def test_plot_classifier_errors(self):
"""
Tests the plot_classifier_errors method.
"""
loader = converters.Loader(classname="weka.core.converters.ArffLoader")
bolts_data = loader.load_file(self.datafile("bolts.arff"))
self.assertIsNotNone(bolts_data)
bolts_data.class_is_last()
classifier = classifiers.Classifier(classname="weka.classifiers.functions.LinearRegression", options=["-S", "1", "-C"])
evaluation = classifiers.Evaluation(bolts_data)
evaluation.crossvalidate_model(classifier, bolts_data, 10, Random(42))
plot.plot_classifier_errors(evaluation.predictions, wait=False)
def test_plot_classifier_errors(self):
"""
Tests the plot_classifier_errors method.
"""
loader = converters.Loader(classname="weka.core.converters.ArffLoader")
bolts_data = loader.load_file(self.datafile("bolts.arff"))
self.assertIsNotNone(bolts_data)
bolts_data.class_is_last()
classifier = classifiers.Classifier(classname="weka.classifiers.functions.LinearRegression", options=["-S", "1", "-C"])
evaluation = classifiers.Evaluation(bolts_data)
evaluation.crossvalidate_model(classifier, bolts_data, 10, Random(42))
plot.plot_classifier_errors(evaluation.predictions, wait=False)
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
result = None
evl = self.input.payload
pltclassifier.plot_classifier_errors(
evl.predictions,
absolute=bool(self.resolve_option("absolute")),
max_relative_size=int(self.resolve_option("max_relative_size")),
absolute_size=int(self.resolve_option("absolute_size")),
title=self.resolve_option("title"),
outfile=self.resolve_option("outfile"),
wait=bool(self.resolve_option("wait")))
return result
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
result = None
evl = self.input.payload
pltclassifier.plot_classifier_errors(
evl.predictions,
absolute=bool(self.resolve_option("absolute")),
max_relative_size=int(self.resolve_option("max_relative_size")),
absolute_size=int(self.resolve_option("absolute_size")),
title=self.resolve_option("title"),
outfile=self.resolve_option("outfile"),
wait=bool(self.resolve_option("wait")))
return result
print("\nLoading dataset: " + fname + "\n")
loader = Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(fname)
data.set_class_index(data.num_attributes() - 1)
# plot
pld.scatter_plot(
data, data.get_attribute_by_name("petalwidth").get_index(),
data.get_attribute_by_name("petallength").get_index(),
wait=False)
# add classifier errors to dataset
addcls = Filter(
classname="weka.filters.supervised.attribute.AddClassification",
options=["-W", "weka.classifiers.trees.J48", "-classification", "-error"])
addcls.set_inputformat(data)
filtered = addcls.filter(data)
print(filtered)
# build J48
cls = Classifier(classname="weka.classifiers.trees.J48")
cls.build_classifier(data)
evl = Evaluation(data)
evl.test_model(cls, data)
# plot classifier errors
plc.plot_classifier_errors(evl.predictions(), wait=True)
jvm.stop()
import weka.core.jvm as jvm
import weka.core.converters as conv
from weka.classifiers import Evaluation, Classifier
from weka.core.classes import Random
import weka.plot.classifiers as plcls
import os
jvm.start(packages=True)
data = conv.load_any_file("Dataset/test.arff")
#print(data)
data.class_is_last()
cls = Classifier(classname="weka.classifiers.trees.J48", options=["-C", "0.3"])
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, 15, Random(1))
#print(evl.summary("=== J48 on anneal (stats) === Rafael Manja", False))
#print(evl.matrix("Matriz do Rafael"))
plcls.plot_classifier_errors(evl.predictions, absolute=False, wait=True)
jvm.stop()
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.class_is_last()
# classifier help
helper.print_title("Creating help string")
classifier = Classifier(classname="weka.classifiers.trees.J48")
print(classifier.to_help())
# partial classname
helper.print_title("Creating classifier from partial classname")
clsname = ".J48"
classifier = Classifier(classname=clsname)
print(clsname + " --> " + classifier.classname)
# classifier from commandline
helper.print_title("Creating SMO from command-line string")
cmdline = 'weka.classifiers.functions.SMO -K "weka.classifiers.functions.supportVector.NormalizedPolyKernel -E 3.0"'
classifier = from_commandline(cmdline,
classname="weka.classifiers.Classifier")
classifier.build_classifier(iris_data)
print("input: " + cmdline)
print("output: " + classifier.to_commandline())
print("model:\n" + str(classifier))
# kernel classifier
helper.print_title("Creating SMO as KernelClassifier")
kernel = Kernel(
classname="weka.classifiers.functions.supportVector.RBFKernel",
options=["-G", "0.001"])
classifier = KernelClassifier(classname="weka.classifiers.functions.SMO",
options=["-M"])
classifier.kernel = kernel
classifier.build_classifier(iris_data)
print("classifier: " + classifier.to_commandline())
print("model:\n" + str(classifier))
# build a classifier and output model
helper.print_title("Training J48 classifier on iris")
classifier = Classifier(classname="weka.classifiers.trees.J48")
# Instead of using 'options=["-C", "0.3"]' in the constructor, we can also set the "confidenceFactor"
# property of the J48 classifier itself. However, being of type float rather than double, we need
# to convert it to the correct type first using the double_to_float function:
classifier.set_property("confidenceFactor", types.double_to_float(0.3))
classifier.build_classifier(iris_data)
print(classifier)
print(classifier.graph)
print(classifier.to_source("MyJ48"))
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.summary())
# evaluate model on train/test split
helper.print_title("Evaluating J48 classifier on iris (random split 66%)")
classifier = Classifier(classname="weka.classifiers.trees.J48",
options=["-C", "0.3"])
evaluation = Evaluation(iris_data)
evaluation.evaluate_train_test_split(classifier, iris_data, 66.0,
Random(1))
print(evaluation.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.class_is_last()
classifier = Classifier(
classname="weka.classifiers.bayes.NaiveBayesUpdateable")
classifier.build_classifier(iris_inc)
for inst in loader:
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.classifier = Classifier(
classname="weka.classifiers.functions.LinearRegression")
flter = Filter("weka.filters.unsupervised.attribute.Remove")
flter.options = ["-R", "first"]
meta.set_property("filter", flter.jobject)
print(meta.to_commandline())
# direct FilteredClassifier instantiation
print("direct FilteredClassifier instantiation")
meta = FilteredClassifier()
meta.classifier = Classifier(
classname="weka.classifiers.functions.LinearRegression")
flter = Filter("weka.filters.unsupervised.attribute.Remove")
flter.options = ["-R", "first"]
meta.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.classifiers = classifiers
print(meta.to_commandline())
# cross-validate nominal classifier
helper.print_title("Cross-validating NaiveBayes on diabetes")
diabetes_file = helper.get_data_dir() + os.sep + "diabetes.arff"
helper.print_info("Loading dataset: " + diabetes_file)
loader = Loader("weka.core.converters.ArffLoader")
diabetes_data = loader.load_file(diabetes_file)
diabetes_data.class_is_last()
classifier = Classifier(classname="weka.classifiers.bayes.NaiveBayes")
pred_output = PredictionOutput(
classname="weka.classifiers.evaluation.output.prediction.PlainText",
options=["-distribution"])
evaluation = Evaluation(diabetes_data)
evaluation.crossvalidate_model(classifier,
diabetes_data,
10,
Random(42),
output=pred_output)
print(evaluation.summary())
print(evaluation.class_details())
print(evaluation.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.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))
print("prediction output:\n" + str(pred_output))
plot_cls.plot_roc(evaluation,
title="ROC diabetes",
class_index=range(
0, diabetes_data.class_attribute.num_values),
wait=False)
plot_cls.plot_prc(evaluation,
title="PRC diabetes",
class_index=range(
0, diabetes_data.class_attribute.num_values),
wait=False)
# train 2nd classifier on diabetes dataset
classifier2 = Classifier(classname="weka.classifiers.trees.RandomForest")
evaluation2 = Evaluation(diabetes_data)
evaluation2.crossvalidate_model(classifier2, diabetes_data, 10, Random(42))
plot_cls.plot_rocs({
"NB": evaluation,
"RF": evaluation2
},
title="ROC diabetes",
class_index=0,
wait=False)
plot_cls.plot_prcs({
"NB": evaluation,
"RF": evaluation2
},
title="PRC diabetes",
class_index=0,
wait=False)
# 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.class_is_last()
# build a classifier and output model
helper.print_title("Training LinearRegression on bolts")
classifier = Classifier(
classname="weka.classifiers.functions.LinearRegression",
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",
options=["-S", "1", "-C"])
evaluation = Evaluation(bolts_data)
evaluation.crossvalidate_model(classifier, bolts_data, 10, Random(42))
print(evaluation.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")
for index, pred in enumerate(evaluation.predictions):
print(
str(index + 1) + ": " + str(pred) + " -> error=" + str(pred.error))
plot_cls.plot_classifier_errors(evaluation.predictions, wait=False)
# train 2nd classifier and show errors in same plot
classifier2 = Classifier(classname="weka.classifiers.functions.SMOreg")
evaluation2 = Evaluation(bolts_data)
evaluation2.crossvalidate_model(classifier2, bolts_data, 10, Random(42))
plot_cls.plot_classifier_errors(
{
"LR": evaluation.predictions,
"SMOreg": evaluation2.predictions
},
wait=False)
# learning curve
cls = [
Classifier(classname="weka.classifiers.trees.J48"),
Classifier(classname="weka.classifiers.bayes.NaiveBayesUpdateable")
]
plot_cls.plot_learning_curve(cls,
diabetes_data,
increments=0.05,
label_template="[#] !",
metric="percent_correct",
wait=True)
# access classifier's Java API
labor_file = helper.get_data_dir() + os.sep + "labor.arff"
helper.print_info("Loading dataset: " + labor_file)
loader = Loader("weka.core.converters.ArffLoader")
labor_data = loader.load_file(labor_file)
labor_data.class_is_last()
helper.print_title("Using JRip's Java API to access rules")
jrip = Classifier(classname="weka.classifiers.rules.JRip")
jrip.build_classifier(labor_data)
rset = jrip.jwrapper.getRuleset()
for i in xrange(rset.size()):
r = rset.get(i)
print(str(r.toString(labor_data.class_attribute.jobject)))
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.class_is_last()
# classifier help
helper.print_title("Creating help string")
classifier = Classifier(classname="weka.classifiers.trees.J48")
print(classifier.to_help())
# partial classname
helper.print_title("Creating classifier from partial classname")
clsname = ".J48"
classifier = Classifier(classname=clsname)
print(clsname + " --> " + classifier.classname)
# classifier from commandline
helper.print_title("Creating SMO from command-line string")
cmdline = 'weka.classifiers.functions.SMO -K "weka.classifiers.functions.supportVector.NormalizedPolyKernel -E 3.0"'
classifier = from_commandline(cmdline, classname="weka.classifiers.Classifier")
classifier.build_classifier(iris_data)
print("input: " + cmdline)
print("output: " + classifier.to_commandline())
print("model:\n" + str(classifier))
# kernel classifier
helper.print_title("Creating SMO as KernelClassifier")
kernel = Kernel(classname="weka.classifiers.functions.supportVector.RBFKernel", options=["-G", "0.001"])
classifier = KernelClassifier(classname="weka.classifiers.functions.SMO", options=["-M"])
classifier.kernel = kernel
classifier.build_classifier(iris_data)
print("classifier: " + classifier.to_commandline())
print("model:\n" + str(classifier))
# build a classifier and output model
helper.print_title("Training J48 classifier on iris")
classifier = Classifier(classname="weka.classifiers.trees.J48")
# Instead of using 'options=["-C", "0.3"]' in the constructor, we can also set the "confidenceFactor"
# property of the J48 classifier itself. However, being of type float rather than double, we need
# to convert it to the correct type first using the double_to_float function:
classifier.set_property("confidenceFactor", typeconv.double_to_float(0.3))
classifier.build_classifier(iris_data)
print(classifier)
print(classifier.graph)
print(classifier.to_source("MyJ48"))
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.summary())
# evaluate model on train/test split
helper.print_title("Evaluating J48 classifier on iris (random split 66%)")
classifier = Classifier(classname="weka.classifiers.trees.J48", options=["-C", "0.3"])
evaluation = Evaluation(iris_data)
evaluation.evaluate_train_test_split(classifier, iris_data, 66.0, Random(1))
print(evaluation.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.class_is_last()
classifier = Classifier(classname="weka.classifiers.bayes.NaiveBayesUpdateable")
classifier.build_classifier(iris_inc)
for inst in loader:
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.classifier = Classifier(classname="weka.classifiers.functions.LinearRegression")
flter = Filter("weka.filters.unsupervised.attribute.Remove")
flter.options = ["-R", "first"]
meta.set_property("filter", flter.jobject)
print(meta.to_commandline())
# direct FilteredClassifier instantiation
print("direct FilteredClassifier instantiation")
meta = FilteredClassifier()
meta.classifier = Classifier(classname="weka.classifiers.functions.LinearRegression")
flter = Filter("weka.filters.unsupervised.attribute.Remove")
flter.options = ["-R", "first"]
meta.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.classifiers = classifiers
print(meta.to_commandline())
# cross-validate nominal classifier
helper.print_title("Cross-validating NaiveBayes on diabetes")
diabetes_file = helper.get_data_dir() + os.sep + "diabetes.arff"
helper.print_info("Loading dataset: " + diabetes_file)
loader = Loader("weka.core.converters.ArffLoader")
diabetes_data = loader.load_file(diabetes_file)
diabetes_data.class_is_last()
classifier = Classifier(classname="weka.classifiers.bayes.NaiveBayes")
pred_output = PredictionOutput(
classname="weka.classifiers.evaluation.output.prediction.PlainText", options=["-distribution"])
evaluation = Evaluation(diabetes_data)
evaluation.crossvalidate_model(classifier, diabetes_data, 10, Random(42), output=pred_output)
print(evaluation.summary())
print(evaluation.class_details())
print(evaluation.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.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))
print("prediction output:\n" + str(pred_output))
plot_cls.plot_roc(
evaluation, title="ROC diabetes",
class_index=range(0, diabetes_data.class_attribute.num_values), wait=False)
plot_cls.plot_prc(
evaluation, title="PRC diabetes",
class_index=range(0, diabetes_data.class_attribute.num_values), wait=False)
# 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.class_is_last()
# build a classifier and output model
helper.print_title("Training LinearRegression on bolts")
classifier = Classifier(classname="weka.classifiers.functions.LinearRegression", 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", options=["-S", "1", "-C"])
evaluation = Evaluation(bolts_data)
evaluation.crossvalidate_model(classifier, bolts_data, 10, Random(42))
print(evaluation.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")
for index, pred in enumerate(evaluation.predictions):
print(str(index+1) + ": " + str(pred) + " -> error=" + str(pred.error))
plot_cls.plot_classifier_errors(evaluation.predictions, wait=False)
# learning curve
cls = [
Classifier(classname="weka.classifiers.trees.J48"),
Classifier(classname="weka.classifiers.bayes.NaiveBayesUpdateable")]
plot_cls.plot_learning_curve(
cls, diabetes_data, increments=0.05, label_template="[#] !", metric="percent_correct", wait=True)
# access classifier's Java API
labor_file = helper.get_data_dir() + os.sep + "labor.arff"
helper.print_info("Loading dataset: " + labor_file)
loader = Loader("weka.core.converters.ArffLoader")
labor_data = loader.load_file(labor_file)
labor_data.class_is_last()
helper.print_title("Using JRip's Java API to access rules")
jrip = Classifier(classname="weka.classifiers.rules.JRip")
jrip.build_classifier(labor_data)
rset = jrip.jwrapper.getRuleset()
for i in range(rset.size()):
r = rset.get(i)
print(str(r.toString(labor_data.class_attribute.jobject)))
def runner(self, cdat, heap_size = 16384, seed = None, verbose = True):
self.set_status(Pipeline.RUNNING)
self.logs.append('Initializing Pipeline')
para = self.config
self.logs.append('Reading Pipeline Configuration')
head = ''
name = get_rand_uuid_str()
self.logs.append('Reading Input File')
for i, stage in enumerate(self.stages):
if stage.code in ('dat.fle', 'prp.bgc', 'prp.nrm', 'prp.pmc', 'prp.sum'):
self.stages[i].status = Pipeline.RUNNING
if stage.code == 'dat.fle':
head = os.path.abspath(stage.value.path)
name, _ = os.path.splitext(stage.value.name)
self.logs.append('Parsing to ARFF')
path = os.path.join(head, '{name}.arff'.format(name = name))
# This bug, I don't know why, using Config.schema instead.
# cdat.toARFF(path, express_config = para.Preprocess.schema, verbose = verbose)
for i, stage in enumerate(self.stages):
if stage.code in ('dat.fle', 'prp.bgc', 'prp.nrm', 'prp.pmc', 'prp.sum'):
self.stages[i].status = Pipeline.COMPLETE
self.logs.append('Saved ARFF at {path}'.format(path = path))
self.logs.append('Splitting to Training and Testing Sets')
JVM.start(max_heap_size = '{size}m'.format(size = heap_size))
load = Loader(classname = 'weka.core.converters.ArffLoader')
# data = load.load_file(path)
# save = Saver(classname = 'weka.core.converters.ArffSaver')
data = load.load_file(os.path.join(head, 'iris.arff')) # For Debugging Purposes Only
data.class_is_last() # For Debugging Purposes Only
# data.class_index = cdat.iclss
for i, stage in enumerate(self.stages):
if stage.code == 'prp.kcv':
self.stages[i].status = Pipeline.RUNNING
self.logs.append('Splitting Training Set')
# TODO - Check if this seed is worth it.
seed = assign_if_none(seed, random.randint(0, 1000))
opts = ['-S', str(seed), '-N', str(para.Preprocess.FOLDS)]
wobj = Filter(classname = 'weka.filters.supervised.instance.StratifiedRemoveFolds', options = opts + ['-V'])
wobj.inputformat(data)
tran = wobj.filter(data)
self.logs.append('Splitting Testing Set')
wobj.options = opts
test = wobj.filter(data)
for i, stage in enumerate(self.stages):
if stage.code == 'prp.kcv':
self.stages[i].status = Pipeline.COMPLETE
self.logs.append('Performing Feature Selection')
feat = [ ]
for comb in para.FEATURE_SELECTION:
if comb.USE:
for i, stage in enumerate(self.stages):
if stage.code == 'ats':
search = stage.value.search.name
evaluator = stage.value.evaluator.name
if search == comb.Search.NAME and evaluator == comb.Evaluator.NAME:
self.stages[i].status = Pipeline.RUNNING
srch = ASSearch(classname = 'weka.attributeSelection.{classname}'.format(
classname = comb.Search.NAME,
options = assign_if_none(comb.Search.OPTIONS, [ ])
))
ewal = ASEvaluation(classname = 'weka.attributeSelection.{classname}'.format(
classname = comb.Evaluator.NAME,
options = assign_if_none(comb.Evaluator.OPTIONS, [ ])
))
attr = AttributeSelection()
attr.search(srch)
attr.evaluator(ewal)
attr.select_attributes(tran)
meta = addict.Dict()
meta.search = comb.Search.NAME
meta.evaluator = comb.Evaluator.NAME
meta.features = [tran.attribute(index).name for index in attr.selected_attributes]
feat.append(meta)
for i, stage in enumerate(self.stages):
if stage.code == 'ats':
search = stage.value.search.name
evaluator = stage.value.evaluator.name
if search == comb.Search.NAME and evaluator == comb.Evaluator.NAME:
self.stages[i].status = Pipeline.COMPLETE
models = [ ]
for model in para.MODEL:
if model.USE:
summary = addict.Dict()
self.logs.append('Modelling {model}'.format(model = model.LABEL))
summary.label = model.LABEL
summary.name = model.NAME
summary.options = assign_if_none(model.OPTIONS, [ ])
for i, stage in enumerate(self.stages):
if stage.code == 'lrn' and stage.value.name == model.NAME:
self.stages[i].status = Pipeline.RUNNING
for i, instance in enumerate(data):
iclass = list(range(instance.num_classes))
options = assign_if_none(model.OPTIONS, [ ])
classifier = Classifier(classname = 'weka.classifiers.{classname}'.format(classname = model.NAME), options = options)
classifier.build_classifier(tran)
serializer.write(os.path.join(head, '{name}.{classname}.model'.format(
name = name,
classname = model.NAME
)), classifier)
self.logs.append('Testing model {model}'.format(model = model.LABEL))
evaluation = Evaluation(tran)
evaluation.test_model(classifier, test)
summary.summary = evaluation.summary()
frame = pd.DataFrame(data = evaluation.confusion_matrix)
axes = sns.heatmap(frame, cbar = False, annot = True)
b64str = get_b64_plot(axes)
summary.confusion_matrix = addict.Dict({
'value': evaluation.confusion_matrix.tolist(),
'plot': b64str
})
self.logs.append('Plotting Learning Curve for {model}'.format(model = model.LABEL))
buffer = io.BytesIO()
plot_classifier_errors(evaluation.predictions, tran, test, outfile = buffer, wait = False)
b64str = buffer_to_b64(buffer)
summary.learning_curve = b64str
buffer = io.BytesIO()
plot_roc(evaluation, class_index = iclass, outfile = buffer, wait = False)
b64str = buffer_to_b64(buffer)
summary.roc_curve = b64str
buffer = io.BytesIO()
plot_prc(evaluation, class_index = iclass, outfile = buffer, wait = False)
b64str = buffer_to_b64(buffer)
summary.prc_curve = b64str
if classifier.graph:
summary.graph = classifier.graph
for i, instance in enumerate(test):
prediction = classifier.classify_instance(instance)
for i, stage in enumerate(self.stages):
if stage.code == 'lrn' and stage.value.name == model.NAME:
self.stages[i].status = Pipeline.COMPLETE
models.append(summary)
self.gist.models = models
JVM.stop()
JSON.write(os.path.join(head, '{name}.cgist'.format(name = name)), self.gist)
self.logs.append('Pipeline Complete')
self.set_status(Pipeline.COMPLETE)
print("\nLoading dataset: " + fname + "\n")
loader = Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(fname)
data.class_is_last()
# plot
pld.scatter_plot(
data, data.attribute_by_name("petalwidth").index,
data.attribute_by_name("petallength").index,
wait=False)
# add classifier errors to dataset
addcls = Filter(
classname="weka.filters.supervised.attribute.AddClassification",
options=["-W", "weka.classifiers.trees.J48", "-classification", "-error"])
addcls.inputformat(data)
filtered = addcls.filter(data)
print(filtered)
# build J48
cls = Classifier(classname="weka.classifiers.trees.J48")
cls.build_classifier(data)
evl = Evaluation(data)
evl.test_model(cls, data)
# plot classifier errors
plc.plot_classifier_errors(evl.predictions, wait=True)
jvm.stop()
