Skip to content

check_search_fit_sets_attributes

yohou.testing.search.check_search_fit_sets_attributes(search_cv, y, X_actual=None, forecasting_horizon=3, X_future=None, X_forecast=None)

Check fit() sets required search CV attributes.

Validates that fit() creates all required attributes including cv_results_, best_forecaster_, best_params_, best_score_, best_index_, scorer_, n_splits_, and multimetric_.

Parameters

Name Type Description Default
search_cv BaseSearchCV

Unfitted search CV instance

 required
y DataFrame

Training target data with "time" column

 required
X_actual DataFrame

Training features with "time" column

 None
forecasting_horizon int

Number of steps ahead to forecast

 3

Raises

Type Description
AssertionError

If required attributes are not set after fit()

Source Code

View on GitHub

Show/Hide source 
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
def check_search_fit_sets_attributes(
    search_cv,
    y: pl.DataFrame,
    X_actual: pl.DataFrame | None = None,
    forecasting_horizon: int = 3,
    X_future: pl.DataFrame | None = None,
    X_forecast: pl.DataFrame | None = None,
) -> None:
    """Check fit() sets required search CV attributes.

    Validates that fit() creates all required attributes including cv_results_,
    best_forecaster_, best_params_, best_score_, best_index_, scorer_, n_splits_,
    and multimetric_.

    Parameters
    ----------
    search_cv : BaseSearchCV
        Unfitted search CV instance
    y : pl.DataFrame
        Training target data with "time" column
    X_actual : pl.DataFrame, optional
        Training features with "time" column
    forecasting_horizon : int, default=3
        Number of steps ahead to forecast

    Raises
    ------
    AssertionError
        If required attributes are not set after fit()

    """
    search_cv_clone = clone(search_cv)
    search_cv_clone.fit(y, X_actual, forecasting_horizon=forecasting_horizon, X_future=X_future, X_forecast=X_forecast)

    # Check core fitted attributes
    assert hasattr(search_cv_clone, "cv_results_"), "fit() must set cv_results_ attribute"
    assert isinstance(search_cv_clone.cv_results_, dict), (
        f"cv_results_ should be dict, got {type(search_cv_clone.cv_results_)}"
    )

    assert hasattr(search_cv_clone, "best_params_"), "fit() must set best_params_ attribute"
    assert isinstance(search_cv_clone.best_params_, dict), (
        f"best_params_ should be dict, got {type(search_cv_clone.best_params_)}"
    )

    assert hasattr(search_cv_clone, "best_score_"), "fit() must set best_score_ attribute"
    assert isinstance(search_cv_clone.best_score_, int | float | np.number), (
        f"best_score_ should be numeric, got {type(search_cv_clone.best_score_)}"
    )

    assert hasattr(search_cv_clone, "best_index_"), "fit() must set best_index_ attribute"
    assert isinstance(search_cv_clone.best_index_, int | np.integer), (
        f"best_index_ should be int, got {type(search_cv_clone.best_index_)}"
    )

    assert hasattr(search_cv_clone, "scorer_"), "fit() must set scorer_ attribute"

    assert hasattr(search_cv_clone, "n_splits_"), "fit() must set n_splits_ attribute"
    assert isinstance(search_cv_clone.n_splits_, int | np.integer), (
        f"n_splits_ should be int, got {type(search_cv_clone.n_splits_)}"
    )

    assert hasattr(search_cv_clone, "multimetric_"), "fit() must set multimetric_ attribute"
    assert isinstance(search_cv_clone.multimetric_, bool), (
        f"multimetric_ should be bool, got {type(search_cv_clone.multimetric_)}"
    )

    # Check best_forecaster_ when refit=True
    if search_cv_clone.refit:
        assert hasattr(search_cv_clone, "best_forecaster_"), "fit() must set best_forecaster_ when refit=True"
        assert hasattr(search_cv_clone, "refit_time_"), "fit() must set refit_time_ when refit=True"

        # Depth assertion: step columns propagate through search to best_forecaster_
        if X_future is not None or X_forecast is not None:
            assert hasattr(search_cv_clone.best_forecaster_, "_step_column_names_"), (
                "best_forecaster_ must have _step_column_names_ when X_future/X_forecast provided"
            )
            assert len(search_cv_clone.best_forecaster_._step_column_names_) > 0, (
                "best_forecaster_._step_column_names_ should be non-empty when X_future/X_forecast provided"
            )