python-weka-wrapper-0.1.0/ 0000775 0001750 0001750 00000000000 12327147451 017050 5 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.1.0/setup.cfg 0000664 0001750 0001750 00000000073 12327147451 020671 0 ustar fracpete fracpete 0000000 0000000 [egg_info]
tag_build =
tag_date = 0
tag_svn_revision = 0
python-weka-wrapper-0.1.0/PKG-INFO 0000664 0001750 0001750 00000001441 12327147451 020145 0 ustar fracpete fracpete 0000000 0000000 Metadata-Version: 1.1
Name: python-weka-wrapper
Version: 0.1.0
Summary: Python wrapper for the Weka Machine Learning Workbench
Home-page: https://github.com/fracpete/python-weka-wrapper
Author: Peter "fracpete" Reutemann
Author-email: fracpete 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 algorihtms and filters from within Python. It offers access to Weka
API using thin wrappers around JNI calls using the "javabridge" package.
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python
python-weka-wrapper-0.1.0/python/ 0000775 0001750 0001750 00000000000 12327147451 020371 5 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.1.0/python/python_weka_wrapper.egg-info/ 0000775 0001750 0001750 00000000000 12327147451 026153 5 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.1.0/python/python_weka_wrapper.egg-info/requires.txt 0000664 0001750 0001750 00000000044 12327147450 030550 0 ustar fracpete fracpete 0000000 0000000 javabridge
matplotlib
pygraphviz
PIL python-weka-wrapper-0.1.0/python/python_weka_wrapper.egg-info/top_level.txt 0000664 0001750 0001750 00000000022 12327147450 030676 0 ustar fracpete fracpete 0000000 0000000 wekaexamples
weka
python-weka-wrapper-0.1.0/python/python_weka_wrapper.egg-info/dependency_links.txt 0000664 0001750 0001750 00000000001 12327147450 032220 0 ustar fracpete fracpete 0000000 0000000
python-weka-wrapper-0.1.0/python/python_weka_wrapper.egg-info/PKG-INFO 0000664 0001750 0001750 00000001441 12327147450 027247 0 ustar fracpete fracpete 0000000 0000000 Metadata-Version: 1.1
Name: python-weka-wrapper
Version: 0.1.0
Summary: Python wrapper for the Weka Machine Learning Workbench
Home-page: https://github.com/fracpete/python-weka-wrapper
Author: Peter "fracpete" Reutemann
Author-email: fracpete 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 algorihtms and filters from within Python. It offers access to Weka
API using thin wrappers around JNI calls using the "javabridge" package.
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python
python-weka-wrapper-0.1.0/python/python_weka_wrapper.egg-info/SOURCES.txt 0000664 0001750 0001750 00000002654 12327147451 030046 0 ustar fracpete fracpete 0000000 0000000 MANIFEST.in
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/arrays.py
python/weka/core/capabilities.py
python/weka/core/classes.py
python/weka/core/converters.py
python/weka/core/dataset.py
python/weka/core/jvm.py
python/weka/core/serialization.py
python/weka/core/utils.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/graph.py
python/wekaexamples/__init__.py
python/wekaexamples/associations.py
python/wekaexamples/attribute_selection.py
python/wekaexamples/classifiers.py
python/wekaexamples/clusterers.py
python/wekaexamples/datagenerators.py
python/wekaexamples/experiments.py
python/wekaexamples/filters.py
python/wekaexamples/helper.py
python/wekaexamples/serialization.py
python/wekaexamples/data/anneal.arff
python/wekaexamples/data/bodyfat.arff
python/wekaexamples/data/bolts.arff
python/wekaexamples/data/iris.arff
python/wekaexamples/data/vote.arff python-weka-wrapper-0.1.0/python/weka/ 0000775 0001750 0001750 00000000000 12327147451 021320 5 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.1.0/python/weka/clusterers.py 0000664 0001750 0001750 00000030404 12327054230 024056 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 Fracpete (fracpete at gmail dot com)
import javabridge
import logging
import os
import sys
import getopt
import weka.core.jvm as jvm
import weka.core.utils as utils
from weka.core.classes import JavaObject
from weka.core.classes import OptionHandler
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=None, jobject=None):
"""
Initializes the specified clusterer using either the classname or the supplied JB_Object.
:param classname: the classname of the clusterer
:param jobject: the JB_Object to use
"""
if jobject is None:
jobject = Clusterer.new_instance(classname)
if classname is None:
classname = utils.get_classname(jobject)
self.classname = 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)
def get_capabilities(self):
"""
Returns the capabilities of the clusterer.
: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
"""
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
"""
if self.is_updateable:
javabridge.call(self.jobject, "updateClusterer", "(Lweka/core/Instance;)V", inst.jobject)
else:
logger.critical(self.classname + " 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(self.classname + " is not updateable!")
def cluster_instance(self, inst):
"""
Peforms a prediction.
:param inst: the instance to determine the cluster for
"""
return javabridge.call(self.jobject, "clusterInstance", "(Lweka/core/Instance;)D", 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
:rtype: float[]
"""
pred = javabridge.call(self.jobject, "distributionForInstance", "(Lweka/core/Instance;)[D", inst.jobject)
return jvm.ENV.get_float_array_elements(pred)
def number_of_clusters(self, inst):
"""
Returns the number of clusters found.
:rtype: int
"""
return javabridge.call(self.jobject, "numberOfClusters", "()I")
def graph_type(self):
"""
Returns the graph type if classifier implements weka.core.Drawable, otherwise -1.
:rtype: int
"""
if self.is_drawable:
return javabridge.call(self.jobject, "graphType", "()I")
else:
return -1
def graph(self):
"""
Returns the graph if classifier implements weka.core.Drawable, otherwise None.
:rtype: str
"""
if self.is_drawable:
return javabridge.call(self.jobject, "graph", "()Ljava/lang/String;")
else:
return None
class SingleClustererEnhancer(Clusterer):
"""
Wrapper class for clusterers that use a single base clusterer.
"""
def __init__(self, classname=None, jobject=None):
"""
Initializes the specified clusterer using either the classname or the supplied JB_Object.
:param classname: the classname of the clusterer
:param jobject: the JB_Object to use
"""
if jobject is None:
jobject = Clusterer.new_instance(classname)
if classname is None:
classname = utils.get_classname(jobject)
jobject = SingleClustererEnhancer.new_instance(classname)
self.enforce_type(jobject, "weka.clusterers.SingleClustererEnhancer")
super(SingleClustererEnhancer, self).__init__(classname, jobject)
def set_clusterer(self, clusterer):
"""
Sets the base clusterer.
:param clusterer: the base clusterer to use
"""
javabridge.call(self.jobject, "setClusterer", "(Lweka/clusterers/Clusterer;)V", clusterer.jobject)
def get_clusterer(self):
"""
Returns the base clusterer.
:rtype: Clusterer
"""
return Clusterer(javabridge.call(self.jobject, "getClusterer", "()Lweka/clusterers/Clusterer;"))
class FilteredClusterer(SingleClustererEnhancer):
"""
Wrapper class for the filtered clusterer.
"""
def __init__(self, jobject=None):
"""
Initializes the specified clusterer using either the classname or the supplied JB_Object.
:param jobject: the JB_Object to use
"""
if jobject is None:
classname = "weka.clusterers.FilteredClusterer"
jobject = Clusterer.new_instance(classname)
else:
classname = utils.get_classname(jobject)
jobject = FilteredClusterer.new_instance(classname)
self.enforce_type(jobject, "weka.clusterers.FilteredClusterer")
super(FilteredClusterer, self).__init__(classname, jobject)
def set_filter(self, filtr):
"""
Sets the filter.
:param filtr: the filter to use
"""
javabridge.call(self.jobject, "setFilter", "(Lweka/filters/Filter;)V", filtr.jobject)
def get_filter(self):
"""
Returns the filter.
:rtype: Filter
"""
return Filter(javabridge.call(self.jobject, "getFilter", "()Lweka/filters/Filter;"))
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
"""
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
"""
javabridge.call(self.jobject, "evaluateClusterer", "(Lweka/core/Instances;)V", test.jobject)
def get_cluster_results(self):
"""
The cluster results as string.
:rtype : str
"""
return javabridge.call(self.jobject, "clusterResultsToString", "()Ljava/lang/String;")
def get_cluster_assignments(self):
"""
Return an array of cluster assignments corresponding to the most recent set of instances clustered.
:rtype : ndarray
"""
array = javabridge.call(self.jobject, "getClusterAssignments", "()[D")
if array is None:
return None
else:
return jvm.ENV.get_double_array_elements(array)
def get_num_clusters(self):
"""
Returns the number of clusters.
:rtype : int
"""
return javabridge.call(self.jobject, "getNumClusters", "()I")
def get_log_likelihood(self):
"""
Returns the log likelihood.
:rtype : float
"""
return javabridge.call(self.jobject, "getLogLikelihood", "()D")
def get_classes_to_clusters(self):
"""
Return the array (ordered by cluster number) of minimum error class to cluster mappings..
:rtype : ndarray
"""
array = javabridge.call(self.jobject, "getClassesToClusters", "()[I")
if array is None:
return None
else:
return jvm.ENV.get_int_array_elements(array)
@classmethod
def evaluate_clusterer(cls, clusterer, args):
"""
Evaluates the clusterer with the given options.
:rtype : str
:param clusterer: the clusterer instance to evaluate
:param args: the command-line arguments
"""
return javabridge.static_call(
"Lweka/clusterers/ClusterEvaluation;", "evaluateClusterer",
"(Lweka/clusterers/Clusterer;[Ljava/lang/String;)Ljava/lang/String;",
clusterer.jobject, args)
def main(args):
"""
Runs a clusterer from the command-line. Calls JVM start/stop automatically.
Options:
[-j jar1[:jar2...]]
[-X max heap size]
-t train
[-T test]
[-d output model file]
[-l input model file]
[-p attribute range]
[-x num folds]
[-s seed]
[-c classindex]
[-g graph file]
clusterer classname
[clusterer options]
"""
usage = "Usage: weka.clusterers [-j jar1[" + os.pathsep + "jar2...]] [-X max heap size] " \
+ "-t train [-T test] [-d output model file] [-l input model file] " \
+ "[-p attribute range] [-x num folds] [-s seed] [-c classindex] " \
+ "[-g graph file] clusterer classname [clusterer options]"
optlist, optargs = getopt.getopt(args, "j:X:t:T:d:l:p:x:s:c:g:h")
if len(optargs) == 0:
raise Exception("No clusterer classname provided!\n" + usage)
for opt in optlist:
if opt[0] == "-h":
print(usage)
return
jars = []
params = []
train = None
heap = None
for opt in optlist:
if opt[0] == "-j":
jars = opt[1].split(os.pathsep)
elif opt[0] == "-X":
heap = opt[1]
elif opt[0] == "-t":
params.append(opt[0])
params.append(opt[1])
train = opt[1]
elif opt[0] == "-T":
params.append(opt[0])
params.append(opt[1])
elif opt[0] == "-d":
params.append(opt[0])
params.append(opt[1])
elif opt[0] == "-l":
params.append(opt[0])
params.append(opt[1])
elif opt[0] == "-p":
params.append(opt[0])
params.append(opt[1])
elif opt[0] == "-x":
params.append(opt[0])
params.append(opt[1])
elif opt[0] == "-s":
params.append(opt[0])
params.append(opt[1])
elif opt[0] == "-c":
params.append(opt[0])
params.append(opt[1])
elif opt[0] == "-g":
params.append(opt[0])
params.append(opt[1])
# check parameters
if train is None:
raise Exception("No train file provided ('-t ...')!")
jvm.start(jars, max_heap_size=heap, packages=True)
logger.debug("Commandline: " + utils.join_options(args))
try:
clusterer = Clusterer(classname=optargs[0])
optargs = optargs[1:]
if len(optargs) > 0:
clusterer.set_options(optargs)
print(ClusterEvaluation.evaluate_clusterer(clusterer, params))
except Exception, ex:
print(ex)
finally:
jvm.stop()
if __name__ == "__main__":
try:
main(sys.argv[1:])
except Exception, e:
print(e)
python-weka-wrapper-0.1.0/python/weka/filters.py 0000664 0001750 0001750 00000014016 12327066431 023342 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 .
# filters.py
# Copyright (C) 2014 Fracpete (fracpete at gmail dot com)
import javabridge
import logging
import os
import sys
import getopt
import weka.core.jvm as jvm
import weka.core.utils as utils
from weka.core.classes import OptionHandler
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
# logging setup
logger = logging.getLogger("weka.filters")
class Filter(OptionHandler):
"""
Wrapper class for filters.
"""
def __init__(self, classname=None, jobject=None):
"""
Initializes the specified filter using either the classname or the supplied JB_Object.
:param classname: the classname of the filter
:param jobject: the JB_Object to use
"""
if jobject is None:
jobject = Filter.new_instance(classname)
if classname is None:
classname = utils.get_classname(jobject)
self.classname = classname
self.enforce_type(jobject, "weka.filters.Filter")
super(Filter, self).__init__(jobject)
def get_capabilities(self):
"""
Returns the capabilities of the filter.
:rtype: Capabilities
"""
return Capabilities(javabridge.call(self.jobject, "getCapabilities", "()Lweka/core/Capabilities;"))
def set_inputformat(self, data):
"""
Sets the input format.
:param data: the data to use as input
"""
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
"""
return javabridge.call(self.jobject, "input", "(Lweka/core/Instance;)Z", inst.jobject)
def get_outputformat(self):
"""
Returns the output format.
:rtype: Instances
"""
inst = javabridge.call(self.jobject, "getOutputFormat", "()Lweka/core/Instances;")
if inst is None:
return None
else:
return Instances(inst)
def output(self):
"""
Outputs the filtered Instance.
:rtype : an Instance object
"""
return Instance(javabridge.call(self.jobject, "output", "()Lweka/core/Instance;"))
def filter(self, data):
"""
Filters the dataset.
:param data: the Instances to filter
:rtype : the filtered Instances object
"""
return Instances(javabridge.static_call(
"Lweka/filters/Filter;", "useFilter",
"(Lweka/core/Instances;Lweka/filters/Filter;)Lweka/core/Instances;",
data.jobject, self.jobject))
def main(args):
"""
Runs a filter from the command-line. Calls JVM start/stop automatically.
Options:
[-j jar1[:jar2...]]
[-X max heap size]
-i input1
-o output1
[-r input2]
[-s output2]
[-c classindex]
filter classname
[filter options]
"""
usage = "Usage: weka.filters [-j jar1[" + os.pathsep + "jar2...]] [-X max heap size] -i input1 -o output1 " \
+ "[-r input2 -s output2] [-c classindex] filterclass [filter options]"
optlist, optargs = getopt.getopt(args, "j:X:i:o:r:s:c:h")
if len(optargs) == 0:
raise Exception("No filter classname provided!\n" + usage)
for opt in optlist:
if opt[0] == "-h":
print(usage)
return
jars = []
input1 = None
output1 = None
input2 = None
output2 = None
cls = "-1"
heap = None
for opt in optlist:
if opt[0] == "-j":
jars = opt[1].split(os.pathsep)
elif opt[0] == "-X":
heap = opt[1]
elif opt[0] == "-i":
input1 = opt[1]
elif opt[0] == "-o":
output1 = opt[1]
elif opt[0] == "-r":
input2 = opt[1]
elif opt[0] == "-s":
output2 = opt[1]
elif opt[0] == "-c":
cls = opt[1]
# check parameters
if input1 is None:
raise Exception("No input file provided ('-i ...')!")
if output1 is None:
raise Exception("No output file provided ('-o ...')!")
if not input2 is None and output2 is None:
raise Exception("No 2nd output file provided ('-s ...')!")
jvm.start(jars, max_heap_size=heap, packages=True)
logger.debug("Commandline: " + utils.join_options(args))
try:
flter = Filter(classname=optargs[0])
optargs = optargs[1:]
if len(optargs) > 0:
flter.set_options(optargs)
loader = Loader("weka.core.converters.ArffLoader")
in1 = loader.load_file(input1)
if str(cls) == "first":
cls = "0"
if str(cls) == "last":
cls = str(in1.num_attributes() - 1)
in1.set_class_index(int(cls))
flter.set_inputformat(in1)
out1 = flter.filter(in1)
saver = Saver("weka.core.converters.ArffSaver")
saver.save_file(out1, output1)
if not input2 is None:
in2 = loader.load_file(input2)
in2.set_class_index(int(cls))
out2 = flter.filter(in2)
saver.save_file(out2, output2)
except Exception, ex:
print(ex)
finally:
jvm.stop()
if __name__ == "__main__":
try:
main(sys.argv[1:])
except Exception, e:
print(e)
python-weka-wrapper-0.1.0/python/weka/classifiers.py 0000664 0001750 0001750 00000077605 12327074420 024213 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 Fracpete (fracpete at gmail dot com)
import javabridge
import logging
import os
import sys
import getopt
import weka.core.jvm as jvm
import weka.core.utils as utils
import weka.core.arrays as arrays
from weka.core.classes import JavaObject
from weka.core.classes import OptionHandler
from weka.core.classes import Random
from weka.core.capabilities import Capabilities
from weka.core.dataset import Instances
from weka.filters import Filter
# logging setup
logger = logging.getLogger("weka.classifiers")
class Classifier(OptionHandler):
"""
Wrapper class for classifiers.
"""
def __init__(self, classname=None, jobject=None):
"""
Initializes the specified classifier using either the classname or the supplied JB_Object.
:param classname: the classname of the classifier
:param jobject: the JB_Object to use
"""
if jobject is None:
jobject = Classifier.new_instance(classname)
if classname is None:
classname = utils.get_classname(jobject)
self.classname = 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")
super(Classifier, self).__init__(jobject)
def get_capabilities(self):
"""
Returns the capabilities of the classifier.
: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
"""
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
"""
if self.is_updateable:
javabridge.call(self.jobject, "updateClassifier", "(Lweka/core/Instance;)V", inst.jobject)
else:
logger.critical(self.classname + " is not updateable!")
def classify_instance(self, inst):
"""
Peforms a prediction.
:param inst: the Instance to get a prediction for
:rtype: float
"""
return javabridge.call(self.jobject, "classifyInstance", "(Lweka/core/Instance;)D", 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
:rtype: float[]
"""
pred = javabridge.call(self.jobject, "distributionForInstance", "(Lweka/core/Instance;)[D", inst.jobject)
return jvm.ENV.get_float_array_elements(pred)
def graph_type(self):
"""
Returns the graph type if classifier implements weka.core.Drawable, otherwise -1.
:rtype: int
"""
if self.is_drawable:
return javabridge.call(self.jobject, "graphType", "()I")
else:
return -1
def graph(self):
"""
Returns the graph if classifier implements weka.core.Drawable, otherwise None.
:rtype: str
"""
if self.is_drawable:
return javabridge.call(self.jobject, "graph", "()Ljava/lang/String;")
else:
return None
class SingleClassifierEnhancer(Classifier):
"""
Wrapper class for classifiers that use a single base classifier.
"""
def __init__(self, classname=None, jobject=None):
"""
Initializes the specified classifier using either the classname or the supplied JB_Object.
:param classname: the classname of the classifier
:param jobject: the JB_Object to use
"""
if jobject is None:
jobject = Classifier.new_instance(classname)
if classname is None:
classname = utils.get_classname(jobject)
jobject = SingleClassifierEnhancer.new_instance(classname)
self.enforce_type(jobject, "weka.classifiers.SingleClassifierEnhancer")
super(SingleClassifierEnhancer, self).__init__(classname, jobject)
def set_classifier(self, classifier):
"""
Sets the base classifier.
:param classifier: the base classifier to use
"""
javabridge.call(self.jobject, "setClassifier", "(Lweka/classifiers/Classifier;)V", classifier.jobject)
def get_classifier(self):
"""
Returns the base classifier.
:rtype: Classifier
"""
return Classifier(javabridge.call(self.jobject, "getClassifier", "()Lweka/classifiers/Classifier;"))
class FilteredClassifier(SingleClassifierEnhancer):
"""
Wrapper class for the filtered classifier.
"""
def __init__(self, jobject=None):
"""
Initializes the specified classifier using either the classname or the supplied JB_Object.
:param jobject: the JB_Object to use
"""
if jobject is None:
classname = "weka.classifiers.meta.FilteredClassifier"
jobject = Classifier.new_instance(classname)
else:
classname = utils.get_classname(jobject)
jobject = FilteredClassifier.new_instance(classname)
self.enforce_type(jobject, "weka.classifiers.meta.FilteredClassifier")
super(FilteredClassifier, self).__init__(classname, jobject)
def set_filter(self, filtr):
"""
Sets the filter.
:param filtr: the filter to use
"""
javabridge.call(self.jobject, "setFilter", "(Lweka/filters/Filter;)V", filtr.jobject)
def get_filter(self):
"""
Returns the filter.
:rtype: Filter
"""
return Filter(javabridge.call(self.jobject, "getFilter", "()Lweka/filters/Filter;"))
class MultipleClassifiersCombiner(Classifier):
"""
Wrapper class for classifiers that use a multiple base classifiers.
"""
def __init__(self, classname=None, jobject=None):
"""
Initializes the specified classifier using either the classname or the supplied JB_Object.
:param classname: the classname of the classifier
:param jobject: the JB_Object to use
"""
if jobject is None:
jobject = Classifier.new_instance(classname)
if classname is None:
classname = utils.get_classname(jobject)
jobject = MultipleClassifiersCombiner.new_instance(classname)
self.enforce_type(jobject, "weka.classifiers.MultipleClassifiersCombiner")
super(MultipleClassifiersCombiner, self).__init__(classname, jobject)
def set_classifiers(self, classifiers):
"""
Sets the base classifiers.
:param classifiers: the list of base classifiers to use
"""
obj = []
for classifier in classifiers:
obj.append(classifier.jobject)
javabridge.call(self.jobject, "setClassifiers", "([Lweka/classifiers/Classifier;)V", obj)
def get_classifiers(self):
"""
Returns the list of base classifiers.
:rtype: list
"""
objects = javabridge.get_object_array_elements(
javabridge.call(self.jobject, "getClassifiers", "()[Lweka/classifiers/Classifier;"))
result = []
for obj in objects:
result.append(Classifier(jobject=obj))
return result
class Prediction(JavaObject):
"""
Wrapper class for a prediction.
"""
def __init__(self, jobject):
"""
Initializes the wrapper.
:param jobject: the prediction to wrap
"""
self.enforce_type(jobject, "weka.classifiers.evaluation.Prediction")
super(Prediction, self).__init__(jobject)
def actual(self):
"""
Returns the actual value.
:rtype: float
"""
return javabridge.call(self.jobject, "actual", "()D")
def predicted(self):
"""
Returns the predicted value.
:rtype: float
"""
return javabridge.call(self.jobject, "predicted", "()D")
def weight(self):
"""
Returns the weight.
: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
"""
self.enforce_type(jobject, "weka.classifiers.evaluation.NominalPrediction")
super(NominalPrediction, self).__init__(jobject)
def distribution(self):
"""
Returns the class distribution.
:rtype: list
"""
return javabridge.call(self.jobject, "distribution", "()[D")
def margin(self):
"""
Returns 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
"""
self.enforce_type(jobject, "weka.classifiers.evaluation.NumericPrediction")
super(NumericPrediction, self).__init__(jobject)
def error(self):
"""
Returns the error.
:rtype: float
"""
return javabridge.call(self.jobject, "error", "()D")
def prediction_intervals(self):
"""
Returns the prediction intervals.
:rtype: ndarray
"""
return arrays.double_matrix_to_ndarray(javabridge.call(self.jobject, "predictionIntervals", "()[[D"))
class Evaluation(JavaObject):
"""
Evaluation class for classifiers.
"""
def __init__(self, data):
"""
Initializes an Evaluation object.
:param data: the data to use to initialize the priors with
"""
jobject = javabridge.make_instance(
"weka/classifiers/EvaluationWrapper", "(Lweka/core/Instances;)V", data.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, random):
"""
Crossvalidates the model using the specified data, number of folds and random number generator wrapper.
:param classifier: the classifier to cross-validate
:param data: the data to evaluate on
:param num_folds: the number of folds
:param random: the random number generator to use
"""
javabridge.call(
self.jobject, "crossValidateModel",
"(Lweka/classifiers/Classifier;Lweka/core/Instances;ILjava/util/Random;[Ljava/lang/Object;)V",
classifier.jobject, data.jobject, num_folds, random.jobject, [])
def test_model(self, classifier, data):
"""
Evaluates the built model using the specified test data and returns the classifications.
:param classifier: the trained classifier to evaluate
:param data: the data to evaluate on
:rtype: ndarray
"""
array = javabridge.call(
self.jobject, "evaluateModel",
"(Lweka/classifiers/Classifier;Lweka/core/Instances;[Ljava/lang/Object;)[D",
classifier.jobject, data.jobject, [])
if array is None:
return None
else:
return jvm.ENV.get_double_array_elements(array)
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
:param inst: the Instance to evaluate on
:rtype: float
"""
return javabridge.call(
self.jobject, "evaluateModelOnce",
"(Lweka/classifiers/Classifier;Lweka/core/Instance;)D",
classifier.jobject, inst.jobject)
def to_summary(self, title=None):
"""
Generates a summary.
:param title: optional title
: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;)Ljava/lang/String;", title)
def to_class_details(self, title=None):
"""
Generates the class details.
:param title: optional title
: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 to_matrix(self, title=None):
"""
Generates the confusion matrix.
:param title: optional title
: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
:rtype: float
"""
return javabridge.call(self.jobject, "areaUnderPRC", "(I)D", class_index)
def weighted_area_under_prc(self):
"""
Returns the weighted area under precision recall curve.
: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
:rtype: float
"""
return javabridge.call(self.jobject, "areaUnderROC", "(I)D", class_index)
def weighted_area_under_roc(self):
"""
Returns the weighted area under receiver operator characteristic curve.
:rtype: float
"""
return javabridge.call(self.jobject, "weightedAreaUnderROC", "()D")
def avg_cost(self):
"""
Returns the average cost.
:rtype: float
"""
return javabridge.call(self.jobject, "avgCost", "()D")
def total_cost(self):
"""
Returns the total cost.
:rtype: float
"""
return javabridge.call(self.jobject, "totalCost", "()D")
def confusion_matrix(self):
"""
Returns the confusion matrix.
:rtype: float[][]
"""
return arrays.double_matrix_to_ndarray(javabridge.call(self.jobject, "confusionMatrix", "()[[D"))
def correct(self):
"""
Returns the correct count (nominal classes).
:rtype: float
"""
return javabridge.call(self.jobject, "correct", "()D")
def incorrect(self):
"""
Returns the incorrect count (nominal classes).
:rtype: float
"""
return javabridge.call(self.jobject, "incorrect", "()D")
def unclassified(self):
"""
Returns the unclassified count.
:rtype: float
"""
return javabridge.call(self.jobject, "unclassified", "()D")
def num_instances(self):
"""
Returns the number of instances that had a know class value.
:rtype: float
"""
return javabridge.call(self.jobject, "numInstances", "()D")
def percent_correct(self):
"""
Returns the percent correct (nominal classes).
:rtype: float
"""
return javabridge.call(self.jobject, "pctCorrect", "()D")
def percent_incorrect(self):
"""
Returns the percent incorrect (nominal classes).
:rtype: float
"""
return javabridge.call(self.jobject, "pctIncorrect", "()D")
def percent_unclassified(self):
"""
Returns the percent unclassified.
:rtype: float
"""
return javabridge.call(self.jobject, "pctUnclassified", "()D")
def correlation_coefficient(self):
"""
Returns the correlation coefficient (numeric classes).
: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
:rtype: float
"""
return javabridge.call(self.jobject, "matthewsCorrelationCoefficient", "(I)D", class_index)
def weighted_matthews_correlation(self):
"""
Returns the weighted Matthews correlation (nominal classes).
:rtype: float
"""
return javabridge.call(self.jobject, "weightedMatthewsCorrelation", "()D")
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.
:rtype: float
"""
return javabridge.call(self.jobject, "coverageOfTestCasesByPredictedRegions", "()D")
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.
:rtype: float
"""
return javabridge.call(self.jobject, "sizeOfPredictedRegions", "()D")
def error_rate(self):
"""
Returns the error rate (numeric classes).
:rtype: float
"""
return javabridge.call(self.jobject, "errorRate", "()D")
def mean_absolute_error(self):
"""
Returns the mean absolute error.
:rtype: float
"""
return javabridge.call(self.jobject, "meanAbsoluteError", "()D")
def relative_absolute_error(self):
"""
Returns the relative absolute error.
:rtype: float
"""
return javabridge.call(self.jobject, "relativeAbsoluteError", "()D")
def root_mean_squared_error(self):
"""
Returns the root mean squared error.
:rtype: float
"""
return javabridge.call(self.jobject, "rootMeanSquaredError", "()D")
def root_relative_squared_error(self):
"""
Returns the root relative squared error.
:rtype: float
"""
return javabridge.call(self.jobject, "rootRelativeSquaredError", "()D")
def root_mean_prior_squared_error(self):
"""
Returns the root mean prior squared error.
:rtype: float
"""
return javabridge.call(self.jobject, "rootMeanPriorSquaredError", "()D")
def mean_prior_absolute_error(self):
"""
Returns the mean prior absolute 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
:rtype: float
"""
return javabridge.call(self.jobject, "falseNegativeRate", "(I)D", class_index)
def weighted_false_negative_rate(self):
"""
Returns the weighted false negative 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
:rtype: float
"""
return javabridge.call(self.jobject, "falsePositiveRate", "(I)D", class_index)
def weighted_false_positive_rate(self):
"""
Returns the weighted false positive 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
: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
:rtype: float
"""
return javabridge.call(self.jobject, "trueNegativeRate", "(I)D", class_index)
def weighted_true_negative_rate(self):
"""
Returns the weighted true negative 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
: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
: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
:rtype: float
"""
return javabridge.call(self.jobject, "truePositiveRate", "(I)D", class_index)
def weighted_true_positive_rate(self):
"""
Returns the weighted true positive 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
: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
:rtype: float
"""
return javabridge.call(self.jobject, "fMeasure", "(I)D", class_index)
def weighted_f_measure(self):
"""
Returns the weighted f measure.
:rtype: float
"""
return javabridge.call(self.jobject, "weightedFMeasure", "()D")
def unweighted_macro_f_measure(self):
"""
Returns the unweighted macro-averaged F-measure.
:rtype: float
"""
return javabridge.call(self.jobject, "unweightedMacroFmeasure", "()D")
def unweighted_micro_f_measure(self):
"""
Returns the unweighted micro-averaged F-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
:rtype: float
"""
return javabridge.call(self.jobject, "precision", "(I)D", class_index)
def weighted_precision(self):
"""
Returns the weighted 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
:rtype: float
"""
return javabridge.call(self.jobject, "recall", "(I)D", class_index)
def weighted_recall(self):
"""
Returns the weighted recall.
:rtype: float
"""
return javabridge.call(self.jobject, "weightedRecall", "()D")
def kappa(self):
"""
Returns kappa.
:rtype: float
"""
return javabridge.call(self.jobject, "kappa", "()D")
def kb_information(self):
"""
Returns KB information.
:rtype: float
"""
return javabridge.call(self.jobject, "KBInformation", "()D")
def kb_mean_information(self):
"""
Returns KB mean information.
:rtype: float
"""
return javabridge.call(self.jobject, "KBMeanInformation", "()D")
def kb_relative_information(self):
"""
Returns KB relative information.
:rtype: float
"""
return javabridge.call(self.jobject, "KBRelativeInformation", "()D")
def sf_entropy_gain(self):
"""
Returns the total SF, which is the null model entropy minus the scheme entropy.
:rtype: float
"""
return javabridge.call(self.jobject, "SFEntropyGain", "()D")
def sf_mean_entropy_gain(self):
"""
Returns the SF per instance, which is the null model entropy minus the scheme entropy, per instance.
:rtype: float
"""
return javabridge.call(self.jobject, "SFMeanEntropyGain", "()D")
def sf_mean_prior_entropy(self):
"""
Returns the entropy per instance for the null model.
:rtype: float
"""
return javabridge.call(self.jobject, "SFMeanPriorEntropy", "()D")
def sf_mean_scheme_entropy(self):
"""
Returns the entropy per instance for the scheme.
:rtype: float
"""
return javabridge.call(self.jobject, "SFMeanSchemeEntropy", "()D")
def set_class_priors(self, data):
"""
Sets the class priors derived from the dataset.
:param data: the dataset to derive the priors from
"""
return javabridge.call(self.jobject, "setClassPriors", "(Lweka/core/Instances;)V", data)
def get_class_priors(self):
"""
Returns the class priors.
:rtype: ndarray
"""
return jvm.ENV.get_float_array_elements(javabridge.call(self.jobject, "getClassPriors", "()[D"))
def header(self):
"""
Returns the header format.
:rtype: Instances
"""
return Instances(javabridge.call(self.jobject, "getHeader", "()Lweka/core/Instances;"))
def set_discard_predictions(self, discard):
"""
Sets whether to discard predictions (saves memory).
:param discard: True if to discard predictions
"""
javabridge.call(self.jobject, "setDiscardPredictions", "(Z)V", discard)
def get_discard_predictions(self):
"""
Returns whether to discard predictions (saves memory).
:rtype: bool
"""
return javabridge.call(self.jobject, "getDiscardPredictions", "()Z")
def predictions(self):
"""
Returns the predictions.
:rtype: list
"""
preds = javabridge.get_collection_wrapper(
javabridge.call(self.jobject, "predictions", "()Ljava/util/ArrayList;"))
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.
:rtype : str
:param classifier: the classifier instance to use
:param args: the command-line arguments to use
"""
return javabridge.static_call(
"Lweka/classifiers/Evaluation;", "evaluateModel",
"(Lweka/classifiers/Classifier;[Ljava/lang/String;)Ljava/lang/String;",
classifier.jobject, args)
def main(args):
"""
Runs a classifier from the command-line. Calls JVM start/stop automatically.
Options:
[-j jar1[:jar2...]]
[-X max heap size]
-t train
[-T test]
[-c classindex]
[-d output model file]
[-l input model file]
[-x num folds]
[-s seed]
[-v # no stats for training]
[-o # only stats, no model]
[-i # information-retrieval stats per class]
[-k # information-theoretic stats]
[-m cost matrix file]
[-g graph file]
classifier classname
[classifier options]
"""
usage = "Usage: weka.classifiers [-j jar1[" + os.pathsep + "jar2...]] [-X max heap size] -t train " \
+ "[-T test] [-c classindex] " \
+ "[-d output model file] [-l input model file] [-x num folds] [-s seed] [-v # no stats for training] " \
+ "[-o # only stats, no model] [-i # information-retrieval stats per class] " \
+ "-kl # information-theoretic stats] [-m cost matrix file] [-g graph file] " \
+ "classifier classname [classifier options]"
optlist, optargs = getopt.getopt(args, "j:X:t:T:c:d:l:x:s:voikm:g:h")
if len(optargs) == 0:
raise Exception("No classifier classname provided!\n" + usage)
for opt in optlist:
if opt[0] == "-h":
print(usage)
return
jars = []
params = []
train = None
heap = None
for opt in optlist:
if opt[0] == "-j":
jars = opt[1].split(os.pathsep)
elif opt[0] == "-X":
heap = opt[1]
elif opt[0] == "-t":
params.append(opt[0])
params.append(opt[1])
train = opt[1]
elif opt[0] == "-T":
params.append(opt[0])
params.append(opt[1])
elif opt[0] == "-c":
params.append(opt[0])
params.append(opt[1])
elif opt[0] == "-d":
params.append(opt[0])
params.append(opt[1])
elif opt[0] == "-l":
params.append(opt[0])
params.append(opt[1])
elif opt[0] == "-x":
params.append(opt[0])
params.append(opt[1])
elif opt[0] == "-s":
params.append(opt[0])
params.append(opt[1])
elif opt[0] == "-v":
params.append(opt[0])
elif opt[0] == "-o":
params.append(opt[0])
elif opt[0] == "-i":
params.append(opt[0])
elif opt[0] == "-k":
params.append(opt[0])
elif opt[0] == "-m":
params.append(opt[0])
params.append(opt[1])
elif opt[0] == "-g":
params.append(opt[0])
params.append(opt[1])
# check parameters
if train is None:
raise Exception("No train file provided ('-t ...')!")
jvm.start(jars, max_heap_size=heap, packages=True)
logger.debug("Commandline: " + utils.join_options(args))
try:
classifier = Classifier(classname=optargs[0])
optargs = optargs[1:]
if len(optargs) > 0:
classifier.set_options(optargs)
print(Evaluation.evaluate_model(classifier, params))
except Exception, e:
print(e)
finally:
jvm.stop()
if __name__ == "__main__":
try:
main(sys.argv[1:])
except Exception, e:
print(e)
python-weka-wrapper-0.1.0/python/weka/__init__.py 0000664 0001750 0001750 00000000000 12327054230 023407 0 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.1.0/python/weka/attribute_selection.py 0000664 0001750 0001750 00000026115 12327054230 025737 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 Fracpete (fracpete at gmail dot com)
import javabridge
import logging
import os
import sys
import getopt
import weka.core.jvm as jvm
import weka.core.utils as utils
import weka.core.arrays as arrays
from weka.core.classes import JavaObject
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=None, jobject=None):
"""
Initializes the specified search algorithm using either the classname or the supplied JB_Object.
:param classname: the classname of the search algorithms
:param jobject: the JB_Object to use
"""
if jobject is None:
jobject = ASSearch.new_instance(classname)
if classname is None:
classname = utils.get_classname(jobject)
self.classname = classname
self.enforce_type(jobject, "weka.attributeSelection.ASSearch")
super(ASSearch, self).__init__(jobject)
def search(self, evaluation, data):
"""
Performs the search and returns the indices of the selected attributes.
:param evaluation: the evaluation algorithm to use
:param data: the data to use
: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:
jvm.ENV.get_int_array_elements(array)
class ASEvaluation(OptionHandler):
"""
Wrapper class for attribute selection evaluation algorithm.
"""
def __init__(self, classname=None, jobject=None):
"""
Initializes the specified search algorithm using either the classname or the supplied JB_Object.
:param classname: the classname of the search algorithms
:param jobject: the JB_Object to use
"""
if jobject is None:
jobject = ASEvaluation.new_instance(classname)
if classname is None:
classname = utils.get_classname(jobject)
self.classname = classname
self.enforce_type(jobject, "weka.attributeSelection.ASEvaluation")
super(ASEvaluation, self).__init__(jobject)
def get_capabilities(self):
"""
Returns the capabilities of the classifier.
: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
"""
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
:rtype: ndarray
"""
array = javabridge.call(self.jobject, "postProcess", "([I)[I", indices)
if array is None:
return None
else:
return jvm.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 set_evaluator(self, evaluator):
"""
Sets the evaluator to use.
:param evaluator: the evaluator to use.
"""
javabridge.call(self.jobject, "setEvaluator", "(Lweka/attributeSelection/ASEvaluation;)V", evaluator.jobject)
def set_search(self, search):
"""
Sets the search algorithm to use.
:param search: the search algorithm
"""
javabridge.call(self.jobject, "setSearch", "(Lweka/attributeSelection/ASSearch;)V", search.jobject)
def set_folds(self, folds):
"""
Sets the number of folds to use for cross-validation.
:param folds: the number of folds
"""
javabridge.call(self.jobject, "setFolds", "(I)V", folds)
def set_ranking(self, ranking):
"""
Sets whether to perform a ranking, if possible.
:param ranking: whether to perform a ranking
"""
javabridge.call(self.jobject, "setRanking", "(Z)V", ranking)
def set_seed(self, seed):
"""
Sets the seed for cross-validation.
:param seed: the seed value
"""
javabridge.call(self.jobject, "setSeed", "(I)V", seed)
def set_crossvalidation(self, crossvalidation):
"""
Sets whether to perform cross-validation.
:param crossvalidation: whether to perform cross-validation
"""
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
"""
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
"""
javabridge.call(self.jobject, "selectAttributesCVSplit", "(Lweka/core/Instances;)V", instances.jobject)
def get_selected_attributes(self):
"""
Returns the selected attributes from the last run.
:rtype: ndarray
"""
array = javabridge.call(self.jobject, "selectedAttributes", "()[I")
if array is None:
return None
else:
return jvm.ENV.get_int_array_elements(array)
def to_results_string(self):
"""
Generates a results string from the last attribute selection.
:rtype: str
"""
return javabridge.call(self.jobject, "toResultsString", "()Ljava/lang/String;")
def get_cv_results_string(self):
"""
Generates a results string from the last cross-validation attribute selection.
:rtype: str
"""
return javabridge.call(self.jobject, "CVResultsString", "()Ljava/lang/String;")
def get_number_attributes_selected(self):
"""
Returns the number of attributes that were selected.
:rtype: int
"""
return javabridge.call(self.jobject, "numberAttributesSelected", "()I")
def get_ranked_attributes(self):
"""
Returns the matrix of ranked attributes from the last run.
: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.
:rtype: same as input
"""
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
:param args: the command-line args for the attribute selection
:rtype; str
"""
return javabridge.static_call(
"Lweka/attributeSelection/AttributeSelection;", "SelectAttributes",
"(Lweka/attributeSelection/ASEvaluation;[Ljava/lang/String;)Ljava/lang/String;",
evaluator.jobject, args)
def main(args):
"""
Runs attribute selection from the command-line. Calls JVM start/stop automatically.
Options:
[-j jar1[:jar2...]]
[-X max heap size]
-i train
[-c classindex]
[-s search method]
[-x num folds]
[-n seed]
evaluator classname
"""
usage = "Usage: weka.attribute_selection [-j jar1[" + os.pathsep + "jar2...]] [-X max heap size] " \
+ "-i input file [-c classindex] " \
+ "[-s search method][-x num folds] [-n seed] " \
+ "evaluator classname [evaluator options]"
optlist, optargs = getopt.getopt(args, "j:X:i:c:s:x:n:h")
if len(optargs) == 0:
raise Exception("No evaluator classname provided!\n" + usage)
for opt in optlist:
if opt[0] == "-h":
print(usage)
return
jars = []
params = []
input = None
heap = None
for opt in optlist:
if opt[0] == "-j":
jars = opt[1].split(os.pathsep)
elif opt[0] == "-X":
heap = opt[1]
elif opt[0] == "-i":
params.append(opt[0])
params.append(opt[1])
input = opt[1]
elif opt[0] == "-c":
params.append(opt[0])
params.append(opt[1])
elif opt[0] == "-s":
params.append(opt[0])
params.append(opt[1])
elif opt[0] == "-x":
params.append(opt[0])
params.append(opt[1])
elif opt[0] == "-n":
params.append(opt[0])
params.append(opt[1])
# check parameters
if input is None:
raise Exception("No input file provided ('-i ...')!")
jvm.start(jars, max_heap_size=heap, packages=True)
logger.debug("Commandline: " + utils.join_options(args))
try:
evaluation = ASEvaluation(classname=optargs[0])
optargs = optargs[1:]
if len(optargs) > 0:
evaluation.set_options(optargs)
print(AttributeSelection.attribute_selection(evaluation, params))
except Exception, e:
print(e)
finally:
jvm.stop()
if __name__ == "__main__":
try:
main(sys.argv[1:])
except Exception, e:
print(e)
python-weka-wrapper-0.1.0/python/weka/core/ 0000775 0001750 0001750 00000000000 12327147451 022250 5 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.1.0/python/weka/core/serialization.py 0000664 0001750 0001750 00000005522 12327054231 025474 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 Fracpete (fracpete at gmail dot com)
import javabridge
import logging
import weka.core.jvm as jvm
from weka.core.classes import JavaObject
# logging setup
logger = logging.getLogger(__name__)
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
:rtype: 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
: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 jvm.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
:param jobject: the object to serialize
"""
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
:param jobjects: the list of objects to serialize
"""
array = jvm.ENV.make_object_array(len(jobjects), jvm.ENV.find_class("java/lang/Object"))
for i in xrange(len(jobjects)):
obj = jobjects[i]
if isinstance(obj, JavaObject):
obj = obj.jobject
jvm.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-0.1.0/python/weka/core/utils.py 0000664 0001750 0001750 00000005012 12327054231 023751 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 .
# utils.py
# Copyright (C) 2014 Fracpete (fracpete at gmail dot com)
import javabridge
import logging
import weka.core.arrays as arrays
from weka.core.classes import OptionHandler
# logging setup
logger = logging.getLogger(__name__)
def split_options(cmdline):
"""
Splits the commandline into a list of options.
:param cmdline: the commandline string to split into individual options
:rtype: list
"""
return arrays.string_array_to_list(
javabridge.static_call(
"Lweka/core/Utils;", "splitOptions",
"(Ljava/lang/String;)[Ljava/lang/String;",
cmdline))
def join_options(options):
"""
Turns the list of options back into a single commandline string.
:param options: the list of options to process
:rtype: str
"""
return javabridge.static_call(
"Lweka/core/Utils;", "joinOptions",
"([Ljava/lang/String;)Ljava/lang/String;",
options)
def to_commandline(optionhandler):
"""
Generates a commandline string from the OptionHandler instance.
:param optionhandler: the OptionHandler instance to turn into a commandline
:rtype: str
"""
return javabridge.static_call(
"Lweka/core/Utils;", "toCommandLine",
"(Ljava/lang/Object;)Ljava/lang/String;",
optionhandler.jobject)
def from_commandline(cmdline):
"""
Creates an OptionHandler based on the provided commandline string.
:param cmdline: the commandline string to use
:rtype: OptionHandler
"""
params = split_options(cmdline)
cls = params[0]
params = params[1:]
result = OptionHandler(cls)
result.set_options(params)
return result
def get_classname(jobject):
"""
Returns the classname of the JB_Object.
:rtype: str
"""
cls = javabridge.call(jobject, "getClass", "()Ljava/lang/Class;")
return javabridge.call(cls, "getName", "()Ljava/lang/String;")
python-weka-wrapper-0.1.0/python/weka/core/__init__.py 0000664 0001750 0001750 00000000000 12327054230 024337 0 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.1.0/python/weka/core/converters.py 0000664 0001750 0001750 00000014636 12327054230 025016 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 Fracpete (fracpete at gmail dot com)
import javabridge
import weka.core.jvm as jvm
from weka.core.classes import OptionHandler
from weka.core.capabilities import Capabilities
from weka.core.dataset import Instances, Instance
import weka.core.utils as utils
class Loader(OptionHandler):
"""
Wrapper class for Loaders.
"""
def __init__(self, classname="weka.core.converters.ArffLoader", jobject=None):
"""
Initializes the specified loader either using the classname or the JB_Object.
:param classname: the classname of the loader
:param jobject: the JB_Object to use
"""
if jobject is None:
jobject = Loader.new_instance(classname)
if classname is None:
classname = utils.get_classname(jobject)
self.enforce_type(jobject, "weka.core.converters.Loader")
super(Loader, self).__init__(jobject)
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
:param incremental: whether to load the dataset incrementally
:rtype: Instances
"""
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", jvm.ENV.new_string_utf(str(dfile)))
javabridge.call(self.jobject, "reset", "()V")
javabridge.call(self.jobject, "setFile", "(Ljava/io/File;)V", dfile)
if incremental:
return Instances(javabridge.call(self.jobject, "getStructure", "()Lweka/core/Instances;"))
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
:param incremental: whether to load the dataset incrementally
:rtype: Instances
"""
self.enforce_type(self.jobject, "weka.core.converters.URLSourcedLoader")
javabridge.call(self.jobject, "reset", "()V")
javabridge.call(self.jobject, "setURL", "(Ljava/lang/String;)V", str(url))
if incremental:
return Instances(javabridge.call(self.jobject, "getStructure", "()Lweka/core/Instances;"))
else:
return Instances(javabridge.call(self.jobject, "getDataSet", "()Lweka/core/Instances;"))
def next_instance(self, structure):
"""
Returns the next Instance object in case the dataset is being loaded incrementally.
Returns None if there are no more instances available.
:param structure: the Instances object this instance belongs to
:rtype: Instance
"""
inst = javabridge.call(self.jobject, "getNextInstance", "(Lweka/core/Instances;)Lweka/core/Instance;", structure.jobject)
if inst is None:
return None
else:
return Instance(inst)
class Saver(OptionHandler):
"""
Wrapper class for Savers.
"""
def __init__(self, classname="weka.core.converters.ArffSaver", jobject=None):
"""
Initializes the specified saver either using the classname or the provided JB_Object.
:param classname: the classname of the saver
:param jobject: the JB_Object to use
"""
if jobject is None:
jobject = Saver.new_instance(classname)
if classname is None:
classname = utils.get_classname(jobject)
self.enforce_type(jobject, "weka.core.converters.Saver")
super(Saver, self).__init__(jobject)
def get_capabilities(self):
"""
Returns the capabilities of the saver.
: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
:param dfile: the file to save the data to
"""
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", jvm.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
: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 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
: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)
python-weka-wrapper-0.1.0/python/weka/core/dataset.py 0000664 0001750 0001750 00000040061 12327054231 024241 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 Fracpete (fracpete at gmail dot com)
import javabridge
import logging
import weka.core.jvm as jvm
from weka.core.classes import JavaObject
# 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
"""
self.enforce_type(jobject, "weka.core.Instances")
super(Instances, self).__init__(jobject)
def get_relationname(self):
"""
Returns the name of the dataset.
:rtype: str
"""
return javabridge.call(self.jobject, "relationName", "()Ljava/lang/String;")
def num_attributes(self):
"""
Returns the number of attributes.
:rtype: int
"""
return javabridge.call(self.jobject, "numAttributes", "()I")
def get_attribute(self, index):
"""
Returns the specified attribute.
:param index: the 0-based index of the attribute
:rtype: Attribute
"""
return Attribute(javabridge.call(self.jobject, "attribute", "(I)Lweka/core/Attribute;"))
def get_attribute_by_name(self, name):
"""
Returns the specified attribute, None if not found.
:param name: the name of the attribute
:rtype: Attribute
"""
att = javabridge.call(self.jobject, "attribute", "(Ljava/lang/String;)Lweka/core/Attribute;", name)
if att is None:
return None
else:
return Attribute(att)
def num_instances(self):
"""
Returns the number of instances.
:rtype: int
"""
return javabridge.call(self.jobject, "numInstances", "()I")
def get_class_attribute(self):
"""
Returns the currently set class attribute.
:rtype: Attribute
"""
return Attribute(javabridge.call(self.jobject, "classAttribute", "()Lweka/core/Attribute;"))
def get_class_index(self):
"""
Returns the currently set class index (0-based).
:rtype: int
"""
return javabridge.call(self.jobject, "classIndex", "()I")
def set_class_index(self, index):
""" Sets the class index (0-based). """
return javabridge.call(self.jobject, "setClassIndex", "(I)V", index)
def get_instance(self, index):
"""
Returns the Instance object at the specified location.
:param index: the 0-based index of the instance
:rtype: Instance
"""
return Instance(javabridge.call(self.jobject, "instance", "(I)Lweka/core/Instance;", index))
def add_instance(self, inst, index=None):
"""
Adds the specified instance to the dataset.
:param inst: Instance
:param index: the 0-based index where to add the Instance
"""
if index is None:
javabridge.call(self.jobject, "add", "(Lweka/core/Instance;)Z", inst.jobject)
else:
javabridge.call(self.jobject, "add", "(ILweka/core/Instance;)V", 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
:param inst: the Instance to set
:rtype: Instance
"""
return Instance(javabridge.call(self.jobject, "set", "(ILweka/core/Instance;)Lweka/core/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
"""
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
"""
javabridge.call(self.jobject, "deleteAttributeAt", "(I)V", index)
def delete_attribute_type(self, type):
"""
Deletes all attributes of the given type in the dataset.
:param type: the attribute type to remove, see weka.core.Attribute Javadoc
"""
javabridge.call(self.jobject, "deleteAttributeType", "(I)V", type)
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
"""
javabridge.call(self.jobject, "sort", "(I)V", index)
@classmethod
def copy_instances(cls, dataset):
"""
Creates a copy of the Instances.
:param dataset: the original dataset
:rtype: Instances
"""
return Instances(
javabridge.make_instance(
"weka/core/Instances", "(Lweka/core/Instances;)V", dataset.jobject))
@classmethod
def template_instances(cls, dataset, capacity=0):
"""
Uses the Instances as template to create an empty dataset.
:param dataset: the original dataset
:param capacity: how many data rows to reserve initially (see compactify)
: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
:param atts: the list of attributes to use for the dataset
:param capacity: how many data rows to reserve initially (see compactify)
: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))
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
"""
self.enforce_type(jobject, "weka.core.Instance")
super(Instance, self).__init__(jobject)
def set_dataset(self, dataset):
"""
Sets the dataset that this instance belongs to (for attribute information).
:param dataset: the dataset this instance belongs to.
"""
javabridge.call(self.jobject, "setDataset", "(Lweka/core/Instances;)V", dataset.jobject)
def get_dataset(self):
"""
Returns the dataset that this instance belongs to.
:rtype: Instances
"""
return Instances(javabridge.call(self.jobject, "dataset", "()Lweka/core/Instances"))
def num_attributes(self):
"""
Returns the number of attributes.
:rtype: int
"""
return javabridge.call(self.jobject, "numAttributes", "()I")
def get_class_index(self):
"""
Returns the currently set class index.
:rtype: int
"""
return javabridge.call(self.jobject, "classIndex", "()I")
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
:rtype: float
"""
return javabridge.call(self.jobject, "value", "(I)D", index)
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
:param value: the internal float value to set
"""
javabridge.call(self.jobject, "value", "(ID)V", index, value)
def get_weight(self):
"""
Returns the currently set weight.
:rtype: float
"""
return javabridge.call(self.jobject, "weight", "()D")
def set_weight(self, weight):
"""
Sets the weight.
:param weight: the float weight to set
"""
javabridge.call(self.jobject, "setWeight", "(D)V", weight)
def get_values(self):
"""
Returns the internal values of this instance.
:rtype: ndarray
"""
return jvm.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 double values (internal format) to use (numpy array)
:param classname: the classname of the instance (eg weka.core.DenseInstance).
:param weight: the weight of the instance
"""
jni_classname = classname.replace(".", "/")
return Instance(javabridge.make_instance(jni_classname, "(D[D)V", weight, jvm.ENV.make_double_array(values)))
class Attribute(JavaObject):
"""
Wrapper class for weka.core.Attribute.
"""
def __init__(self, jobject):
""" Initializes the weka.core.Attribute wrapper. """
self.enforce_type(jobject, "weka.core.Attribute")
super(Attribute, self).__init__(jobject)
def get_name(self):
"""
Returns the name of the attribute.
:rtype: str
"""
return javabridge.call(self.jobject, "name", "()Ljava/lang/String;")
def get_index(self):
"""
Returns the index of this attribute.
:rtype: int
"""
return javabridge.call(self.jobject, "index", "()I")
def set_weight(self, weight):
"""
Sets the weight of the attribute.
:param weight: the weight of the attribute
"""
javabridge.call(self.jobject, "setWeight", "(D)V")
def get_weight(self):
"""
Returns the weight of the attribute.
:rtype: float
"""
return javabridge.call(self.jobject, "weight", "()D")
def index_of(self, label):
"""
Returns the index of the label in this attribute.
:param label: the string label to get the index for
:rtype: int
"""
return javabridge.call(self.jobject, "indexOf", "(Ljava/lang/String;)I")
def value(self, index):
"""
Returns the label for the index.
:param index: the 0-based index of the label to return
:rtype: str
"""
return javabridge.call(self.jobject, "value", "(I)Ljava/lang/String;")
def get_type(self):
"""
Returns the type of the attribute.
:rtype: int
"""
return javabridge.call(self.jobject, "type", "()I")
def is_date(self):
"""
Returns whether the attribute is a date one.
:rtype: bool
"""
return javabridge.call(self.jobject, "isDate", "()Z")
def is_nominal(self):
"""
Returns whether the attribute is a nominal one.
:rtype: bool
"""
return javabridge.call(self.jobject, "isNominal", "()Z")
def is_numeric(self):
"""
Returns whether the attribute is a numeric one.
:rtype: bool
"""
return javabridge.call(self.jobject, "isNumeric", "()Z")
def is_relation_valued(self):
"""
Returns whether the attribute is a relation valued one.
:rtype: bool
"""
return javabridge.call(self.jobject, "isRelationValued", "()Z")
def is_string(self):
"""
Returns whether the attribute is a string attribute.
:rtype: bool
"""
return javabridge.call(self.jobject, "isString", "()Z")
def get_date_format(self):
"""
Returns the format of this data attribute.
:rtype: str
"""
return javabridge.call(self.jobject, "getDateFormat", "()Ljava/lang/String;")
def get_lower_numeric_bound(self):
"""
Returns the lower numeric bound of the numeric attribute.
:rtype: float
"""
return javabridge.call(self.jobject, "getLowerNumericBound", "()D")
def get_upper_numeric_bound(self):
"""
Returns the upper numeric bound of the numeric attribute.
: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
: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
:rtype: int
"""
return javabridge.call(self.jobject, "addStringValue", "(S)I", s)
def add_relation(self, instances):
"""
Adds the relation value, returns the index.
:param instances: the Instances object to add
: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
: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
: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
:rtype: str
"""
return javabridge.call(self.jobject, "equalsMsg", "(Lweka/core/Attribute;)Ljava/lang/String;", att.jobject)
@classmethod
def create_numeric(cls, name):
"""
Creates a numeric attribute.
:param name: the name of the attribute
"""
return Attribute(
javabridge.make_instance(
"weka/core/Attribute", "(Ljava/lang/String;)V", name))
@classmethod
def create_date(cls, name, format="yyyy-MM-dd'T'HH:mm:ss"):
"""
Creates a date attribute.
:param name: the name of the attribute
:param format: the date format, see Javadoc for java.text.SimpleDateFormat
"""
return Attribute(
javabridge.make_instance(
"weka/core/Attribute", "(Ljava/lang/String;Ljava/lang/String;)V", name, format))
@classmethod
def create_nominal(cls, name, labels):
"""
Creates a date attribute.
:param name: the name of the attribute
:param labels: the list of string labels to use
"""
return Attribute(
javabridge.make_instance(
"weka/core/Attribute", "(Ljava/lang/String;Ljava/util/List;)V", name, javabridge.make_list(labels)))
python-weka-wrapper-0.1.0/python/weka/core/arrays.py 0000664 0001750 0001750 00000004150 12327066262 024123 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 .
# arrays.py
# Copyright (C) 2014 Fracpete (fracpete at gmail dot com)
import logging
import weka.core.jvm as jvm
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
:rtype: list
"""
result = []
len = jvm.ENV.get_array_length(a)
wrapped = jvm.ENV.get_object_array_elements(a)
for i in xrange(len):
result.append(jvm.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
:rtype: java string array
"""
result = jvm.ENV.make_object_array(len(l), jvm.ENV.find_class("java/lang/String"))
for i in xrange(len(l)):
jvm.ENV.set_object_array_element(result, i, jvm.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
:rtype: numpy.darray
"""
rows = jvm.ENV.get_object_array_elements(m)
num = jvm.ENV.get_array_length(m)
result = numpy.zeros(num * num).reshape((num, num))
i = 0
for row in rows:
elements = jvm.ENV.get_double_array_elements(row)
n = 0
for element in elements:
result[i][n] = element
n += 1
i += 1
return result
python-weka-wrapper-0.1.0/python/weka/core/jvm.py 0000664 0001750 0001750 00000007414 12327075423 023423 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 Fracpete (fracpete at gmail dot com)
import javabridge
import os
import glob
import logging
ENV = 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():
"""
Adds the jars from all Weka packages to the JVM's classpath.
"""
package_dir = os.path.expanduser("~" + os.sep + "wekafiles" + os.sep + "packages")
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 not os.environ['CLASSPATH'] is None:
parts = not os.environ['CLASSPATH'].split(os.pathsep)
for part in parts:
javabridge.JARS.append(part)
def start(class_path=[], 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
:param bundled: whether to add jars from the "lib" directory
:param packages: whether to add jars from Weka packages as well
:param system_cp: whether to add the system classpath as well
:param max_heap_size: the maximum heap size (-Xmx parameter, eg 512m or 4g)
"""
global ENV
if not ENV is None:
logger.info("JVM already running, call jvm.stop() first")
return
# add user-defined jars first
for cp in class_path:
logger.debug("Adding user-supplied classpath=" + class_path)
javabridge.JARS.append(cp)
if bundled:
logger.debug("Adding bundled jars")
add_bundled_jars()
if packages:
logger.debug("Adding Weka packages")
add_weka_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()
ENV = javabridge.get_env()
def stop():
""" Kills the JVM. """
global ENV
if not ENV is None:
ENV = None
javabridge.kill_vm()
python-weka-wrapper-0.1.0/python/weka/core/capabilities.py 0000664 0001750 0001750 00000025161 12327054230 025250 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 Fracpete (fracpete at gmail dot com)
import javabridge
from weka.core.classes import JavaObject
class Capability(JavaObject):
""" Wrapper for a Capability. """
def __init__(self, jobject):
"""
Initializes the wrapper with the specified Capability object.
:param jobject: the Capability object to wrap
"""
Capability.enforce_type(jobject, "weka.core.Capabilities$Capability")
super(Capability, self).__init__(jobject)
@classmethod
def parse(cls, s):
"""
Tries to instantiate a Capability object from the string representation.
:param s: the string representing a Capability
"""
return Capability(
javabridge.static_call(
"weka/core/Capabilities$Capability", "valueOf",
"(Ljava/lang/String;)Lweka/core/Capabilities$Capability;", s))
class Capabilities(JavaObject):
""" Wrapper for Capabilities. """
def __init__(self, jobject):
"""
Initializes the wrapper with the specified Capabilities object.
:param jobject: the Capabilities object to wrap
"""
Capabilities.enforce_type(jobject, "weka.core.Capabilities")
super(Capabilities, self).__init__(jobject)
def capabilities(self):
"""
Returns all the capabilities.
:rtype: list
"""
result = []
iter = javabridge.iterate_java(javabridge.call(self.jobject, "capabilities", "()Ljava/util/Iterator;"))
for c in iter:
result.append(Capability(c))
return result
def get_attribute_capabilities(self):
"""
Returns all the attribute capabilities.
:rtype: Capabilities
"""
return Capabilities(
javabridge.call(self.jobject, "getAttributeCapabilities", "()Lweka/core/Capabilities;"))
def get_class_capabilities(self):
"""
Returns all the class capabilities.
:rtype: Capabilities
"""
return Capabilities(
javabridge.call(self.jobject, "getClassCapabilities", "()Lweka/core/Capabilities;"))
def get_other_capabilities(self):
"""
Returns all other capabilities.
:rtype: Capabilities
"""
return Capabilities(
javabridge.call(self.jobject, "getOtherCapabilities", "()Lweka/core/Capabilities;"))
def dependencies(self):
"""
Returns all the dependencies.
:rtype: list
"""
result = []
iter = javabridge.iterate_java(javabridge.call(self.jobject, "dependencies", "()Ljava/util/Iterator;"))
for c in iter:
result.append(Capability(c))
return result
def enable_all(self):
"""
enables all capabilities.
"""
javabridge.call(self.jobject, "enableAll", "()V")
def enable_all_attributes(self):
"""
enables all attributes.
"""
javabridge.call(self.jobject, "enableAllAttributes", "()V")
def enable_all_classes(self):
"""
enables all classes.
"""
javabridge.call(self.jobject, "enableAllClasses", "()V")
def enable(self, capability):
"""
enables the specified capability.
"""
javabridge.call(self.jobject, "enable", "(Lweka/core/Capabilities$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
"""
javabridge.call(
self.jobject, "enableDependency", "(Lweka/core/Capabilities$Capability;)V", capability.jobject)
def disable_all(self):
"""
Disables all capabilities.
"""
javabridge.call(self.jobject, "disableAll", "()V")
def disable_all_attributes(self):
"""
Disables all attributes.
"""
javabridge.call(self.jobject, "disableAllAttributes", "()V")
def disable_all_classes(self):
"""
Disables all classes.
"""
javabridge.call(self.jobject, "disableAllClasses", "()V")
def disable(self, capability):
"""
Disables the specified capability.
"""
javabridge.call(self.jobject, "disable", "(Lweka/core/Capabilities$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
"""
javabridge.call(
self.jobject, "disableDependency", "(Lweka/core/Capabilities$Capability;)V", capability.jobject)
def has_dependencies(self):
"""
Returns whether any dependencies are set.
:rtype: bool
"""
return javabridge.call(self.jobject, "hasDependencies", "()Z")
def has_dependency(self, capability):
"""
Returns whether any dependencies are set.
:rtype: bool
"""
return javabridge.call(
self.jobject, "hasDependency", "(Lweka/core/Capabilities$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!)
: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!)
:rtype: bool
"""
return javabridge.call(self.jobject, "supportsMaybe", "(Lweka/core/Capabilities;)Z", capabilities.jobject)
def set_min_instances(self, min):
"""
Sets the minimum number of instances that must be supported.
:param min: the minimum number
"""
javabridge.call(self.jobject, "setMinimumNumberInstances", "(I)V", min)
def get_min_instances(self):
"""
Returns the minimum number of instances that must be supported.
:rtype: int
"""
return javabridge.call(self.jobject, "getMinimumNumberInstances", "()I")
def test_attribute(self, att, is_class=None, fail=False):
"""
Tests whether the attribute meets the conditions.
:param att: the Attribute to test
:param is_class: whether this attribute is the class attribute
:param fail: whether to fail with an exception in case the test fails
: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
:param from_index: the first attribute to include
:param to_index: the last attribute to include
: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.
"""
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-0.1.0/python/weka/core/classes.py 0000664 0001750 0001750 00000024647 12327054230 024264 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 Fracpete (fracpete at gmail dot com)
import arrays
import javabridge
from javabridge.jutil import JavaException
import weka.core.jvm as jvm
class JavaObject(object):
""" Basic Java object. """
def __init__(self, jobject):
"""
Initializes the wrapper with the specified Java object.
:param jobject: the Java object to wrap
"""
if jobject is None:
raise Exception("No Java object supplied!")
self.jobject = jobject
def __str__(self):
"""
Just calls the toString() method.
:rtype: str
"""
return javabridge.to_string(self.jobject)
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
:param jobject: the Java object to set; if instance of JavaObject class, the jobject member is automatically used
"""
# 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
:rtype: JavaObject
"""
return JavaObject(javabridge.static_call(
"Lweka/core/PropertyPath;", "getValue",
"(Ljava/lang/Object;Ljava/lang/String;)Ljava/lang/Object;",
self.jobject, path))
@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
:param intf_or_class: the classname in Java notation (eg "weka.core.Instance")
:param jni_intf_or_class: the classname in JNI notation (eg "Lweka/core/Instance;")
: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
:param intf_or_class: the classname in Java notation (eg "weka.core.Instance")
:param jni_intf_or_class: the classname in JNI notation (eg "Lweka/core/Instance;")
"""
if not cls.check_type(jobject, intf_or_class, jni_intf_or_class):
raise TypeError("Object does not implement or subclass " + intf_or_class + "!")
@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.Instance")
:param jni_classname: the classname in JNI notation (eg "Lweka/core/Instance;")
:rtype: 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 + ": " + e)
return None
class Random(JavaObject):
"""
Wrapper for the java.util.Random class.
"""
def __init__(self, seed):
"""
The seed value.
:param seed: the seed value
"""
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
:rtype: int
"""
if n is None:
return javabridge.call(self.jobject, "nextInt", "()I")
else:
return javabridge.call(self.jobject, "nextInt", "(I)I")
def next_double(self):
"""
Next random double.
:rtype: double
"""
return javabridge.call(self.jobject, "nextDouble", "()D")
class OptionHandler(JavaObject):
"""
Ancestor for option-handling classes.
Classes should implement the weka.core.OptionHandler interface to have any effect.
"""
def __init__(self, jobject):
"""
Initializes the wrapper with the specified Java object.
:param jobject: the java object to wrap
"""
super(OptionHandler, self).__init__(jobject)
self.is_optionhandler = OptionHandler.check_type(jobject, "weka.core.OptionHandler")
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, e:
return None
def set_options(self, options):
"""
Sets the command-line options (as list).
:param options: the list of command-line options to set
"""
if self.is_optionhandler:
javabridge.call(self.jobject, "setOptions", "([Ljava/lang/String;)V", arrays.string_list_to_array(options))
def get_options(self):
"""
Obtains the currently set options as list.
:rtype: list
"""
if self.is_optionhandler:
return arrays.string_array_to_list(javabridge.call(self.jobject, "getOptions", "()[Ljava/lang/String;"))
else:
return []
def to_commandline(self):
"""
Generates a commandline string from the JavaObject instance.
:rtype: str
"""
return javabridge.static_call(
"Lweka/core/Utils;", "toCommandLine",
"(Ljava/lang/Object;)Ljava/lang/String;",
self.jobject)
def __str__(self):
"""
Obtains the currently set options as list.
:rtype: str
"""
return javabridge.to_string(self.jobject)
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
:param index: the string index to use
"""
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 set_upper(self, upper):
"""
Sets the upper limit.
:param upper: the upper limit
"""
javabridge.call(self.jobject, "setUpper", "(I)V", upper)
def get_index(self):
"""
Returns the integer index.
:rtype: int
"""
return javabridge.call(self.jobject, "getIndex", "()I")
def get_single_index(self):
"""
Returns the string index.
:rtype: str
"""
return javabridge.call(self.jobject, "getSingleIndex", "()Ljava/lang/String;")
def set_single_index(self, index):
"""
Sets the string index.
:rtype: 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
:param ranges: the string range to use
"""
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 set_upper(self, upper):
"""
Sets the upper limit.
:param upper: the upper limit
"""
javabridge.call(self.jobject, "setUpper", "(I)V", upper)
def get_selection(self):
"""
Returns the selection list.
:rtype: list
"""
return jvm.ENV.get_int_array_elements(javabridge.call(self.jobject, "getSelection", "()[I"))
def get_ranges(self):
"""
Returns the string rage.
:rtype: str
"""
return javabridge.call(self.jobject, "getRanges", "()Ljava/lang/String;")
def set_ranges(self, range):
"""
Sets the string range.
:param range: the range to set
:rtype: str
"""
javabridge.call(self.jobject, "setRanges", "(Ljava/lang/String;)V", range)
python-weka-wrapper-0.1.0/python/weka/experiments.py 0000664 0001750 0001750 00000053543 12327054231 024240 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 Fracpete (fracpete at gmail dot com)
import logging
import javabridge
import weka.core.jvm as jvm
import weka.core.utils as utils
from weka.core.classes import OptionHandler, Range
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=None, jobject=None):
"""
Initializes the specified experiment using either the classname or the supplied JB_Object.
:param classname: the classname of the experiment
:param jobject: the JB_Object to use
"""
if jobject is None:
jobject = Experiment.new_instance(classname)
if classname is None:
classname = utils.get_classname(jobject)
self.classname = classname
super(Experiment, self).__init__(jobject)
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, jobject=None, classification=True, runs=10, datasets=[], classifiers=[], result=None):
"""
Initializes the experiment.
:param classification: whether to perform classification or regression
:param runs: the number of runs to perform
:param datasets: the filenames of datasets to use in the experiment
:param classifiers: the Classifier objects or commandline strings to use in the experiment
:param result: the filename of the file to store the results in
"""
if not jobject is None:
self.enforce_type(jobject, "weka.experiment.Experiment")
self.classification = classification
self.runs = runs
self.datasets = datasets[:]
self.classifiers = classifiers[:]
self.result = result
self.jobject = jobject
def configure_splitevaluator(self):
"""
Configures and returns the SplitEvaluator and Classifier instance as tuple.
: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.
:rtype: tuple
"""
raise Exception("Not implemented!")
def setup(self):
"""
Initializes the experiment.
"""
if not self.jobject is None:
return
self.jobject = javabridge.make_instance("weka/experiment/Experiment", "()V")
# basic options
javabridge.call(
self.jobject, "setPropertyArray", "(Ljava/lang/Object;)V",
jvm.ENV.make_object_array(0, jvm.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 = jvm.ENV.make_object_array(len(self.classifiers), jvm.ENV.find_class("weka/classifiers/Classifier"))
for i, classifier in enumerate(self.classifiers):
if type(classifier) is Classifier:
jvm.ENV.set_object_array_element(classifiers, i, classifier.jobject)
else:
jvm.ENV.set_object_array_element(classifiers, i, utils.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 get_experiment(self):
"""
Returns the internal experiment, if set up, otherwise None.
:rtype: Experiment
"""
if self.jobject is None:
return None
else:
return Experiment(self.jobject)
@classmethod
def load(cls, filename):
"""
Loads the experiment from disk.
:param filename: the filename of the experiment to load
: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
:param experiment: the Experiment to save
"""
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, classification=True, runs=10, folds=10, datasets=[], classifiers=[], result=None):
"""
Initializes the experiment.
:param runs: the number of runs to perform
:param folds: the number folds to use for CV
:param datasets: the filenames of datasets to use in the experiment
:param classifiers: the Classifier objects to use in the experiment
:param result: the filename of the file to store the results in
"""
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.
: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 = jvm.ENV.make_object_array(2, jvm.ENV.find_class("weka/experiment/PropertyNode"))
cls = jvm.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)
jvm.ENV.set_object_array_element(prop_path, 0, node)
cls = jvm.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",
jvm.ENV.get_object_class(speval), desc, cls)
jvm.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, classification=True, runs=10, percentage=66.6, preserve_order=False, datasets=[], classifiers=[], result=None):
"""
Initializes the experiment.
:param runs: the number of runs to perform
:param percentage: the percentage to use for training
:param preserve_order: whether to preserve the order in the datasets
:param datasets: the filenames of datasets to use in the experiment
:param classifiers: the Classifier objects to use in the experiment
:param result: the filename of the file to store the results in
"""
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.
: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 = jvm.ENV.make_object_array(2, jvm.ENV.find_class("weka/experiment/PropertyNode"))
cls = jvm.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)
jvm.ENV.set_object_array_element(prop_path, 0, node)
cls = jvm.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",
jvm.ENV.get_object_class(speval), desc, cls)
jvm.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=None, jobject=None):
"""
Initializes the specified ResultMatrix using either the classname or the supplied JB_Object.
:param classname: the classname of the ResultMatrix
:param jobject: the JB_Object to use
"""
if jobject is None:
jobject = ResultMatrix.new_instance(classname)
if classname is None:
classname = utils.get_classname(jobject)
self.classname = classname
self.enforce_type(jobject, "weka.experiment.ResultMatrix")
super(ResultMatrix, self).__init__(jobject)
def to_string_matrix(self):
"""
Returns the matrix as a string.
: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.
:rtype: str
"""
return javabridge.call(self.jobject, "toStringKey", "()V")
def to_string_header(self):
"""
Returns the header of the matrix as a string.
:rtype: str
"""
return javabridge.call(self.jobject, "toStringHeader", "()V")
def to_string_summary(self):
"""
returns the summary as string.
:rtype: str
"""
return javabridge.call(self.jobject, "toStringSummary", "()V")
def to_string_ranking(self):
"""
Returns the ranking in a string representation.
:rtype: str
"""
return javabridge.call(self.jobject, "toStringRanking", "()V")
class Tester(OptionHandler):
"""
For generating statistical results from an experiment.
"""
def __init__(self, classname=None, jobject=None):
"""
Initializes the specified tester using either the classname or the supplied JB_Object.
:param classname: the classname of the tester
:param jobject: the JB_Object to use
"""
if jobject is None:
jobject = Tester.new_instance(classname)
if classname is None:
classname = utils.get_classname(jobject)
self.classname = 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)
def set_resultmatrix(self, matrix):
"""
Sets the ResultMatrix to use.
:param matrix: the ResultMatrix instance to use
"""
javabridge.call(self.jobject, "setResultMatrix", "(Lweka/experiment/ResultMatrix;)V", matrix.jobject)
def get_resultmatrix(self):
"""
Returns the ResultMatrix instance in use.
:rtype: ResultMatrix
"""
return ResultMatrix(javabridge.call(self.jobject, "getResultMatrix", "()Lweka/experiment/ResultMatrix;"))
def set_instances(self, data):
"""
Sets the data to use for analysis.
:param inst: the Instances to analyze
"""
javabridge.call(self.jobject, "setInstances", "(Lweka/core/Instances;)V", data.jobject)
self.columns_determined = False
def get_instances(self):
"""
Retruns the data used in the analysis.
:rtype: Instances
"""
inst = javabridge.call(self.jobject, "getInstances", "()Lweka/core/Instances;")
if inst is None:
return None
else:
return Instances(inst)
def set_dataset_columns(self, col_names):
"""
Sets the list of column names that identify uniquely a dataset.
:param col_names: the list of attribute names
"""
self.dataset_columns = col_names[:]
def set_run_column(self, col_name):
"""
Sets the column name that holds the Run number.
:param col_name: the attribute name
"""
self.run_column = col_name
def set_fold_column(self, col_name):
"""
Sets the column name that holds the Fold number.
:param col_name: the attribute name
"""
self.fold_column = col_name
def set_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
"""
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.get_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.get_attribute_by_name(name)
if att is None:
raise Exception("Dataset column not found: " + name)
if len(cols) > 0:
cols += ","
cols += str(att.get_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.get_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.get_index())
# fold
if not self.fold_column is None:
att = data.get_attribute_by_name(self.fold_column)
if att is None:
index = -1
else:
index = att.get_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.get_attribute_by_name(name)
if att is None:
raise Exception("Result column not found: " + name)
if len(cols) > 0:
cols += ","
cols += str(att.get_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
: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 index of the base resultset
:param comparison_column: the index of the column to compare against
: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 index of the column to compare against
: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 index of the column to compare against
:rtype: str
"""
return javabridge.call(
self.jobject, "multiResultsetSummary", "(I)Ljava/lang/String;", comparison_column)
python-weka-wrapper-0.1.0/python/weka/datagenerators.py 0000664 0001750 0001750 00000013543 12327054231 024674 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 Fracpete (fracpete at gmail dot com)
import javabridge
import logging
import os
import sys
import getopt
import weka.core.jvm as jvm
import weka.core.utils as utils
from weka.core.classes import OptionHandler
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=None, jobject=None):
"""
Initializes the specified datagenerator using either the classname or the supplied JB_Object.
:param classname: the classname of the datagenerator
:param jobject: the JB_Object to use
"""
if jobject is None:
jobject = DataGenerator.new_instance(classname)
if classname is None:
classname = utils.get_classname(jobject)
self.classname = classname
self.enforce_type(jobject, "weka.datagenerators.DataGenerator")
super(DataGenerator, self).__init__(jobject)
def define_data_format(self):
"""
Returns the data format.
:rtype: Instances
"""
data = javabridge.call(self.jobject, "defineDataFormat", "()Lweka/core/Instances;")
if data is None:
return None
else:
return Instances(data)
def get_single_mode_flag(self):
"""
Returns whether data is generated row by row (True) or in one go (False).
:rtype: bool
"""
return javabridge.call(self.jobject, "getSingleModeFlag", "()Z")
def get_dataset_format(self):
"""
Returns the dataset format.
:rtype: Instances
"""
data = javabridge.call(self.jobject, "getDatasetFormat", "()Lweka/core/Instances;")
if data is None:
return None
else:
return Instances(data)
def set_dataset_format(self, inst):
"""
Sets the dataset format.
:param inst: the Instances to use as dataset format
"""
javabridge.call(self.jobject, "setDatasetFormat", "(Lweka/core/Instances;)V", inst.jobject)
def generate_start(self):
"""
Returns a "start" string.
:rtype: str
"""
return javabridge.call(self.jobject, "generateStart", "()Ljava/lang/String;")
def get_num_examples_act(self):
"""
Returns a actual number of examples to generate.
:rtype: int
"""
return javabridge.call(self.jobject, "getNumExamplesAct", "()I")
def generate_example(self):
"""
Returns a single Instance.
: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.
: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.
: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.
:rtype : Instances
:param generator: the generator instance to use
:param args: the command-line arguments
"""
return javabridge.static_call(
"Lweka/datagenerators/DataGenerator;", "makeData",
"(Lweka/datagenerators/DataGenerator;[Ljava/lang/String;)V",
generator.jobject, args)
def main(args):
"""
Runs a datagenerator from the command-line. Calls JVM start/stop automatically.
Options:
[-j jar1[:jar2...]]
[-X max heap size]
-o output
[-S seed]
[-r relation]
datagenerator classname
[datagenerator options]
"""
usage = "Usage: weka.datagenerators [-j jar1[" + os.pathsep + "jar2...]] [-X max heap size] " \
+ "datagenerator classname -o output [-S seed] [-r relation] [datagenerator options]"
optlist, optargs = getopt.getopt(args, "j:X:h")
if len(optargs) == 0:
raise Exception("No datagenerator classname provided!\n" + usage)
for opt in optlist:
if opt[0] == "-h":
print(usage)
return
jars = []
heap = None
for opt in optlist:
if opt[0] == "-j":
jars = opt[1].split(os.pathsep)
elif opt[0] == "-X":
heap = opt[1]
jvm.start(jars, max_heap_size=heap, packages=True)
logger.debug("Commandline: " + utils.join_options(args))
try:
generator = DataGenerator(classname=optargs[0])
optargs = optargs[1:]
if len(optargs) > 0:
generator.set_options(optargs)
DataGenerator.make_data(generator, optargs)
except Exception, ex:
print(ex)
finally:
jvm.stop()
if __name__ == "__main__":
try:
main(sys.argv[1:])
except Exception, e:
print(e)
python-weka-wrapper-0.1.0/python/weka/plot/ 0000775 0001750 0001750 00000000000 12327147451 022276 5 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.1.0/python/weka/plot/graph.py 0000664 0001750 0001750 00000002337 12327054231 023747 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 Fracpete (fracpete at gmail dot com)
from pygraphviz.agraph import AGraph
import tempfile
from PIL import Image
def plot_dot_graph(graph, filename=None):
"""
Plots a graph in graphviz dot notation.
:param graph: the dot notation graph
:param filename: the (optional) file to save the generated plot to. The extension determines the file format.
"""
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-0.1.0/python/weka/plot/classifiers.py 0000664 0001750 0001750 00000004114 12327066171 025156 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 Fracpete (fracpete at gmail dot com)
import matplotlib.pyplot as plt
from weka.classifiers import NumericPrediction
def plot_classifier_errors(predictions, absolute=True, max_relative_size=20, outfile=None):
"""
Plots the classifers for the given list of predictions.
NB: The plot window blocks execution till closed.
:param predictions: the predictions to plot
:param absolute: whether to use absolute errors as size or relative ones
"""
actual = []
predicted = []
error = 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()))
fig, ax = plt.subplots()
if error is None:
ax.scatter(actual, predicted)
else:
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)
ax.set_xlabel("actual")
ax.set_ylabel("predicted")
ax.set_title("Classifier errors")
ax.plot(ax.get_xlim(), ax.get_ylim(), ls="--", c="0.3")
ax.grid(True)
plt.draw()
if not outfile is None:
plt.savefig(outfile)
plt.show()
python-weka-wrapper-0.1.0/python/weka/plot/__init__.py 0000664 0001750 0001750 00000000030 12327054231 024371 0 ustar fracpete fracpete 0000000 0000000 __author__ = 'fracpete'
python-weka-wrapper-0.1.0/python/weka/associations.py 0000664 0001750 0001750 00000007715 12327054230 024373 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 Fracpete (fracpete at gmail dot com)
import javabridge
import logging
import os
import sys
import getopt
import weka.core.jvm as jvm
import weka.core.utils as utils
import weka.core.converters as converters
from weka.core.classes import OptionHandler
from weka.core.capabilities import Capabilities
from weka.core.converters import Loader
# logging setup
logger = logging.getLogger("weka.associations")
class Associator(OptionHandler):
"""
Wrapper class for associators.
"""
def __init__(self, classname=None, jobject=None):
"""
Initializes the specified associator using either the classname or the supplied JB_Object.
:param classname: the classname of the associator
:param jobject: the JB_Object to use
"""
if jobject is None:
jobject = Associator.new_instance(classname)
if classname is None:
classname = utils.get_classname(jobject)
self.classname = classname
self.enforce_type(jobject, "weka.associations.Associator")
super(Associator, self).__init__(jobject)
def get_capabilities(self):
"""
Returns the capabilities of the associator.
: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
"""
javabridge.call(self.jobject, "buildAssociations", "(Lweka/core/Instances;)V", data.jobject)
def main(args):
"""
Runs a associator from the command-line. Calls JVM start/stop automatically.
Options:
[-j jar1[:jar2...]]
[-X max heap size]
-t train
associator classname
[associator options]
"""
usage = "Usage: weka.associators [-j jar1[" + os.pathsep + "jar2...]] [-X max heap size] -t train [-T test] " \
+ "associator classname [associator options]"
optlist, optargs = getopt.getopt(args, "j:X:t:h")
if len(optargs) == 0:
raise Exception("No associator classname provided!\n" + usage)
for opt in optlist:
if opt[0] == "-h":
print(usage)
return
jars = []
params = []
train = None
heap = None
for opt in optlist:
if opt[0] == "-j":
jars = opt[1].split(os.pathsep)
elif opt[0] == "-X":
heap = opt[1]
elif opt[0] == "-t":
params.append(opt[0])
params.append(opt[1])
train = opt[1]
# check parameters
if train is None:
raise Exception("No train file provided ('-t ...')!")
jvm.start(jars, max_heap_size=heap, packages=True)
logger.debug("Commandline: " + utils.join_options(args))
try:
associator = Associator(classname=optargs[0])
optargs = optargs[1:]
if len(optargs) > 0:
associator.set_options(optargs)
loader = converters.loader_for_file(train)
data = loader.load_file(train)
associator.build_associations(data)
print(str(associator))
except Exception, e:
print(e)
finally:
jvm.stop()
if __name__ == "__main__":
try:
main(sys.argv[1:])
except Exception, e:
print(e)
python-weka-wrapper-0.1.0/python/weka/lib/ 0000775 0001750 0001750 00000000000 12327147451 022066 5 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.1.0/python/weka/lib/python-weka-wrapper.jar 0000664 0001750 0001750 00000002277 12327054230 026510 0 ustar fracpete fracpete 0000000 0000000 PK ‡³D META-INF/þÊ PK PK ‡³D META-INF/MANIFEST.MFóMÌËLK-.Ñ
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™ùG¸ y[ç;ÕÞ{¤rH„ŒI§Öͺc¤½4–bØùóeà <Ò'Ÿâ®ÙÝ)É”àAöåèw?=ÚA,<‰5è¿ 7Ö™Ñ.G{Šj…No ÏÓºáœ9_‘{¶u†ügÞ¡¶÷³ üÅÖ)J#ðC¦SA•þÇ æé^Eª6¡áÛÄ´p‰Ò\AºŽ[X Ž*r?uá¨rj` µy[Ä屑:ŒÖç
ï3ÇZ6ïP]ʼ_ùPK“†òOö 7 PK ‡³D META-INF/þÊ PK ‡³D¶¸œnG G = META-INF/MANIFEST.MFPK
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ðX“D é weka/classifiers/PK ðX“D¿”=õ C ' weka/classifiers/EvaluationWrapper.javaPK D b python-weka-wrapper-0.1.0/python/wekaexamples/ 0000775 0001750 0001750 00000000000 12327147451 023057 5 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.1.0/python/wekaexamples/clusterers.py 0000664 0001750 0001750 00000007244 12327054231 025624 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 Fracpete (fracpete at gmail dot com)
import os
import weka.core.jvm as jvm
import wekaexamples.helper as helper
from weka.core.converters import Loader
from weka.clusterers import Clusterer, FilteredClusterer, ClusterEvaluation
from weka.filters import Filter
import weka.plot.graph as plot_graph
def main():
"""
Just runs some example code.
"""
# load a dataset
iris_file = helper.get_data_dir() + os.sep + "iris.arff"
helper.print_info("Loading dataset: " + iris_file)
loader = Loader("weka.core.converters.ArffLoader")
data = loader.load_file(iris_file)
# remove class attribute
helper.print_info("Removing class attribute")
remove = Filter("weka.filters.unsupervised.attribute.Remove")
remove.set_options(["-R", "last"])
remove.set_inputformat(data)
data = remove.filter(data)
# build a clusterer and output model
helper.print_title("Training SimpleKMeans clusterer")
clusterer = Clusterer(classname="weka.clusterers.SimpleKMeans")
clusterer.set_options(["-N", "3"])
clusterer.build_clusterer(data)
print(clusterer)
helper.print_info("Evaluating on data")
evaluation = ClusterEvaluation()
evaluation.set_model(clusterer)
evaluation.test_model(data)
print("# clusters: " + str(evaluation.get_num_clusters()))
print("log likelihood: " + str(evaluation.get_log_likelihood()))
print("cluster assignments:\n" + str(evaluation.get_cluster_assignments()))
# using a filtered clusterer
helper.print_title("Filtered clusterer")
loader = Loader("weka.core.converters.ArffLoader")
data = loader.load_file(iris_file)
clusterer = Clusterer("weka.clusterers.SimpleKMeans")
clusterer.set_options(["-N", "3"])
remove = Filter("weka.filters.unsupervised.attribute.Remove")
remove.set_options(["-R", "last"])
fclusterer = FilteredClusterer()
fclusterer.set_clusterer(clusterer)
fclusterer.set_filter(remove)
fclusterer.build_clusterer(data)
print(fclusterer)
# load a dataset incrementally and build clusterer incrementally
helper.print_title("Incremental clusterer")
loader = Loader("weka.core.converters.ArffLoader")
iris_inc = loader.load_file(iris_file, incremental=True)
clusterer = Clusterer("weka.clusterers.Cobweb")
remove = Filter("weka.filters.unsupervised.attribute.Remove")
remove.set_options(["-R", "last"])
remove.set_inputformat(iris_inc)
iris_filtered = remove.get_outputformat()
clusterer.build_clusterer(iris_filtered)
while True:
inst = loader.next_instance(iris_filtered)
if inst is None:
break
remove.input(inst)
inst_filtered = remove.output()
clusterer.update_clusterer(inst_filtered)
clusterer.update_finished()
print(clusterer.to_commandline())
print(clusterer)
print(clusterer.graph())
plot_graph.plot_dot_graph(clusterer.graph())
if __name__ == "__main__":
try:
jvm.start()
main()
except Exception, e:
print(e)
finally:
jvm.stop()
python-weka-wrapper-0.1.0/python/wekaexamples/serialization.py 0000664 0001750 0001750 00000004655 12327054231 026311 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 Fracpete (fracpete at gmail dot com)
import os
import tempfile
import weka.core.jvm as jvm
import wekaexamples.helper as helper
from weka.core.converters import Loader
from weka.core.dataset import Instances
from weka.classifiers import Classifier
import weka.core.serialization as serialization
def main():
"""
Just runs some example code.
"""
# load a dataset
iris_file = helper.get_data_dir() + os.sep + "iris.arff"
helper.print_info("Loading dataset: " + iris_file)
loader = Loader("weka.core.converters.ArffLoader")
iris_data = loader.load_file(iris_file)
iris_data.set_class_index(iris_data.num_attributes() - 1)
# train classifier
classifier = Classifier("weka.classifiers.trees.J48")
classifier.build_classifier(iris_data)
# save and read object
helper.print_title("I/O: single object")
outfile = tempfile.gettempdir() + os.sep + "j48.model"
serialization.write(outfile, classifier)
model = Classifier(jobject=serialization.read(outfile))
print(model)
# save classifier and dataset header (multiple objects)
helper.print_title("I/O: single object")
serialization.write_all(outfile, [classifier, Instances.template_instances(iris_data)])
objects = serialization.read_all(outfile)
for i, obj in enumerate(objects):
helper.print_info("Object #" + str(i+1) + ":")
if jvm.ENV.is_instance_of(obj, jvm.ENV.find_class("weka/core/Instances")):
obj = Instances(jobject=obj)
elif jvm.ENV.is_instance_of(obj, jvm.ENV.find_class("weka/classifiers/Classifier")):
obj = Classifier(jobject=obj)
print(obj)
if __name__ == "__main__":
try:
jvm.start()
main()
except Exception, e:
print(e)
finally:
jvm.stop()
python-weka-wrapper-0.1.0/python/wekaexamples/filters.py 0000664 0001750 0001750 00000003205 12327054231 025072 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 .
# filters.py
# Copyright (C) 2014 Fracpete (fracpete at gmail dot com)
import os
import weka.core.jvm as jvm
import wekaexamples.helper as helper
from weka.core.converters import Loader
from weka.filters import Filter
def main():
"""
Just runs some example code.
"""
# load a dataset
iris = helper.get_data_dir() + os.sep + "iris.arff"
helper.print_info("Loading dataset: " + iris)
loader = Loader("weka.core.converters.ArffLoader")
data = loader.load_file(iris)
# remove class attribute
helper.print_info("Removing class attribute")
remove = Filter(classname="weka.filters.unsupervised.attribute.Remove")
remove.set_options(["-R", "last"])
remove.set_inputformat(data)
filtered = remove.filter(data)
# output datasets
helper.print_title("Input")
print(data)
helper.print_title("Output")
print(filtered)
if __name__ == "__main__":
try:
jvm.start()
main()
except Exception, e:
print(e)
finally:
jvm.stop()
python-weka-wrapper-0.1.0/python/wekaexamples/data/ 0000775 0001750 0001750 00000000000 12327147451 023770 5 ustar fracpete fracpete 0000000 0000000 python-weka-wrapper-0.1.0/python/wekaexamples/data/bodyfat.arff 0000664 0001750 0001750 00000057561 12327054230 026266 0 ustar fracpete fracpete 0000000 0000000 %
% !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
%
% Bodyfat treated as the class attribute.
%
% As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
% using instance-based learning with encoding length selection. In Progress
% in Connectionist-Based Information Systems. Singapore: Springer-Verlag.
%
% !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
%
% Short Summary:
% Lists estimates of the percentage of body fat determined by underwater
% weighing and various body circumference measurements for 252 men.
%
% Classroom use of this data set:
% This data set can be used to illustrate multiple regression techniques.
% Accurate measurement of body fat is inconvenient/costly and it is
% desirable to have easy methods of estimating body fat that are not
% inconvenient/costly.
%
% More Details:
% A variety of popular health books suggest that the readers assess their
% health, at least in part, by estimating their percentage of body fat. In
% Bailey (1994), for instance, the reader can estimate body fat from tables
% using their age and various skin-fold measurements obtained by using a
% caliper. Other texts give predictive equations for body fat using body
% circumference measurements (e.g. abdominal circumference) and/or skin-fold
% measurements. See, for instance, Behnke and Wilmore (1974), pp. 66-67;
% Wilmore (1976), p. 247; or Katch and McArdle (1977), pp. 120-132).
%
% Percentage of body fat for an individual can be estimated once body density
% has been determined. Folks (e.g. Siri (1956)) assume that the body consists
% of two components - lean body tissue and fat tissue. Letting
%
% D = Body Density (gm/cm^3)
% A = proportion of lean body tissue
% B = proportion of fat tissue (A+B=1)
% a = density of lean body tissue (gm/cm^3)
% b = density of fat tissue (gm/cm^3)
%
% we have
%
% D = 1/[(A/a) + (B/b)]
%
% solving for B we find
%
% B = (1/D)*[ab/(a-b)] - [b/(a-b)].
%
% Using the estimates a=1.10 gm/cm^3 and b=0.90 gm/cm^3 (see Katch and McArdle
% (1977), p. 111 or Wilmore (1976), p. 123) we come up with "Siri's equation":
%
% Percentage of Body Fat (i.e. 100*B) = 495/D - 450.
%
% Volume, and hence body density, can be accurately measured a variety of ways.
% The technique of underwater weighing "computes body volume as the difference
% between body weight measured in air and weight measured during water
% submersion. In other words, body volume is equal to the loss of weight in
% water with the appropriate temperature correction for the water's density"
% (Katch and McArdle (1977), p. 113). Using this technique,
%
% Body Density = WA/[(WA-WW)/c.f. - LV]
%
% where
%
% WA = Weight in air (kg)
% WW = Weight in water (kg)
% c.f. = Water correction factor (=1 at 39.2 deg F as one-gram of water
% occupies exactly one cm^3 at this temperature, =.997 at 76-78 deg F)
% LV = Residual Lung Volume (liters)
%
% (Katch and McArdle (1977), p. 115). Other methods of determining body volume
% are given in Behnke and Wilmore (1974), p. 22 ff.
%
%
% The variables listed below, from left to right, are:
%
% Density determined from underwater weighing
% Percent body fat from Siri's (1956) equation
% Age (years)
% Weight (lbs)
% Height (inches)
% Neck circumference (cm)
% Chest circumference (cm)
% Abdomen 2 circumference (cm)
% Hip circumference (cm)
% Thigh circumference (cm)
% Knee circumference (cm)
% Ankle circumference (cm)
% Biceps (extended) circumference (cm)
% Forearm circumference (cm)
% Wrist circumference (cm)
%
% (Measurement standards are apparently those listed in Benhke and Wilmore
% (1974), pp. 45-48 where, for instance, the abdomen 2 circumference is
% measured "laterally, at the level of the iliac crests, and anteriorly, at
% the umbilicus".)
%
% These data are used to produce the predictive equations for lean
% body weight given in the abstract "Generalized body composition prediction
% equation for men using simple measurement techniques", K.W. Penrose, A.G.
% Nelson, A.G. Fisher, FACSM, Human Performance Research Center, Brigham Young
% University, Provo, Utah 84602 as listed in _Medicine and Science in Sports
% and Exercise_, vol. 17, no. 2, April 1985, p. 189. (The predictive equations
% were obtained from the first 143 of the 252 cases that are listed below).
% The data were generously supplied by Dr. A. Garth Fisher who gave permission to
% freely distribute the data and use for non-commercial purposes.
%
%
% References:
%
% Bailey, Covert (1994). _Smart Exercise: Burning Fat, Getting Fit_,
% Houghton-Mifflin Co., Boston, pp. 179-186.
%
% Behnke, A.R. and Wilmore, J.H. (1974). _Evaluation and Regulation of Body
% Build and Composition_, Prentice-Hall, Englewood Cliffs, N.J.
%
% Siri, W.E. (1956), "Gross composition of the body", in _Advances in
% Biological and Medical Physics_, vol. IV, edited by J.H. Lawrence and C.A.
% Tobias, Academic Press, Inc., New York.
%
% Katch, Frank and McArdle, William (1977). _Nutrition, Weight Control, and
% Exercise_, Houghton Mifflin Co., Boston.
%
% Wilmore, Jack (1976). _Athletic Training and Physical Fitness: Physiological
% Principles of the Conditioning Process_, Allyn and Bacon, Inc., Boston.
%
@relation 'bodyfat.names'
@attribute Density real
@attribute Age real
@attribute Weight real
@attribute Height real
@attribute Neck real
@attribute Chest real
@attribute Abdomen real
@attribute Hip real
@attribute Thigh real
@attribute Knee real
@attribute Ankle real
@attribute Biceps real
@attribute Forearm real
@attribute Wrist real
@attribute class real
@data
1.0708,23,154.25,67.75,36.2,93.1,85.2,94.5,59,37.3,21.9,32,27.4,17.1,12.3
1.0853,22,173.25,72.25,38.5,93.6,83,98.7,58.7,37.3,23.4,30.5,28.9,18.2,6.1
1.0414,22,154,66.25,34,95.8,87.9,99.2,59.6,38.9,24,28.8,25.2,16.6,25.3
1.0751,26,184.75,72.25,37.4,101.8,86.4,101.2,60.1,37.3,22.8,32.4,29.4,18.2,10.4
1.034,24,184.25,71.25,34.4,97.3,100,101.9,63.2,42.2,24,32.2,27.7,17.7,28.7
1.0502,24,210.25,74.75,39,104.5,94.4,107.8,66,42,25.6,35.7,30.6,18.8,20.9
1.0549,26,181,69.75,36.4,105.1,90.7,100.3,58.4,38.3,22.9,31.9,27.8,17.7,19.2
1.0704,25,176,72.5,37.8,99.6,88.5,97.1,60,39.4,23.2,30.5,29,18.8,12.4
1.09,25,191,74,38.1,100.9,82.5,99.9,62.9,38.3,23.8,35.9,31.1,18.2,4.1
1.0722,23,198.25,73.5,42.1,99.6,88.6,104.1,63.1,41.7,25,35.6,30,19.2,11.7
1.083,26,186.25,74.5,38.5,101.5,83.6,98.2,59.7,39.7,25.2,32.8,29.4,18.5,7.1
1.0812,27,216,76,39.4,103.6,90.9,107.7,66.2,39.2,25.9,37.2,30.2,19,7.8
1.0513,32,180.5,69.5,38.4,102,91.6,103.9,63.4,38.3,21.5,32.5,28.6,17.7,20.8
1.0505,30,205.25,71.25,39.4,104.1,101.8,108.6,66,41.5,23.7,36.9,31.6,18.8,21.2
1.0484,35,187.75,69.5,40.5,101.3,96.4,100.1,69,39,23.1,36.1,30.5,18.2,22.1
1.0512,35,162.75,66,36.4,99.1,92.8,99.2,63.1,38.7,21.7,31.1,26.4,16.9,20.9
1.0333,34,195.75,71,38.9,101.9,96.4,105.2,64.8,40.8,23.1,36.2,30.8,17.3,29
1.0468,32,209.25,71,42.1,107.6,97.5,107,66.9,40,24.4,38.2,31.6,19.3,22.9
1.0622,28,183.75,67.75,38,106.8,89.6,102.4,64.2,38.7,22.9,37.2,30.5,18.5,16
1.061,33,211.75,73.5,40,106.2,100.5,109,65.8,40.6,24,37.1,30.1,18.2,16.5
1.0551,28,179,68,39.1,103.3,95.9,104.9,63.5,38,22.1,32.5,30.3,18.4,19.1
1.064,28,200.5,69.75,41.3,111.4,98.8,104.8,63.4,40.6,24.6,33,32.8,19.9,15.2
1.0631,31,140.25,68.25,33.9,86,76.4,94.6,57.4,35.3,22.2,27.9,25.9,16.7,15.6
1.0584,32,148.75,70,35.5,86.7,80,93.4,54.9,36.2,22.1,29.8,26.7,17.1,17.7
1.0668,28,151.25,67.75,34.5,90.2,76.3,95.8,58.4,35.5,22.9,31.1,28,17.6,14
1.0911,27,159.25,71.5,35.7,89.6,79.7,96.5,55,36.7,22.5,29.9,28.2,17.7,3.7
1.0811,34,131.5,67.5,36.2,88.6,74.6,85.3,51.7,34.7,21.4,28.7,27,16.5,7.9
1.0468,31,148,67.5,38.8,97.4,88.7,94.7,57.5,36,21,29.2,26.6,17,22.9
1.091,27,133.25,64.75,36.4,93.5,73.9,88.5,50.1,34.5,21.3,30.5,27.9,17.2,3.7
1.079,29,160.75,69,36.7,97.4,83.5,98.7,58.9,35.3,22.6,30.1,26.7,17.6,8.8
1.0716,32,182,73.75,38.7,100.5,88.7,99.8,57.5,38.7,33.9,32.5,27.7,18.4,11.9
1.0862,29,160.25,71.25,37.3,93.5,84.5,100.6,58.5,38.8,21.5,30.1,26.4,17.9,5.7
1.0719,27,168,71.25,38.1,93,79.1,94.5,57.3,36.2,24.5,29,30,18.8,11.8
1.0502,41,218.5,71,39.8,111.7,100.5,108.3,67.1,44.2,25.2,37.5,31.5,18.7,21.3
1.0263,41,247.25,73.5,42.1,117,115.6,116.1,71.2,43.3,26.3,37.3,31.7,19.7,32.3
1.0101,49,191.75,65,38.4,118.5,113.1,113.8,61.9,38.3,21.9,32,29.8,17,40.1
1.0438,40,202.25,70,38.5,106.5,100.9,106.2,63.5,39.9,22.6,35.1,30.6,19,24.2
1.0346,50,196.75,68.25,42.1,105.6,98.8,104.8,66,41.5,24.7,33.2,30.5,19.4,28.4
1.0202,46,363.15,72.25,51.2,136.2,148.1,147.7,87.3,49.1,29.6,45,29,21.4,35.2
1.0258,50,203,67,40.2,114.8,108.1,102.5,61.3,41.1,24.7,34.1,31,18.3,32.6
1.0217,45,262.75,68.75,43.2,128.3,126.2,125.6,72.5,39.6,26.6,36.4,32.7,21.4,34.5
1.025,44,205,29.5,36.6,106,104.3,115.5,70.6,42.5,23.7,33.6,28.7,17.4,32.9
1.0279,48,217,70,37.3,113.3,111.2,114.1,67.7,40.9,25,36.7,29.8,18.4,31.6
1.0269,41,212,71.5,41.5,106.6,104.3,106,65,40.2,23,35.8,31.5,18.8,32
1.0814,39,125.25,68,31.5,85.1,76,88.2,50,34.7,21,26.1,23.1,16.1,7.7
1.067,43,164.25,73.25,35.7,96.6,81.5,97.2,58.4,38.2,23.4,29.7,27.4,18.3,13.9
1.0742,40,133.5,67.5,33.6,88.2,73.7,88.5,53.3,34.5,22.5,27.9,26.2,17.3,10.8
1.0665,39,148.5,71.25,34.6,89.8,79.5,92.7,52.7,37.5,21.9,28.8,26.8,17.9,5.6
1.0678,45,135.75,68.5,32.8,92.3,83.4,90.4,52,35.8,20.6,28.8,25.5,16.3,13.6
1.0903,47,127.5,66.75,34,83.4,70.4,87.2,50.6,34.4,21.9,26.8,25.8,16.8,4
1.0756,47,158.25,72.25,34.9,90.2,86.7,98.3,52.6,37.2,22.4,26,25.8,17.3,10.2
1.084,40,139.25,69,34.3,89.2,77.9,91,51.4,34.9,21,26.7,26.1,17.2,6.6
1.0807,51,137.25,67.75,36.5,89.7,82,89.1,49.3,33.7,21.4,29.6,26,16.9,8
1.0848,49,152.75,73.5,35.1,93.3,79.6,91.6,52.6,37.6,22.6,38.5,27.4,18.5,6.3
1.0906,42,136.25,67.5,37.8,87.6,77.6,88.6,51.9,34.9,22.5,27.7,27.5,18.5,3.9
1.0473,54,198,72,39.9,107.6,100,99.6,57.2,38,22,35.9,30.2,18.9,22.6
1.0524,58,181.5,68,39.1,100,99.8,102.5,62.1,39.6,22.5,33.1,28.3,18.5,20.4
1.0356,62,201.25,69.5,40.5,111.5,104.2,105.8,61.8,39.8,22.7,37.7,30.9,19.2,28
1.028,54,202.5,70.75,40.5,115.4,105.3,97,59.1,38,22.5,31.6,28.8,18.2,31.5
1.043,61,179.75,65.75,38.4,104.8,98.3,99.6,60.6,37.7,22.9,34.5,29.6,18.5,24.6
1.0396,62,216,73.25,41.4,112.3,104.8,103.1,61.6,40.9,23.1,36.2,31.8,20.2,26.1
1.0317,56,178.75,68.5,35.6,102.9,94.7,100.8,60.9,38,22.1,32.5,29.8,18.3,29.8
1.0298,54,193.25,70.25,38,107.6,102.4,99.4,61,39.4,23.6,32.7,29.9,19.1,30.7
1.0403,61,178,67,37.4,105.3,99.7,99.7,60.8,40.1,22.7,33.6,29,18.8,25.8
1.0264,57,205.5,70,40.1,105.3,105.5,108.3,65,41.2,24.7,35.3,31.1,18.4,32.3
1.0313,55,183.5,67.5,40.9,103,100.3,104.2,64.8,40.2,22.7,34.8,30.1,18.7,30
1.0499,54,151.5,70.75,35.6,90,83.9,93.9,55,36.1,21.7,29.6,27.4,17.4,21.5
1.0673,55,154.75,71.5,36.9,95.4,86.6,91.8,54.3,35.4,21.5,32.8,27.4,18.7,13.8
1.0847,54,155.25,69.25,37.5,89.3,78.4,96.1,56,37.4,22.4,32.6,28.1,18.1,6.3
1.0693,55,156.75,71.5,36.3,94.4,84.6,94.3,51.2,37.4,21.6,27.3,27.1,17.3,12.9
1.0439,62,167.5,71.5,35.5,97.6,91.5,98.5,56.6,38.6,22.4,31.5,27.3,18.6,24.3
1.0788,55,146.75,68.75,38.7,88.5,82.8,95.5,58.9,37.6,21.6,30.3,27.3,18.3,8.8
1.0796,56,160.75,73.75,36.4,93.6,82.9,96.3,52.9,37.5,23.1,29.7,27.3,18.2,8.5
1.068,55,125,64,33.2,87.7,76,88.6,50.9,35.4,19.1,29.3,25.7,16.9,13.5
1.072,61,143,65.75,36.5,93.4,83.3,93,55.5,35.2,20.9,29.4,27,16.8,11.8
1.0666,61,148.25,67.5,36,91.6,81.8,94.8,54.5,37,21.4,29.3,27,18.3,18.5
1.079,57,162.5,69.5,38.7,91.6,78.8,94.3,56.7,39.7,24.2,30.2,29.2,18.1,8.8
1.0483,69,177.75,68.5,38.7,102,95,98.3,55,38.3,21.8,30.8,25.7,18.8,22.2
1.0498,81,161.25,70.25,37.8,96.4,95.4,99.3,53.5,37.5,21.5,31.4,26.8,18.3,21.5
1.056,66,171.25,69.25,37.4,102.7,98.6,100.2,56.5,39.3,22.7,30.3,28.7,19,18.8
1.0283,67,163.75,67.75,38.4,97.7,95.8,97.1,54.8,38.2,23.7,29.4,27.2,19,31.4
1.0382,64,150.25,67.25,38.1,97.1,89,96.9,54.8,38,22,29.9,25.2,17.7,26.8
1.0568,64,190.25,72.75,39.3,103.1,97.8,99.6,58.9,39,23,34.3,29.6,19,18.4
1.0377,70,170.75,70,38.7,101.8,94.9,95,56,36.5,24.1,31.2,27.3,19.2,27
1.0378,72,168,69.25,38.5,101.4,99.8,96.2,56.3,36.6,22,29.7,26.3,18,27
1.0386,67,167,67.5,36.5,98.9,89.7,96.2,54.7,37.8,33.7,32.4,27.7,18.2,26.6
1.0648,72,157.75,67.25,37.7,97.5,88.1,96.9,57.2,37.7,21.8,32.6,28,18.8,14.9
1.0462,64,160,65.75,36.5,104.3,90.9,93.8,57.8,39.5,23.3,29.2,28.4,18.1,23.1
1.08,46,176.75,72.5,38,97.3,86,99.3,61,38.4,23.8,30.2,29.3,18.8,8.3
1.0666,48,176,73,36.7,96.7,86.5,98.3,60.4,39.9,24.4,28.8,29.6,18.7,14.1
1.052,46,177,70,37.2,99.7,95.6,102.2,58.3,38.2,22.5,29.1,27.7,17.7,20.5
1.0573,44,179.75,69.5,39.2,101.9,93.2,100.6,58.9,39.7,23.1,31.4,28.4,18.8,18.2
1.0795,47,165.25,70.5,37.5,97.2,83.1,95.4,56.9,38.3,22.1,30.1,28.2,18.4,8.5
1.0424,46,192.5,71.75,38,106.6,97.5,100.6,58.9,40.5,24.5,33.3,29.6,19.1,24.9
1.0785,47,184.25,74.5,37.3,99.6,88.8,101.4,57.4,39.6,24.6,30.3,27.9,17.8,9
1.0991,53,224.5,77.75,41.1,113.2,99.2,107.5,61.7,42.3,23.2,32.9,30.8,20.4,17.4
1.077,38,188.75,73.25,37.5,99.1,91.6,102.4,60.6,39.4,22.9,31.6,30.1,18.5,9.6
1.073,50,162.5,66.5,38.7,99.4,86.7,96.2,62.1,39.3,23.3,30.6,27.8,18.2,11.3
1.0582,46,156.5,68.25,35.9,95.1,88.2,92.8,54.7,37.3,21.9,31.6,27.5,18.2,17.8
1.0484,47,197,72,40,107.5,94,103.7,62.7,39,22.3,35.3,30.9,18.3,22.2
1.0506,49,198.5,73.5,40.1,106.5,95,101.7,59,39.4,22.3,32.2,31,18.6,21.2
1.0524,48,173.75,72,37,99.1,92,98.3,59.3,38.4,22.4,27.9,26.2,17,20.4
1.053,41,172.75,71.25,36.3,96.7,89.2,98.3,60,38.4,23.2,31,29.2,18.4,20.1
1.048,49,196.75,73.75,40.7,103.5,95.5,101.6,59.1,39.8,25.4,31,30.3,19.7,22.3
1.0412,43,177,69.25,39.6,104,98.6,99.5,59.5,36.1,22,30.1,27.2,17.7,25.4
1.0578,43,165.5,68.5,31.1,93.1,87.3,96.6,54.7,39,24.8,31,29.4,18.8,18
1.0547,43,200.25,73.5,38.6,105.2,102.8,103.6,61.2,39.3,23.5,30.5,28.5,18.1,19.3
1.0569,52,203.25,74.25,42,110,101.6,100.7,55.8,38.7,23.4,35.1,29.6,19.1,18.3
1.0593,43,194,75.5,38.5,110.1,88.7,102.1,57.5,40,24.8,35.1,30.7,19.2,17.3
1.05,40,168.5,69.25,34.2,97.8,92.3,100.6,57.5,36.8,22.8,32.1,26,17.3,21.4
1.0538,43,170.75,68.5,37.2,96.3,90.6,99.3,61.9,38,22.3,33.3,28.2,18.1,19.7
1.0355,43,183.25,70,37.1,108,105,103,63.7,40,23.6,33.5,27.8,17.4,28
1.0486,47,178.25,70,40.2,99.7,95,98.6,62.3,38.1,23.9,35.3,31.1,19.8,22.1
1.0503,42,163,70.25,35.3,93.5,89.6,99.8,61.5,37.8,21.9,30.7,27.6,17.4,21.3
1.0384,48,175.25,71.75,38,100.7,92.4,97.5,59.3,38.1,21.8,31.8,27.3,17.5,26.7
1.0607,40,158,69.25,36.3,97,86.6,92.6,55.9,36.3,22.1,29.8,26.3,17.3,16.7
1.0529,48,177.25,72.75,36.8,96,90,99.7,58.8,38.4,22.8,29.9,28,18.1,20.1
1.0671,51,179,72,41,99.2,90,96.4,56.8,38.8,23.3,33.4,29.8,19.5,13.9
1.0404,40,191,74,38.3,95.4,92.4,104.3,64.6,41.1,24.8,33.6,29.5,18.5,25.8
1.0575,44,187.5,72.25,38,101.8,87.5,101,58.5,39.2,24.5,32.1,28.6,18,18.1
1.0358,52,206.5,74.5,40.8,104.3,99.2,104.1,58.5,39.3,24.6,33.9,31.2,19.5,27.9
1.0414,44,185.25,71.5,39.5,99.2,98.1,101.4,57.1,40.5,23.2,33,29.6,18.4,25.3
1.0652,40,160.25,68.75,36.9,99.3,83.3,97.5,60.5,38.7,22.6,34.4,28,17.6,14.7
1.0623,47,151.5,66.75,36.9,94,86.1,95.2,58.1,36.5,22.1,30.6,27.5,17.6,16
1.0674,50,161,66.5,37.7,98.9,84.1,94,58.5,36.6,23.5,34.4,29.2,18,13.8
1.0587,46,167,67,36.6,101,89.9,100,60.7,36,21.9,35.6,30.2,17.6,17.5
1.0373,42,177.5,68.75,38.9,98.7,92.1,98.5,60.7,36.8,22.2,33.8,30.3,17.2,27.2
1.059,43,152.25,67.75,37.5,95.9,78,93.2,53.5,35.8,20.8,33.9,28.2,17.4,17.4
1.0515,40,192.25,73.25,39.8,103.9,93.5,99.5,61.7,39,21.8,33.3,29.6,18.1,20.8
1.0648,42,165.25,69.75,38.3,96.2,87,97.8,57.4,36.9,22.2,31.6,27.8,17.7,14.9
1.0575,49,171.75,71.5,35.5,97.8,90.1,95.8,57,38.7,23.2,27.5,26.5,17.6,18.1
1.0472,40,171.25,70.5,36.3,94.6,90.3,99.1,60.3,38.5,23,31.2,28.4,17.1,22.7
1.0452,47,197,73.25,37.8,103.6,99.8,103.2,61.2,38.1,22.6,33.5,28.6,17.9,23.6
1.0398,50,157,66.75,37.8,100.4,89.4,92.3,56.1,35.6,20.5,33.6,29.3,17.3,26.1
1.0435,41,168.25,69.5,36.5,98.4,87.2,98.4,56,36.9,23,34,29.8,18.1,24.4
1.0374,44,186,69.75,37.8,104.6,101.1,102.1,58.9,37.9,22.7,30.9,28.8,17.6,27.1
1.0491,39,166.75,70.75,37,92.9,86.1,95.6,58.8,36.1,22.4,32.7,28.3,17.1,21.8
1.0325,43,187.75,74,37.7,97.8,98.6,100.6,63.6,39.2,23.8,34.3,28.4,17.7,29.4
1.0481,40,168.25,71.25,34.3,98.3,88.5,98.3,58.1,38.4,22.5,31.7,27.4,17.6,22.4
1.0522,49,212.75,75,40.8,104.7,106.6,107.7,66.5,42.5,24.5,35.5,29.8,18.7,20.4
1.0422,40,176.75,71,37.4,98.6,93.1,101.6,59.1,39.6,21.6,30.8,27.9,16.6,24.9
1.0571,40,173.25,69.5,36.5,99.5,93,99.3,60.4,38.2,22,32,28.5,17.8,18.3
1.0459,52,167,67.75,37.5,102.7,91,98.9,57.1,36.7,22.3,31.6,27.5,17.9,23.3
1.0775,23,159.75,72.25,35.5,92.1,77.1,93.9,56.1,36.1,22.7,30.5,27.2,18.2,9.4
1.0754,23,188.15,77.5,38,96.6,85.3,102.5,59.1,37.6,23.2,31.8,29.7,18.3,10.3
1.0664,24,156,70.75,35.7,92.7,81.9,95.3,56.4,36.5,22,33.5,28.3,17.3,14.2
1.055,24,208.5,72.75,39.2,102,99.1,110.1,71.2,43.5,25.2,36.1,30.3,18.7,19.2
1.0322,25,206.5,69.75,40.9,110.9,100.5,106.2,68.4,40.8,24.6,33.3,29.7,18.4,29.6
1.0873,25,143.75,72.5,35.2,92.3,76.5,92.1,51.9,35.7,22,25.8,25.2,16.9,5.3
1.0416,26,223,70.25,40.6,114.1,106.8,113.9,67.6,42.7,24.7,36,30.4,18.4,25.2
1.0776,26,152.25,69,35.4,92.9,77.6,93.5,56.9,35.9,20.4,31.6,29,17.8,9.4
1.0542,26,241.75,74.5,41.8,108.3,102.9,114.4,72.9,43.5,25.1,38.5,33.8,19.6,19.6
1.0758,27,146,72.25,34.1,88.5,72.8,91.1,53.6,36.8,23.8,27.8,26.3,17.4,10.1
1.061,27,156.75,67.25,37.9,94,88.2,95.2,56.8,37.4,22.8,30.6,28.3,17.9,16.5
1.051,27,200.25,73.5,38.2,101.1,100.1,105,62.1,40,24.9,33.7,29.2,19.4,21
1.0594,28,171.5,75.25,35.6,92.1,83.5,98.3,57.3,37.8,21.7,32.2,27.7,17.7,17.3
1.0287,28,205.75,69,38.5,105.6,105,106.4,68.6,40,25.2,35.2,30.7,19.1,31.2
1.0761,28,182.5,72.25,37,98.5,90.8,102.5,60.8,38.5,25,31.6,28,18.6,10
1.0704,30,136.5,68.75,35.9,88.7,76.6,89.8,50.1,34.8,21.8,27,34.9,16.9,12.5
1.0477,31,177.25,71.5,36.2,101.1,92.4,99.3,59.4,39,24.6,30.1,28.2,18.2,22.5
1.0775,31,151.25,72.25,35,94,81.2,91.5,52.5,36.6,21,27,26.3,16.5,9.4
1.0653,33,196,73,38.5,103.8,95.6,105.1,61.4,40.6,25,31.3,29.2,19.1,14.6
1.069,33,184.25,68.75,40.7,98.9,92.1,103.5,64,37.3,23.5,33.5,30.6,19.7,13
1.0644,34,140,70.5,36,89.2,83.4,89.6,52.4,35.6,20.4,28.3,26.2,16.5,15.1
1.037,34,218.75,72,39.5,111.4,106,108.8,63.8,42,23.4,34,31.2,18.5,27.3
1.0549,35,217,73.75,40.5,107.5,95.1,104.5,64.8,41.3,25.6,36.4,33.7,19.4,19.2
1.0492,35,166.25,68,38.5,99.1,90.4,95.6,55.5,34.2,21.9,30.2,28.7,17.7,21.8
1.0525,35,224.75,72.25,43.9,108.2,100.4,106.8,63.3,41.7,24.6,37.2,33.1,19.8,20.3
1.018,35,228.25,69.5,40.4,114.9,115.9,111.9,74.4,40.6,24,36.1,31.8,18.8,34.3
1.061,35,172.75,69.5,37.6,99.1,90.8,98.1,60.1,39.1,23.4,32.5,29.8,17.4,16.5
1.0926,35,152.25,67.75,37,92.2,81.9,92.8,54.7,36.2,22.1,30.4,27.4,17.7,3
1.0983,35,125.75,65.5,34,90.8,75,89.2,50,34.8,22,24.8,25.9,16.9,0.7
1.0521,35,177.25,71,38.4,100.5,90.3,98.7,57.8,37.3,22.4,31,28.7,17.7,20.5
1.0603,36,176.25,71.5,38.7,98.2,90.3,99.9,59.2,37.7,21.5,32.4,28.4,17.8,16.9
1.0414,36,226.75,71.75,41.5,115.3,108.8,114.4,69.2,42.4,24,35.4,21,20.1,25.3
1.0763,37,145.25,69.25,36,96.8,79.4,89.2,50.3,34.8,22.2,31,26.9,16.9,9.9
1.0689,37,151,67,35.3,92.6,83.2,96.4,60,38.1,22,31.5,26.6,16.7,13.1
1.0316,37,241.25,71.5,42.1,119.2,110.3,113.9,69.8,42.6,24.8,34.4,29.5,18.4,29.9
1.0477,38,187.25,69.25,38,102.7,92.7,101.9,64.7,39.5,24.7,34.8,30.3,18.1,22.5
1.0603,39,234.75,74.5,42.8,109.5,104.5,109.9,69.5,43.1,25.8,39.1,32.5,19.9,16.9
1.0387,39,219.25,74.25,40,108.5,104.6,109.8,68.1,42.8,24.1,35.6,29,19,26.6
1.1089,40,118.5,68,33.8,79.3,69.4,85,47.2,33.5,20.2,27.7,24.6,16.5,0
1.0725,40,145.75,67.25,35.5,95.5,83.6,91.6,54.1,36.2,21.8,31.4,28.3,17.2,11.5
1.0713,40,159.25,69.75,35.3,92.3,86.8,96.1,58,39.4,22.7,30,26.4,17.4,12.1
1.0587,40,170.5,74.25,37.7,98.9,90.4,95.5,55.4,38.9,22.4,30.5,28.9,17.7,17.5
1.0794,40,167.5,71.5,39.4,89.5,83.7,98.1,57.3,39.7,22.6,32.9,29.3,18.2,8.6
1.0453,41,232.75,74.25,41.9,117.5,109.3,108.8,67.7,41.3,24.7,37.2,31.8,20,23.6
1.0524,41,210.5,72,38.5,107.4,98.9,104.1,63.5,39.8,23.5,36.4,30.4,19.1,20.4
1.052,41,202.25,72.5,40.8,109.2,98,101.8,62.8,41.3,24.8,36.6,32.4,18.8,20.5
1.0434,41,185,68.25,38,103.4,101.2,103.1,61.5,40.4,22.9,33.4,29.2,18.5,24.4
1.0728,41,153,69.25,36.4,91.4,80.6,92.3,54.3,36.3,21.8,29.6,27.3,17.9,11.4
1.014,42,244.25,76,41.8,115.2,113.7,112.4,68.5,45,25.5,37.1,31.2,19.9,38.1
1.0624,42,193.5,70.5,40.7,104.9,94.1,102.7,60.6,38.6,24.7,34,30.1,18.7,15.9
1.0429,42,224.75,74.75,38.5,106.7,105.7,111.8,65.3,43.3,26,33.7,29.9,18.5,24.7
1.047,42,162.75,72.75,35.4,92.2,85.6,96.5,60.2,38.9,22.4,31.7,27.1,17.1,22.8
1.0411,42,180,68.25,38.5,101.6,96.6,100.6,61.1,38.4,24.1,32.9,29.8,18.8,25.5
1.0488,42,156.25,69,35.5,97.8,86,96.2,57.7,38.6,24,31.2,27.3,17.4,22
1.0583,42,168,71.5,36.5,92,89.7,101,62.3,38,22.3,30.8,27.8,16.9,17.7
1.0841,42,167.25,72.75,37.6,94,78,99,57.5,40,22.5,30.6,30,18.5,6.6
1.0462,43,170.75,67.5,37.4,103.7,89.7,94.2,58.5,39,24.1,33.8,28.8,18.8,23.6
1.0709,43,178.25,70.25,37.8,102.7,89.2,99.2,60.2,39.2,23.8,31.7,28.4,18.6,12.2
1.0484,43,150,69.25,35.2,91.1,85.7,96.9,55.5,35.7,22,29.4,26.6,17.4,22.1
1.034,43,200.5,71.5,37.9,107.2,103.1,105.5,68.8,38.3,23.7,32.1,28.9,18.7,28.7
1.0854,44,184,74,37.9,100.8,89.1,102.6,60.6,39,24,32.9,29.2,18.4,6
1.0209,44,223,69.75,40.9,121.6,113.9,107.1,63.5,40.3,21.8,34.8,30.7,17.4,34.8
1.061,44,208.75,73,41.9,105.6,96.3,102,63.3,39.8,24.1,37.3,23.1,19.4,16.6
1.025,44,166,65.5,39.1,100.6,93.9,100.1,58.9,37.6,21.4,33.1,29.5,17.3,32.9
1.0254,47,195,72.5,40.2,102.7,101.3,101.7,60.7,39.4,23.3,36.7,31.6,18.4,32.8
1.0771,47,160.5,70.25,36,99.8,83.9,91.8,53,36.2,22.5,31.4,27.5,17.7,9.6
1.0742,47,159.75,70.75,34.5,92.9,84.4,94,56,38.2,22.6,29,26.2,17.6,10.8
1.0829,49,140.5,68,35.8,91.2,79.4,89,51.1,35,21.7,30.9,28.8,17.4,7.1
1.0373,49,216.25,74.5,40.2,115.6,104,109,63.7,40.3,23.2,36.8,31,18.9,27.2
1.0543,49,168.25,71.75,38.3,98.3,89.7,99.1,56.3,38.8,23,29.5,27.9,18.6,19.5
1.0561,50,194.75,70.75,39,103.7,97.6,104.2,60,40.9,25.5,32.7,30,19,18.7
1.0543,50,172.75,73,37.4,98.7,87.6,96.1,57.1,38.1,21.8,28.6,26.7,18,19.5
0.995,51,219,64,41.2,119.8,122.1,112.8,62.5,36.9,23.6,34.7,29.1,18.4,47.5
1.0678,51,149.25,69.75,34.8,92.8,81.1,96.3,53.8,36.5,21.5,31.3,26.3,17.8,13.6
1.0819,51,154.5,70,36.9,93.3,81.5,94.4,54.7,39,22.6,27.5,25.9,18.6,7.5
1.0433,52,199.25,71.75,39.4,106.8,100,105,63.9,39.2,22.9,35.7,30.4,19.2,24.5
1.0646,53,154.5,69.25,37.6,93.9,88.7,94.5,53.7,36.2,22,28.5,25.7,17.1,15
1.0706,54,153.25,70.5,38.5,99,91.8,96.2,57.7,38.1,23.9,31.4,29.9,18.9,12.4
1.0399,54,230,72.25,42.5,119.9,110.4,105.5,64.2,42.7,27,38.4,32,19.6,26
1.0726,54,161.75,67.5,37.4,94.2,87.6,95.6,59.7,40.2,23.4,27.9,27,17.8,11.5
1.0874,55,142.25,67.25,35.2,92.7,82.8,91.9,54.4,35.2,22.5,29.4,26.8,17,5.2
1.074,55,179.75,68.75,41.1,106.9,95.3,98.2,57.4,37.1,21.8,34.1,31.1,19.2,10.9
1.0703,55,126.5,66.75,33.4,88.8,78.2,87.5,50.8,33,19.7,25.3,22,15.8,12.5
1.065,55,169.5,68.25,37.2,101.7,91.1,97.1,56.6,38.5,22.6,33.4,29.3,18.8,14.8
1.0418,55,198.5,74.25,38.3,105.3,96.7,106.6,64,42.6,23.4,33.2,30,18.4,25.2
1.0647,56,174.5,69.5,38.1,104,89.4,98.4,58.4,37.4,22.5,34.6,30.1,18.8,14.9
1.0601,56,167.75,68.5,37.4,98.6,93,97,55.4,38.8,23.2,32.4,29.7,19,17
1.0745,57,147.75,65.75,35.2,99.6,86.4,90.1,53,35,21.3,31.7,27.3,16.9,10.6
1.062,57,182.25,71.75,39.4,103.4,96.7,100.7,59.3,38.6,22.8,31.8,29.1,19,16.1
1.0636,58,175.5,71.5,38,100.2,88.1,97.8,57.1,38.9,23.6,30.9,29.6,18,15.4
1.0384,58,161.75,67.25,35.1,94.9,94.9,100.2,56.8,35.9,21,27.8,26.1,17.6,26.7
1.0403,60,157.75,67.5,40.4,97.2,93.3,94,54.3,35.7,21,31.3,28.7,18.3,25.8
1.0563,62,168.75,67.5,38.3,104.7,95.6,93.7,54.4,37.1,22.7,30.3,26.3,18.3,18.6
1.0424,62,191.5,72.25,40.6,104,98.2,101.1,59.3,40.3,23,32.6,28.5,19,24.8
1.0372,63,219.15,69.5,40.2,117.6,113.8,111.8,63.4,41.1,22.3,35.1,29.6,18.5,27.3
1.0705,64,155.25,69.5,37.9,95.8,82.8,94.5,61.2,39.1,22.3,29.8,28.9,18.3,12.4
1.0316,65,189.75,65.75,40.8,106.4,100.5,100.5,59.2,38.1,24,35.9,30.5,19.1,29.9
1.0599,65,127.5,65.75,34.7,93,79.7,87.6,50.7,33.4,20.1,28.5,24.8,16.5,17
1.0207,65,224.5,68.25,38.8,119.6,118,114.3,61.3,42.1,23.4,34.9,30.1,19.4,35
1.0304,66,234.25,72,41.4,119.7,109,109.1,63.7,42.4,24.6,35.6,30.7,19.5,30.4
1.0256,67,227.75,72.75,41.3,115.8,113.4,109.8,65.6,46,25.4,35.3,29.8,19.5,32.6
1.0334,67,199.5,68.5,40.7,118.3,106.1,101.6,58.2,38.8,24.1,32.1,29.3,18.5,29
1.0641,68,155.5,69.25,36.3,97.4,84.3,94.4,54.3,37.5,22.6,29.2,27.3,18.5,15.2
1.0308,69,215.5,70.5,40.8,113.7,107.6,110,63.3,44,22.6,37.5,32.6,18.8,30.2
1.0736,70,134.25,67,34.9,89.2,83.6,88.8,49.6,34.8,21.5,25.6,25.7,18.5,11
1.0236,72,201,69.75,40.9,108.5,105,104.5,59.6,40.8,23.2,35.2,28.6,20.1,33.6
1.0328,72,186.75,66,38.9,111.1,111.5,101.7,60.3,37.3,21.5,31.3,27.2,18,29.3
1.0399,72,190.75,70.5,38.9,108.3,101.3,97.8,56,41.6,22.7,30.5,29.4,19.8,26
1.0271,74,207.5,70,40.8,112.4,108.5,107.1,59.3,42.2,24.6,33.7,30,20.9,31.9
python-weka-wrapper-0.1.0/python/wekaexamples/data/vote.arff 0000664 0001750 0001750 00000116505 12327054230 025605 0 ustar fracpete fracpete 0000000 0000000 % 1. Title: 1984 United States Congressional Voting Records Database
%
% 2. Source Information:
% (a) Source: Congressional Quarterly Almanac, 98th Congress,
% 2nd session 1984, Volume XL: Congressional Quarterly Inc.
% Washington, D.C., 1985.
% (b) Donor: Jeff Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu)
% (c) Date: 27 April 1987
%
% 3. Past Usage
% - Publications
% 1. Schlimmer, J. C. (1987). Concept acquisition through
% representational adjustment. Doctoral dissertation, Department of
% Information and Computer Science, University of California, Irvine, CA.
% -- Results: about 90%-95% accuracy appears to be STAGGER's asymptote
% - Predicted attribute: party affiliation (2 classes)
%
% 4. Relevant Information:
% This data set includes votes for each of the U.S. House of
% Representatives Congressmen on the 16 key votes identified by the
% CQA. The CQA lists nine different types of votes: voted for, paired
% for, and announced for (these three simplified to yea), voted
% against, paired against, and announced against (these three
% simplified to nay), voted present, voted present to avoid conflict
% of interest, and did not vote or otherwise make a position known
% (these three simplified to an unknown disposition).
%
% 5. Number of Instances: 435 (267 democrats, 168 republicans)
%
% 6. Number of Attributes: 16 + class name = 17 (all Boolean valued)
%
% 7. Attribute Information:
% 1. Class Name: 2 (democrat, republican)
% 2. handicapped-infants: 2 (y,n)
% 3. water-project-cost-sharing: 2 (y,n)
% 4. adoption-of-the-budget-resolution: 2 (y,n)
% 5. physician-fee-freeze: 2 (y,n)
% 6. el-salvador-aid: 2 (y,n)
% 7. religious-groups-in-schools: 2 (y,n)
% 8. anti-satellite-test-ban: 2 (y,n)
% 9. aid-to-nicaraguan-contras: 2 (y,n)
% 10. mx-missile: 2 (y,n)
% 11. immigration: 2 (y,n)
% 12. synfuels-corporation-cutback: 2 (y,n)
% 13. education-spending: 2 (y,n)
% 14. superfund-right-to-sue: 2 (y,n)
% 15. crime: 2 (y,n)
% 16. duty-free-exports: 2 (y,n)
% 17. export-administration-act-south-africa: 2 (y,n)
%
% 8. Missing Attribute Values: Denoted by "?"
%
% NOTE: It is important to recognize that "?" in this database does
% not mean that the value of the attribute is unknown. It
% means simply, that the value is not "yea" or "nay" (see
% "Relevant Information" section above).
%
% Attribute: #Missing Values:
% 1: 0
% 2: 0
% 3: 12
% 4: 48
% 5: 11
% 6: 11
% 7: 15
% 8: 11
% 9: 14
% 10: 15
% 11: 22
% 12: 7
% 13: 21
% 14: 31
% 15: 25
% 16: 17
% 17: 28
%
% 9. Class Distribution: (2 classes)
% 1. 45.2 percent are democrat
% 2. 54.8 percent are republican
%
% Class predictiveness and predictability: Pr(C|A=V) and Pr(A=V|C)
% Attribute 1: (A = handicapped-infants)
% 0.91; 1.21 (C=democrat; V=y)
% 0.09; 0.10 (C=republican; V=y)
% 0.43; 0.38 (C=democrat; V=n)
% 0.57; 0.41 (C=republican; V=n)
% 0.75; 0.03 (C=democrat; V=?)
% 0.25; 0.01 (C=republican; V=?)
% Attribute 2: (A = water-project-cost-sharing)
% 0.62; 0.45 (C=democrat; V=y)
% 0.38; 0.23 (C=republican; V=y)
% 0.62; 0.45 (C=democrat; V=n)
% 0.38; 0.23 (C=republican; V=n)
% 0.58; 0.10 (C=democrat; V=?)
% 0.42; 0.06 (C=republican; V=?)
% Attribute 3: (A = adoption-of-the-budget-resolution)
% 0.91; 0.87 (C=democrat; V=y)
% 0.09; 0.07 (C=republican; V=y)
% 0.17; 0.11 (C=democrat; V=n)
% 0.83; 0.44 (C=republican; V=n)
% 0.64; 0.03 (C=democrat; V=?)
% 0.36; 0.01 (C=republican; V=?)
% Attribute 4: (A = physician-fee-freeze)
% 0.08; 0.05 (C=democrat; V=y)
% 0.92; 0.50 (C=republican; V=y)
% 0.99; 0.92 (C=democrat; V=n)
% 0.01; 0.01 (C=republican; V=n)
% 0.73; 0.03 (C=democrat; V=?)
% 0.27; 0.01 (C=republican; V=?)
% Attribute 5: (A = el-salvador-aid)
% 0.26; 0.21 (C=democrat; V=y)
% 0.74; 0.48 (C=republican; V=y)
% 0.96; 0.75 (C=democrat; V=n)
% 0.04; 0.02 (C=republican; V=n)
% 0.80; 0.04 (C=democrat; V=?)
% 0.20; 0.01 (C=republican; V=?)
% Attribute 6: (A = religious-groups-in-schools)
% 0.45; 0.46 (C=democrat; V=y)
% 0.55; 0.46 (C=republican; V=y)
% 0.89; 0.51 (C=democrat; V=n)
% 0.11; 0.05 (C=republican; V=n)
% 0.82; 0.03 (C=democrat; V=?)
% 0.18; 0.01 (C=republican; V=?)
% Attribute 7: (A = anti-satellite-test-ban)
% 0.84; 0.75 (C=democrat; V=y)
% 0.16; 0.12 (C=republican; V=y)
% 0.32; 0.22 (C=democrat; V=n)
% 0.68; 0.38 (C=republican; V=n)
% 0.57; 0.03 (C=democrat; V=?)
% 0.43; 0.02 (C=republican; V=?)
% Attribute 8: (A = aid-to-nicaraguan-contras)
% 0.90; 0.82 (C=democrat; V=y)
% 0.10; 0.07 (C=republican; V=y)
% 0.25; 0.17 (C=democrat; V=n)
% 0.75; 0.41 (C=republican; V=n)
% 0.27; 0.01 (C=democrat; V=?)
% 0.73; 0.03 (C=republican; V=?)
% Attribute 9: (A = mx-missile)
% 0.91; 0.70 (C=democrat; V=y)
% 0.09; 0.06 (C=republican; V=y)
% 0.29; 0.22 (C=democrat; V=n)
% 0.71; 0.45 (C=republican; V=n)
% 0.86; 0.07 (C=democrat; V=?)
% 0.14; 0.01 (C=republican; V=?)
% Attribute 10: (A = immigration)
% 0.57; 0.46 (C=democrat; V=y)
% 0.43; 0.28 (C=republican; V=y)
% 0.66; 0.52 (C=democrat; V=n)
% 0.34; 0.23 (C=republican; V=n)
% 0.57; 0.01 (C=democrat; V=?)
% 0.43; 0.01 (C=republican; V=?)
% Attribute 11: (A = synfuels-corporation-cutback)
% 0.86; 0.48 (C=democrat; V=y)
% 0.14; 0.06 (C=republican; V=y)
% 0.48; 0.47 (C=democrat; V=n)
% 0.52; 0.43 (C=republican; V=n)
% 0.57; 0.04 (C=democrat; V=?)
% 0.43; 0.03 (C=republican; V=?)
% Attribute 12: (A = education-spending)
% 0.21; 0.13 (C=democrat; V=y)
% 0.79; 0.42 (C=republican; V=y)
% 0.91; 0.80 (C=democrat; V=n)
% 0.09; 0.06 (C=republican; V=n)
% 0.58; 0.07 (C=democrat; V=?)
% 0.42; 0.04 (C=republican; V=?)
% Attribute 13: (A = superfund-right-to-sue)
% 0.35; 0.27 (C=democrat; V=y)
% 0.65; 0.42 (C=republican; V=y)
% 0.89; 0.67 (C=democrat; V=n)
% 0.11; 0.07 (C=republican; V=n)
% 0.60; 0.06 (C=democrat; V=?)
% 0.40; 0.03 (C=republican; V=?)
% Attribute 14: (A = crime)
% 0.36; 0.34 (C=democrat; V=y)
% 0.64; 0.49 (C=republican; V=y)
% 0.98; 0.63 (C=democrat; V=n)
% 0.02; 0.01 (C=republican; V=n)
% 0.59; 0.04 (C=democrat; V=?)
% 0.41; 0.02 (C=republican; V=?)
% Attribute 15: (A = duty-free-exports)
% 0.92; 0.60 (C=democrat; V=y)
% 0.08; 0.04 (C=republican; V=y)
% 0.39; 0.34 (C=democrat; V=n)
% 0.61; 0.44 (C=republican; V=n)
% 0.57; 0.06 (C=democrat; V=?)
% 0.43; 0.04 (C=republican; V=?)
% Attribute 16: (A = export-administration-act-south-africa)
% 0.64; 0.65 (C=democrat; V=y)
% 0.36; 0.30 (C=republican; V=y)
% 0.19; 0.04 (C=democrat; V=n)
% 0.81; 0.15 (C=republican; V=n)
% 0.79; 0.31 (C=democrat; V=?)
% 0.21; 0.07 (C=republican; V=?)
%
@relation vote
@attribute 'handicapped-infants' { 'n', 'y'}
@attribute 'water-project-cost-sharing' { 'n', 'y'}
@attribute 'adoption-of-the-budget-resolution' { 'n', 'y'}
@attribute 'physician-fee-freeze' { 'n', 'y'}
@attribute 'el-salvador-aid' { 'n', 'y'}
@attribute 'religious-groups-in-schools' { 'n', 'y'}
@attribute 'anti-satellite-test-ban' { 'n', 'y'}
@attribute 'aid-to-nicaraguan-contras' { 'n', 'y'}
@attribute 'mx-missile' { 'n', 'y'}
@attribute 'immigration' { 'n', 'y'}
@attribute 'synfuels-corporation-cutback' { 'n', 'y'}
@attribute 'education-spending' { 'n', 'y'}
@attribute 'superfund-right-to-sue' { 'n', 'y'}
@attribute 'crime' { 'n', 'y'}
@attribute 'duty-free-exports' { 'n', 'y'}
@attribute 'export-administration-act-south-africa' { 'n', 'y'}
@attribute 'Class' { 'democrat', 'republican'}
@data
'n','y','n','y','y','y','n','n','n','y',?,'y','y','y','n','y','republican'
'n','y','n','y','y','y','n','n','n','n','n','y','y','y','n',?,'republican'
?,'y','y',?,'y','y','n','n','n','n','y','n','y','y','n','n','democrat'
'n','y','y','n',?,'y','n','n','n','n','y','n','y','n','n','y','democrat'
'y','y','y','n','y','y','n','n','n','n','y',?,'y','y','y','y','democrat'
'n','y','y','n','y','y','n','n','n','n','n','n','y','y','y','y','democrat'
'n','y','n','y','y','y','n','n','n','n','n','n',?,'y','y','y','democrat'
'n','y','n','y','y','y','n','n','n','n','n','n','y','y',?,'y','republican'
'n','y','n','y','y','y','n','n','n','n','n','y','y','y','n','y','republican'
'y','y','y','n','n','n','y','y','y','n','n','n','n','n',?,?,'democrat'
'n','y','n','y','y','n','n','n','n','n',?,?,'y','y','n','n','republican'
'n','y','n','y','y','y','n','n','n','n','y',?,'y','y',?,?,'republican'
'n','y','y','n','n','n','y','y','y','n','n','n','y','n',?,?,'democrat'
'y','y','y','n','n','y','y','y',?,'y','y',?,'n','n','y',?,'democrat'
'n','y','n','y','y','y','n','n','n','n','n','y',?,?,'n',?,'republican'
'n','y','n','y','y','y','n','n','n','y','n','y','y',?,'n',?,'republican'
'y','n','y','n','n','y','n','y',?,'y','y','y',?,'n','n','y','democrat'
'y',?,'y','n','n','n','y','y','y','n','n','n','y','n','y','y','democrat'
'n','y','n','y','y','y','n','n','n','n','n',?,'y','y','n','n','republican'
'y','y','y','n','n','n','y','y','y','n','y','n','n','n','y','y','democrat'
'y','y','y','n','n',?,'y','y','n','n','y','n','n','n','y','y','democrat'
'y','y','y','n','n','n','y','y','y','n','n','n',?,?,'y','y','democrat'
'y',?,'y','n','n','n','y','y','y','n','n',?,'n','n','y','y','democrat'
'y','y','y','n','n','n','y','y','y','n','n','n','n','n','y','y','democrat'
'y','n','y','n','n','n','y','y','y','n','n','n','n','n','y',?,'democrat'
'y','n','y','n','n','n','y','y','y','y','n','n','n','n','y','y','democrat'
'y','n','y','n','n','n','y','y','y','n','y','n','n','n','y','y','democrat'
'y','y','y','n','n','n','y','y','y','n','y','n','n','n','y','y','democrat'
'y','n','n','y','y','n','y','y','y','n','n','y','y','y','n','y','republican'
'y','y','y','n','n','n','y','y','y','n','y','n','n','n','y','y','democrat'
'n','y','n','y','y','y','n','n','n','n','n','y','y','y','n','n','republican'
'y','y','y','n','n','n','y','y','y','n','y','n','n','n','y',?,'democrat'
'y','y','y','n','n','n','y','y','y','y','n','n','y','n','y','y','democrat'
'n','y','n','y','y','y','n','n','n','n','n','y','y','y','n','y','republican'
'y','y','y','n','n','n','y','y','y','n','n','n','n','n','y','y','democrat'
'n','y','n','y','y','y','n','n','n','n','n','y','y','y','n','n','republican'
'y',?,'n','y','y','y','n','n','n','y','n','y',?,'y','n','y','republican'
'y','y','n','y','y','y','n','n','n','n','n','n','y','y','n','y','republican'
'n','y','n','y','y','y','n','n','n','y','n','y','y','y','n','n','republican'
'y','n','y','n','n','n','y','y','y','y','y','n','y','n','y','y','democrat'
'y','y','y','n','n','n','y','y','y','n',?,'n','n','n','n',?,'democrat'
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?,?,?,?,?,?,?,?,'y',?,?,?,?,?,?,?,'democrat'
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?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,'republican'
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'y','n','y','n','n',?,'y','y','y','n',?,?,'n',?,?,?,'democrat'
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'n','n','n','y','y','y','n','n','n','n','n','y','y','y','y','n','republican'
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'n','n','n','y','y','y','n','n','n','n','n','y','y','y','n','y','republican'
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'n','n','n','y','y','y','n','n','n','n','n','y','y','y','n','y','republican'
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'n','n','y','n','n','y',?,'y',?,'y','y','y','n','y','y',?,'democrat'
'y','y','y',?,'n','y','y','y','y','n','y','n','y','n',?,'y','democrat'
'y','y','y','n','y','y','n','y','n','y','y','n','y','y','y','y','democrat'
'y','y','y','n','y','y','n','y','n','y','y','n','y','y','n',?,'democrat'
'y','n','y','n',?,'y',?,'y','y','y','n','n','y','y','n','y','democrat'
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'n','y','y','n','n','y','y','y','n','y','y','n','y','y','n',?,'democrat'
'n','y','n','y','y','y',?,?,'n','y','n','y',?,?,?,?,'republican'
'n','n','y','y','y','y','n','n','n','y','n','y','y','y','y','y','republican'
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'n','n',?,'n','n','y','y','y','y','n','n','n','n','n','y','y','democrat'
'n','n','n','y','y','y','y','n','n','y','n','y','y','y','n','y','republican'
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'n','y','n','y','y','y',?,?,'n','n',?,?,'y',?,?,?,'republican'
'n','n','n','y','y','y','n','n','n','n','n','y','y','y','n','y','republican'
'n','n','n','y','y','y','n','n','n','n','n','y','y','y','n','y','republican'
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?,?,'n','n',?,'y',?,'n','n','n','y','y','n','y','n',?,'democrat'
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?,?,?,?,'n','y','n','y','y','n','n','y','y','n','n',?,'republican'
'y','y',?,?,?,'y','n','n','n','n','y','n','y','n','n','y','democrat'
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'n','y','n','y','y','y','n',?,'n','n',?,?,?,'y','n',?,'republican'
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?,'n','y','y','n','y','y','y','y','y','n','y','n','y','n','y','republican'
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'y','n','y','n','n','y','y','y','y','y','y','n','n','n','n','y','democrat'
'n','n','n','y','y','y','n','n','n','y','n','y','y','y','n','y','republican'
'y','y','y','y','y','y','y','y','n','y',?,?,?,'y','n','y','republican'
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'y','y','y','y','y','y','y','y','n','y','n','n','y','y','n','y','republican'
'n','y','y','n','y','y','y','y','n','n','y','n','y','n','y','y','democrat'
'n','n','y','n','n','y','y','y','y','n','y','n','n','n','y','y','democrat'
'n','y','y','n','n','y','y','y','y','n','y','n','n','y','y','y','democrat'
'n','y','y','n','n',?,'y','y','y','y','y','n',?,'y','y','y','democrat'
'n','n','y','n','n','n','y','y','n','y','y','n','n','n','y',?,'democrat'
'y','n','y','n','n','n','y','y','y','y','n','n','n','n','y','y','democrat'
'n','n','n','y','y','y','y','y','n','y','n','y','y','y','n','y','republican'
?,?,?,'n','n','n','y','y','y','y','n','n','y','n','y','y','democrat'
'y','n','y','n',?,'n','y','y','y','y','n','y','n',?,'y','y','democrat'
'n','n','y','y','y','y','n','n','y','y','n','y','y','y','n','y','republican'
'n','n','y','n','n','n','y','y','y','y','n','n','n','n','n','y','democrat'
'n',?,'n','y','y','y','n','n','n','n','y','y','y','y','n','y','republican'
'n','n','n','y','y','y',?,?,?,?,'n','y','y','y','n','y','republican'
'n','y','n','y','y','y','n','n','n','y','n','y','y','y',?,'n','republican'
%
%
%
python-weka-wrapper-0.1.0/python/wekaexamples/data/anneal.arff 0000664 0001750 0001750 00000427750 12327054230 026075 0 ustar fracpete fracpete 0000000 0000000 % 1. Title of Database: Annealing Data
%
% 2. Source Information: donated by David Sterling and Wray Buntine.
%
% 3. Past Usage: unknown
%
% 4. Relevant Information:
% -- Explanation: I suspect this was left by Ross Quinlan in 1987 at the
% 4th Machine Learning Workshop. I'd have to check with Jeff Schlimmer
% to double check this.
%
% 5. Number of Instances: 798
%
% 6. Number of Attributes: 38
% -- 6 continuously-valued
% -- 3 integer-valued
% -- 29 nominal-valued
%
% 7. Attribute Information:
% 1. family: --,GB,GK,GS,TN,ZA,ZF,ZH,ZM,ZS
% 2. product-type: C, H, G
% 3. steel: -,R,A,U,K,M,S,W,V
% 4. carbon: continuous
% 5. hardness: continuous
% 6. temper_rolling: -,T
% 7. condition: -,S,A,X
% 8. formability: -,1,2,3,4,5
% 9. strength: continuous
% 10. non-ageing: -,N
% 11. surface-finish: P,M,-
% 12. surface-quality: -,D,E,F,G
% 13. enamelability: -,1,2,3,4,5
% 14. bc: Y,-
% 15. bf: Y,-
% 16. bt: Y,-
% 17. bw/me: B,M,-
% 18. bl: Y,-
% 19. m: Y,-
% 20. chrom: C,-
% 21. phos: P,-
% 22. cbond: Y,-
% 23. marvi: Y,-
% 24. exptl: Y,-
% 25. ferro: Y,-
% 26. corr: Y,-
% 27. blue/bright/varn/clean: B,R,V,C,-
% 28. lustre: Y,-
% 29. jurofm: Y,-
% 30. s: Y,-
% 31. p: Y,-
% 32. shape: COIL, SHEET
% 33. thick: continuous
% 34. width: continuous
% 35. len: continuous
% 36. oil: -,Y,N
% 37. bore: 0000,0500,0600,0760
% 38. packing: -,1,2,3
% classes: 1,2,3,4,5,U
%
% -- The '-' values are actually 'not_applicable' values rather than
% 'missing_values' (and so can be treated as legal discrete
% values rather than as showing the absence of a discrete value).
%
% 8. Missing Attribute Values: Signified with "?"
% Attribute: Number of instances missing its value:
% 1 0
% 2 0
% 3 70
% 4 0
% 5 0
% 6 675
% 7 271
% 8 283
% 9 0
% 10 703
% 11 790
% 12 217
% 13 785
% 14 797
% 15 680
% 16 736
% 17 609
% 18 662
% 19 798
% 20 775
% 21 791
% 22 730
% 23 798
% 24 796
% 25 772
% 26 798
% 27 793
% 28 753
% 29 798
% 30 798
% 31 798
% 32 0
% 33 0
% 34 0
% 35 0
% 36 740
% 37 0
% 38 789
% 39 0
%
% 9. Distribution of Classes
% Class Name: Number of Instances:
% 1 8
% 2 88
% 3 608
% 4 0
% 5 60
% U 34
% ---
% 798
%
@relation anneal
@attribute 'family' {'?','GB','GK','GS','TN','ZA','ZF','ZH','ZM','ZS'}
@attribute 'product-type' {'C','H','G'}
@attribute 'steel' {'?','R','A','U','K','M','S','W','V'}
@attribute 'carbon' real
@attribute 'hardness' real
@attribute 'temper_rolling' {'?','T'}
@attribute 'condition' {'?','S','A','X'}
@attribute 'formability' {'?','1','2','3','4','5'}
@attribute 'strength' real
@attribute 'non-ageing' {'?','N'}
@attribute 'surface-finish' {'?','P','M'}
@attribute 'surface-quality' {'?','D','E','F','G'}
@attribute 'enamelability' {'?','1','2','3','4','5'}
@attribute 'bc' {'?','Y'}
@attribute 'bf' {'?','Y'}
@attribute 'bt' {'?','Y'}
@attribute 'bw/me' {'?','B','M'}
@attribute 'bl' {'?','Y'}
@attribute 'm' {'?','Y'}
@attribute 'chrom' {'?','C'}
@attribute 'phos' {'?','P'}
@attribute 'cbond' {'?','Y'}
@attribute 'marvi' {'?','Y'}
@attribute 'exptl' {'?','Y'}
@attribute 'ferro' {'?','Y'}
@attribute 'corr' {'?','Y'}
@attribute 'blue/bright/varn/clean' {'?','B','R','V','C'}
@attribute 'lustre' {'?','Y'}
@attribute 'jurofm' {'?','Y'}
@attribute 's' {'?','Y'}
@attribute 'p' {'?','Y'}
@attribute 'shape' {'COIL','SHEET'}
@attribute 'thick' real
@attribute 'width' real
@attribute 'len' real
@attribute 'oil' {'?','Y','N'}
@attribute 'bore' {'0','500','600','760'}
@attribute 'packing' {'?','1','2','3'}
@attribute 'class' {'1','2','3','4','5','U'}
@data
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'?','C','W',0,0,'?','?','?',310,'?','?','G','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',0.8,610,762,'?','0','?','3'
'?','C','?',0,0,'?','A','2',0,'?','?','G','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','COIL',0.7,610,0,'?','0','?','3'
'?','C','R',0,0,'?','S','3',0,'?','?','E','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',0.601,610,762,'?','0','?','3'
'ZS','C','A',0,0,'?','S','3',0,'N','?','E','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','COIL',2,1250,0,'?','0','?','3'
'?','C','R',0,0,'?','S','2',0,'?','?','E','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',0.699,1320,761,'?','0','?','3'
'?','C','A',0,0,'?','S','2',0,'?','?','G','?','?','Y','?','B','?','?','?','?','?','?','?','?','?','?','?','?','?','?','COIL',0.699,1320,0,'?','0','?','3'
'ZS','C','A',0,0,'?','S','3',0,'N','?','E','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',0.7,609.9,4170,'?','0','?','3'
'?','C','R',0,0,'?','S','3',0,'?','?','E','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',0.601,609,4880,'?','0','?','3'
'?','C','M',0,0,'?','?','?',600,'?','?','G','?','?','?','Y','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',0.9,1050,1220,'?','0','?','2'
'?','C','A',0,0,'?','S','2',0,'?','?','E','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',0.7,1525,762,'?','0','?','3'
'?','C','R',0,0,'?','S','2',0,'?','?','E','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','Y','?','?','?','COIL',3.2,609.9,0,'?','0','?','3'
'?','C','A',0,0,'?','S','2',0,'?','?','E','?','?','?','?','?','?','?','?','?','Y','?','?','?','?','?','?','?','?','?','COIL',0.7,599.9,0,'?','0','?','3'
'?','C','R',0,0,'?','S','2',0,'?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','COIL',1.6,609.9,0,'?','0','?','2'
'?','C','M',0,0,'?','?','?',350,'?','?','G','?','?','?','Y','M','?','?','?','?','?','?','?','?','?','?','?','?','?','?','COIL',1.6,610,0,'?','0','?','3'
'?','C','R',0,0,'?','S','2',0,'?','?','E','?','?','?','?','?','?','?','?','?','Y','?','?','?','?','?','?','?','?','?','SHEET',1.2,375,612,'?','0','?','3'
'?','C','A',8,0,'?','S','?',0,'?','?','G','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','COIL',3.2,609.9,0,'?','0','?','3'
'?','C','?',0,0,'?','S','1',0,'?','?','G','?','?','?','?','B','Y','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',0.699,1300,4880,'?','0','?','3'
'?','C','K',45,0,'?','?','?',0,'?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','COIL',1.1,610,0,'?','0','?','3'
'?','C','V',0,0,'?','S','2',0,'?','?','?','2','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',0.8,609.9,762,'?','0','?','2'
'?','C','A',0,0,'?','S','2',0,'?','?','E','?','?','?','Y','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','COIL',0.3,1320,0,'?','0','?','3'
'ZS','C','A',0,85,'T','?','?',0,'?','?','E','?','?','?','Y','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',0.8,610,4880,'?','0','?','U'
'?','C','A',0,0,'?','S','2',0,'?','?','F','?','?','Y','?','B','Y','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',0.6,520,0,'?','0','?','3'
'?','C','A',0,80,'T','?','?',0,'?','?','G','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',1.6,1320,4170,'Y','0','?','U'
'?','C','A',0,60,'T','?','?',0,'?','?','G','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','COIL',2.801,50,0,'?','0','?','3'
'?','C','A',0,0,'?','S','2',0,'?','?','E','?','?','?','?','B','Y','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',3.2,1320,762,'?','0','?','3'
'ZS','C','A',0,0,'?','S','5',0,'N','?','E','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','COIL',1,609.9,0,'?','0','?','3'
'?','C','A',0,45,'?','S','?',0,'?','?','D','?','?','?','?','B','?','?','?','?','?','?','?','?','?','?','?','?','?','?','COIL',0.699,610,0,'?','0','?','3'
'?','C','W',0,0,'?','?','?',310,'?','?','G','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',1.599,610,762,'?','0','?','3'
'?','C','?',0,70,'T','?','?',0,'?','?','G','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','Y','?','?','?','COIL',0.6,610,0,'?','600','?','3'
'?','C','K',55,0,'?','?','?',0,'?','?','?','?','?','?','?','M','?','?','?','?','?','?','?','?','?','?','?','?','?','?','COIL',2.2,640,0,'?','0','?','3'
'?','C','A',0,45,'?','S','?',0,'?','?','D','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','COIL',0.7,1320,0,'?','0','?','3'
'?','C','A',0,0,'?','S','2',0,'?','?','F','?','?','Y','?','?','Y','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',0.7,1220,762,'?','0','?','3'
'?','C','R',0,0,'?','S','3',0,'?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',1.599,610,4880,'?','0','?','2'
'?','C','?',0,0,'?','S','1',0,'?','?','G','?','?','?','?','?','?','?','?','?','Y','?','?','?','?','?','Y','?','?','?','COIL',0.24,20,0,'?','0','?','3'
'?','C','A',0,0,'?','S','2',0,'?','?','G','?','?','?','Y','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','COIL',0.3,1320,0,'?','0','?','3'
'?','C','R',0,0,'?','S','2',0,'?','?','E','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',0.699,375,612,'?','0','?','3'
'ZS','C','A',0,50,'T','?','?',0,'?','?','E','?','?','?','?','B','?','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',0.451,1250,762,'?','0','?','3'
'?','C','K',45,0,'?','?','?',0,'?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','COIL',1.5,900,0,'?','0','?','3'
'?','C','A',0,0,'?','S','2',0,'?','?','G','?','?','Y','?','B','Y','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',1.599,1300,4880,'?','0','?','3'
'?','C','R',0,0,'?','S','2',0,'?','?','E','?','?','?','?','?','Y','?','?','?','?','?','?','?','?','?','?','?','?','?','COIL',3.2,1320,0,'?','0','?','3'
'?','C','K',45,0,'?','?','?',0,'?','?','?','?','?','?','Y','M','?','?','?','?','?','?','?','?','?','?','?','?','?','?','COIL',3.2,640,0,'?','500','?','3'
'TN','C','A',0,0,'?','?','3',0,'N','?','?','?','?','?','?','B','?','?','?','?','?','?','?','Y','?','?','?','?','?','?','SHEET',1.6,609.9,3000,'?','0','?','5'
'TN','C','?',0,0,'?','A','1',0,'?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','Y','?','?','?','?','?','?','SHEET',1.6,609.9,612,'?','0','?','5'
'?','C','R',0,0,'?','S','2',0,'?','?','?','?','?','?','?','?','Y','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',1.6,1500,4170,'?','0','?','2'
'TN','C','A',0,0,'?','?','3',0,'N','?','?','?','?','?','?','?','?','?','C','?','?','?','?','?','?','?','?','?','?','?','SHEET',1.2,609.9,1,'?','0','?','5'
'?','C','R',0,0,'?','S','3',0,'?','?','?','?','?','?','?','B','?','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',1.599,610,762,'?','0','?','2'
'?','C','R',0,0,'?','S','3',0,'?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',1.601,830,880,'?','0','?','2'
'?','C','V',0,0,'?','S','2',0,'?','?','?','2','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','SHEET',1.599,150,762,'?','0','?','2'
'?','C','A',0,85,'T','?','?',0,'?','?','G','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','?','COIL',0.4,20,0,'?','0','?','U'
'?','C','A',0,85,'T','?','?',0,'?','?','G','?','?','?','Y','M','?','?','?','?','?','?','?','?','?','?','?','?','?','?','COIL',4,610,0,'?','500','?','U'
%
%
%
python-weka-wrapper-0.1.0/python/wekaexamples/data/bolts.arff 0000664 0001750 0001750 00000010666 12327054230 025754 0 ustar fracpete fracpete 0000000 0000000 % Data from StatLib (ftp stat.cmu.edu/datasets)
%
% SUMMARY:
%
% Data from an experiment on the affects of machine adjustments on
% the time to count bolts. Data appear as the STATS (Issue 10) Challenge.
%
% DATA:
%
% Submitted by W. Robert Stephenson, Iowa State University
% email: wrstephe@iastate.edu
%
% A manufacturer of automotive accessories provides hardware, e.g. nuts,
% bolts, washers and screws, to fasten the accessory to the car or truck.
% Hardware is counted and packaged automatically. Specifically, bolts
% are dumped into a large metal dish. A plate that forms the bottom of
% the dish rotates counterclockwise. This rotation forces bolts to the
% outside of the dish and up along a narrow ledge. Due to the vibration
% of the dish caused by the spinning bottom plate, some bolts fall off
% the ledge and back into the dish. The ledge spirals up to a point
% where the bolts are allowed to drop into a pan on a conveyor belt.
% As a bolt drops, it passes by an electronic eye that counts it. When
% the electronic counter reaches the preset number of bolts, the
% rotation is stopped and the conveyor belt is moved forward.
%
% There are several adjustments on the machine that affect its operation.
% These include; a speed setting that controls the speed of rotation
% (SPEED1) of the plate at the bottom of the dish, a total number of
% bolts (TOTAL) to be counted, a second speed setting (SPEED2) that is
% used to change the speed of rotation (usually slowing it down) for the
% last few bolts, the number of bolts to be counted at this second speed
% (NUMBER2), and the sensitivity of the electronic eye (SENS). The
% sensitivity setting is to insure that the correct number of bolts are
% counted. Too few bolts packaged causes customer complaints. Too many
% bolts packaged increases costs. For each run conducted in this
% experiment the correct number of bolts was counted. From an
% engineering standpoint if the correct number of bolts is counted, the
% sensitivity should not affect the time to count bolts. The measured
% response is the time (TIME), in seconds, it takes to count the desired
% number of bolts. In order to put times on a equal footing the
% response to be analyzed is the time to count 20 bolts (T20BOLT).
% Below are the data for 40 combinations of settings. RUN is the order
% in which the data were collected.
%
% Analyze the data. What adjustments have the greatest effect on the
% time to count 20 bolts? How would you adjust the machine to get
% the shortest time to count 20 bolts? Are there any unusual features
% to the data?
%
% The data description and data may be freely used for non-commercial
% purposes and can be freely distributed. Copyright remains with the
% author and STATS Magazine.
%
@relation bolts
@attribute RUN integer
@attribute SPEED1 integer
@attribute TOTAL integer
@attribute SPEED2 integer
@attribute NUMBER2 integer
@attribute SENS integer
@attribute TIME real
@attribute T20BOLT real
@data
25, 2, 10, 1.5, 0, 6, 5.70, 11.40
24, 2, 10, 1.5, 0, 10, 17.56, 35.12
30, 2, 10, 1.5, 2, 6, 11.28, 22.56
2, 2, 10, 1.5, 2, 10, 8.39, 16.78
40, 2, 10, 2.5, 0, 6, 16.67, 33.34
37, 2, 10, 2.5, 0, 10, 12.04, 24.08
16, 2, 10, 2.5, 2, 6, 9.22, 18.44
22, 2, 10, 2.5, 2, 10, 3.94, 7.88
33, 2, 30, 1.5, 0, 6, 27.02, 18.01
17, 2, 30, 1.5, 0, 10, 19.46, 12.97
28, 2, 30, 1.5, 2, 6, 18.54, 12.36
27, 2, 30, 1.5, 2, 10, 25.70, 17.13
14, 2, 30, 2.5, 0, 6, 19.02, 12.68
13, 2, 30, 2.5, 0, 10, 22.39, 14.93
4, 2, 30, 2.5, 2, 6, 23.85, 15.90
21, 2, 30, 2.5, 2, 10, 30.12, 20.08
23, 6, 10, 1.5, 0, 6, 13.42, 26.84
35, 6, 10, 1.5, 0, 10, 34.26, 68.52
19, 6, 10, 1.5, 2, 6, 39.74, 79.48
34, 6, 10, 1.5, 2, 10, 10.60, 21.20
31, 6, 10, 2.5, 0, 6, 28.89, 57.78
9, 6, 10, 2.5, 0, 10, 35.61, 71.22
38, 6, 10, 2.5, 2, 6, 17.20, 34.40
15, 6, 10, 2.5, 2, 10, 6.00, 12.00
39, 6, 30, 1.5, 0, 6, 129.45, 86.30
8, 6, 30, 1.5, 0, 0, 107.38, 71.59
26, 6, 30, 1.5, 2, 6, 111.66, 74.44
11, 6, 30, 1.5, 2, 0, 109.10, 72.73
6, 6, 30, 2.5, 0, 6, 100.43, 66.95
20, 6, 30, 2.5, 0, 0, 109.28, 72.85
10, 6, 30, 2.5, 2, 6, 106.46, 70.97
32, 6, 30, 2.5, 2, 0, 134.01, 89.34
1, 4, 20, 2.0, 1, 8, 10.78, 10.78
3, 4, 20, 2.0, 1, 8, 9.39, 9.39
5, 4, 20, 2.0, 1, 8, 9.84, 9.84
7, 4, 20, 2.0, 1, 8, 13.94, 13.94
12, 4, 20, 2.0, 1, 8, 12.33, 12.33
18, 4, 20, 2.0, 1, 8, 7.32, 7.32
29, 4, 20, 2.0, 1, 8, 7.91, 7.91
36, 4, 20, 2.0, 1, 8, 15.58, 15.58
python-weka-wrapper-0.1.0/python/wekaexamples/data/iris.arff 0000664 0001750 0001750 00000016476 12327054230 025604 0 ustar fracpete fracpete 0000000 0000000 % 1. Title: Iris Plants Database
%
% 2. Sources:
% (a) Creator: R.A. Fisher
% (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
% (c) Date: July, 1988
%
% 3. Past Usage:
% - Publications: too many to mention!!! Here are a few.
% 1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
% Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
% to Mathematical Statistics" (John Wiley, NY, 1950).
% 2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
% (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
% 3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
% Structure and Classification Rule for Recognition in Partially Exposed
% Environments". IEEE Transactions on Pattern Analysis and Machine
% Intelligence, Vol. PAMI-2, No. 1, 67-71.
% -- Results:
% -- very low misclassification rates (0% for the setosa class)
% 4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE
% Transactions on Information Theory, May 1972, 431-433.
% -- Results:
% -- very low misclassification rates again
% 5. See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II
% conceptual clustering system finds 3 classes in the data.
%
% 4. Relevant Information:
% --- This is perhaps the best known database to be found in the pattern
% recognition literature. Fisher's paper is a classic in the field
% and is referenced frequently to this day. (See Duda & Hart, for
% example.) The data set contains 3 classes of 50 instances each,
% where each class refers to a type of iris plant. One class is
% linearly separable from the other 2; the latter are NOT linearly
% separable from each other.
% --- Predicted attribute: class of iris plant.
% --- This is an exceedingly simple domain.
%
% 5. Number of Instances: 150 (50 in each of three classes)
%
% 6. Number of Attributes: 4 numeric, predictive attributes and the class
%
% 7. Attribute Information:
% 1. sepal length in cm
% 2. sepal width in cm
% 3. petal length in cm
% 4. petal width in cm
% 5. class:
% -- Iris Setosa
% -- Iris Versicolour
% -- Iris Virginica
%
% 8. Missing Attribute Values: None
%
% Summary Statistics:
% Min Max Mean SD Class Correlation
% sepal length: 4.3 7.9 5.84 0.83 0.7826
% sepal width: 2.0 4.4 3.05 0.43 -0.4194
% petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
% petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
%
% 9. Class Distribution: 33.3% for each of 3 classes.
@RELATION iris
@ATTRIBUTE sepallength REAL
@ATTRIBUTE sepalwidth REAL
@ATTRIBUTE petallength REAL
@ATTRIBUTE petalwidth REAL
@ATTRIBUTE class {Iris-setosa,Iris-versicolor,Iris-virginica}
@DATA
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.4,3.7,1.5,0.2,Iris-setosa
4.8,3.4,1.6,0.2,Iris-setosa
4.8,3.0,1.4,0.1,Iris-setosa
4.3,3.0,1.1,0.1,Iris-setosa
5.8,4.0,1.2,0.2,Iris-setosa
5.7,4.4,1.5,0.4,Iris-setosa
5.4,3.9,1.3,0.4,Iris-setosa
5.1,3.5,1.4,0.3,Iris-setosa
5.7,3.8,1.7,0.3,Iris-setosa
5.1,3.8,1.5,0.3,Iris-setosa
5.4,3.4,1.7,0.2,Iris-setosa
5.1,3.7,1.5,0.4,Iris-setosa
4.6,3.6,1.0,0.2,Iris-setosa
5.1,3.3,1.7,0.5,Iris-setosa
4.8,3.4,1.9,0.2,Iris-setosa
5.0,3.0,1.6,0.2,Iris-setosa
5.0,3.4,1.6,0.4,Iris-setosa
5.2,3.5,1.5,0.2,Iris-setosa
5.2,3.4,1.4,0.2,Iris-setosa
4.7,3.2,1.6,0.2,Iris-setosa
4.8,3.1,1.6,0.2,Iris-setosa
5.4,3.4,1.5,0.4,Iris-setosa
5.2,4.1,1.5,0.1,Iris-setosa
5.5,4.2,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.0,3.2,1.2,0.2,Iris-setosa
5.5,3.5,1.3,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
4.4,3.0,1.3,0.2,Iris-setosa
5.1,3.4,1.5,0.2,Iris-setosa
5.0,3.5,1.3,0.3,Iris-setosa
4.5,2.3,1.3,0.3,Iris-setosa
4.4,3.2,1.3,0.2,Iris-setosa
5.0,3.5,1.6,0.6,Iris-setosa
5.1,3.8,1.9,0.4,Iris-setosa
4.8,3.0,1.4,0.3,Iris-setosa
5.1,3.8,1.6,0.2,Iris-setosa
4.6,3.2,1.4,0.2,Iris-setosa
5.3,3.7,1.5,0.2,Iris-setosa
5.0,3.3,1.4,0.2,Iris-setosa
7.0,3.2,4.7,1.4,Iris-versicolor
6.4,3.2,4.5,1.5,Iris-versicolor
6.9,3.1,4.9,1.5,Iris-versicolor
5.5,2.3,4.0,1.3,Iris-versicolor
6.5,2.8,4.6,1.5,Iris-versicolor
5.7,2.8,4.5,1.3,Iris-versicolor
6.3,3.3,4.7,1.6,Iris-versicolor
4.9,2.4,3.3,1.0,Iris-versicolor
6.6,2.9,4.6,1.3,Iris-versicolor
5.2,2.7,3.9,1.4,Iris-versicolor
5.0,2.0,3.5,1.0,Iris-versicolor
5.9,3.0,4.2,1.5,Iris-versicolor
6.0,2.2,4.0,1.0,Iris-versicolor
6.1,2.9,4.7,1.4,Iris-versicolor
5.6,2.9,3.6,1.3,Iris-versicolor
6.7,3.1,4.4,1.4,Iris-versicolor
5.6,3.0,4.5,1.5,Iris-versicolor
5.8,2.7,4.1,1.0,Iris-versicolor
6.2,2.2,4.5,1.5,Iris-versicolor
5.6,2.5,3.9,1.1,Iris-versicolor
5.9,3.2,4.8,1.8,Iris-versicolor
6.1,2.8,4.0,1.3,Iris-versicolor
6.3,2.5,4.9,1.5,Iris-versicolor
6.1,2.8,4.7,1.2,Iris-versicolor
6.4,2.9,4.3,1.3,Iris-versicolor
6.6,3.0,4.4,1.4,Iris-versicolor
6.8,2.8,4.8,1.4,Iris-versicolor
6.7,3.0,5.0,1.7,Iris-versicolor
6.0,2.9,4.5,1.5,Iris-versicolor
5.7,2.6,3.5,1.0,Iris-versicolor
5.5,2.4,3.8,1.1,Iris-versicolor
5.5,2.4,3.7,1.0,Iris-versicolor
5.8,2.7,3.9,1.2,Iris-versicolor
6.0,2.7,5.1,1.6,Iris-versicolor
5.4,3.0,4.5,1.5,Iris-versicolor
6.0,3.4,4.5,1.6,Iris-versicolor
6.7,3.1,4.7,1.5,Iris-versicolor
6.3,2.3,4.4,1.3,Iris-versicolor
5.6,3.0,4.1,1.3,Iris-versicolor
5.5,2.5,4.0,1.3,Iris-versicolor
5.5,2.6,4.4,1.2,Iris-versicolor
6.1,3.0,4.6,1.4,Iris-versicolor
5.8,2.6,4.0,1.2,Iris-versicolor
5.0,2.3,3.3,1.0,Iris-versicolor
5.6,2.7,4.2,1.3,Iris-versicolor
5.7,3.0,4.2,1.2,Iris-versicolor
5.7,2.9,4.2,1.3,Iris-versicolor
6.2,2.9,4.3,1.3,Iris-versicolor
5.1,2.5,3.0,1.1,Iris-versicolor
5.7,2.8,4.1,1.3,Iris-versicolor
6.3,3.3,6.0,2.5,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
7.1,3.0,5.9,2.1,Iris-virginica
6.3,2.9,5.6,1.8,Iris-virginica
6.5,3.0,5.8,2.2,Iris-virginica
7.6,3.0,6.6,2.1,Iris-virginica
4.9,2.5,4.5,1.7,Iris-virginica
7.3,2.9,6.3,1.8,Iris-virginica
6.7,2.5,5.8,1.8,Iris-virginica
7.2,3.6,6.1,2.5,Iris-virginica
6.5,3.2,5.1,2.0,Iris-virginica
6.4,2.7,5.3,1.9,Iris-virginica
6.8,3.0,5.5,2.1,Iris-virginica
5.7,2.5,5.0,2.0,Iris-virginica
5.8,2.8,5.1,2.4,Iris-virginica
6.4,3.2,5.3,2.3,Iris-virginica
6.5,3.0,5.5,1.8,Iris-virginica
7.7,3.8,6.7,2.2,Iris-virginica
7.7,2.6,6.9,2.3,Iris-virginica
6.0,2.2,5.0,1.5,Iris-virginica
6.9,3.2,5.7,2.3,Iris-virginica
5.6,2.8,4.9,2.0,Iris-virginica
7.7,2.8,6.7,2.0,Iris-virginica
6.3,2.7,4.9,1.8,Iris-virginica
6.7,3.3,5.7,2.1,Iris-virginica
7.2,3.2,6.0,1.8,Iris-virginica
6.2,2.8,4.8,1.8,Iris-virginica
6.1,3.0,4.9,1.8,Iris-virginica
6.4,2.8,5.6,2.1,Iris-virginica
7.2,3.0,5.8,1.6,Iris-virginica
7.4,2.8,6.1,1.9,Iris-virginica
7.9,3.8,6.4,2.0,Iris-virginica
6.4,2.8,5.6,2.2,Iris-virginica
6.3,2.8,5.1,1.5,Iris-virginica
6.1,2.6,5.6,1.4,Iris-virginica
7.7,3.0,6.1,2.3,Iris-virginica
6.3,3.4,5.6,2.4,Iris-virginica
6.4,3.1,5.5,1.8,Iris-virginica
6.0,3.0,4.8,1.8,Iris-virginica
6.9,3.1,5.4,2.1,Iris-virginica
6.7,3.1,5.6,2.4,Iris-virginica
6.9,3.1,5.1,2.3,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
6.8,3.2,5.9,2.3,Iris-virginica
6.7,3.3,5.7,2.5,Iris-virginica
6.7,3.0,5.2,2.3,Iris-virginica
6.3,2.5,5.0,1.9,Iris-virginica
6.5,3.0,5.2,2.0,Iris-virginica
6.2,3.4,5.4,2.3,Iris-virginica
5.9,3.0,5.1,1.8,Iris-virginica
%
%
%
python-weka-wrapper-0.1.0/python/wekaexamples/helper.py 0000664 0001750 0001750 00000002242 12327070666 024713 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 .
# helper.py
# Copyright (C) 2014 Fracpete (fracpete at gmail dot com)
import os
def get_data_dir():
"""
Returns the data directory.
:rtype: str
"""
rootdir = os.path.dirname(__file__)
libdir = rootdir + os.sep + "data"
return libdir
def print_title(title):
"""
Prints the title underlined.
:param title: the title to print
"""
print("\n" + title)
print("=" * len(title))
def print_info(info):
"""
Prints the info.
:param info: the info to print
"""
print("\n" + info)
python-weka-wrapper-0.1.0/python/wekaexamples/classifiers.py 0000664 0001750 0001750 00000023412 12327066206 025740 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 Fracpete (fracpete at gmail dot com)
import os
import weka.core.jvm as jvm
import wekaexamples.helper as helper
from weka.core.converters import Loader
from weka.classifiers import Classifier, SingleClassifierEnhancer, MultipleClassifiersCombiner, FilteredClassifier
from weka.classifiers import Evaluation
from weka.filters import Filter
from weka.core.classes import Random
import weka.plot.classifiers as plot_cls
import weka.plot.graph as plot_graph
def main():
"""
Just runs some example code.
"""
# load a dataset
iris_file = helper.get_data_dir() + os.sep + "iris.arff"
helper.print_info("Loading dataset: " + iris_file)
loader = Loader("weka.core.converters.ArffLoader")
iris_data = loader.load_file(iris_file)
iris_data.set_class_index(iris_data.num_attributes() - 1)
# build a classifier and output model
helper.print_title("Training J48 classifier on iris")
classifier = Classifier(classname="weka.classifiers.trees.J48")
classifier.set_options(["-C", "0.3"])
classifier.build_classifier(iris_data)
print(classifier)
print(classifier.graph())
plot_graph.plot_dot_graph(classifier.graph())
# evaluate model on test set
helper.print_title("Evaluating J48 classifier on iris")
evaluation = Evaluation(iris_data)
evl = evaluation.test_model(classifier, iris_data)
print(evl)
print(evaluation.to_summary())
# load a dataset incrementally and build classifier incrementally
helper.print_title("Build classifier incrementally on iris")
helper.print_info("Loading dataset: " + iris_file)
loader = Loader("weka.core.converters.ArffLoader")
iris_inc = loader.load_file(iris_file, incremental=True)
iris_inc.set_class_index(iris_inc.num_attributes() - 1)
classifier = Classifier("weka.classifiers.bayes.NaiveBayesUpdateable")
classifier.build_classifier(iris_inc)
while True:
inst = loader.next_instance(iris_inc)
if inst is None:
break
classifier.update_classifier(inst)
print(classifier)
# construct meta-classifiers
helper.print_title("Meta classifiers")
# generic FilteredClassifier instantiation
print("generic FilteredClassifier instantiation")
meta = SingleClassifierEnhancer(classname="weka.classifiers.meta.FilteredClassifier")
meta.set_classifier(Classifier(classname="weka.classifiers.functions.LinearRegression"))
flter = Filter("weka.filters.unsupervised.attribute.Remove")
flter.set_options(["-R", "first"])
meta.set_property("filter", flter.jobject)
print(meta.to_commandline())
# direct FilteredClassifier instantiation
print("direct FilteredClassifier instantiation")
meta = FilteredClassifier()
meta.set_classifier(Classifier(classname="weka.classifiers.functions.LinearRegression"))
flter = Filter("weka.filters.unsupervised.attribute.Remove")
flter.set_options(["-R", "first"])
meta.set_filter(flter)
print(meta.to_commandline())
# generic Vote
print("generic Vote instantiation")
meta = MultipleClassifiersCombiner(classname="weka.classifiers.meta.Vote")
classifiers = [
Classifier(classname="weka.classifiers.functions.SMO"),
Classifier(classname="weka.classifiers.trees.J48")
]
meta.set_classifiers(classifiers)
print(meta.to_commandline())
# cross-validate nominal classifier
helper.print_title("Cross-validating SMO on iris")
classifier = Classifier(classname="weka.classifiers.functions.SMO")
classifier.set_options(["-M"])
evaluation = Evaluation(iris_data)
evaluation.crossvalidate_model(classifier, iris_data, 10, Random(42))
print(evaluation.to_summary())
print(evaluation.to_class_details())
print(evaluation.to_matrix())
print("areaUnderPRC/0: " + str(evaluation.area_under_prc(0)))
print("weightedAreaUnderPRC: " + str(evaluation.weighted_area_under_prc()))
print("areaUnderROC/1: " + str(evaluation.area_under_roc(1)))
print("weightedAreaUnderROC: " + str(evaluation.weighted_area_under_roc()))
print("avgCost: " + str(evaluation.avg_cost()))
print("totalCost: " + str(evaluation.total_cost()))
print("confusionMatrix: " + str(evaluation.confusion_matrix()))
print("correct: " + str(evaluation.correct()))
print("pctCorrect: " + str(evaluation.percent_correct()))
print("incorrect: " + str(evaluation.incorrect()))
print("pctIncorrect: " + str(evaluation.percent_incorrect()))
print("unclassified: " + str(evaluation.unclassified()))
print("pctUnclassified: " + str(evaluation.percent_unclassified()))
print("coverageOfTestCasesByPredictedRegions: " + str(evaluation.coverage_of_test_cases_by_predicted_regions()))
print("sizeOfPredictedRegions: " + str(evaluation.size_of_predicted_regions()))
print("falseNegativeRate: " + str(evaluation.false_negative_rate(1)))
print("weightedFalseNegativeRate: " + str(evaluation.weighted_false_negative_rate()))
print("numFalseNegatives: " + str(evaluation.num_false_negatives(1)))
print("trueNegativeRate: " + str(evaluation.true_negative_rate(1)))
print("weightedTrueNegativeRate: " + str(evaluation.weighted_true_negative_rate()))
print("numTrueNegatives: " + str(evaluation.num_true_negatives(1)))
print("falsePositiveRate: " + str(evaluation.false_positive_rate(1)))
print("weightedFalsePositiveRate: " + str(evaluation.weighted_false_positive_rate()))
print("numFalsePositives: " + str(evaluation.num_false_positives(1)))
print("truePositiveRate: " + str(evaluation.true_positive_rate(1)))
print("weightedTruePositiveRate: " + str(evaluation.weighted_true_positive_rate()))
print("numTruePositives: " + str(evaluation.num_true_positives(1)))
print("fMeasure: " + str(evaluation.f_measure(1)))
print("weightedFMeasure: " + str(evaluation.weighted_f_measure()))
print("unweightedMacroFmeasure: " + str(evaluation.unweighted_macro_f_measure()))
print("unweightedMicroFmeasure: " + str(evaluation.unweighted_micro_f_measure()))
print("precision: " + str(evaluation.precision(1)))
print("weightedPrecision: " + str(evaluation.weighted_precision()))
print("recall: " + str(evaluation.recall(1)))
print("weightedRecall: " + str(evaluation.weighted_recall()))
print("kappa: " + str(evaluation.kappa()))
print("KBInformation: " + str(evaluation.kb_information()))
print("KBMeanInformation: " + str(evaluation.kb_mean_information()))
print("KBRelativeInformation: " + str(evaluation.kb_relative_information()))
print("SFEntropyGain: " + str(evaluation.sf_entropy_gain()))
print("SFMeanEntropyGain: " + str(evaluation.sf_mean_entropy_gain()))
print("SFMeanPriorEntropy: " + str(evaluation.sf_mean_prior_entropy()))
print("SFMeanSchemeEntropy: " + str(evaluation.sf_mean_scheme_entropy()))
print("matthewsCorrelationCoefficient: " + str(evaluation.matthews_correlation_coefficient(1)))
print("weightedMatthewsCorrelation: " + str(evaluation.weighted_matthews_correlation()))
print("class priors: " + str(evaluation.get_class_priors()))
print("numInstances: " + str(evaluation.num_instances()))
print("meanAbsoluteError: " + str(evaluation.mean_absolute_error()))
print("meanPriorAbsoluteError: " + str(evaluation.mean_prior_absolute_error()))
print("relativeAbsoluteError: " + str(evaluation.relative_absolute_error()))
print("rootMeanSquaredError: " + str(evaluation.root_mean_squared_error()))
print("rootMeanPriorSquaredError: " + str(evaluation.root_mean_prior_squared_error()))
print("rootRelativeSquaredError: " + str(evaluation.root_relative_squared_error()))
# load a numeric dataset
bolts_file = helper.get_data_dir() + os.sep + "bolts.arff"
helper.print_info("Loading dataset: " + bolts_file)
loader = Loader("weka.core.converters.ArffLoader")
bolts_data = loader.load_file(bolts_file)
bolts_data.set_class_index(bolts_data.num_attributes() - 1)
# build a classifier and output model
helper.print_title("Training LinearRegression on bolts")
classifier = Classifier(classname="weka.classifiers.functions.LinearRegression")
classifier.set_options(["-S", "1", "-C"])
classifier.build_classifier(bolts_data)
print(classifier)
# cross-validate numeric classifier
helper.print_title("Cross-validating LinearRegression on bolts")
classifier = Classifier(classname="weka.classifiers.functions.LinearRegression")
classifier.set_options(["-S", "1", "-C"])
evaluation = Evaluation(bolts_data)
evaluation.crossvalidate_model(classifier, bolts_data, 10, Random(42))
print(evaluation.to_summary())
print("correlationCoefficient: " + str(evaluation.correlation_coefficient()))
print("errorRate: " + str(evaluation.error_rate()))
helper.print_title("Header - bolts")
print(str(evaluation.header()))
helper.print_title("Predictions on bolts")
i = 0
preds = evaluation.predictions()
for pred in preds:
i += 1
print(str(i) + ": " + str(pred) + " -> error=" + str(pred.error()))
plot_cls.plot_classifier_errors(preds, outfile="/tmp/out2.png")
if __name__ == "__main__":
try:
jvm.start()
main()
except Exception, e:
print(e)
finally:
jvm.stop()
python-weka-wrapper-0.1.0/python/wekaexamples/__init__.py 0000664 0001750 0001750 00000000030 12327054231 025152 0 ustar fracpete fracpete 0000000 0000000 __author__ = 'fracpete'
python-weka-wrapper-0.1.0/python/wekaexamples/attribute_selection.py 0000664 0001750 0001750 00000005361 12327054231 027477 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 Fracpete (fracpete at gmail dot com)
import os
import weka.core.jvm as jvm
import wekaexamples.helper as helper
from weka.core.converters import Loader
from weka.attribute_selection import ASSearch
from weka.attribute_selection import ASEvaluation
from weka.attribute_selection import AttributeSelection
def main():
"""
Just runs some example code.
"""
# load a dataset
anneal_file = helper.get_data_dir() + os.sep + "anneal.arff"
helper.print_info("Loading dataset: " + anneal_file)
loader = Loader("weka.core.converters.ArffLoader")
anneal_data = loader.load_file(anneal_file)
anneal_data.set_class_index(anneal_data.num_attributes() - 1)
# perform attribute selection
helper.print_title("Attribute selection")
search = ASSearch("weka.attributeSelection.BestFirst")
search.set_options(["-D", "1", "-N", "5"])
evaluation = ASEvaluation("weka.attributeSelection.CfsSubsetEval")
evaluation.set_options(["-P", "1", "-E", "1"])
attsel = AttributeSelection()
attsel.set_search(search)
attsel.set_evaluator(evaluation)
attsel.select_attributes(anneal_data)
print("# attributes: " + str(attsel.get_number_attributes_selected()))
print("attributes: " + str(attsel.get_selected_attributes()))
print("result string:\n" + attsel.to_results_string())
# perform ranking
helper.print_title("Attribute ranking (2-fold CV)")
search = ASSearch("weka.attributeSelection.Ranker")
search.set_options(["-N", "-1"])
evaluation = ASEvaluation("weka.attributeSelection.InfoGainAttributeEval")
attsel = AttributeSelection()
attsel.set_ranking(True)
attsel.set_folds(2)
attsel.set_crossvalidation(True)
attsel.set_seed(42)
attsel.set_search(search)
attsel.set_evaluator(evaluation)
attsel.select_attributes(anneal_data)
print("ranked attributes:\n" + str(attsel.get_ranked_attributes()))
print("result string:\n" + attsel.to_results_string())
if __name__ == "__main__":
try:
jvm.start()
main()
except Exception, e:
print(e)
finally:
jvm.stop()
python-weka-wrapper-0.1.0/python/wekaexamples/experiments.py 0000664 0001750 0001750 00000006616 12327054231 025776 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 Fracpete (fracpete at gmail dot com)
import os
import tempfile
import weka.core.jvm as jvm
import weka.core.converters as converters
import wekaexamples.helper as helper
from weka.classifiers import Classifier
from weka.experiments import SimpleCrossValidationExperiment, SimpleRandomSplitExperiment, Tester, ResultMatrix
def main():
"""
Just runs some example code.
"""
print(helper.get_data_dir())
# cross-validation + classification
helper.print_title("Experiment: Cross-validation + classification")
datasets = [helper.get_data_dir() + os.sep + "iris.arff", helper.get_data_dir() + os.sep + "anneal.arff"]
classifiers = [Classifier("weka.classifiers.rules.ZeroR"), Classifier("weka.classifiers.trees.J48")]
outfile = tempfile.gettempdir() + os.sep + "results-cv.arff"
exp = SimpleCrossValidationExperiment(
classification=True,
runs=10,
folds=10,
datasets=datasets,
classifiers=classifiers,
result=outfile)
exp.setup()
exp.run()
# evaluate
loader = converters.loader_for_file(outfile)
data = loader.load_file(outfile)
matrix = ResultMatrix("weka.experiment.ResultMatrixPlainText")
tester = Tester("weka.experiment.PairedCorrectedTTester")
tester.set_resultmatrix(matrix)
comparison_col = data.get_attribute_by_name("Percent_correct").get_index()
tester.set_instances(data)
print(tester.header(comparison_col))
print(tester.multi_resultset_full(0, comparison_col))
# random split + regression
helper.print_title("Experiment: Random split + regression")
datasets = [helper.get_data_dir() + os.sep + "bolts.arff", helper.get_data_dir() + os.sep + "bodyfat.arff"]
classifiers = [Classifier("weka.classifiers.rules.ZeroR"), Classifier("weka.classifiers.functions.LinearRegression")]
outfile = tempfile.gettempdir() + os.sep + "results-rs.arff"
exp = SimpleRandomSplitExperiment(
classification=False,
runs=10,
percentage=66.6,
preserve_order=False,
datasets=datasets,
classifiers=classifiers,
result=outfile)
exp.setup()
exp.run()
# evaluate
loader = converters.loader_for_file(outfile)
data = loader.load_file(outfile)
matrix = ResultMatrix("weka.experiment.ResultMatrixPlainText")
tester = Tester("weka.experiment.PairedCorrectedTTester")
tester.set_resultmatrix(matrix)
comparison_col = data.get_attribute_by_name("Correlation_coefficient").get_index()
tester.set_instances(data)
print(tester.header(comparison_col))
print(tester.multi_resultset_full(0, comparison_col))
if __name__ == "__main__":
try:
jvm.start()
main()
except Exception, e:
print(e)
finally:
jvm.stop()
python-weka-wrapper-0.1.0/python/wekaexamples/datagenerators.py 0000664 0001750 0001750 00000002774 12327054231 026437 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 Fracpete (fracpete at gmail dot com)
import weka.core.jvm as jvm
import wekaexamples.helper as helper
from weka.datagenerators import DataGenerator
def main():
"""
Just runs some example code.
"""
helper.print_title("Generate data")
generator = DataGenerator("weka.datagenerators.classifiers.classification.Agrawal")
generator.set_options(["-n", "10", "-r", "agrawal"])
generator.set_dataset_format(generator.define_data_format())
print(generator.get_dataset_format())
if generator.get_single_mode_flag():
for i in xrange(generator.get_num_examples_act()):
print(generator.generate_example())
else:
print(generator.generate_examples())
if __name__ == "__main__":
try:
jvm.start()
main()
except Exception, e:
print(e)
finally:
jvm.stop()
python-weka-wrapper-0.1.0/python/wekaexamples/associations.py 0000664 0001750 0001750 00000003057 12327054231 026126 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 Fracpete (fracpete at gmail dot com)
import os
import weka.core.jvm as jvm
import wekaexamples.helper as helper
from weka.core.converters import Loader
from weka.associations import Associator
def main():
"""
Just runs some example code.
"""
# load a dataset
vote_file = helper.get_data_dir() + os.sep + "vote.arff"
helper.print_info("Loading dataset: " + vote_file)
loader = Loader("weka.core.converters.ArffLoader")
vote_data = loader.load_file(vote_file)
vote_data.set_class_index(vote_data.num_attributes() - 1)
# train and output associator
associator = Associator("weka.associations.Apriori")
associator.set_options(["-N", "9", "-I"])
associator.build_associations(vote_data)
print(associator)
if __name__ == "__main__":
try:
jvm.start()
main()
except Exception, e:
print(e)
finally:
jvm.stop()
python-weka-wrapper-0.1.0/MANIFEST.in 0000664 0001750 0001750 00000000207 12327141140 020572 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
python-weka-wrapper-0.1.0/setup.py 0000664 0001750 0001750 00000006703 12327146650 020570 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 Fracpete (fracpete 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:", e.code, url
return False
except URLError, e:
print "URL Error:", e.reason, url
return False
return True
def download_weka():
"""
Downloads the monolithic Weka jar from sourceforget.net if nececssary.
"""
url = "http://sourceforge.net/projects/weka/files/weka-3-7/3.7.11/weka-monolithic.jar/download"
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()
setup(
name="python-weka-wrapper",
description="Python wrapper for the Weka Machine Learning Workbench",
long_description='''The python-weka-wrapper package makes it easy to run
Weka algorihtms and filters from within Python. It offers access to Weka
API using thin wrappers around JNI calls using the "javabridge" package.''',
url="https://github.com/fracpete/python-weka-wrapper",
classifiers=[
'Development Status :: 3 - Alpha',
'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.plot",
"wekaexamples"
],
package_data={
"weka": ["lib/*.jar"],
"wekaexamples": ["data/*.arff"],
},
include_package_data=True,
version="0.1.0",
author='Peter "fracpete" Reutemann',
author_email='fracpete at gmail dot com',
install_requires=[
"javabridge",
"matplotlib",
"pygraphviz",
"PIL"
],
ext_modules=ext_modules(),
)