def test_preprocessnumeric_normalize(self): unnormal_data = [[5.0, 3, 1], [2.0, 2, 1], [10.0, 1, 1], [10.0, 1, 1]] columns = ["col1", "col2", "col3"] data = pd.DataFrame(unnormal_data, columns=columns) preprocess = Data(x_train=data, test_split_percentage=0.5, report_name="test") preprocess.normalize_numeric() validate = preprocess.x_train.values.tolist() self.assertTrue(True)
def test_report_preprocessing_standardize(self): unnormal_data = [[5.0, 3, 1], [2.0, 2, 1], [10.0, 1, 1]] columns = ["col1", "col2", "col3"] data = pd.DataFrame(unnormal_data, columns=columns) preprocess = Data(x_train=data, test_split_percentage=0.5, split=False, report_name="test") preprocess.normalize_numeric() with open(preprocess.report.filename) as f: content = f.read() validate = "col1" in content and "col2" in content and "col3" in content os.remove(preprocess.report.filename) self.assertTrue(validate)
def test_preprocess_traindata(self): unnormal_x_train = [[5.0, 3, 1], [2.0, 2, 1], [10.0, 1, 1]] unnormal_x_test = [[5.0, 3, 1], [2.0, 2, 1], [10.0, 1, 1]] columns = ["col1", "col2", "col3"] x_train = pd.DataFrame(unnormal_x_train, columns=columns) x_test = pd.DataFrame(unnormal_x_test, columns=columns) preprocess = Data( x_train=x_train, x_test=x_test, test_split_percentage=0.5, report_name="test", ) preprocess.normalize_numeric("col1", "col2", "col3") validate_train = preprocess.x_train.values.tolist() validate_test = preprocess.x_test.values.tolist() self.assertListEqual(validate_train, validate_test)