def test_get_gridsearch(gridsearch_params, expected_estimator, expected_error):
if expected_error is not None:
with pytest.raises(expected_error):
get_gridsearch(**gridsearch_params)
else:
res = get_gridsearch(**gridsearch_params)
print(res)
assert isinstance(res, GridSearchCV)
assert isinstance(res.cv, FinerTimeSplit)
assert res.error_score is np.nan
assert res.refit is False
assert isinstance(res.estimator["exog_passthrough"],
expected_estimator["exog_passthrough"])
assert isinstance(res.estimator["holiday"],
expected_estimator["holiday"])
if expected_estimator["holiday_step"]:
assert all([
isinstance(holiday_step[1], expected_estimator["holiday_step"])
for holiday_step in res.estimator["holiday"].steps
])
assert all([(holiday_step[1].country_code is code) &
(holiday_step[1].country_code_column is col)
for holiday_step, code, col in zip(
res.estimator["holiday"].steps,
expected_estimator["holiday_steps_codes"],
expected_estimator["holiday_steps_columns"],
)])
assert isinstance(res.estimator["model"], expected_estimator["model"])
def test_add_model_to_gridsearch():
gs = get_gridsearch(frequency="D", sklearn_models=False)
model = ProphetWrapper()
gs = add_model_to_gridsearch(model, gs)
assert len(gs.param_grid) == 1
assert str(gs.param_grid[0]["model"][0].get_params()) == str(model.get_params())
gs = get_gridsearch(frequency="D", sklearn_models=False)
model = [ProphetWrapper(), ProphetWrapper(clip_predictions_lower=0.0)]
gs = add_model_to_gridsearch(model, gs)
assert len(gs.param_grid) == 2
assert str(gs.param_grid[0]["model"][0].get_params()) == str(model[0].get_params())
assert str(gs.param_grid[1]["model"][0].get_params()) == str(model[1].get_params())
def test_get_gridsearch(gridsearch_params, expected_estimator, expected_error):
if expected_error is not None:
with pytest.raises(expected_error):
get_gridsearch(**gridsearch_params)
else:
res = get_gridsearch(**gridsearch_params)
print(res)
assert isinstance(res, GridSearchCV)
assert isinstance(res.cv, FinerTimeSplit)
assert res.error_score is np.nan
assert res.refit is False
assert isinstance(res.estimator["exog_passthrough"],
expected_estimator["exog_passthrough"])
assert isinstance(res.estimator["holiday"],
expected_estimator["holiday"])
assert isinstance(res.estimator["model"], expected_estimator["model"])