"""Utilities to get the response values of a classifier or a regressor.

It allows to make uniform checks and validation.
"""
import numpy as np

from ..base import is_classifier
from .validation import _check_response_method, check_is_fitted


def _get_response_values(
    estimator,
    X,
    response_method,
    pos_label=None,
):
    """Compute the response values of a classifier or a regressor.

    The response values are predictions, one scalar value for each sample in X
    that depends on the specific choice of `response_method`.

    If `estimator` is a binary classifier, also return the label for the
    effective positive class.

    .. versionadded:: 1.3

    Parameters
    ----------
    estimator : estimator instance
        Fitted classifier or regressor or a fitted :class:`~sklearn.pipeline.Pipeline`
        in which the last estimator is a classifier or a regressor.

    X : {array-like, sparse matrix} of shape (n_samples, n_features)
        Input values.

    response_method : {"predict_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:`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`.

    pos_label : int, float, bool or str, default=None
        The class considered as the positive class when computing
        the metrics. By default, `estimators.classes_[1]` is
        considered as the positive class.

    Returns
    -------
    y_pred : ndarray of shape (n_samples,)
        Target scores calculated from the provided response_method
        and `pos_label`.

    pos_label : int, float, bool, str or None
        The class considered as the positive class when computing
        the metrics. Returns `None` if `estimator` is a regressor.

    Raises
    ------
    ValueError
        If `pos_label` is not a valid label.
        If the shape of `y_pred` is not consistent for binary classifier.
        If the response method can be applied to a classifier only and
        `estimator` is a regressor.
    """
    from sklearn.base import is_classifier  # noqa

    if is_classifier(estimator):
        prediction_method = _check_response_method(estimator, response_method)
        classes = estimator.classes_
        target_type = "binary" if len(classes) <= 2 else "multiclass"

        if pos_label is not None and pos_label not in classes.tolist():
            raise ValueError(
                f"pos_label={pos_label} is not a valid label: It should be "
                f"one of {classes}"
            )
        elif pos_label is None and target_type == "binary":
            pos_label = pos_label if pos_label is not None else classes[-1]

        y_pred = prediction_method(X)
        if prediction_method.__name__ == "predict_proba":
            if target_type == "binary" and y_pred.shape[1] <= 2:
                if y_pred.shape[1] == 2:
                    col_idx = np.flatnonzero(classes == pos_label)[0]
                    y_pred = y_pred[:, col_idx]
                else:
                    err_msg = (
                        f"Got predict_proba of shape {y_pred.shape}, but need "
                        "classifier with two classes."
                    )
                    raise ValueError(err_msg)
        elif prediction_method.__name__ == "decision_function":
            if target_type == "binary":
                if pos_label == classes[0]:
                    y_pred *= -1
    else:  # estimator is a regressor
        if response_method != "predict":
            raise ValueError(
                f"{estimator.__class__.__name__} should either be a classifier to be "
                f"used with response_method={response_method} or the response_method "
                "should be 'predict'. Got a regressor with response_method="
                f"{response_method} instead."
            )
        y_pred, pos_label = estimator.predict(X), None

    return y_pred, pos_label


def _get_response_values_binary(estimator, X, response_method, pos_label=None):
    """Compute the response values of a binary classifier.

    Parameters
    ----------
    estimator : estimator instance
        Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline`
        in which the last estimator is a binary classifier.

    X : {array-like, sparse matrix} of shape (n_samples, n_features)
        Input values.

    response_method : {'auto', 'predict_proba', 'decision_function'}
        Specifies whether to use :term:`predict_proba` or
        :term:`decision_function` as the target response. If set to 'auto',
        :term:`predict_proba` is tried first and if it does not exist
        :term:`decision_function` is tried next.

    pos_label : int, float, bool or str, default=None
        The class considered as the positive class when computing
        the metrics. By default, `estimators.classes_[1]` is
        considered as the positive class.

    Returns
    -------
    y_pred : ndarray of shape (n_samples,)
        Target scores calculated from the provided response_method
        and pos_label.

    pos_label : int, float, bool or str
        The class considered as the positive class when computing
        the metrics.
    """
    classification_error = "Expected 'estimator' to be a binary classifier."

    check_is_fitted(estimator)
    if not is_classifier(estimator):
        raise ValueError(
            classification_error + f" Got {estimator.__class__.__name__} instead."
        )
    elif len(estimator.classes_) != 2:
        raise ValueError(
            classification_error + f" Got {len(estimator.classes_)} classes instead."
        )

    if response_method == "auto":
        response_method = ["predict_proba", "decision_function"]

    return _get_response_values(
        estimator,
        X,
        response_method,
        pos_label=pos_label,
    )