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)
}
