import pickle
import tempfile
import shutil
import os
import numbers
from unittest.mock import Mock
from functools import partial

import numpy as np
import pytest
import joblib

from numpy.testing import assert_allclose
from sklearn.utils._testing import assert_almost_equal
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import ignore_warnings

from sklearn.base import BaseEstimator
from sklearn.metrics import (f1_score, r2_score, roc_auc_score, fbeta_score,
                             log_loss, precision_score, recall_score,
                             jaccard_score)
from sklearn.metrics import cluster as cluster_module
from sklearn.metrics import check_scoring
from sklearn.metrics._scorer import (_PredictScorer, _passthrough_scorer,
                                     _MultimetricScorer,
                                     _check_multimetric_scoring)
from sklearn.metrics import accuracy_score
from sklearn.metrics import make_scorer, get_scorer, SCORERS
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC
from sklearn.pipeline import make_pipeline
from sklearn.cluster import KMeans
from sklearn.linear_model import Ridge, LogisticRegression, Perceptron
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.datasets import make_blobs
from sklearn.datasets import make_classification
from sklearn.datasets import make_multilabel_classification
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.multiclass import OneVsRestClassifier


REGRESSION_SCORERS = ['explained_variance', 'r2',
                      'neg_mean_absolute_error', 'neg_mean_squared_error',
                      'neg_mean_squared_log_error',
                      'neg_median_absolute_error',
                      'neg_root_mean_squared_error',
                      'mean_absolute_error',
                      'mean_squared_error', 'median_absolute_error',
                      'max_error', 'neg_mean_poisson_deviance',
                      'neg_mean_gamma_deviance']

CLF_SCORERS = ['accuracy', 'balanced_accuracy',
               'f1', 'f1_weighted', 'f1_macro', 'f1_micro',
               'roc_auc', 'average_precision', 'precision',
               'precision_weighted', 'precision_macro', 'precision_micro',
               'recall', 'recall_weighted', 'recall_macro', 'recall_micro',
               'neg_log_loss', 'log_loss', 'neg_brier_score',
               'jaccard', 'jaccard_weighted', 'jaccard_macro',
               'jaccard_micro', 'roc_auc_ovr', 'roc_auc_ovo',
               'roc_auc_ovr_weighted', 'roc_auc_ovo_weighted']

# All supervised cluster scorers (They behave like classification metric)
CLUSTER_SCORERS = ["adjusted_rand_score",
                   "homogeneity_score",
                   "completeness_score",
                   "v_measure_score",
                   "mutual_info_score",
                   "adjusted_mutual_info_score",
                   "normalized_mutual_info_score",
                   "fowlkes_mallows_score"]

MULTILABEL_ONLY_SCORERS = ['precision_samples', 'recall_samples', 'f1_samples',
                           'jaccard_samples']

REQUIRE_POSITIVE_Y_SCORERS = ['neg_mean_poisson_deviance',
                              'neg_mean_gamma_deviance']


def _require_positive_y(y):
    """Make targets strictly positive"""
    offset = abs(y.min()) + 1
    y = y + offset
    return y


def _make_estimators(X_train, y_train, y_ml_train):
    # Make estimators that make sense to test various scoring methods
    sensible_regr = DecisionTreeRegressor(random_state=0)
    # some of the regressions scorers require strictly positive input.
    sensible_regr.fit(X_train, y_train + 1)
    sensible_clf = DecisionTreeClassifier(random_state=0)
    sensible_clf.fit(X_train, y_train)
    sensible_ml_clf = DecisionTreeClassifier(random_state=0)
    sensible_ml_clf.fit(X_train, y_ml_train)
    return dict(
        [(name, sensible_regr) for name in REGRESSION_SCORERS] +
        [(name, sensible_clf) for name in CLF_SCORERS] +
        [(name, sensible_clf) for name in CLUSTER_SCORERS] +
        [(name, sensible_ml_clf) for name in MULTILABEL_ONLY_SCORERS]
    )


X_mm, y_mm, y_ml_mm = None, None, None
ESTIMATORS = None
TEMP_FOLDER = None


def setup_module():
    # Create some memory mapped data
    global X_mm, y_mm, y_ml_mm, TEMP_FOLDER, ESTIMATORS
    TEMP_FOLDER = tempfile.mkdtemp(prefix='sklearn_test_score_objects_')
    X, y = make_classification(n_samples=30, n_features=5, random_state=0)
    _, y_ml = make_multilabel_classification(n_samples=X.shape[0],
                                             random_state=0)
    filename = os.path.join(TEMP_FOLDER, 'test_data.pkl')
    joblib.dump((X, y, y_ml), filename)
    X_mm, y_mm, y_ml_mm = joblib.load(filename, mmap_mode='r')
    ESTIMATORS = _make_estimators(X_mm, y_mm, y_ml_mm)


def teardown_module():
    global X_mm, y_mm, y_ml_mm, TEMP_FOLDER, ESTIMATORS
    # GC closes the mmap file descriptors
    X_mm, y_mm, y_ml_mm, ESTIMATORS = None, None, None, None
    shutil.rmtree(TEMP_FOLDER)


class EstimatorWithoutFit:
    """Dummy estimator to test scoring validators"""
    pass


class EstimatorWithFit(BaseEstimator):
    """Dummy estimator to test scoring validators"""
    def fit(self, X, y):
        return self


class EstimatorWithFitAndScore:
    """Dummy estimator to test scoring validators"""
    def fit(self, X, y):
        return self

    def score(self, X, y):
        return 1.0


class EstimatorWithFitAndPredict:
    """Dummy estimator to test scoring validators"""
    def fit(self, X, y):
        self.y = y
        return self

    def predict(self, X):
        return self.y


class DummyScorer:
    """Dummy scorer that always returns 1."""
    def __call__(self, est, X, y):
        return 1


def test_all_scorers_repr():
    # Test that all scorers have a working repr
    for name, scorer in SCORERS.items():
        repr(scorer)


def check_scoring_validator_for_single_metric_usecases(scoring_validator):
    # Test all branches of single metric usecases
    estimator = EstimatorWithoutFit()
    pattern = (r"estimator should be an estimator implementing 'fit' method,"
               r" .* was passed")
    with pytest.raises(TypeError, match=pattern):
        scoring_validator(estimator)

    estimator = EstimatorWithFitAndScore()
    estimator.fit([[1]], [1])
    scorer = scoring_validator(estimator)
    assert scorer is _passthrough_scorer
    assert_almost_equal(scorer(estimator, [[1]], [1]), 1.0)

    estimator = EstimatorWithFitAndPredict()
    estimator.fit([[1]], [1])
    pattern = (r"If no scoring is specified, the estimator passed should have"
               r" a 'score' method\. The estimator .* does not\.")
    with pytest.raises(TypeError, match=pattern):
        scoring_validator(estimator)

    scorer = scoring_validator(estimator, scoring="accuracy")
    assert_almost_equal(scorer(estimator, [[1]], [1]), 1.0)

    estimator = EstimatorWithFit()
    scorer = scoring_validator(estimator, scoring="accuracy")
    assert isinstance(scorer, _PredictScorer)

    # Test the allow_none parameter for check_scoring alone
    if scoring_validator is check_scoring:
        estimator = EstimatorWithFit()
        scorer = scoring_validator(estimator, allow_none=True)
        assert scorer is None


def check_multimetric_scoring_single_metric_wrapper(*args, **kwargs):
    # This wraps the _check_multimetric_scoring to take in
    # single metric scoring parameter so we can run the tests
    # that we will run for check_scoring, for check_multimetric_scoring
    # too for single-metric usecases

    scorers, is_multi = _check_multimetric_scoring(*args, **kwargs)
    # For all single metric use cases, it should register as not multimetric
    assert not is_multi
    if args[0] is not None:
        assert scorers is not None
        names, scorers = zip(*scorers.items())
        assert len(scorers) == 1
        assert names[0] == 'score'
        scorers = scorers[0]
    return scorers


def test_check_scoring_and_check_multimetric_scoring():
    check_scoring_validator_for_single_metric_usecases(check_scoring)
    # To make sure the check_scoring is correctly applied to the constituent
    # scorers
    check_scoring_validator_for_single_metric_usecases(
        check_multimetric_scoring_single_metric_wrapper)

    # For multiple metric use cases
    # Make sure it works for the valid cases
    for scoring in (('accuracy',), ['precision'],
                    {'acc': 'accuracy', 'precision': 'precision'},
                    ('accuracy', 'precision'), ['precision', 'accuracy'],
                    {'accuracy': make_scorer(accuracy_score),
                     'precision': make_scorer(precision_score)}):
        estimator = LinearSVC(random_state=0)
        estimator.fit([[1], [2], [3]], [1, 1, 0])

        scorers, is_multi = _check_multimetric_scoring(estimator, scoring)
        assert is_multi
        assert isinstance(scorers, dict)
        assert sorted(scorers.keys()) == sorted(list(scoring))
        assert all([isinstance(scorer, _PredictScorer)
                    for scorer in list(scorers.values())])

        if 'acc' in scoring:
            assert_almost_equal(scorers['acc'](
                estimator, [[1], [2], [3]], [1, 0, 0]), 2. / 3.)
        if 'accuracy' in scoring:
            assert_almost_equal(scorers['accuracy'](
                estimator, [[1], [2], [3]], [1, 0, 0]), 2. / 3.)
        if 'precision' in scoring:
            assert_almost_equal(scorers['precision'](
                estimator, [[1], [2], [3]], [1, 0, 0]), 0.5)

    estimator = EstimatorWithFitAndPredict()
    estimator.fit([[1]], [1])

    # Make sure it raises errors when scoring parameter is not valid.
    # More weird corner cases are tested at test_validation.py
    error_message_regexp = ".*must be unique strings.*"
    for scoring in ((make_scorer(precision_score),  # Tuple of callables
                     make_scorer(accuracy_score)), [5],
                    (make_scorer(precision_score),), (), ('f1', 'f1')):
        with pytest.raises(ValueError, match=error_message_regexp):
            _check_multimetric_scoring(estimator, scoring=scoring)


def test_check_scoring_gridsearchcv():
    # test that check_scoring works on GridSearchCV and pipeline.
    # slightly redundant non-regression test.

    grid = GridSearchCV(LinearSVC(), param_grid={'C': [.1, 1]}, cv=3)
    scorer = check_scoring(grid, scoring="f1")
    assert isinstance(scorer, _PredictScorer)

    pipe = make_pipeline(LinearSVC())
    scorer = check_scoring(pipe, scoring="f1")
    assert isinstance(scorer, _PredictScorer)

    # check that cross_val_score definitely calls the scorer
    # and doesn't make any assumptions about the estimator apart from having a
    # fit.
    scores = cross_val_score(EstimatorWithFit(), [[1], [2], [3]], [1, 0, 1],
                             scoring=DummyScorer(), cv=3)
    assert_array_equal(scores, 1)


def test_make_scorer():
    # Sanity check on the make_scorer factory function.
    f = lambda *args: 0
    with pytest.raises(ValueError):
        make_scorer(f, needs_threshold=True, needs_proba=True)


def test_classification_scores():
    # Test classification scorers.
    X, y = make_blobs(random_state=0, centers=2)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    clf = LinearSVC(random_state=0)
    clf.fit(X_train, y_train)

    for prefix, metric in [('f1', f1_score), ('precision', precision_score),
                           ('recall', recall_score),
                           ('jaccard', jaccard_score)]:

        score1 = get_scorer('%s_weighted' % prefix)(clf, X_test, y_test)
        score2 = metric(y_test, clf.predict(X_test), pos_label=None,
                        average='weighted')
        assert_almost_equal(score1, score2)

        score1 = get_scorer('%s_macro' % prefix)(clf, X_test, y_test)
        score2 = metric(y_test, clf.predict(X_test), pos_label=None,
                        average='macro')
        assert_almost_equal(score1, score2)

        score1 = get_scorer('%s_micro' % prefix)(clf, X_test, y_test)
        score2 = metric(y_test, clf.predict(X_test), pos_label=None,
                        average='micro')
        assert_almost_equal(score1, score2)

        score1 = get_scorer('%s' % prefix)(clf, X_test, y_test)
        score2 = metric(y_test, clf.predict(X_test), pos_label=1)
        assert_almost_equal(score1, score2)

    # test fbeta score that takes an argument
    scorer = make_scorer(fbeta_score, beta=2)
    score1 = scorer(clf, X_test, y_test)
    score2 = fbeta_score(y_test, clf.predict(X_test), beta=2)
    assert_almost_equal(score1, score2)

    # test that custom scorer can be pickled
    unpickled_scorer = pickle.loads(pickle.dumps(scorer))
    score3 = unpickled_scorer(clf, X_test, y_test)
    assert_almost_equal(score1, score3)

    # smoke test the repr:
    repr(fbeta_score)


def test_regression_scorers():
    # Test regression scorers.
    diabetes = load_diabetes()
    X, y = diabetes.data, diabetes.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    clf = Ridge()
    clf.fit(X_train, y_train)
    score1 = get_scorer('r2')(clf, X_test, y_test)
    score2 = r2_score(y_test, clf.predict(X_test))
    assert_almost_equal(score1, score2)


def test_thresholded_scorers():
    # Test scorers that take thresholds.
    X, y = make_blobs(random_state=0, centers=2)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    clf = LogisticRegression(random_state=0)
    clf.fit(X_train, y_train)
    score1 = get_scorer('roc_auc')(clf, X_test, y_test)
    score2 = roc_auc_score(y_test, clf.decision_function(X_test))
    score3 = roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1])
    assert_almost_equal(score1, score2)
    assert_almost_equal(score1, score3)

    logscore = get_scorer('neg_log_loss')(clf, X_test, y_test)
    logloss = log_loss(y_test, clf.predict_proba(X_test))
    assert_almost_equal(-logscore, logloss)

    # same for an estimator without decision_function
    clf = DecisionTreeClassifier()
    clf.fit(X_train, y_train)
    score1 = get_scorer('roc_auc')(clf, X_test, y_test)
    score2 = roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1])
    assert_almost_equal(score1, score2)

    # test with a regressor (no decision_function)
    reg = DecisionTreeRegressor()
    reg.fit(X_train, y_train)
    score1 = get_scorer('roc_auc')(reg, X_test, y_test)
    score2 = roc_auc_score(y_test, reg.predict(X_test))
    assert_almost_equal(score1, score2)

    # Test that an exception is raised on more than two classes
    X, y = make_blobs(random_state=0, centers=3)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    clf.fit(X_train, y_train)
    with pytest.raises(ValueError, match="multiclass format is not supported"):
        get_scorer('roc_auc')(clf, X_test, y_test)

    # test error is raised with a single class present in model
    # (predict_proba shape is not suitable for binary auc)
    X, y = make_blobs(random_state=0, centers=2)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    clf = DecisionTreeClassifier()
    clf.fit(X_train, np.zeros_like(y_train))
    with pytest.raises(ValueError, match="need classifier with two classes"):
        get_scorer('roc_auc')(clf, X_test, y_test)

    # for proba scorers
    with pytest.raises(ValueError, match="need classifier with two classes"):
        get_scorer('neg_log_loss')(clf, X_test, y_test)


def test_thresholded_scorers_multilabel_indicator_data():
    # Test that the scorer work with multilabel-indicator format
    # for multilabel and multi-output multi-class classifier
    X, y = make_multilabel_classification(allow_unlabeled=False,
                                          random_state=0)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    # Multi-output multi-class predict_proba
    clf = DecisionTreeClassifier()
    clf.fit(X_train, y_train)
    y_proba = clf.predict_proba(X_test)
    score1 = get_scorer('roc_auc')(clf, X_test, y_test)
    score2 = roc_auc_score(y_test, np.vstack([p[:, -1] for p in y_proba]).T)
    assert_almost_equal(score1, score2)

    # Multi-output multi-class decision_function
    # TODO Is there any yet?
    clf = DecisionTreeClassifier()
    clf.fit(X_train, y_train)
    clf._predict_proba = clf.predict_proba
    clf.predict_proba = None
    clf.decision_function = lambda X: [p[:, 1] for p in clf._predict_proba(X)]

    y_proba = clf.decision_function(X_test)
    score1 = get_scorer('roc_auc')(clf, X_test, y_test)
    score2 = roc_auc_score(y_test, np.vstack([p for p in y_proba]).T)
    assert_almost_equal(score1, score2)

    # Multilabel predict_proba
    clf = OneVsRestClassifier(DecisionTreeClassifier())
    clf.fit(X_train, y_train)
    score1 = get_scorer('roc_auc')(clf, X_test, y_test)
    score2 = roc_auc_score(y_test, clf.predict_proba(X_test))
    assert_almost_equal(score1, score2)

    # Multilabel decision function
    clf = OneVsRestClassifier(LinearSVC(random_state=0))
    clf.fit(X_train, y_train)
    score1 = get_scorer('roc_auc')(clf, X_test, y_test)
    score2 = roc_auc_score(y_test, clf.decision_function(X_test))
    assert_almost_equal(score1, score2)


def test_supervised_cluster_scorers():
    # Test clustering scorers against gold standard labeling.
    X, y = make_blobs(random_state=0, centers=2)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    km = KMeans(n_clusters=3)
    km.fit(X_train)
    for name in CLUSTER_SCORERS:
        score1 = get_scorer(name)(km, X_test, y_test)
        score2 = getattr(cluster_module, name)(y_test, km.predict(X_test))
        assert_almost_equal(score1, score2)


@ignore_warnings
def test_raises_on_score_list():
    # Test that when a list of scores is returned, we raise proper errors.
    X, y = make_blobs(random_state=0)
    f1_scorer_no_average = make_scorer(f1_score, average=None)
    clf = DecisionTreeClassifier()
    with pytest.raises(ValueError):
        cross_val_score(clf, X, y, scoring=f1_scorer_no_average)
    grid_search = GridSearchCV(clf, scoring=f1_scorer_no_average,
                               param_grid={'max_depth': [1, 2]})
    with pytest.raises(ValueError):
        grid_search.fit(X, y)


@ignore_warnings
def test_scorer_sample_weight():
    # Test that scorers support sample_weight or raise sensible errors

    # Unlike the metrics invariance test, in the scorer case it's harder
    # to ensure that, on the classifier output, weighted and unweighted
    # scores really should be unequal.
    X, y = make_classification(random_state=0)
    _, y_ml = make_multilabel_classification(n_samples=X.shape[0],
                                             random_state=0)
    split = train_test_split(X, y, y_ml, random_state=0)
    X_train, X_test, y_train, y_test, y_ml_train, y_ml_test = split

    sample_weight = np.ones_like(y_test)
    sample_weight[:10] = 0

    # get sensible estimators for each metric
    estimator = _make_estimators(X_train, y_train, y_ml_train)

    for name, scorer in SCORERS.items():
        if name in MULTILABEL_ONLY_SCORERS:
            target = y_ml_test
        else:
            target = y_test
        if name in REQUIRE_POSITIVE_Y_SCORERS:
            target = _require_positive_y(target)
        try:
            weighted = scorer(estimator[name], X_test, target,
                              sample_weight=sample_weight)
            ignored = scorer(estimator[name], X_test[10:], target[10:])
            unweighted = scorer(estimator[name], X_test, target)
            assert weighted != unweighted, (
                "scorer {0} behaves identically when "
                "called with sample weights: {1} vs "
                "{2}".format(name, weighted, unweighted))
            assert_almost_equal(weighted, ignored,
                                err_msg="scorer {0} behaves differently when "
                                "ignoring samples and setting sample_weight to"
                                " 0: {1} vs {2}".format(name, weighted,
                                                        ignored))

        except TypeError as e:
            assert "sample_weight" in str(e), (
                "scorer {0} raises unhelpful exception when called "
                "with sample weights: {1}".format(name, str(e)))


@pytest.mark.parametrize('name', SCORERS)
def test_scorer_memmap_input(name):
    # Non-regression test for #6147: some score functions would
    # return singleton memmap when computed on memmap data instead of scalar
    # float values.

    if name in REQUIRE_POSITIVE_Y_SCORERS:
        y_mm_1 = _require_positive_y(y_mm)
        y_ml_mm_1 = _require_positive_y(y_ml_mm)
    else:
        y_mm_1, y_ml_mm_1 = y_mm, y_ml_mm

    # UndefinedMetricWarning for P / R scores
    with ignore_warnings():
        scorer, estimator = SCORERS[name], ESTIMATORS[name]
        if name in MULTILABEL_ONLY_SCORERS:
            score = scorer(estimator, X_mm, y_ml_mm_1)
        else:
            score = scorer(estimator, X_mm, y_mm_1)
        assert isinstance(score, numbers.Number), name


def test_scoring_is_not_metric():
    with pytest.raises(ValueError, match='make_scorer'):
        check_scoring(LogisticRegression(), scoring=f1_score)
    with pytest.raises(ValueError, match='make_scorer'):
        check_scoring(LogisticRegression(), scoring=roc_auc_score)
    with pytest.raises(ValueError, match='make_scorer'):
        check_scoring(Ridge(), scoring=r2_score)
    with pytest.raises(ValueError, match='make_scorer'):
        check_scoring(KMeans(), scoring=cluster_module.adjusted_rand_score)


def test_deprecated_scorer():
    X, y = make_blobs(random_state=0, centers=2)
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
    clf = DecisionTreeClassifier()
    clf.fit(X_train, y_train)

    deprecated_scorer = get_scorer('brier_score_loss')
    with pytest.warns(FutureWarning):
        deprecated_scorer(clf, X_test, y_test)


@pytest.mark.parametrize(
    ("scorers,expected_predict_count,"
     "expected_predict_proba_count,expected_decision_func_count"),
    [({'a1': 'accuracy', 'a2': 'accuracy',
       'll1': 'neg_log_loss', 'll2': 'neg_log_loss',
        'ra1': 'roc_auc', 'ra2': 'roc_auc'}, 1, 1, 1),
     (['roc_auc', 'accuracy'], 1, 0, 1),
     (['neg_log_loss', 'accuracy'], 1, 1, 0)])
def test_multimetric_scorer_calls_method_once(scorers, expected_predict_count,
                                              expected_predict_proba_count,
                                              expected_decision_func_count):
    X, y = np.array([[1], [1], [0], [0], [0]]), np.array([0, 1, 1, 1, 0])

    mock_est = Mock()
    fit_func = Mock(return_value=mock_est)
    predict_func = Mock(return_value=y)

    pos_proba = np.random.rand(X.shape[0])
    proba = np.c_[1 - pos_proba, pos_proba]
    predict_proba_func = Mock(return_value=proba)
    decision_function_func = Mock(return_value=pos_proba)

    mock_est.fit = fit_func
    mock_est.predict = predict_func
    mock_est.predict_proba = predict_proba_func
    mock_est.decision_function = decision_function_func

    scorer_dict, _ = _check_multimetric_scoring(LogisticRegression(), scorers)
    multi_scorer = _MultimetricScorer(**scorer_dict)
    results = multi_scorer(mock_est, X, y)

    assert set(scorers) == set(results)  # compare dict keys

    assert predict_func.call_count == expected_predict_count
    assert predict_proba_func.call_count == expected_predict_proba_count
    assert decision_function_func.call_count == expected_decision_func_count


def test_multimetric_scorer_calls_method_once_classifier_no_decision():
    predict_proba_call_cnt = 0

    class MockKNeighborsClassifier(KNeighborsClassifier):
        def predict_proba(self, X):
            nonlocal predict_proba_call_cnt
            predict_proba_call_cnt += 1
            return super().predict_proba(X)

    X, y = np.array([[1], [1], [0], [0], [0]]), np.array([0, 1, 1, 1, 0])

    # no decision function
    clf = MockKNeighborsClassifier(n_neighbors=1)
    clf.fit(X, y)

    scorers = ['roc_auc', 'neg_log_loss']
    scorer_dict, _ = _check_multimetric_scoring(clf, scorers)
    scorer = _MultimetricScorer(**scorer_dict)
    scorer(clf, X, y)

    assert predict_proba_call_cnt == 1


def test_multimetric_scorer_calls_method_once_regressor_threshold():
    predict_called_cnt = 0

    class MockDecisionTreeRegressor(DecisionTreeRegressor):
        def predict(self, X):
            nonlocal predict_called_cnt
            predict_called_cnt += 1
            return super().predict(X)

    X, y = np.array([[1], [1], [0], [0], [0]]), np.array([0, 1, 1, 1, 0])

    # no decision function
    clf = MockDecisionTreeRegressor()
    clf.fit(X, y)

    scorers = {'neg_mse': 'neg_mean_squared_error', 'r2': 'roc_auc'}
    scorer_dict, _ = _check_multimetric_scoring(clf, scorers)
    scorer = _MultimetricScorer(**scorer_dict)
    scorer(clf, X, y)

    assert predict_called_cnt == 1


def test_multimetric_scorer_sanity_check():
    # scoring dictionary returned is the same as calling each scorer separately
    scorers = {'a1': 'accuracy', 'a2': 'accuracy',
               'll1': 'neg_log_loss', 'll2': 'neg_log_loss',
               'ra1': 'roc_auc', 'ra2': 'roc_auc'}

    X, y = make_classification(random_state=0)

    clf = DecisionTreeClassifier()
    clf.fit(X, y)

    scorer_dict, _ = _check_multimetric_scoring(clf, scorers)
    multi_scorer = _MultimetricScorer(**scorer_dict)

    result = multi_scorer(clf, X, y)

    separate_scores = {
        name: get_scorer(name)(clf, X, y)
        for name in ['accuracy', 'neg_log_loss', 'roc_auc']}

    for key, value in result.items():
        score_name = scorers[key]
        assert_allclose(value, separate_scores[score_name])


@pytest.mark.parametrize('scorer_name, metric', [
    ('roc_auc_ovr', partial(roc_auc_score, multi_class='ovr')),
    ('roc_auc_ovo', partial(roc_auc_score, multi_class='ovo')),
    ('roc_auc_ovr_weighted', partial(roc_auc_score, multi_class='ovr',
                                     average='weighted')),
    ('roc_auc_ovo_weighted', partial(roc_auc_score, multi_class='ovo',
                                     average='weighted'))])
def test_multiclass_roc_proba_scorer(scorer_name, metric):
    scorer = get_scorer(scorer_name)
    X, y = make_classification(n_classes=3, n_informative=3, n_samples=20,
                               random_state=0)
    lr = LogisticRegression(multi_class="multinomial").fit(X, y)
    y_proba = lr.predict_proba(X)
    expected_score = metric(y, y_proba)

    assert scorer(lr, X, y) == pytest.approx(expected_score)


def test_multiclass_roc_proba_scorer_label():
    scorer = make_scorer(roc_auc_score, multi_class='ovo',
                         labels=[0, 1, 2], needs_proba=True)
    X, y = make_classification(n_classes=3, n_informative=3, n_samples=20,
                               random_state=0)
    lr = LogisticRegression(multi_class="multinomial").fit(X, y)
    y_proba = lr.predict_proba(X)

    y_binary = y == 0
    expected_score = roc_auc_score(y_binary, y_proba,
                                   multi_class='ovo',
                                   labels=[0, 1, 2])

    assert scorer(lr, X, y_binary) == pytest.approx(expected_score)


@pytest.mark.parametrize('scorer_name', [
    'roc_auc_ovr', 'roc_auc_ovo',
    'roc_auc_ovr_weighted', 'roc_auc_ovo_weighted'])
def test_multiclass_roc_no_proba_scorer_errors(scorer_name):
    # Perceptron has no predict_proba
    scorer = get_scorer(scorer_name)
    X, y = make_classification(n_classes=3, n_informative=3, n_samples=20,
                               random_state=0)
    lr = Perceptron().fit(X, y)
    msg = "'Perceptron' object has no attribute 'predict_proba'"
    with pytest.raises(AttributeError, match=msg):
        scorer(lr, X, y)