def test_score_function(daily_data_with_reg):
"""Checks score function without null model, with regressors"""
model = BaseSilverkiteEstimator()
train_df = daily_data_with_reg["train_df"]
test_df = daily_data_with_reg["test_df"]
# every subclass `fit` follows these steps
model.fit(X=train_df, time_col=cst.TIME_COL, value_col=cst.VALUE_COL)
silverkite = SilverkiteForecast()
model.model_dict = silverkite.forecast(
df=train_df,
time_col=cst.TIME_COL,
value_col=cst.VALUE_COL,
origin_for_time_vars=None,
extra_pred_cols=["ct1", "regressor1", "regressor2"],
train_test_thresh=None,
training_fraction=None,
fit_algorithm="linear",
fit_algorithm_params=None,
daily_event_df_dict=None,
changepoints_dict=None,
fs_components_df=pd.DataFrame({
"name": ["tod", "tow", "conti_year"],
"period": [24.0, 7.0, 1.0],
"order": [3, 3, 5],
"seas_names": ["daily", "weekly", "yearly"]
}),
autoreg_dict=None,
min_admissible_value=None,
max_admissible_value=None,
uncertainty_dict=None)
model.finish_fit()
score = model.score(test_df, test_df[cst.VALUE_COL])
pred_df = model.predict(test_df)
assert list(pred_df.columns) == [cst.TIME_COL, cst.PREDICTED_COL]
assert score == pytest.approx(
mean_squared_error(pred_df[cst.PREDICTED_COL], test_df[cst.VALUE_COL]))
assert score == pytest.approx(4.6, rel=1e-1)
def test_fit_predict(daily_data):
"""Checks fit and predict function with null model"""
model = BaseSilverkiteEstimator(null_model_params={"strategy": "mean"})
train_df = daily_data["train_df"]
test_df = daily_data["test_df"]
assert model.last_predicted_X_ is None
assert model.cached_predictions_ is None
with pytest.raises(NotFittedError,
match="Call `fit` before calling `predict`."):
model.predict(train_df)
# Every subclass `fit` follows these steps
model.fit(train_df, time_col=cst.TIME_COL, value_col=cst.VALUE_COL)
# Checks that `df` is set, but other variables aren't
assert_equal(model.df, train_df)
assert model.pred_cols is None
assert model.feature_cols is None
assert model.coef_ is None
with pytest.raises(
ValueError,
match="Must set `self.model_dict` before calling this function."):
model.finish_fit()
silverkite = SilverkiteForecast()
model.model_dict = silverkite.forecast(df=train_df,
time_col=cst.TIME_COL,
value_col=cst.VALUE_COL,
origin_for_time_vars=None,
extra_pred_cols=None,
train_test_thresh=None,
training_fraction=None,
fit_algorithm="linear",
fit_algorithm_params=None,
daily_event_df_dict=None,
changepoints_dict=None,
fs_components_df=pd.DataFrame({
"name":
["tod", "tow", "conti_year"],
"period": [24.0, 7.0, 1.0],
"order": [3, 3, 5],
"seas_names":
["daily", "weekly", "yearly"]
}),
autoreg_dict=None,
min_admissible_value=None,
max_admissible_value=None,
uncertainty_dict=None)
with pytest.raises(
NotFittedError,
match="Subclass must call `finish_fit` inside the `fit` method."):
model.predict(train_df)
assert model.last_predicted_X_ is not None # attempted prediction
assert model.cached_predictions_ is None
model.finish_fit()
# Checks that other variables are set
assert_equal(model.pred_cols, model.model_dict["pred_cols"])
assert_equal(model.feature_cols, model.model_dict["x_mat"].columns)
assert_equal(
model.coef_,
pd.DataFrame(model.model_dict["ml_model"].coef_,
index=model.feature_cols))
# Predicts on a new dataset
with LogCapture(cst.LOGGER_NAME) as log_capture:
predicted = model.predict(test_df)
assert_equal(model.last_predicted_X_, test_df)
assert_equal(model.cached_predictions_, predicted)
log_capture.check() # no log messages (not using cached predictions)
# Uses cached predictions
with LogCapture(cst.LOGGER_NAME) as log_capture:
assert_equal(model.predict(test_df), predicted)
log_capture.check(
(cst.LOGGER_NAME, "DEBUG", "Returning cached predictions."))
# Predicts on a different dataset
with LogCapture(cst.LOGGER_NAME) as log_capture:
predicted = model.predict(train_df)
assert_equal(model.last_predicted_X_, train_df)
assert_equal(model.cached_predictions_, predicted)
log_capture.check() # no log messages (not using cached predictions)
# .fit() clears the cached result
model.fit(train_df, time_col=cst.TIME_COL, value_col=cst.VALUE_COL)
assert model.last_predicted_X_ is None
assert model.cached_predictions_ is None