def check_methods_do_not_change_state(Estimator):
# Check that methods that are not supposed to change attributes of the
# estimators do not change anything (including hyper-parameters and
# fitted parameters)
estimator = _construct_instance(Estimator)
set_random_state(estimator)
fit_args = _make_args(estimator, "fit")
estimator.fit(*fit_args)
dict_before = estimator.__dict__.copy()
for method in NON_STATE_CHANGING_METHODS:
if hasattr(estimator, method):
args = _make_args(estimator, method)
getattr(estimator, method)(*args)
if method == "transform" and _has_tag(Estimator,
"fit-in-transform"):
# Some transformers fit during transform, as they apply
# some transformation to each series passed to transform,
# so transform will actually change the state of these estimator.
continue
assert (
estimator.__dict__ == dict_before
), f"Estimator: {estimator} changes __dict__ during {method}"
def check_series_to_primitive_transform_multivariate(Estimator):
n_columns = 3
if _has_tag(Estimator, "univariate-only"):
_check_raises_error(Estimator, n_columns=n_columns)
else:
out = _construct_fit_transform(Estimator, n_columns=n_columns)
assert isinstance(out, (pd.Series, np.ndarray))
assert out.shape == (n_columns,)
def check_panel_to_tabular_transform_multivariate(Estimator):
n_instances = 5
if _has_tag(Estimator, "univariate-only"):
_check_raises_error(Estimator, n_instances=n_instances, n_columns=3)
else:
out = _construct_fit_transform(Estimator, n_instances=n_instances, n_columns=3)
assert isinstance(out, (pd.DataFrame, np.ndarray))
assert out.shape[0] == n_instances
def _check_raises_error(Estimator, **kwargs):
with pytest.raises(ValueError, match=r"univariate"):
if _has_tag(Estimator, "fit-in-transform"):
# As some estimators have an empty fit method, we here check if they
# raise the appropriate error in transform rather than fit.
_construct_fit_transform(Estimator, **kwargs)
else:
# All other estimators should raise the error in fit.
_construct_fit(Estimator, **kwargs)
def check_series_to_series_transform_multivariate(Estimator):
n_columns = 3
n_timepoints = 5
if _has_tag(Estimator, "univariate-only"):
_check_raises_error(Estimator, n_timepoints=n_timepoints, n_columns=n_columns)
else:
out = _construct_fit_transform(
Estimator, n_timepoints=n_timepoints, n_columns=n_columns
)
assert isinstance(out, (pd.DataFrame, np.ndarray))
assert out.shape == (n_timepoints, n_columns)
def _yield_transformer_checks(Estimator):
yield from all_transformer_checks
if hasattr(Estimator, "inverse_transform"):
yield check_transform_inverse_transform_equivalent
if issubclass(Estimator, _SeriesToPrimitivesTransformer):
yield from series_to_primitive_checks
if issubclass(Estimator, _SeriesToSeriesTransformer):
yield from series_to_series_checks
if issubclass(Estimator, _PanelToTabularTransformer):
yield from panel_to_tabular_checks
if issubclass(Estimator, _PanelToPanelTransformer):
yield from panel_to_panel_checks
if _has_tag(Estimator, "transform-returns-same-time-index"):
yield check_transform_returns_same_time_index