def test_base_dlt_init(estimator):
dlt = DLT(estimator=estimator)
is_fitted = dlt.is_fitted()
model_data_input = dlt.get_training_data_input()
model_param_names = dlt._model.get_model_param_names()
init_values = dlt._model.get_init_values()
# model is not yet fitted
assert not is_fitted
# should only be initialized and not set
assert not model_data_input
# model param names should already be set
assert model_param_names
# callable is not implemented yet
assert not init_values
def test_dlt_is_fitted(iclaims_training_data, estimator, keep_samples, point_method):
df = iclaims_training_data
df['claims'] = np.log(df['claims'])
regressor_col = ['trend.unemploy']
dlt = DLT(
response_col='claims',
date_col='week',
regressor_col=regressor_col,
seasonality=52,
seed=8888,
num_warmup=50,
num_sample=50,
verbose=False,
estimator=estimator
)
dlt.fit(df, keep_samples=keep_samples, point_method=point_method)
is_fitted = dlt.is_fitted()
# still True when keep_samples is False
assert is_fitted