def test_inverse_transform():
X = np.array([1, 4, 9, 16]).reshape((2, 2))
# Test that inverse_transform works correctly
F = FunctionTransformer(
func=np.sqrt,
inverse_func=np.around,
inv_kw_args=dict(decimals=3),
)
assert_array_equal(
F.inverse_transform(F.transform(X)),
np.around(np.sqrt(X), decimals=3),
)
def test_check_inverse():
X_dense = np.array([1, 4, 9, 16], dtype=np.float64).reshape((2, 2))
X_list = [X_dense, sparse.csr_matrix(X_dense), sparse.csc_matrix(X_dense)]
for X in X_list:
if sparse.issparse(X):
accept_sparse = True
else:
accept_sparse = False
trans = FunctionTransformer(func=np.sqrt,
inverse_func=np.around,
accept_sparse=accept_sparse,
check_inverse=True,
validate=True)
assert_warns_message(
UserWarning, "The provided functions are not strictly"
" inverse of each other. If you are sure you"
" want to proceed regardless, set"
" 'check_inverse=False'.", trans.fit, X)
trans = FunctionTransformer(func=np.expm1,
inverse_func=np.log1p,
accept_sparse=accept_sparse,
check_inverse=True,
validate=True)
Xt = assert_no_warnings(trans.fit_transform, X)
assert_allclose_dense_sparse(X, trans.inverse_transform(Xt))
# check that we don't check inverse when one of the func or inverse is not
# provided.
trans = FunctionTransformer(func=np.expm1,
inverse_func=None,
check_inverse=True,
validate=True)
assert_no_warnings(trans.fit, X_dense)
trans = FunctionTransformer(func=None,
inverse_func=np.expm1,
check_inverse=True,
validate=True)
assert_no_warnings(trans.fit, X_dense)