def test_log_reg_accuracy(attribute_id, C): data_set, labels = cd.create_data(cd.test_classes, attribute_id) data = cd.flatten_data_set(data_set) filename = INPUT_LOG_REG_PATTERN % (cd.attributenames[attribute_id], str(C)) return test_classifier(data, labels, attribute_id, filename)
def train_SVM(attribute_id): data, labels = cd.create_data(cd.train_classes, attribute_id) train_data = cd.flatten_data_set(data) svm = SVC(C=10., kernel='rbf', probability=True) svm.fit(train_data, labels) filename = OUTPUT_SVM_PATTERN % cd.attributenames[attribute_id] bz_pickle(svm, filename)
def train_logistic_regression(attribute_id, C): data, labels = cd.create_data(cd.train_classes, attribute_id) train_data = cd.flatten_data_set(data) logreg = LogisticRegression('l2', C=C, solver='saga') logreg.fit(train_data, labels) filename = OUTPUT_LOG_REG_PATTERN % (cd.attributenames[attribute_id], str(C)) bz_pickle(logreg, filename)
def train_logistic_regression_CV(attribute_id): data, labels = cd.create_data(cd.train_classes, attribute_id) train_data = cd.flatten_data_set(data) logreg = LogisticRegressionCV(Cs=[0.01, 0.1, 1., 10., 100.], cv=5, dual=False, penalty='l2', solver='saga', refit=True) logreg.fit(train_data, labels) filename = OUTPUT_LOG_REG_CV_PATTERN % cd.attributenames[attribute_id] bz_pickle(logreg, filename)