Feature importance

evaluate_feature_importance(model, x_test, y_test, feature_names, n_repeats=50, random_state=None)

Evaluate the feature importance of a sklearn classifier or regressor.

Parameters:

Name	Type	Description	Default
model	BaseEstimator	A trained and fitted Sklearn model.	required
x_test	ndarray	Testing feature data (X data need to be normalized / standardized).	required
y_test	ndarray	Testing label data.	required
feature_names	Sequence[str]	Names of the feature columns.	required
n_repeats	int	Number of iteration used when calculate feature importance. Defaults to 50.	50
random_state	Optional[int]	random state for repeatability of results. Optional parameter.	None

Returns:

Type	Description
DataFrame	A dataframe containing features and their importance.
dict	A dictionary containing importance mean, importance std, and overall importance.

Raises:

Type	Description
InvalidDatasetException	Either array is empty.
InvalidParameterValueException	Value for 'n_repeats' is not at least one.

Source code in eis_toolkit/exploratory_analyses/feature_importance.py

@beartype
def evaluate_feature_importance(
    model: sklearn.base.BaseEstimator,
    x_test: np.ndarray,
    y_test: np.ndarray,
    feature_names: Sequence[str],
    n_repeats: int = 50,
    random_state: Optional[int] = None,
) -> tuple[pd.DataFrame, dict]:
    """
    Evaluate the feature importance of a sklearn classifier or regressor.

    Args:
        model: A trained and fitted Sklearn model.
        x_test: Testing feature data (X data need to be normalized / standardized).
        y_test: Testing label data.
        feature_names: Names of the feature columns.
        n_repeats: Number of iteration used when calculate feature importance. Defaults to 50.
        random_state: random state for repeatability of results. Optional parameter.

    Returns:
        A dataframe containing features and their importance.
        A dictionary containing importance mean, importance std, and overall importance.

    Raises:
        InvalidDatasetException: Either array is empty.
        InvalidParameterValueException: Value for 'n_repeats' is not at least one.
    """

    if x_test.size == 0:
        raise InvalidDatasetException("Array 'x_test' is empty.")

    if y_test.size == 0:
        raise InvalidDatasetException("Array 'y_test' is empty.")

    if n_repeats < 1:
        raise InvalidParameterValueException("Value for 'n_repeats' is less than one.")

    result = permutation_importance(model, x_test, y_test.ravel(), n_repeats=n_repeats, random_state=random_state)

    feature_importance = pd.DataFrame({"Feature": feature_names, "Importance": result.importances_mean * 100})

    feature_importance = feature_importance.sort_values(by="Importance", ascending=False)

    return feature_importance, result