def test_poly_features(
clf_dataset,
degree, # noqa: F811
interaction_only,
include_bias,
order):
X_np, X = clf_dataset
polyfeatures = cuPolynomialFeatures(degree=degree,
order=order,
interaction_only=interaction_only,
include_bias=include_bias)
t_X = polyfeatures.fit_transform(X)
assert type(X) == type(t_X)
if isinstance(t_X, np.ndarray):
if order == 'C':
assert t_X.flags['C_CONTIGUOUS']
elif order == 'F':
assert t_X.flags['F_CONTIGUOUS']
polyfeatures = skPolynomialFeatures(degree=degree,
order=order,
interaction_only=interaction_only,
include_bias=include_bias)
sk_t_X = polyfeatures.fit_transform(X_np)
assert_allclose(t_X, sk_t_X, rtol=0.1, atol=0.1)
def test_column_transformer_named_transformers_(clf_dataset): # noqa: F811
X_np, X = clf_dataset
cu_transformers = [("PolynomialFeatures", cuPolynomialFeatures(), [0, 2])]
transformer = cuColumnTransformer(cu_transformers)
transformer.fit_transform(X)
cu_named_transformers = transformer.named_transformers_
sk_transformers = [("PolynomialFeatures", skPolynomialFeatures(), [0, 2])]
transformer = skColumnTransformer(sk_transformers)
transformer.fit_transform(X_np)
sk_named_transformers = transformer.named_transformers_
assert cu_named_transformers.keys() == sk_named_transformers.keys()
def test_column_transformer_get_feature_names(clf_dataset): # noqa: F811
X_np, X = clf_dataset
cu_transformers = [("PolynomialFeatures", cuPolynomialFeatures(), [0, 2])]
transformer = cuColumnTransformer(cu_transformers)
transformer.fit_transform(X)
cu_feature_names = transformer.get_feature_names()
sk_transformers = [("PolynomialFeatures", skPolynomialFeatures(), [0, 2])]
transformer = skColumnTransformer(sk_transformers)
transformer.fit_transform(X_np)
sk_feature_names = transformer.get_feature_names()
assert cu_feature_names == sk_feature_names
def test_poly_features_sparse(sparse_clf_dataset, degree, # noqa: F811
interaction_only, include_bias):
X_np, X = sparse_clf_dataset
polyfeatures = cuPolynomialFeatures(degree=degree,
interaction_only=interaction_only,
include_bias=include_bias)
t_X = polyfeatures.fit_transform(X)
assert type(t_X) == type(X)
polyfeatures = skPolynomialFeatures(degree=degree,
interaction_only=interaction_only,
include_bias=include_bias)
sk_t_X = polyfeatures.fit_transform(X_np)
assert_allclose(t_X, sk_t_X, rtol=0.1, atol=0.1)
def test_poly_features_sparse(failure_logger, sparse_clf_dataset, # noqa: F811
degree, interaction_only, include_bias):
X_np, X = sparse_clf_dataset
polyfeatures = cuPolynomialFeatures(degree=degree,
interaction_only=interaction_only,
include_bias=include_bias)
t_X = polyfeatures.fit_transform(X)
# assert type(t_X) == type(X)
if cpx.scipy.sparse.issparse(X):
assert cpx.scipy.sparse.issparse(t_X)
if scipy.sparse.issparse(X):
assert scipy.sparse.issparse(t_X)
polyfeatures = skPolynomialFeatures(degree=degree,
interaction_only=interaction_only,
include_bias=include_bias)
sk_t_X = polyfeatures.fit_transform(X_np)
assert_allclose(t_X, sk_t_X, rtol=0.1, atol=0.1)
