def test_score_function_null(daily_data):
"""Tests fit and its compatibility with predict/score.
Checks score function accuracy with null model
"""
model = SilverkiteEstimator(
null_model_params={"strategy": "mean"},
fit_algorithm_dict={
"fit_algorithm_params": {"fit_intercept": False}
}
)
assert model.fit_algorithm_dict == {
"fit_algorithm_params": {"fit_intercept": False}
}
train_df = daily_data["train_df"]
model.fit(
train_df,
time_col=cst.TIME_COL,
value_col=cst.VALUE_COL)
assert model.fit_algorithm_dict == {
"fit_algorithm": "linear",
"fit_algorithm_params": {"fit_intercept": False}
}
score = model.score(
daily_data["test_df"],
daily_data["test_df"][cst.VALUE_COL])
assert score == pytest.approx(0.90, rel=1e-2)
def test_score_function(daily_data_with_reg):
"""Tests fit and its compatibility with predict/score.
Checks score function accuracy without null model
"""
model = SilverkiteEstimator(
extra_pred_cols=["ct1", "regressor1", "regressor2"],
impute_dict={
"func": impute_with_lags,
"params": {"orders": [7]}}
)
train_df = daily_data_with_reg["train_df"]
test_df = daily_data_with_reg["test_df"]
model.fit(
X=train_df,
time_col=cst.TIME_COL,
value_col=cst.VALUE_COL)
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-2)