def check_get_params(Estimator):
# Check get params works correctly
estimator = _construct_instance(Estimator)
params = estimator.get_params()
assert isinstance(params, dict)
_check_get_params_invariance(estimator.__class__.__name__, estimator)
def test_predict_time_index(Forecaster, fh, y_train):
f = _construct_instance(Forecaster)
f.fit(y_train, fh)
y_pred = f.predict()
assert_correct_pred_time_index(y_pred, y_train, fh)
def test_update_predict_single(Forecaster, fh):
f = _construct_instance(Forecaster)
f.fit(y_train, fh)
y_pred = f.update_predict_single(y_test)
assert_correct_pred_time_index(y_pred, y_test, fh)
def test_no_fh_in_fit_req(Forecaster):
f = _construct_instance(Forecaster)
# fh required in fit, raises error if not passed
with pytest.raises(ValueError):
f.fit(y_train)
def test_same_fh_in_fit_and_predict_req(Forecaster):
f = _construct_instance(Forecaster)
f.fit(y_train, FH0)
np.testing.assert_array_equal(f.fh, FH0)
f.predict(FH0)
np.testing.assert_array_equal(f.fh, FH0)
def test_update_predict_single(Forecaster, fh):
# Check correct time index of update-predict
f = _construct_instance(Forecaster)
f.fit(y_train, fh=fh)
y_pred = f.update_predict_single(y_test)
assert_correct_pred_time_index(y_pred.index, y_test.index[-1], fh)
def test_same_fh_in_fit_and_predict_opt(Forecaster):
f = _construct_instance(Forecaster)
# passing the same fh to both fit and predict works
f.fit(y_train, FH0)
f.predict(FH0)
np.testing.assert_array_equal(f.fh, FH0)
def check_constructor(Estimator):
# Check that the constructor behaves correctly
estimator = _construct_instance(Estimator)
# Check that init does not construct object of other class than itself
assert isinstance(estimator, Estimator)
# Ensure that each parameter is set in init
init_params = _get_args(type(estimator).__init__)
invalid_attr = set(init_params) - set(vars(estimator)) - {"self"}
assert not invalid_attr, (
"Estimator %s should store all parameters"
" as an attribute during init. Did not find "
"attributes `%s`." %
(estimator.__class__.__name__, sorted(invalid_attr)))
# Ensure that init does nothing but set parameters
# No logic/interaction with other parameters
def param_filter(p):
"""Identify hyper parameters of an estimator"""
return (p.name != 'self' and p.kind != p.VAR_KEYWORD
and p.kind != p.VAR_POSITIONAL)
init_params = [
p for p in signature(estimator.__init__).parameters.values()
if param_filter(p)
]
params = estimator.get_params()
# Filter out required parameters with no default value and parameters
# set for running tests
required_params = getattr(estimator, '_required_parameters', [])
test_config_params = ESTIMATOR_TEST_PARAMS.get(Estimator, {}).keys()
init_params = [
param for param in init_params if param.name not in required_params
and param.name not in test_config_params
]
for param in init_params:
assert param.default != param.empty, (
"parameter `%s` for %s has no default value and is not "
"included in `_required_parameters`" %
(param.name, estimator.__class__.__name__))
if type(param.default) is type:
assert param.default in [np.float64, np.int64]
else:
assert (type(param.default) in [
str, int, float, bool, tuple,
type(None), np.float64, types.FunctionType, joblib.Memory
])
param_value = params[param.name]
if isinstance(param_value, np.ndarray):
np.testing.assert_array_equal(param_value, param.default)
else:
if bool(
isinstance(param_value, numbers.Real)
and np.isnan(param_value)):
# Allows to set default parameters to np.nan
assert param_value is param.default, param.name
else:
assert param_value == param.default, param.name
def test_no_fh_opt(Forecaster):
f = _construct_instance(Forecaster)
f.fit(y_train)
# not passing fh to either fit or predict raises error
with pytest.raises(ValueError):
f.predict()
def _construct_fit(Estimator, **kwargs):
estimator = _construct_instance(Estimator)
args = _make_args(estimator, "fit", **kwargs)[:2]
return estimator.fit(*args)
def check_repr(Estimator):
# Check we can call repr
estimator = _construct_instance(Estimator)
repr(estimator)
def check_transform_inverse_transform_equivalent(Estimator):
estimator = _construct_instance(Estimator)
X = _make_args(estimator, "fit")[0]
Xt = estimator.fit_transform(X)
Xit = estimator.inverse_transform(Xt)
_assert_array_almost_equal(X, Xit)
def test_y_invalid_type_raises_error(Forecaster, y):
with pytest.raises(TypeError, match=r"type"):
f = _construct_instance(Forecaster)
f.fit(y, fh=FH0)
def test_y_multivariate_raises_error(Forecaster):
# Check that multivariate y raises an appropriate error message.
y = _make_series(n_columns=2)
with pytest.raises(ValueError, match=r"univariate"):
f = _construct_instance(Forecaster)
f.fit(y, fh=FH0)
def check_set_params(Estimator):
# Check set_params works correctly
estimator = _construct_instance(Estimator)
params = estimator.get_params()
assert estimator.set_params(**params) is estimator
_check_set_params(estimator.__class__.__name__, estimator)
def test_fh_in_predict_opt(Forecaster):
f = _construct_instance(Forecaster)
f.fit(y_train)
f.predict(FH0)
np.testing.assert_array_equal(f.fh, FH0)
def check_clone(Estimator):
# Check we can call clone from scikit-learn
estimator = _construct_instance(Estimator)
clone(estimator)
def test_transformed_data_has_same_index_as_input_data(Transformer):
transformer = _construct_instance(Transformer)
X, y = _make_args(transformer, "fit")
Xt = transformer.fit_transform(X, y)
np.testing.assert_array_equal(X.index, Xt.index)
