def filter_data(self, data): print("Filtering Data..\n") flter = Filter( classname="weka.filters.supervised.attribute.AttributeSelection") aseval = ASEvaluation( classname="weka.attributeSelection.CfsSubsetEval", options=["-P", "1", "-E", "1"]) assearch = ASSearch(classname="weka.attributeSelection.BestFirst", options=["-D", "1", "-N", "5"]) flter.set_property("evaluator", aseval.jobject) flter.set_property("search", assearch.jobject) flter.inputformat(data) filtered = flter.filter(data) return filtered
def use_filter(data): """ Uses the AttributeSelection filter for attribute selection. :param data: the dataset to use :type data: Instances """ print("\n2. Filter") flter = Filter(classname="weka.filters.supervised.attribute.AttributeSelection") aseval = ASEvaluation(classname="weka.attributeSelection.CfsSubsetEval") assearch = ASSearch(classname="weka.attributeSelection.GreedyStepwise", options=["-B"]) flter.set_property("evaluator", aseval.jobject) flter.set_property("search", assearch.jobject) flter.inputformat(data) filtered = flter.filter(data) print(str(filtered))
def classification(data, train, test, num_clases): baseClassifiers_list = [ "weka.classifiers.bayes.NaiveBayes", "weka.classifiers.functions.MultilayerPerceptron", "weka.classifiers.functions.SMO", "weka.classifiers.lazy.IBk", "weka.classifiers.lazy.KStar", "weka.classifiers.meta.AdaBoostM1", "weka.classifiers.meta.Bagging", "weka.classifiers.meta.LogitBoost", "weka.classifiers.trees.J48", "weka.classifiers.trees.DecisionStump", "weka.classifiers.trees.LMT", "weka.classifiers.trees.RandomForest", "weka.classifiers.trees.REPTree", "weka.classifiers.rules.PART", "weka.classifiers.rules.JRip", "weka.classifiers.functions.Logistic", "weka.classifiers.meta.ClassificationViaRegression", "weka.classifiers.bayes.BayesNet" ] results_train = pd.DataFrame() results_test = pd.DataFrame() cost_matrix_list = [ "[]", "[0]", "[0.0 1.0; 1.0 0.0]", "[0.0 1.0 2.0; 1.0 0.0 1.0; 2.0 1.0 0.0]", "[0.0 1.0 2.0 3.0; 1.0 0.0 1.0 2.0; 2.0 1.0 0.0 1.0; 3.0 2.0 1.0 0.0]", "[0.0 1.0 2.0 3.0 4.0; 1.0 0.0 1.0 2.0 3.0; 2.0 1.0 0.0 1.0 2.0; 3.0 2.0 1.0 0.0 1.0; 4.0 3.0 2.0 1.0 0.0]", "[0.0 1.0 2.0 3.0 4.0 5.0; 1.0 0.0 1.0 2.0 3.0 4.0; 2.0 1.0 0.0 1.0 2.0 3.0; 3.0 2.0 1.0 0.0 1.0 2.0; 4.0 3.0 2.0 1.0 0.0 1.0; 5.0 4.0 3.0 2.0 1.0 0.0]", "[0.0 1.0 2.0 3.0 4.0 5.0 6.0; 1.0 0.0 1.0 2.0 3.0 4.0 5.0; 2.0 1.0 0.0 1.0 2.0 3.0 4.0; 3.0 2.0 1.0 0.0 1.0 2.0 3.0; 4.0 3.0 2.0 1.0 0.0 1.0 2.0; 5.0 4.0 3.0 2.0 1.0 0.0 1.0; 6.0 5.0 4.0 3.0 2.0 1.0 0.0]", "[0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0; 1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0; 2.0 1.0 0.0 1.0 2.0 3.0 4.0 5.0; 3.0 2.0 1.0 0.0 1.0 2.0 3.0 4.0; 4.0 3.0 2.0 1.0 0.0 1.0 2.0 3.0; 5.0 4.0 3.0 2.0 1.0 0.0 1.0 2.0; 6.0 5.0 4.0 3.0 2.0 1.0 0.0 1.0; 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0]", "[0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0; 1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0; 2.0 1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0; 3.0 2.0 1.0 0.0 1.0 2.0 3.0 4.0 5.0; 4.0 3.0 2.0 1.0 0.0 1.0 2.0 3.0 4.0; 5.0 4.0 3.0 2.0 1.0 0.0 1.0 2.0 3.0; 6.0 5.0 4.0 3.0 2.0 1.0 0.0 1.0 2.0; 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 1.0; 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0]", "[0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0; 1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0; 2.0 1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0; 3.0 2.0 1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0; 4.0 3.0 2.0 1.0 0.0 1.0 2.0 3.0 4.0 5.0; 5.0 4.0 3.0 2.0 1.0 0.0 1.0 2.0 3.0 4.0; 6.0 5.0 4.0 3.0 2.0 1.0 0.0 1.0 2.0 3.0; 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 1.0 2.0; 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 1.0; 9.0 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0]" ] real_train = [] # the real label of the dataset for i in range(train.num_instances): real_train.append( train.get_instance(i).values[(train.num_attributes - 1)]) results_train['real'] = real_train real_test = [] # the real label of the dataset for i in range(test.num_instances): real_test.append( test.get_instance(i).values[(test.num_attributes - 1)]) results_test['real'] = real_test num = 0 for clas in baseClassifiers_list: column = "p" + np.str(num) #classifier classifier = SingleClassifierEnhancer( classname="weka.classifiers.meta.CostSensitiveClassifier", options=[ "-cost-matrix", cost_matrix_list[num_clases], "-M", "-S", "1" ]) base = Classifier(classname=clas) classifier.classifier = base predicted_data_train = None predicted_data_test = None evaluation = Evaluation(data) classifier.build_classifier(train) #evaluation.test_model(classifier, train) # add predictions addcls = Filter( classname="weka.filters.supervised.attribute.AddClassification", options=["-classification"]) addcls.set_property("classifier", Classifier.make_copy(classifier)) addcls.inputformat(train) #addcls.filter(train) # trains the classifier pred_train = addcls.filter(train) pred_test = addcls.filter(test) if predicted_data_train is None: predicted_data_train = Instances.template_instances(pred_train, 0) for n in range(pred_train.num_instances): predicted_data_train.add_instance(pred_train.get_instance(n)) if predicted_data_test is None: predicted_data_test = Instances.template_instances(pred_test, 0) for n in range(pred_test.num_instances): predicted_data_test.add_instance(pred_test.get_instance(n)) preds_train = [ ] #labels predicted for the classifer trained in the iteration preds_test = [] for i in range(predicted_data_train.num_instances): preds_train.append( predicted_data_train.get_instance(i).values[( predicted_data_train.num_attributes - 1)]) for i in range(predicted_data_test.num_instances): preds_test.append( predicted_data_test.get_instance(i).values[( predicted_data_test.num_attributes - 1)]) results_train[column] = preds_train results_test[column] = preds_test num = num + 1 return results_train, results_test
def main(): """ Just runs some example code. """ # load a dataset data_file = helper.get_data_dir() + os.sep + "vote.arff" helper.print_info("Loading dataset: " + data_file) loader = Loader("weka.core.converters.ArffLoader") data = loader.load_file(data_file) data.class_is_last() # classifier classifier = Classifier(classname="weka.classifiers.trees.J48") # randomize data folds = 10 seed = 1 rnd = Random(seed) rand_data = Instances.copy_instances(data) rand_data.randomize(rnd) if rand_data.class_attribute.is_nominal: rand_data.stratify(folds) # perform cross-validation and add predictions predicted_data = None evaluation = Evaluation(rand_data) for i in xrange(folds): train = rand_data.train_cv(folds, i) # the above code is used by the StratifiedRemoveFolds filter, # the following code is used by the Explorer/Experimenter # train = rand_data.train_cv(folds, i, rnd) test = rand_data.test_cv(folds, i) # build and evaluate classifier cls = Classifier.make_copy(classifier) cls.build_classifier(train) evaluation.test_model(cls, test) # add predictions addcls = Filter( classname="weka.filters.supervised.attribute.AddClassification", options=["-classification", "-distribution", "-error"]) # setting the java object directory avoids issues with correct quoting in option array addcls.set_property("classifier", Classifier.make_copy(classifier)) addcls.inputformat(train) addcls.filter(train) # trains the classifier pred = addcls.filter(test) if predicted_data is None: predicted_data = Instances.template_instances(pred, 0) for n in xrange(pred.num_instances): predicted_data.add_instance(pred.get_instance(n)) print("") print("=== Setup ===") print("Classifier: " + classifier.to_commandline()) print("Dataset: " + data.relationname) print("Folds: " + str(folds)) print("Seed: " + str(seed)) print("") print(evaluation.summary("=== " + str(folds) + " -fold Cross-Validation ===")) print("") print(predicted_data)
# the above code is used by the StratifiedRemoveFolds filter, # the following code is used by the Explorer/Experimenter # train = rand_data.train_cv(folds, i, rnd) test = rand_data.test_cv(folds, i) # build and evaluate classifier cls = Classifier.make_copy(classifier) cls.build_classifier(train) evaluation.test_model(cls, test) # add predictions addcls = Filter( classname="weka.filters.supervised.attribute.AddClassification", options=["-classification", "-distribution", "-error"]) # setting the java object directory avoids issues with correct quoting in option array addcls.set_property("classifier", Classifier.make_copy(classifier)) addcls.inputformat(train) addcls.filter(train) # trains the classifier pred = addcls.filter(test) if predicted_data is None: predicted_data = Instances.template_instances(pred, 0) for n in xrange(pred.num_instances): predicted_data.add_instance(pred.get_instance(n)) print("") print("=== Setup ===") print("Classifier: " + classifier.to_commandline()) print("Dataset: " + data.relationname) print("Folds: " + str(folds)) print("Seed: " + str(seed)) print("")