def test_setup(params):
"""Tests __init__ and attributes set during fit"""
coverage = 0.95
silverkite = SilverkiteForecast()
model = SilverkiteEstimator(
silverkite=silverkite,
score_func=mean_squared_error,
coverage=coverage,
null_model_params=None,
**params)
assert model.silverkite == silverkite
assert model.score_func == mean_squared_error
assert model.coverage == coverage
assert model.null_model_params is None
# set_params must be able to replicate the init
model2 = SilverkiteEstimator()
model2.set_params(**dict(
silverkite=silverkite,
score_func=mean_squared_error,
coverage=coverage,
null_model_params=None,
**params))
assert model2.__dict__ == model.__dict__
initalized_params = model.__dict__
initalized_params_subset = {
k: v for k, v in initalized_params.items()
if k in params.keys()}
assert_equal(initalized_params_subset, params)
assert model.model_dict is None
assert model.pred_cols is None
assert model.feature_cols is None
assert model.coef_ is None
train_df = daily_data_reg().get("train_df").copy()
model.fit(train_df)
assert model.fit_algorithm_dict == {
"fit_algorithm": "sgd",
"fit_algorithm_params": {"alpha": 0.1},
}
assert model.model_dict is not None
assert type(model.model_dict["ml_model"]) == SGDRegressor
assert model.model_dict["ml_model"].alpha == (
params["fit_algorithm_dict"]["fit_algorithm_params"]["alpha"])
assert model.model_dict["training_evaluation"] is not None
assert model.model_dict["test_evaluation"] is None
assert model.pred_cols is not None
assert model.feature_cols is not None
assert_frame_equal(model.df, train_df)
assert model.coef_ is not None
def test_setup2(params2):
"""Tests __init__ and attributes set during fit"""
coverage = 0.95
silverkite = SilverkiteForecast()
model = SilverkiteEstimator(
silverkite=silverkite,
score_func=mean_squared_error,
coverage=coverage,
null_model_params=None,
**params2)
assert model.silverkite == silverkite
assert model.score_func == mean_squared_error
assert model.coverage == coverage
assert model.null_model_params is None
# set_params must be able to replicate the init
model2 = SilverkiteEstimator()
model2.set_params(**dict(
silverkite=silverkite,
score_func=mean_squared_error,
coverage=coverage,
null_model_params=None,
**params2))
assert model2.__dict__ == model.__dict__
initalized_params = model.__dict__
initalized_params_subset = {
k: v for k, v in initalized_params.items()
if k in params2.keys()}
assert_equal(initalized_params_subset, params2)
assert model.model_dict is None
assert model.pred_cols is None
assert model.feature_cols is None
assert model.coef_ is None
train_df = daily_data_reg().get("train_df").copy()
model.fit(train_df)
assert model.model_dict is not None
assert model.model_dict["training_evaluation"] is not None
assert model.model_dict["test_evaluation"] is None
assert model.pred_cols is not None
assert model.feature_cols is not None
assert_frame_equal(model.df, train_df)
assert model.coef_ is not None