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)))
# CLASSIFIERS classifiers = [ ("Bayesian Network", Classifier(classname="weka.classifiers.bayes.BayesNet")), # ("Decision Tree", Classifier(classname="weka.classifiers.trees.J48")), # ("Logistic Regression", Classifier(classname="weka.classifiers.functions.Logistic")), # ("Multilayer Perceptron", Classifier(classname="weka.classifiers.functions.MultilayerPerceptron")), # ("Naive Bayes", Classifier(classname="weka.classifiers.bayes.NaiveBayes")), # ("Nearest Neighbour", Classifier(classname="weka.classifiers.lazy.IBk"))), ] # EVALUATION for name, cls in classifiers: print(name) evaluation = Evaluation(data) evaluation.crossvalidate_model(cls, data, 10, Random(42)) print(evaluation.summary()) print(evaluation.class_details()) print("areaUnderROC/1: " + str(evaluation.area_under_roc(1))) print("numFalseNegatives: " + str(evaluation.num_false_negatives(1))) print("numTrueNegatives: " + str(evaluation.num_true_negatives(1))) print("numFalsePositives: " + str(evaluation.num_false_positives(1))) print("numTruePositives: " + str(evaluation.num_true_positives(1))) jvm.stop() except: print("runtime error") 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", 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 ClassifyParam(website, mode, binWidths, truncation_modes=["full", "truncated"]): if not os.path.exists("classificationResults"): os.makedirs("classificationResults") if("normal" in mode): for truncation in truncation_modes: file = open("classificationResults/SingleWebsite_%s_%s.csv"%(truncation, website),"w") file.write("BinWidth, Accuracy, FalsePositiveRate, FalseNegativeRate\n") for binWidth in binWidths: train_set_file = "TrainSet_%s_%s.arff"%(truncation, binWidth) train_set = "Data/%s/arff/%s"%(website, train_set_file) test_set = "Data/%s/arff/%s"%(website, train_set_file.replace("TrainSet", "TestSet")) print "Loading Datasets..." print "Train: " + train_set train_data = converters.load_any_file(train_set) print "Test: " + test_set test_data = converters.load_any_file(test_set) #Set class attribute train_data.class_is_last() test_data.class_is_last() print "Dataset Loaded!" classifier_name = "weka.classifiers.meta.FilteredClassifier" classifier = Classifier(classname=classifier_name, options=[ "-F", "weka.filters.unsupervised.attribute.StringToWordVector -R first-last -W 1000 -C -T -N 1 -stemmer weka.core.stemmers.NullStemmer -M 1 -tokenizer \"weka.core.tokenizers.WordTokenizer -delimiters \\\" \\\\r\\\\n\\\\t.,;:\\\\\\\'\\\\\\\"()?!\\\"\"", "-W", "weka.classifiers.bayes.NaiveBayesMultinomial"]) start_train = time.time() classifier.build_classifier(train_data) end_train = time.time() print "Train\t%s\t%s"%(binWidth, end_train-start_train) for index, inst in enumerate(test_data): if(index == 0): start_sample = time.time() classifier.classify_instance(inst) end_sample = time.time() print "Sample\t%s\t%s"%(binWidth, end_sample-start_sample) print "Evaluating w/ Multinomial Naive Bayes classifier. BinWidth = %s"%(binWidth) evaluation = Evaluation(test_data) start_batch = time.time() evaluation.test_model(classifier, test_data) end_batch = time.time() print "Batch\t%s\t%s"%(binWidth,end_batch-start_batch) print evaluation.summary() print evaluation.matrix() #Just as an example, we're measuring the fpr and fnr of the website indexed as class 1 tp = evaluation.num_true_positives(1) tn = evaluation.num_true_negatives(1) fp = evaluation.num_false_positives(1) fn = evaluation.num_false_negatives(1) acc = (tp+tn)/float(tp+tn+fp+fn) fpr = evaluation.false_positive_rate(1) fnr = evaluation.false_negative_rate(1) print "Accuracy: %s"%(acc) print "False Positive Rate: %s"%(fpr) print "False Negative Rate: %s"%(fnr) file.write("%s, %s, %s, %s\n"%(binWidth, acc, fpr, fnr)) file.close()