def get_test_results_newer(clf, test_data, target_column, train_time): analyzer = Analyzer(list(test_data[target_column].unique())) test_input = test_data.drop(target_column, axis=1) test_target = test_data[target_column] prediction_time = 0 for features, target in zip(test_input.iterrows(), test_target): start = time.process_time() prediction = clf.predict(features[1]) end = time.process_time() prediction_time += (end - start) analyzer.addValueInConfusionMatrix(prediction,target) return { 'acc': analyzer.calcAccuracy(), 'fMeasure_micro': analyzer.calcFBethaMeasure(1,"micro"), 'fMeasure_macro': analyzer.calcFBethaMeasure(1,"macro"), 'train_time': train_time, 'pred_time': prediction_time/len(test_data) }