def test_create_ohe():
# check one hot encode
X, y = get_wine()
X['A_FAKE_CAT'] = np.random.randint(4, size=len(y))
X['B_FAKE_CAT'] = np.random.randint(4, size=len(y))
X['C_FAKE_CAT'] = np.random.choice(['SWEET', 'SOUR', 'TART'], len(y))
X['D_FAKE_LABEL_CAT'] = np.random.choice(['BAD', 'OK', 'GOOD', 'GREAT'],
len(y))
X_train, X_test = preproc.preproc(
X,
OHE=np.array(['A_FAKE_CAT', 'B_FAKE_CAT', 'C_FAKE_CAT']),
label_encode={'D_FAKE_LABEL_CAT': ['BAD', 'OK', 'GOOD', 'GREAT']},
standard_scale=[
'alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium',
'total_phenols', 'flavanoids', 'nonflavanoid_phenols',
'proanthocyanins', 'color_intensity', 'hue',
'od280/od315_of_diluted_wines', 'proline'
])
result_df = X_train
assert (np.array_equal(result_df.columns[0:11], [
'A_FAKE_CAT_0', 'A_FAKE_CAT_1', 'A_FAKE_CAT_2', 'A_FAKE_CAT_3',
'B_FAKE_CAT_0', 'B_FAKE_CAT_1', 'B_FAKE_CAT_2', 'B_FAKE_CAT_3',
'C_FAKE_CAT_SOUR', 'C_FAKE_CAT_SWEET', 'C_FAKE_CAT_TART'
]))
tc.assertFalse('A_FAKE_CAT' in result_df.columns)
tc.assertIn(result_df['A_FAKE_CAT_0'][0], [0.0, 1.0])
def raise_excep():
# raise excep if bad data is passed
X, y = get_wine()
X['A_FAKE_CAT'] = np.random.choice(['SWEET', 'SOUR', 'TART'], len(y))
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2)
X_train_new, X_test_new = preproc.preproc(X_train, X_test, auto=[])
def test_PreprocAuto():
# test auto prerpocessing
X, y = get_wine()
X['A_FAKE_CAT'] = np.random.choice(['SWEET', 'SOUR', 'TART'], len(y))
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
X_train_new, X_test_new = preproc.preproc(X_train, X_test, auto=True)
# one hot encoding should add one column
assert (len(X_train_new.columns) == len(X_train.columns) + 1)
def test_does_not_alter_original_df():
# check to see that df was not altered.
X, y = get_wine()
X['A_FAKE_CAT'] = np.random.randint(4, size=len(y))
X['B_FAKE_CAT'] = np.random.randint(4, size=len(y))
X['C_FAKE_CAT'] = np.random.choice(['SWEET', 'SOUR', 'TART'], len(y))
X['D_FAKE_LABEL_CAT'] = np.random.choice(['BAD', 'OK', 'GOOD', 'GREAT'],
len(y))
X_copy = X.copy()
preproc.preproc(
X,
auto=False,
OHE=np.array(['A_FAKE_CAT', 'B_FAKE_CAT', 'C_FAKE_CAT']),
label_encode={'D_FAKE_LABEL_CAT': ['BAD', 'OK', 'GOOD', 'GREAT']},
standard_scale=[
'alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium',
'total_phenols', 'flavanoids', 'nonflavanoid_phenols',
'proanthocyanins', 'color_intensity', 'hue',
'od280/od315_of_diluted_wines', 'proline'
])
assert_frame_equal(X, X_copy)
def test_standard_scaled():
# test standard scaling
X, y = get_wine()
X['A_FAKE_CAT'] = np.random.randint(4, size=len(y))
X['B_FAKE_CAT'] = np.random.randint(4, size=len(y))
X['C_FAKE_CAT'] = np.random.choice(['SWEET', 'SOUR', 'TART'], len(y))
X['D_FAKE_LABEL_CAT'] = np.random.choice(['BAD', 'OK', 'GOOD', 'GREAT'],
len(y))
result = preproc.preproc(X,
standard_scale=[
'alcohol', 'malic_acid', 'ash',
'alcalinity_of_ash', 'magnesium',
'total_phenols', 'flavanoids',
'nonflavanoid_phenols', 'proanthocyanins',
'color_intensity', 'hue',
'od280/od315_of_diluted_wines', 'proline'
])
result_df = result[0]
tc.assertAlmostEqual(result_df['alcohol'].mean(),
result_df['malic_acid'].mean())