def test_fit_cache_stores_all_training_params(
self, time_series, time_series_forecasting_model1_cache):
time_series_forecasting_model1_cache.fit(time_series)
assert hasattr(time_series_forecasting_model1_cache, "model_")
assert hasattr(time_series_forecasting_model1_cache, "X_test_")
assert hasattr(time_series_forecasting_model1_cache, "X_train_")
assert hasattr(time_series_forecasting_model1_cache, "y_train_")
def test_error_fit_twice_set_features_only_models(
self, time_series, time_series_forecasting_model1_cache,
features2):
time_series_forecasting_model1_cache.fit(time_series)
time_series_forecasting_model1_cache.set_params(features=features2)
with pytest.raises(sklearn.exceptions.NotFittedError):
time_series_forecasting_model1_cache.fit(time_series,
only_model=True)
def test_score_y_test(self, time_series,
time_series_forecasting_model1_cache):
time_series_forecasting_model1_cache.fit(time_series)
len_test = time_series_forecasting_model1_cache.horizon + 2
X_test = time_series.iloc[-len_test:]
score = time_series_forecasting_model1_cache.score(X=X_test)
assert score.shape == (1, 2)
assert all(map(lambda x: x >= 0.0, score.iloc[0]))
def test_fit_twice_set_model_only_models(
self, time_series, time_series_forecasting_model1_cache, model2):
time_series_forecasting_model1_cache.fit(time_series)
time_series_forecasting_model1_cache.set_params(model=model2)
time_series_forecasting_model1_cache.fit(time_series, only_model=True)
def test_fit_twice_only_models(self, time_series,
time_series_forecasting_model1_cache):
time_series_forecasting_model1_cache.fit(time_series).fit(
time_series, only_model=True)
def test_error_fit_once_only_models(self, time_series,
time_series_forecasting_model1_cache):
with pytest.raises(sklearn.exceptions.NotFittedError):
time_series_forecasting_model1_cache.fit(time_series,
only_model=True)
def test_score_default(self, time_series,
time_series_forecasting_model1_cache):
time_series_forecasting_model1_cache.fit(time_series)
score = time_series_forecasting_model1_cache.score()
assert score.shape == (1, 2)
assert all(map(lambda x: x >= 0.0, score.iloc[0]))
def test_score_custom(self, time_series,
time_series_forecasting_model1_cache, metrics):
time_series_forecasting_model1_cache.fit(time_series)
score = time_series_forecasting_model1_cache.score(metrics=metrics)
assert score.shape == (2, 2)
assert all(map(lambda x: x >= 0.0, score.iloc[0]))
