def test_validation_unsupported():
"""
Makes sure we raise a proper message to the user,
when providing not supported data input
"""
validator = InputValidator()
with pytest.raises(ValueError,
match=r"Inconsistent number of train datapoints.*"):
validator.fit(
X_train=np.array([[0, 1, 0], [0, 1, 1]]),
y_train=np.array([0, 1, 0, 0, 0, 0]),
)
with pytest.raises(ValueError,
match=r"Inconsistent number of test datapoints.*"):
validator.fit(
X_train=np.array([[0, 1, 0], [0, 1, 1]]),
y_train=np.array([0, 1]),
X_test=np.array([[0, 1, 0], [0, 1, 1]]),
y_test=np.array([0, 1, 0, 0, 0, 0]),
)
with pytest.raises(ValueError,
match=r"Cannot call transform on a validator .*fitted"):
validator.transform(
X=np.array([[0, 1, 0], [0, 1, 1]]),
y=np.array([0, 1]),
)
def test_data_validation_for_regression(openmlid, as_frame):
x, y = sklearn.datasets.fetch_openml(data_id=openmlid,
return_X_y=True,
as_frame=as_frame)
validator = InputValidator(is_classification=False)
if as_frame:
# NaN is not supported in categories, so
# drop columns with them.
nan_cols = [i for i in x.columns if x[i].isnull().any()]
cat_cols = [
i for i in x.columns if x[i].dtype.name in ['category', 'bool']
]
unsupported_columns = list(set(nan_cols) & set(cat_cols))
if len(unsupported_columns) > 0:
x.drop(unsupported_columns, axis=1, inplace=True)
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
x, y, test_size=0.33, random_state=0)
validator.fit(X_train=X_train, y_train=y_train)
X_train_t, y_train_t = validator.transform(X_train, y_train)
assert np.shape(X_train) == np.shape(X_train_t)
# Leave columns that are complete NaN
# The sklearn pipeline will handle that
if as_frame and np.any(pd.isnull(X_train).values.all(axis=0)):
assert np.any(pd.isnull(X_train_t).values.all(axis=0))
elif not as_frame and np.any(pd.isnull(X_train).all(axis=0)):
assert np.any(pd.isnull(X_train_t).all(axis=0))
validator.feature_validator.feat_type is not None
def test_sparse_data_validation_for_regression():
X, y = sklearn.datasets.make_regression(n_samples=100,
n_features=50,
random_state=0)
X_sp = sparse.coo_matrix(X)
validator = InputValidator(is_classification=False)
validator.fit(X_train=X_sp, y_train=y)
X_t, y_t = validator.transform(X, y)
assert np.shape(X) == np.shape(X_t)
# make sure everything was encoded to number
assert np.issubdtype(X_t.dtype, np.number)
assert np.issubdtype(y_t.dtype, np.number)
# Make sure we can change the sparse format
X_t, y_t = validator.transform(sparse.csr_matrix(X), y)
def test_data_validation_for_classification(openmlid, as_frame):
x, y = sklearn.datasets.fetch_openml(data_id=openmlid,
return_X_y=True,
as_frame=as_frame)
validator = InputValidator(is_classification=True)
if as_frame:
# NaN is not supported in categories, so
# drop columns with them.
nan_cols = [i for i in x.columns if x[i].isnull().any()]
cat_cols = [
i for i in x.columns if x[i].dtype.name in ['category', 'bool']
]
unsupported_columns = list(set(nan_cols) & set(cat_cols))
if len(unsupported_columns) > 0:
x.drop(unsupported_columns, axis=1, inplace=True)
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
x, y, test_size=0.33, random_state=0)
validator.fit(X_train=X_train,
y_train=y_train,
X_test=X_test,
y_test=y_test)
X_train_t, y_train_t = validator.transform(X_train, y_train)
assert np.shape(X_train) == np.shape(X_train_t)
# Leave columns that are complete NaN
# The sklearn pipeline will handle that
if as_frame and np.any(pd.isnull(X_train).values.all(axis=0)):
assert np.any(pd.isnull(X_train_t).values.all(axis=0))
elif not as_frame and np.any(pd.isnull(X_train).all(axis=0)):
assert np.any(pd.isnull(X_train_t).all(axis=0))
# make sure everything was encoded to number
assert np.issubdtype(X_train_t.dtype, np.number)
assert np.issubdtype(y_train_t.dtype, np.number)
# Categorical columns are sorted to the beginning
if as_frame:
validator.feature_validator.feat_type is not None
ordered_unique_elements = list(
dict.fromkeys(validator.feature_validator.feat_type))
if len(ordered_unique_elements) > 1:
assert ordered_unique_elements[0] == 'categorical'