"""
Common code for all metrics.

"""

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

from itertools import combinations

import numpy as np

from ..utils import check_array, check_consistent_length
from ..utils.multiclass import type_of_target


def _average_binary_score(binary_metric, y_true, y_score, average, sample_weight=None):
    """Average a binary metric for multilabel classification.

    Parameters
    ----------
    y_true : array, shape = [n_samples] or [n_samples, n_classes]
        True binary labels in binary label indicators.

    y_score : array, shape = [n_samples] or [n_samples, n_classes]
        Target scores, can either be probability estimates of the positive
        class, confidence values, or binary decisions.

    average : {None, 'micro', 'macro', 'samples', 'weighted'}, default='macro'
        If ``None``, the scores for each class are returned. Otherwise,
        this determines the type of averaging performed on the data:

        ``'micro'``:
            Calculate metrics globally by considering each element of the label
            indicator matrix as a label.
        ``'macro'``:
            Calculate metrics for each label, and find their unweighted
            mean.  This does not take label imbalance into account.
        ``'weighted'``:
            Calculate metrics for each label, and find their average, weighted
            by support (the number of true instances for each label).
        ``'samples'``:
            Calculate metrics for each instance, and find their average.

        Will be ignored when ``y_true`` is binary.

    sample_weight : array-like of shape (n_samples,), default=None
        Sample weights.

    binary_metric : callable, returns shape [n_classes]
        The binary metric function to use.

    Returns
    -------
    score : float or array of shape [n_classes]
        If not ``None``, average the score, else return the score for each
        classes.

    """
    average_options = (None, "micro", "macro", "weighted", "samples")
    if average not in average_options:
        raise ValueError("average has to be one of {0}".format(average_options))

    y_type = type_of_target(y_true)
    if y_type not in ("binary", "multilabel-indicator"):
        raise ValueError("{0} format is not supported".format(y_type))

    if y_type == "binary":
        return binary_metric(y_true, y_score, sample_weight=sample_weight)

    check_consistent_length(y_true, y_score, sample_weight)
    y_true = check_array(y_true)
    y_score = check_array(y_score)

    not_average_axis = 1
    score_weight = sample_weight
    average_weight = None

    if average == "micro":
        if score_weight is not None:
            score_weight = np.repeat(score_weight, y_true.shape[1])
        y_true = y_true.ravel()
        y_score = y_score.ravel()

    elif average == "weighted":
        if score_weight is not None:
            average_weight = np.sum(
                np.multiply(y_true, np.reshape(score_weight, (-1, 1))), axis=0
            )
        else:
            average_weight = np.sum(y_true, axis=0)
        if np.isclose(average_weight.sum(), 0.0):
            return 0

    elif average == "samples":
        # swap average_weight <-> score_weight
        average_weight = score_weight
        score_weight = None
        not_average_axis = 0

    if y_true.ndim == 1:
        y_true = y_true.reshape((-1, 1))

    if y_score.ndim == 1:
        y_score = y_score.reshape((-1, 1))

    n_classes = y_score.shape[not_average_axis]
    score = np.zeros((n_classes,))
    for c in range(n_classes):
        y_true_c = y_true.take([c], axis=not_average_axis).ravel()
        y_score_c = y_score.take([c], axis=not_average_axis).ravel()
        score[c] = binary_metric(y_true_c, y_score_c, sample_weight=score_weight)

    # Average the results
    if average is not None:
        if average_weight is not None:
            # Scores with 0 weights are forced to be 0, preventing the average
            # score from being affected by 0-weighted NaN elements.
            average_weight = np.asarray(average_weight)
            score[average_weight == 0] = 0
        return np.average(score, weights=average_weight)
    else:
        return score


def _average_multiclass_ovo_score(binary_metric, y_true, y_score, average="macro"):
    """Average one-versus-one scores for multiclass classification.

    Uses the binary metric for one-vs-one multiclass classification,
    where the score is computed according to the Hand & Till (2001) algorithm.

    Parameters
    ----------
    binary_metric : callable
        The binary metric function to use that accepts the following as input:
            y_true_target : array, shape = [n_samples_target]
                Some sub-array of y_true for a pair of classes designated
                positive and negative in the one-vs-one scheme.
            y_score_target : array, shape = [n_samples_target]
                Scores corresponding to the probability estimates
                of a sample belonging to the designated positive class label

    y_true : array-like of shape (n_samples,)
        True multiclass labels.

    y_score : array-like of shape (n_samples, n_classes)
        Target scores corresponding to probability estimates of a sample
        belonging to a particular class.

    average : {'macro', 'weighted'}, default='macro'
        Determines the type of averaging performed on the pairwise binary
        metric scores:
        ``'macro'``:
            Calculate metrics for each label, and find their unweighted
            mean. This does not take label imbalance into account. Classes
            are assumed to be uniformly distributed.
        ``'weighted'``:
            Calculate metrics for each label, taking into account the
            prevalence of the classes.

    Returns
    -------
    score : float
        Average of the pairwise binary metric scores.
    """
    check_consistent_length(y_true, y_score)

    y_true_unique = np.unique(y_true)
    n_classes = y_true_unique.shape[0]
    n_pairs = n_classes * (n_classes - 1) // 2
    pair_scores = np.empty(n_pairs)

    is_weighted = average == "weighted"
    prevalence = np.empty(n_pairs) if is_weighted else None

    # Compute scores treating a as positive class and b as negative class,
    # then b as positive class and a as negative class
    for ix, (a, b) in enumerate(combinations(y_true_unique, 2)):
        a_mask = y_true == a
        b_mask = y_true == b
        ab_mask = np.logical_or(a_mask, b_mask)

        if is_weighted:
            prevalence[ix] = np.average(ab_mask)

        a_true = a_mask[ab_mask]
        b_true = b_mask[ab_mask]

        a_true_score = binary_metric(a_true, y_score[ab_mask, a])
        b_true_score = binary_metric(b_true, y_score[ab_mask, b])
        pair_scores[ix] = (a_true_score + b_true_score) / 2

    return np.average(pair_scores, weights=prevalence)
