def testNB(training_data, testing_data):
train_data = Instances.copy_instances(training_data)
test_data = Instances.copy_instances(testing_data)
evaluation = Evaluation(train_data)
classifier = Classifier(classname="weka.classifiers.bayes.NaiveBayes")
classifier.build_classifier(
train_data) # build classifier on the training data
evaluation.test_model(classifier,
test_data) # test and evaluate model on the test set
print("")
print("")
print(
evaluation.summary(
"--------------Naive Bayes Evaluation--------------"))
print("Accuracy: " + str(evaluation.percent_correct))
print("")
print("Label\tPrecision\t\tRecall\t\t\tF-Measure")
print("<=50K\t" + str(evaluation.precision(0)) + "\t" +
str(evaluation.recall(0)) + "\t" + str(evaluation.f_measure(0)))
print(">50K\t" + str(evaluation.precision(1)) + "\t" +
str(evaluation.recall(1)) + "\t" + str(evaluation.f_measure(1)))
print("Mean\t" + str(((evaluation.precision(1)) +
(evaluation.precision(0))) / 2) + "\t" +
str(((evaluation.recall(1)) + (evaluation.recall(0))) / 2) + "\t" +
str(((evaluation.f_measure(1)) + (evaluation.f_measure(0))) / 2))
def naiveBayes(data):
classifier = Classifier(classname="weka.classifiers.bayes.NaiveBayes", options=["-D"])
nfolds=13
rnd = Random(0)
evaluation = Evaluation(data)
evaluation.crossvalidate_model(classifier, data,
nfolds, rnd)
print(" Naive Bayes Cross-validation information")
print(evaluation.summary())
print("precision: " + str(evaluation.precision(1)))
print("recall: " + str(evaluation.recall(1)))
print("F-measure: " + str(evaluation.f_measure(1)))
print("==confusion matrix==")
print(" a b")
print(evaluation.confusion_matrix)
print
#write to file
f = open("naiveeval.txt", "w")
f.write(evaluation.summary())
f.write("\n")
f.write("==confusion matrix==\n")
f.write(" a b\n")
for item in evaluation.confusion_matrix:
f.write("%s\n" % item)
f.close()
#plot roc graph
plcls.plot_roc(evaluation, title="Naive Bayes ROC", outfile="NBROC", wait=True)
return evaluation.percent_correct
def experiment_file_random(path_features, path_folder_save_results, options,
classifier, fold, random, name):
print(name + " Start: " + str(datetime.datetime.now()))
time = datetime.datetime.now()
cls = Classifier(classname=classifier,
options=weka.core.classes.split_options(options))
d_results = {
'percent_correct': [],
'percent_incorrect': [],
'precision': [],
'recall': [],
'f-score': [],
'confusion_matrix': []
}
data = converters.load_any_file(path_features)
data.class_is_last()
pout = PredictionOutput(
classname="weka.classifiers.evaluation.output.prediction.CSV")
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, fold, Random(random), pout)
d_results['percent_correct'].append(evl.percent_correct)
d_results['percent_incorrect'].append(evl.percent_incorrect)
d_results['precision'].append(evl.precision(1))
d_results['recall'].append(evl.recall(1))
d_results['f-score'].append(evl.f_measure(1))
d_results['confusion_matrix'].append(
evl.matrix()) # Generates the confusion matrix.
d_results = pd.DataFrame(data=d_results)
d_results.to_csv(path_folder_save_results + '/' + str(name) + '.csv',
index=False)
save = pout.buffer_content()
check_folder_or_create(path_folder_save_results + '/' + 'prediction')
with open(
path_folder_save_results + '/' + 'prediction/' + str(name) +
'.csv', 'w') as f:
f.write(save)
print(name + " End: " + str(datetime.datetime.now() - time))
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)))
classifier = Classifier(classname="weka.classifiers.bayes.NaiveBayes")
pred_output = PredictionOutput(classname="weka.classifiers.evaluation.output.prediction.PlainText", options=["-distribution"])
evaluation = Evaluation(data)
evaluation.crossvalidate_model(classifier, data, 10, Random(42), output=pred_output)
plot_cls.plot_roc(evaluation, title="ROC bugs",class_index=range(0, data.class_attribute.num_values), wait=False)
plot_cls.plot_prc(evaluation, title="PRC bugs - NaiveBayes",class_index=range(0, data.class_attribute.num_values), wait=False)
"""Performance Metrics - Naive Bayes Classifier"""
print(evaluation.summary())
print(evaluation.class_details())
print(evaluation.matrix())
print("confusionMatrix: " + str(evaluation.confusion_matrix))
print("fMeasure: " + str(evaluation.f_measure(1)))
print("precision: " + str(evaluation.precision(1)))
print("recall: " + str(evaluation.recall(1)))
"""Random Forest Classifier"""
classifier2 = Classifier(classname="weka.classifiers.trees.RandomForest")
evaluation2 = Evaluation(data)
evaluation2.crossvalidate_model(classifier2, data, 10, Random(42))
plot_cls.plot_roc(evaluation2, title="ROC bugs",class_index=range(0, data.class_attribute.num_values), wait=False)
plot_cls.plot_prc(evaluation2, title="PRC bugs - RandomForest",class_index=range(0, data.class_attribute.num_values), wait=False)
"""Performance Evaluation Metrics - Random Forest"""
print(evaluation2.summary())
print(evaluation2.class_details())
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 classify_and_save(classifier, name, outfile):
random.seed("ML349")
csv_header = [
"Game Name",
"SteamID",
"Algorithm",
"Number Players",
"%Players of Training Set",
"Accuracy",
"Precision (0)",
"Recall (0)",
"F1 (0)",
"Precision (1)",
"Recall (1)",
"F1 (1)"
]
game_results = []
with open("data/games_by_username_all.csv", "r") as f:
game_list = f.next().rstrip().split(",")
loader = Loader(classname="weka.core.converters.ArffLoader")
train = loader.load_file("data/final_train.arff")
test = loader.load_file("data/final_test.arff")
count = 0
for i in itertools.chain(xrange(0, 50), random.sample(xrange(50, len(game_list)), 450)):
train.class_index = i
test.class_index = i
count += 1
classifier.build_classifier(train)
evaluation = Evaluation(train)
evaluation.test_model(classifier, test)
confusion = evaluation.confusion_matrix
num_players = sum(confusion[1])
steam_id = repr(train.class_attribute).split(" ")[1]
result = [
game_list[i],
steam_id,
name,
int(num_players),
num_players/1955,
evaluation.percent_correct,
evaluation.precision(0),
evaluation.recall(0),
evaluation.f_measure(0),
evaluation.precision(1),
evaluation.recall(1),
evaluation.f_measure(1)
]
game_results.append(result)
print "\nResult #{2}/500 for {0} (SteamID {1}):".format(game_list[i], steam_id, count),
print evaluation.summary()
with open(outfile, "wb") as f:
csv_writer = csv.writer(f, delimiter=",")
csv_writer.writerow(csv_header)
for r in game_results:
csv_writer.writerow(r)
def experiment_sequential_file(path_indices, path_features,
path_folder_save_results, options, classifier,
name, indicator_col, images):
ind_f = load(path_indices)
lst = ind_f.files
for item in lst:
ind = ind_f[item] + 1
cls = Classifier(classname=classifier,
options=weka.core.classes.split_options(options))
data = converters.load_any_file(path_features)
ind = np.append(ind, len(data))
data.class_is_last()
pout = PredictionOutput(
classname="weka.classifiers.evaluation.output.prediction.CSV")
d_results = {
'index': [],
'percent_correct': [],
'percent_incorrect': [],
'precision': [],
'recall': [],
'f-score': [],
'confusion_matrix': []
}
for j in range(len(ind) - 1):
first = ind[j]
if j == len(ind) - 2:
last = ind[j + 1]
else:
last = ind[j + 1] - 1
d_test = data.subset(row_range=str(first) + '-' + str(last))
if j == 0: # first
d_train = data.subset(row_range=str(last + 1) + '-' +
str(ind[-1])) # last element
print(str(last + 1) + '-' + str(ind[-1]))
elif j == len(ind) - 2: # last
d_train = data.subset(row_range='1-' +
str(first - 1)) # last element
print('1-' + str(first - 1))
else: # central
s = '1-' + str(first - 1) + ',' + str(last + 1) + '-' + str(
ind[-1])
print(s)
d_train = data.subset(row_range=s)
cls.build_classifier(d_train)
evl = Evaluation(data)
evl.test_model(cls, d_test, pout)
# print(type(d_train))
# print(type(d_test))
d_results['index'].append(str(ind[j]))
d_results['percent_correct'].append(evl.percent_correct)
d_results['percent_incorrect'].append(evl.percent_incorrect)
d_results['precision'].append(evl.precision(1))
d_results['recall'].append(evl.recall(1))
d_results['f-score'].append(evl.f_measure(1))
d_results['confusion_matrix'].append(
evl.matrix()) # Generates the confusion matrix.
save = pout.buffer_content()
check_folder_or_create(path_folder_save_results + '/' + 'prediction')
with open(
path_folder_save_results + '/' + 'prediction/' + name +
'pred_data.csv', 'w') as f:
f.write(save)
buffer_save = pd.read_csv(path_folder_save_results + '/' + 'prediction/' +
name + 'pred_data.csv',
index_col=False,
header=None)
col_label = buffer_save[1]
col_prediction = buffer_save[2]
col_different = buffer_save[3]
create_prediction(col_label, col_prediction, col_different, indicator_col,
images, name, path_folder_save_results + '/prediction/')
d_results = pd.DataFrame(data=d_results)
d_results.to_csv(path_folder_save_results + '/' + name + 'results.csv',
index=False)
def experiment_more_file(path_files,
path_folder_save_results,
fold,
options,
classifier,
random,
name,
voting=False):
cls = Classifier(classname=classifier,
options=weka.core.classes.split_options(options))
file_list = os.listdir(path_files)
for file in file_list:
if ".csv" not in file:
file_list.remove(file)
d_results = {
'name_file': [],
'percent_correct': [],
'percent_incorrect': [],
'precision': [],
'recall': [],
'f-score': [],
'confusion_matrix': []
}
for file in file_list:
indicator_table = pd.read_csv(path_files + '/indicator/' + file[0] +
'_indicator.csv')
indicator = list(indicator_table['indicator'])
images = list(indicator_table['image'])
data = converters.load_any_file(path_files + "/" + file)
data.class_is_last()
pout = PredictionOutput(
classname="weka.classifiers.evaluation.output.prediction.CSV")
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, fold, Random(random), pout)
d_results['name_file'].append(str(file))
d_results['percent_correct'].append(evl.percent_correct)
d_results['percent_incorrect'].append(evl.percent_incorrect)
d_results['precision'].append(evl.precision(1))
d_results['recall'].append(evl.recall(1))
d_results['f-score'].append(evl.f_measure(1))
d_results['confusion_matrix'].append(
evl.matrix()) # Generates the confusion matrix.
save = pout.buffer_content()
check_folder_or_create(path_folder_save_results + '/' + name + '/' +
'prediction')
with open(
path_folder_save_results + '/' + name + '/' +
'prediction/pred_data.csv', 'w') as f:
f.write(save)
buffer_save = pd.read_csv(path_folder_save_results + '/' + name + '/' +
'prediction/pred_data.csv',
index_col=False)
col_label = buffer_save['actual']
col_prediction = buffer_save['predicted']
col_different = buffer_save['error']
create_prediction(
col_label, col_prediction, col_different, indicator, images,
file[:-4], path_folder_save_results + '/' + name + '/prediction/')
d_results = pd.DataFrame(data=d_results)
d_results.to_csv(path_folder_save_results + '/' + str(name) + ".csv")
