Function _check_response_method

sklearn/utils/validation.py:2248–2291

Check if `response_method` is available in estimator and return it. .. versionadded:: 1.3 Parameters ---------- estimator : estimator instance Classifier or regressor to check. response_method : {"predict_proba", "predict_log_proba", "decision_function", "p

(estimator, response_method)

def _check_response_method(estimator, response_method):
    """Check if `response_method` is available in estimator and return it.

    .. versionadded:: 1.3

    Parameters
    ----------
    estimator : estimator instance
        Classifier or regressor to check.

    response_method : {"predict_proba", "predict_log_proba", "decision_function",
            "predict"} or list of such str
        Specifies the response method to use get prediction from an estimator
        (i.e. :term:`predict_proba`, :term:`predict_log_proba`,
        :term:`decision_function` or :term:`predict`). Possible choices are:
        - if `str`, it corresponds to the name to the method to return;
        - if a list of `str`, it provides the method names in order of
          preference. The method returned corresponds to the first method in
          the list and which is implemented by `estimator`.

    Returns
    -------
    prediction_method : callable
        Prediction method of estimator.

    Raises
    ------
    AttributeError
        If `response_method` is not available in `estimator`.
    """
    if isinstance(response_method, str):
        list_methods = [response_method]
    else:
        list_methods = response_method

    prediction_method = [getattr(estimator, method, None) for method in list_methods]
    prediction_method = reduce(lambda x, y: x or y, prediction_method)
    if prediction_method is None:
        raise AttributeError(
            f"{estimator.__class__.__name__} has none of the following attributes: "
            f"{', '.join(list_methods)}."
        )

    return prediction_method


def _check_method_params(X, params, indices=None):