapper-0.3.6/setup.cfg 0000644 0172670 0172670 00000000264 12677713207 020733 0 ustar fracpete fracpete 0000000 0000000 [build_sphinx]
source-dir = doc/source
build-dir = doc/build
all_files = 1
[upload_sphinx]
upload-dir = doc/build/html
[egg_info]
tag_build =
tag_date = 0
tag_svn_revision = 0
python-weka-wrapper-0.3.6/setup.py 0000644 0172670 0172670 00000007175 12677713153 020634 0 ustar fracpete fracpete 0000000 0000000 # 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 .
# setup.py
# Copyright (C) 2014-2016 Fracpete (pythonwekawrapper at gmail dot com)
import os
from setuptools import setup
from urllib2 import urlopen, URLError, HTTPError
def download_file(url, outfile):
"""
Downloads the file associated with the URL and saves it to the specified output file.
Taken from here: http://stackoverflow.com/a/4028894
:param url: the URL to download
:type url: str
:param outfile: the name of the output file
:type outfile: str
:returns: whether the download was successful
:rtype: bool
"""
try:
# Open the url
f = urlopen(url)
print("Downloading '" + url + "' to '" + outfile + "'")
# Open our local file for writing
with open(outfile, "wb") as local_file:
local_file.write(f.read())
# handle errors
except HTTPError, e:
print("HTTP Error: " + str(e.code) + " " + url)
return False
except URLError, e:
print("URL Error: " + str(e.reason) + " " + url)
return False
return True
def download_weka():
"""
Downloads the monolithic Weka jar from sourceforget.net if nececssary.
"""
url = "https://github.com/fracpete/python-weka-wrapper/blob/e4518862a42f234c2f75da46965d4051b07898b4/python/weka/lib/weka.jar?raw=true"
outfile = os.path.join(os.path.dirname(__file__), "python", "weka", "lib", "weka.jar")
if not os.path.exists(outfile):
if not download_file(url, outfile):
print("Failed to download Weka jar '" + url + "' to '" + outfile + "'!")
else:
print("Download of Weka jar successful!")
def ext_modules():
"""
Initiates Weka jar download.
"""
download_weka()
def _read(f):
"""
Reads in the content of the file.
:param f: the file to read
:type f: str
:return: the content
:rtype: str
"""
return open(f, 'rb').read()
setup(
name="python-weka-wrapper",
description="Python wrapper for the Weka Machine Learning Workbench",
long_description=(
_read('DESCRIPTION.rst') + b'\n' +
_read('CHANGES.rst')).decode('utf-8'),
url="https://github.com/fracpete/python-weka-wrapper",
classifiers=[
'Development Status :: 4 - Beta',
'License :: OSI Approved :: GNU General Public License (GPL)',
'Topic :: Scientific/Engineering :: Artificial Intelligence',
'Programming Language :: Python',
],
license='GNU General Public License version 3.0 (GPLv3)',
package_dir={
'': 'python'
},
packages=[
"weka",
"weka.core",
"weka.flow",
"weka.plot"
],
package_data={
"weka": ["lib/*.jar"],
},
include_package_data=True,
version="0.3.6",
author='Peter "fracpete" Reutemann',
author_email='pythonwekawrapper at gmail dot com',
install_requires=[
"javabridge==1.0.11",
"numpy"
],
extras_require={
'plots': ["matplotlib"],
'graphs': ["pygraphviz", "PIL"],
},
ext_modules=ext_modules(),
)
python-weka-wrapper-0.3.6/DESCRIPTION.rst 0000644 0172670 0172670 00000000315 12341760024 021407 0 ustar fracpete fracpete 0000000 0000000 The **python-weka-wrapper** package makes it easy to run
Weka algorithms and filters from within Python. It offers access to Weka
API using thin wrappers around JNI calls using the **javabridge** package.
python-weka-wrapper-0.3.6/PKG-INFO 0000644 0172670 0172670 00000024717 12677713207 020220 0 ustar fracpete fracpete 0000000 0000000 Metadata-Version: 1.1
Name: python-weka-wrapper
Version: 0.3.6
Summary: Python wrapper for the Weka Machine Learning Workbench
Home-page: https://github.com/fracpete/python-weka-wrapper
Author: Peter "fracpete" Reutemann
Author-email: pythonwekawrapper at gmail dot com
License: GNU General Public License version 3.0 (GPLv3)
Description: The **python-weka-wrapper** package makes it easy to run
Weka algorithms and filters from within Python. It offers access to Weka
API using thin wrappers around JNI calls using the **javabridge** package.
Changelog
=========
0.3.6 (2016-04-02)
------------------
- `Loader.load_file` method now checks whether the dataset file really exists, otherwise a previously loaded
file gets loaded again without an error message (seems to be a Weka issue)
- replaced `org.pentaho.packageManagement` with `weka.core.packageManagement` as the package management code
is now part of Weka rather than a third-party library
- `jvm.start()` no longer tries to load packages and therefore suppresses error message if `$HOME/wekafiles/packages`
should not yet exist
0.3.5 (2016-01-29)
------------------
- added support for `weka.core.BatchPredictor` to class `Classifier` in module `weka.classifiers`
- upgraded Weka to revision 12410 (post 3.7.13) to avoid performance bottleneck when using setOptions method
- fixed class `SetupGenerator` from module `weka.core.classes`
- added `load_any_file` method to the `weka.core.converters` module
- added `save_any_file` method to the `weka.core.converters` module
- if `GridSearch` instantiation (module `weka.classifiers`) fails, it now outputs message whether package
installed and JVM with package support started
0.3.4 (2016-01-15)
------------------
- added convenience method `create_instances_from_lists` to `weka.core.dataset` module to easily create
an `Instances` object from numeric lists (x and y)
- added `get_object_tags` method to `Tags` class from module `weka.core.classes`, to allow obtaining
`weka.core.Tag` array from the method of a `JavaObject` rather than a static field (MultiSearch)
- updated `MultiSearch` wrapper in module `weka.classifiers` to work with the `multi-search` package
version 2016.1.15 or later
0.3.3 (2015-09-26)
------------------
- updated to Weka 3.7.13
- documentation now covers the API as well
0.3.2 (2015-06-29)
------------------
- The `packages` parameter of the `weka.core.jvm.start()` function can be used for specifying an alternative
Weka home directory now as well
- added `train_test_split` method to `weka.core.Instances` class to easily create train/test splits
- `evaluate_train_test_split` method of `weka.classifiers.Evaluation` class now uses the `train_test_split` method
0.3.1 (2015-04-23)
------------------
- added `get_tags` class method to `Tags` method for easier instantiation of Tag arrays
- added `find` method to `Tags` class to locate `Tag` object that matches the string
- fixed `__getitem__` and `__setitem__` methods of the `Tags` class
- added `GridSearch` meta-classifier with convenience properties to module `weka.classifiers`
- added `SetupGenerator` and various parameter classes to `weka.core.classes`
- added `MultiSearch` meta-classifier with convenience properties to module `weka.classifiers`
- added `quote`/`unquote` and `backquote`/`unbackquote` methods to `weka.core.classes` module
- added `main` method to `weka.core.classes` for operations on options: join, split, code
- added support for option handling to `weka.core.classes` module
0.3.0 (2015-04-15)
------------------
- added method `ndarray_to_instances` to `weka.converters` module for converting Numpy 2-dimensional array into `Instances` object
- added method `plot_learning_curve` to `weka.plot.classifiers` module for creating learning curves for multiple classifiers for a specific metric
- added plotting of experiments with `plot_experiment` methid in `weka.plot.experiments` module
- `Instance.create_instance` method now takes list of tuples (index, internal float value) when generating sparse instances
- added `weka.core.database` module for loading data from a database
- added `make_copy` class method to `Clusterer` class
- added `make_copy` class method to `Associator` class
- added `make_copy` class method to `Filter` class
- added `make_copy` class method to `DataGenerator` class
- most classes (like Classifier and Filter) now have a default classname value in the constructor
- added `TextDirectoryLoader` class to `weka.core.converters`
- moved all methods from `weka.core.utils` to `weka.core.classes`
- fixed `Attribute.index_of` method for determining label index
- fixed `Attribute.add_string_value` method (used incorrect JNI parameter)
- `create_instance` and `create_sparse_instance` methods of class `Instance` now ensure that list values are float
- added `to_help` method to `OptionHandler` class which outputs a help string generated from the base class's
`globalInfo` and `listOptions` methods
- fixed `test_model` method of `Evaluation` class when supplying a `PredictionOutput` object (previously generated `No dataset structure provided!` exception)
- added `batch_finished` method to `Filter` class for incremental filtering
- added `line_plot` method to `weka.plot.dataset` module for plotting dataset using internal format (one line plot per instance)
- added `is_serializable` property to `JavaObject` class
- added `has_class` convenience property to `Instance` class
- added `__repr__` method to `JavaObject` classes (simply calls `toString()` method)
- added `Stemmer` class in module `weka.core.stemmers`
- added `Stopwords` class in module `weka.core.stopwords`
- added `Tokenizer` class in module `weka.core.tokenizers`
- added `StringToWordVector` filter class in module `weka.filters`
- added simple workflow engine (see documentation on *Flow*)
0.2.2 (2015-01-05)
------------------
- added convenience methods `no_class` (to unset class) and `has_class` (class set?) to `Instances` class
- switched to using faster method objects for methods `classify_instance`/`distribution_for_instance` in `Classifier` class
- switched to using faster method objects for methods `cluster_instance`/`distribution_for_instance` in `Clusterer` class
- switched to using faster method objects for methods `class_index`, `is_missing`, `get/set_value`, `get/set_string_value`, `weight` in `Instance` class
- switched to using faster method objects for methods `input`, `output`, `outputformat` in `Filter` class
- switched to using faster method objects for methods `attribute`, `attribute_by_name`, `num_attributes`, `num_instances`,
`class_index`, `class_attribute`, `set_instance`, `get_instance`, `add_instance` in `Instances` class
0.2.1 (2015-01-05)
------------------
- added unit testing framework
- added method `refresh_cache()` to `weka/core/packages.py` to allow user to refresh local cache
- method `get_classname` in `weka.core.utils` now handles Python objects and class objects as well
- added convenience method `get_jclass` to `weka.core.utils` to instantiate a Java class
- added a `JavaArray` wrapper for `arrays`, which allows getting/setting elements and iterating
- added property `classname` to class `JavaObject` for easy access to classname of underlying object
- added class method `parse_matlab` for parsing Matlab matrix strings to `CostMatrix` class
- `predictions` method of `Evaluation` class now return `None` if predictions are discarded
- `Associator.get_capabilities()` method is now a property: `Associator.capabilities`
- added wrapper classes for Java enums: `weka.core.classes.Enum`
- fixed retrieval of `sumSq` in `Stats` class (used by `AttributeStats`)
- fixed `cluster_instance` method in `Clusterer` class
- fixed `filter` and `clusterer` properties in clusterer classes (`SingleClustererEnhancer`, `FilteredClusterer`)
- added `crossvalidate_model` method to `ClusterEvaluation`
- added `get_prc` method to `plot/classifiers.py` for calculating the area under the precision-recall curve
- `Filter.filter` method now handles list of `Instances` objects as well, applying the filter sequentially
to all the datasets (allows generation of compatible train/test sets)
0.2.0 (2014-12-22)
------------------
NB: This release is not backwards compatible!
- requires `JavaBridge` 1.0.9 at least
- moved from Java-like get/set (`getIndex()` and `setIndex(int)`) to nicer Python properties
- using Python properties (also only read-only ones) wherevere possible
- added `weka.core.version` for accessing the Weka version currently in use
- added `jwrapper` and `jclasswrapper` methods to `JavaObject` class (the mother of all objects in python-weka-wrapper)
to allow generic access to an object's methods: http://pythonhosted.org//javabridge/highlevel.html#wrapping-java-objects-using-reflection
- added convenience methods `class_is_last()` and `class_is_first()` to `weka.core.Instances` class
- added convenience methods `delete_last_attribute()` and `delete_first_attribute()` to `weka.core.Instances` class
Older releases
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https://github.com/fracpete/python-weka-wrapper/blob/7fd0bba3c74277313eb463e338c1a7e117a1ea22/CHANGES.rst
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python