Sam Chaudry

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	import re
	import warnings
	from functools import partial
	from itertools import chain, permutations, product

	import numpy as np
	import pytest
	from scipy import linalg
	from scipy.spatial.distance import hamming as sp_hamming
	from scipy.stats import bernoulli

	from sklearn import datasets, svm
	from sklearn.datasets import make_multilabel_classification
	from sklearn.exceptions import UndefinedMetricWarning
	from sklearn.metrics import (
	accuracy_score,
	average_precision_score,
	balanced_accuracy_score,
	brier_score_loss,
	class_likelihood_ratios,
	classification_report,
	cohen_kappa_score,
	confusion_matrix,
	f1_score,
	fbeta_score,
	hamming_loss,
	hinge_loss,
	jaccard_score,
	log_loss,
	make_scorer,
	matthews_corrcoef,
	multilabel_confusion_matrix,
	precision_recall_fscore_support,
	precision_score,
	recall_score,
	zero_one_loss,
	)
	from sklearn.metrics._classification import _check_targets, d2_log_loss_score
	from sklearn.model_selection import cross_val_score
	from sklearn.preprocessing import LabelBinarizer, label_binarize
	from sklearn.tree import DecisionTreeClassifier
	from sklearn.utils._mocking import MockDataFrame
	from sklearn.utils._testing import (
	assert_allclose,
	assert_almost_equal,
	assert_array_almost_equal,
	assert_array_equal,
	ignore_warnings,
	)
	from sklearn.utils.extmath import _nanaverage
	from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS
	from sklearn.utils.validation import check_random_state

	###############################################################################
	# Utilities for testing


	def make_prediction(dataset=None, binary=False):
	"""Make some classification predictions on a toy dataset using a SVC

	If binary is True restrict to a binary classification problem instead of a
	multiclass classification problem
	"""

	if dataset is None:
	# import some data to play with
	dataset = datasets.load_iris()

	X = dataset.data
	y = dataset.target

	if binary:
	# restrict to a binary classification task
	X, y = X[y < 2], y[y < 2]

	n_samples, n_features = X.shape
	p = np.arange(n_samples)

	rng = check_random_state(37)
	rng.shuffle(p)
	X, y = X[p], y[p]
	half = int(n_samples / 2)

	# add noisy features to make the problem harder and avoid perfect results
	rng = np.random.RandomState(0)
	X = np.c_[X, rng.randn(n_samples, 200 * n_features)]

	# run classifier, get class probabilities and label predictions
	clf = svm.SVC(kernel="linear", probability=True, random_state=0)
	y_pred_proba = clf.fit(X[:half], y[:half]).predict_proba(X[half:])

	if binary:
	# only interested in probabilities of the positive case
	# XXX: do we really want a special API for the binary case?
	y_pred_proba = y_pred_proba[:, 1]

	y_pred = clf.predict(X[half:])
	y_true = y[half:]
	return y_true, y_pred, y_pred_proba


	###############################################################################
	# Tests


	def test_classification_report_dictionary_output():
	# Test performance report with dictionary output
	iris = datasets.load_iris()
	y_true, y_pred, _ = make_prediction(dataset=iris, binary=False)

	# print classification report with class names
	expected_report = {
	"setosa": {
	"precision": 0.82608695652173914,
	"recall": 0.79166666666666663,
	"f1-score": 0.8085106382978724,
	"support": 24,
	},
	"versicolor": {
	"precision": 0.33333333333333331,
	"recall": 0.096774193548387094,
	"f1-score": 0.15000000000000002,
	"support": 31,
	},
	"virginica": {
	"precision": 0.41860465116279072,
	"recall": 0.90000000000000002,
	"f1-score": 0.57142857142857151,
	"support": 20,
	},
	"macro avg": {
	"f1-score": 0.5099797365754813,
	"precision": 0.5260083136726211,
	"recall": 0.596146953405018,
	"support": 75,
	},
	"accuracy": 0.5333333333333333,
	"weighted avg": {
	"f1-score": 0.47310435663627154,
	"precision": 0.5137535108414785,
	"recall": 0.5333333333333333,
	"support": 75,
	},
	}

	report = classification_report(
	y_true,
	y_pred,
	labels=np.arange(len(iris.target_names)),
	target_names=iris.target_names,
	output_dict=True,
	)

	# assert the 2 dicts are equal.
	assert report.keys() == expected_report.keys()
	for key in expected_report:
	if key == "accuracy":
	assert isinstance(report[key], float)
	assert report[key] == expected_report[key]
	else:
	assert report[key].keys() == expected_report[key].keys()
	for metric in expected_report[key]:
	assert_almost_equal(expected_report[key][metric], report[key][metric])

	assert isinstance(expected_report["setosa"]["precision"], float)
	assert isinstance(expected_report["macro avg"]["precision"], float)
	assert isinstance(expected_report["setosa"]["support"], int)
	assert isinstance(expected_report["macro avg"]["support"], int)


	def test_classification_report_output_dict_empty_input():
	report = classification_report(y_true=[], y_pred=[], output_dict=True)
	expected_report = {
	"accuracy": 0.0,
	"macro avg": {
	"f1-score": np.nan,
	"precision": np.nan,
	"recall": np.nan,
	"support": 0,
	},
	"weighted avg": {
	"f1-score": np.nan,
	"precision": np.nan,
	"recall": np.nan,
	"support": 0,
	},
	}
	assert isinstance(report, dict)
	# assert the 2 dicts are equal.
	assert report.keys() == expected_report.keys()
	for key in expected_report:
	if key == "accuracy":
	assert isinstance(report[key], float)
	assert report[key] == expected_report[key]
	else:
	assert report[key].keys() == expected_report[key].keys()
	for metric in expected_report[key]:
	assert_almost_equal(expected_report[key][metric], report[key][metric])


	@pytest.mark.parametrize("zero_division", ["warn", 0, 1, np.nan])
	def test_classification_report_zero_division_warning(zero_division):
	y_true, y_pred = ["a", "b", "c"], ["a", "b", "d"]
	with warnings.catch_warnings(record=True) as record:
	classification_report(
	y_true, y_pred, zero_division=zero_division, output_dict=True
	)
	if zero_division == "warn":
	assert len(record) > 1
	for item in record:
	msg = "Use `zero_division` parameter to control this behavior."
	assert msg in str(item.message)
	else:
	assert not record


	@pytest.mark.parametrize(
	"labels, show_micro_avg", [([0], True), ([0, 1], False), ([0, 1, 2], False)]
	)
	def test_classification_report_labels_subset_superset(labels, show_micro_avg):
	"""Check the behaviour of passing `labels` as a superset or subset of the labels.
	WHen a superset, we expect to show the "accuracy" in the report while it should be
	the micro-averaging if this is a subset.

	Non-regression test for:
	https://github.com/scikit-learn/scikit-learn/issues/27927
	"""

	y_true, y_pred = [0, 1], [0, 1]

	report = classification_report(y_true, y_pred, labels=labels, output_dict=True)
	if show_micro_avg:
	assert "micro avg" in report
	assert "accuracy" not in report
	else: # accuracy should be shown
	assert "accuracy" in report
	assert "micro avg" not in report


	def test_multilabel_accuracy_score_subset_accuracy():
	# Dense label indicator matrix format
	y1 = np.array([[0, 1, 1], [1, 0, 1]])
	y2 = np.array([[0, 0, 1], [1, 0, 1]])

	assert accuracy_score(y1, y2) == 0.5
	assert accuracy_score(y1, y1) == 1
	assert accuracy_score(y2, y2) == 1
	assert accuracy_score(y2, np.logical_not(y2)) == 0
	assert accuracy_score(y1, np.logical_not(y1)) == 0
	assert accuracy_score(y1, np.zeros(y1.shape)) == 0
	assert accuracy_score(y2, np.zeros(y1.shape)) == 0


	def test_precision_recall_f1_score_binary():
	# Test Precision Recall and F1 Score for binary classification task
	y_true, y_pred, _ = make_prediction(binary=True)

	# detailed measures for each class
	p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average=None)
	assert_array_almost_equal(p, [0.73, 0.85], 2)
	assert_array_almost_equal(r, [0.88, 0.68], 2)
	assert_array_almost_equal(f, [0.80, 0.76], 2)
	assert_array_equal(s, [25, 25])

	# individual scoring function that can be used for grid search: in the
	# binary class case the score is the value of the measure for the positive
	# class (e.g. label == 1). This is deprecated for average != 'binary'.
	for kwargs in [{}, {"average": "binary"}]:
	with warnings.catch_warnings():
	warnings.simplefilter("error")

	ps = precision_score(y_true, y_pred, **kwargs)
	assert_array_almost_equal(ps, 0.85, 2)

	rs = recall_score(y_true, y_pred, **kwargs)
	assert_array_almost_equal(rs, 0.68, 2)

	fs = f1_score(y_true, y_pred, **kwargs)
	assert_array_almost_equal(fs, 0.76, 2)

	assert_almost_equal(
	fbeta_score(y_true, y_pred, beta=2, **kwargs),
	(1 + 2*2) ps * rs / (2*2 ps + rs),
	2,
	)


	@pytest.mark.filterwarnings(r"ignore::sklearn.exceptions.UndefinedMetricWarning")
	def test_precision_recall_f_binary_single_class():
	# Test precision, recall and F-scores behave with a single positive or
	# negative class
	# Such a case may occur with non-stratified cross-validation
	assert 1.0 == precision_score([1, 1], [1, 1])
	assert 1.0 == recall_score([1, 1], [1, 1])
	assert 1.0 == f1_score([1, 1], [1, 1])
	assert 1.0 == fbeta_score([1, 1], [1, 1], beta=0)

	assert 0.0 == precision_score([-1, -1], [-1, -1])
	assert 0.0 == recall_score([-1, -1], [-1, -1])
	assert 0.0 == f1_score([-1, -1], [-1, -1])
	assert 0.0 == fbeta_score([-1, -1], [-1, -1], beta=float("inf"))
	assert fbeta_score([-1, -1], [-1, -1], beta=float("inf")) == pytest.approx(
	fbeta_score([-1, -1], [-1, -1], beta=1e5)
	)


	@pytest.mark.filterwarnings(r"ignore::sklearn.exceptions.UndefinedMetricWarning")
	def test_precision_recall_f_extra_labels():
	# Test handling of explicit additional (not in input) labels to PRF
	y_true = [1, 3, 3, 2]
	y_pred = [1, 1, 3, 2]
	y_true_bin = label_binarize(y_true, classes=np.arange(5))
	y_pred_bin = label_binarize(y_pred, classes=np.arange(5))
	data = [(y_true, y_pred), (y_true_bin, y_pred_bin)]

	for i, (y_true, y_pred) in enumerate(data):
	# No average: zeros in array
	actual = recall_score(y_true, y_pred, labels=[0, 1, 2, 3, 4], average=None)
	assert_array_almost_equal([0.0, 1.0, 1.0, 0.5, 0.0], actual)

	# Macro average is changed
	actual = recall_score(y_true, y_pred, labels=[0, 1, 2, 3, 4], average="macro")
	assert_array_almost_equal(np.mean([0.0, 1.0, 1.0, 0.5, 0.0]), actual)

	# No effect otherwise
	for average in ["micro", "weighted", "samples"]:
	if average == "samples" and i == 0:
	continue
	assert_almost_equal(
	recall_score(y_true, y_pred, labels=[0, 1, 2, 3, 4], average=average),
	recall_score(y_true, y_pred, labels=None, average=average),
	)

	# Error when introducing invalid label in multilabel case
	# (although it would only affect performance if average='macro'/None)
	for average in [None, "macro", "micro", "samples"]:
	with pytest.raises(ValueError):
	recall_score(y_true_bin, y_pred_bin, labels=np.arange(6), average=average)
	with pytest.raises(ValueError):
	recall_score(
	y_true_bin, y_pred_bin, labels=np.arange(-1, 4), average=average
	)

	# tests non-regression on issue #10307
	y_true = np.array([[0, 1, 1], [1, 0, 0]])
	y_pred = np.array([[1, 1, 1], [1, 0, 1]])
	p, r, f, _ = precision_recall_fscore_support(
	y_true, y_pred, average="samples", labels=[0, 1]
	)
	assert_almost_equal(np.array([p, r, f]), np.array([3 / 4, 1, 5 / 6]))


	@pytest.mark.filterwarnings(r"ignore::sklearn.exceptions.UndefinedMetricWarning")
	def test_precision_recall_f_ignored_labels():
	# Test a subset of labels may be requested for PRF
	y_true = [1, 1, 2, 3]
	y_pred = [1, 3, 3, 3]
	y_true_bin = label_binarize(y_true, classes=np.arange(5))
	y_pred_bin = label_binarize(y_pred, classes=np.arange(5))
	data = [(y_true, y_pred), (y_true_bin, y_pred_bin)]

	for i, (y_true, y_pred) in enumerate(data):
	recall_13 = partial(recall_score, y_true, y_pred, labels=[1, 3])
	recall_all = partial(recall_score, y_true, y_pred, labels=None)

	assert_array_almost_equal([0.5, 1.0], recall_13(average=None))
	assert_almost_equal((0.5 + 1.0) / 2, recall_13(average="macro"))
	assert_almost_equal((0.5 * 2 + 1.0 * 1) / 3, recall_13(average="weighted"))
	assert_almost_equal(2.0 / 3, recall_13(average="micro"))

	# ensure the above were meaningful tests:
	for average in ["macro", "weighted", "micro"]:
	assert recall_13(average=average) != recall_all(average=average)


	def test_average_precision_score_non_binary_class():
	"""Test multiclass-multiouptut for `average_precision_score`."""
	y_true = np.array(
	[
	[2, 2, 1],
	[1, 2, 0],
	[0, 1, 2],
	[1, 2, 1],
	[2, 0, 1],
	[1, 2, 1],
	]
	)
	y_score = np.array(
	[
	[0.7, 0.2, 0.1],
	[0.4, 0.3, 0.3],
	[0.1, 0.8, 0.1],
	[0.2, 0.3, 0.5],
	[0.4, 0.4, 0.2],
	[0.1, 0.2, 0.7],
	]
	)
	err_msg = "multiclass-multioutput format is not supported"
	with pytest.raises(ValueError, match=err_msg):
	average_precision_score(y_true, y_score, pos_label=2)


	@pytest.mark.parametrize(
	"y_true, y_score",
	[
	(
	[0, 0, 1, 2],
	np.array(
	[
	[0.7, 0.2, 0.1],
	[0.4, 0.3, 0.3],
	[0.1, 0.8, 0.1],
	[0.2, 0.3, 0.5],
	]
	),
	),
	(
	[0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
	[0, 0.1, 0.1, 0.4, 0.5, 0.6, 0.6, 0.9, 0.9, 1, 1],
	),
	],
	)
	def test_average_precision_score_duplicate_values(y_true, y_score):
	"""
	Duplicate values with precision-recall require a different
	processing than when computing the AUC of a ROC, because the
	precision-recall curve is a decreasing curve
	The following situation corresponds to a perfect
	test statistic, the average_precision_score should be 1.
	"""
	assert average_precision_score(y_true, y_score) == 1


	@pytest.mark.parametrize(
	"y_true, y_score",
	[
	(
	[2, 2, 1, 1, 0],
	np.array(
	[
	[0.2, 0.3, 0.5],
	[0.2, 0.3, 0.5],
	[0.4, 0.5, 0.3],
	[0.4, 0.5, 0.3],
	[0.8, 0.5, 0.3],
	]
	),
	),
	(
	[0, 1, 1],
	[0.5, 0.5, 0.6],
	),
	],
	)
	def test_average_precision_score_tied_values(y_true, y_score):
	# Here if we go from left to right in y_true, the 0 values are
	# separated from the 1 values, so it appears that we've
	# correctly sorted our classifications. But in fact the first two
	# values have the same score (0.5) and so the first two values
	# could be swapped around, creating an imperfect sorting. This
	# imperfection should come through in the end score, making it less
	# than one.
	assert average_precision_score(y_true, y_score) != 1.0


	def test_precision_recall_f_unused_pos_label():
	# Check warning that pos_label unused when set to non-default value
	# but average != 'binary'; even if data is binary.

	msg = (
	r"Note that pos_label \(set to 2\) is "
	r"ignored when average != 'binary' \(got 'macro'\). You "
	r"may use labels=\[pos_label\] to specify a single "
	"positive class."
	)
	with pytest.warns(UserWarning, match=msg):
	precision_recall_fscore_support(
	[1, 2, 1], [1, 2, 2], pos_label=2, average="macro"
	)


	def test_confusion_matrix_binary():
	# Test confusion matrix - binary classification case
	y_true, y_pred, _ = make_prediction(binary=True)

	def test(y_true, y_pred):
	cm = confusion_matrix(y_true, y_pred)
	assert_array_equal(cm, [[22, 3], [8, 17]])

	tp, fp, fn, tn = cm.flatten()
	num = tp * tn - fp * fn
	den = np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))

	true_mcc = 0 if den == 0 else num / den
	mcc = matthews_corrcoef(y_true, y_pred)
	assert_array_almost_equal(mcc, true_mcc, decimal=2)
	assert_array_almost_equal(mcc, 0.57, decimal=2)

	test(y_true, y_pred)
	test([str(y) for y in y_true], [str(y) for y in y_pred])


	def test_multilabel_confusion_matrix_binary():
	# Test multilabel confusion matrix - binary classification case
	y_true, y_pred, _ = make_prediction(binary=True)

	def test(y_true, y_pred):
	cm = multilabel_confusion_matrix(y_true, y_pred)
	assert_array_equal(cm, [[[17, 8], [3, 22]], [[22, 3], [8, 17]]])

	test(y_true, y_pred)
	test([str(y) for y in y_true], [str(y) for y in y_pred])


	def test_multilabel_confusion_matrix_multiclass():
	# Test multilabel confusion matrix - multi-class case
	y_true, y_pred, _ = make_prediction(binary=False)

	def test(y_true, y_pred, string_type=False):
	# compute confusion matrix with default labels introspection
	cm = multilabel_confusion_matrix(y_true, y_pred)
	assert_array_equal(
	cm, [[[47, 4], [5, 19]], [[38, 6], [28, 3]], [[30, 25], [2, 18]]]
	)

	# compute confusion matrix with explicit label ordering
	labels = ["0", "2", "1"] if string_type else [0, 2, 1]
	cm = multilabel_confusion_matrix(y_true, y_pred, labels=labels)
	assert_array_equal(
	cm, [[[47, 4], [5, 19]], [[30, 25], [2, 18]], [[38, 6], [28, 3]]]
	)

	# compute confusion matrix with super set of present labels
	labels = ["0", "2", "1", "3"] if string_type else [0, 2, 1, 3]
	cm = multilabel_confusion_matrix(y_true, y_pred, labels=labels)
	assert_array_equal(
	cm,
	[
	[[47, 4], [5, 19]],
	[[30, 25], [2, 18]],
	[[38, 6], [28, 3]],
	[[75, 0], [0, 0]],
	],
	)

	test(y_true, y_pred)
	test([str(y) for y in y_true], [str(y) for y in y_pred], string_type=True)


	@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
	@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
	def test_multilabel_confusion_matrix_multilabel(csc_container, csr_container):
	# Test multilabel confusion matrix - multilabel-indicator case

	y_true = np.array([[1, 0, 1], [0, 1, 0], [1, 1, 0]])
	y_pred = np.array([[1, 0, 0], [0, 1, 1], [0, 0, 1]])
	y_true_csr = csr_container(y_true)
	y_pred_csr = csr_container(y_pred)
	y_true_csc = csc_container(y_true)
	y_pred_csc = csc_container(y_pred)

	# cross test different types
	sample_weight = np.array([2, 1, 3])
	real_cm = [[[1, 0], [1, 1]], [[1, 0], [1, 1]], [[0, 2], [1, 0]]]
	trues = [y_true, y_true_csr, y_true_csc]
	preds = [y_pred, y_pred_csr, y_pred_csc]

	for y_true_tmp in trues:
	for y_pred_tmp in preds:
	cm = multilabel_confusion_matrix(y_true_tmp, y_pred_tmp)
	assert_array_equal(cm, real_cm)

	# test support for samplewise
	cm = multilabel_confusion_matrix(y_true, y_pred, samplewise=True)
	assert_array_equal(cm, [[[1, 0], [1, 1]], [[1, 1], [0, 1]], [[0, 1], [2, 0]]])

	# test support for labels
	cm = multilabel_confusion_matrix(y_true, y_pred, labels=[2, 0])
	assert_array_equal(cm, [[[0, 2], [1, 0]], [[1, 0], [1, 1]]])

	# test support for labels with samplewise
	cm = multilabel_confusion_matrix(y_true, y_pred, labels=[2, 0], samplewise=True)
	assert_array_equal(cm, [[[0, 0], [1, 1]], [[1, 1], [0, 0]], [[0, 1], [1, 0]]])

	# test support for sample_weight with sample_wise
	cm = multilabel_confusion_matrix(
	y_true, y_pred, sample_weight=sample_weight, samplewise=True
	)
	assert_array_equal(cm, [[[2, 0], [2, 2]], [[1, 1], [0, 1]], [[0, 3], [6, 0]]])


	def test_multilabel_confusion_matrix_errors():
	y_true = np.array([[1, 0, 1], [0, 1, 0], [1, 1, 0]])
	y_pred = np.array([[1, 0, 0], [0, 1, 1], [0, 0, 1]])

	# Bad sample_weight
	with pytest.raises(ValueError, match="inconsistent numbers of samples"):
	multilabel_confusion_matrix(y_true, y_pred, sample_weight=[1, 2])
	with pytest.raises(ValueError, match="should be a 1d array"):
	multilabel_confusion_matrix(
	y_true, y_pred, sample_weight=[[1, 2, 3], [2, 3, 4], [3, 4, 5]]
	)

	# Bad labels
	err_msg = r"All labels must be in \[0, n labels\)"
	with pytest.raises(ValueError, match=err_msg):
	multilabel_confusion_matrix(y_true, y_pred, labels=[-1])
	err_msg = r"All labels must be in \[0, n labels\)"
	with pytest.raises(ValueError, match=err_msg):
	multilabel_confusion_matrix(y_true, y_pred, labels=[3])

	# Using samplewise outside multilabel
	with pytest.raises(ValueError, match="Samplewise metrics"):
	multilabel_confusion_matrix([0, 1, 2], [1, 2, 0], samplewise=True)

	# Bad y_type
	err_msg = "multiclass-multioutput is not supported"
	with pytest.raises(ValueError, match=err_msg):
	multilabel_confusion_matrix([[0, 1, 2], [2, 1, 0]], [[1, 2, 0], [1, 0, 2]])


	@pytest.mark.parametrize(
	"normalize, cm_dtype, expected_results",
	[
	("true", "f", 0.333333333),
	("pred", "f", 0.333333333),
	("all", "f", 0.1111111111),
	(None, "i", 2),
	],
	)
	def test_confusion_matrix_normalize(normalize, cm_dtype, expected_results):
	y_test = [0, 1, 2] * 6
	y_pred = list(chain(*permutations([0, 1, 2])))
	cm = confusion_matrix(y_test, y_pred, normalize=normalize)
	assert_allclose(cm, expected_results)
	assert cm.dtype.kind == cm_dtype


	def test_confusion_matrix_normalize_single_class():
	y_test = [0, 0, 0, 0, 1, 1, 1, 1]
	y_pred = [0, 0, 0, 0, 0, 0, 0, 0]

	cm_true = confusion_matrix(y_test, y_pred, normalize="true")
	assert cm_true.sum() == pytest.approx(2.0)

	# additionally check that no warnings are raised due to a division by zero
	with warnings.catch_warnings():
	warnings.simplefilter("error", RuntimeWarning)
	cm_pred = confusion_matrix(y_test, y_pred, normalize="pred")

	assert cm_pred.sum() == pytest.approx(1.0)

	with warnings.catch_warnings():
	warnings.simplefilter("error", RuntimeWarning)
	confusion_matrix(y_pred, y_test, normalize="true")


	def test_confusion_matrix_single_label():
	"""Test `confusion_matrix` warns when only one label found."""
	y_test = [0, 0, 0, 0]
	y_pred = [0, 0, 0, 0]

	with pytest.warns(UserWarning, match="A single label was found in"):
	confusion_matrix(y_pred, y_test)


	@pytest.mark.parametrize(
	"params, warn_msg",
	[
	# When y_test contains one class only and y_test==y_pred, LR+ is undefined
	(
	{
	"y_true": np.array([0, 0, 0, 0, 0, 0]),
	"y_pred": np.array([0, 0, 0, 0, 0, 0]),
	},
	"samples of only one class were seen during testing",
	),
	# When `fp == 0` and `tp != 0`, LR+ is undefined
	(
	{
	"y_true": np.array([1, 1, 1, 0, 0, 0]),
	"y_pred": np.array([1, 1, 1, 0, 0, 0]),
	},
	"positive_likelihood_ratio ill-defined and being set to nan",
	),
	# When `fp == 0` and `tp == 0`, LR+ is undefined
	(
	{
	"y_true": np.array([1, 1, 1, 0, 0, 0]),
	"y_pred": np.array([0, 0, 0, 0, 0, 0]),
	},
	"no samples predicted for the positive class",
	),
	# When `tn == 0`, LR- is undefined
	(
	{
	"y_true": np.array([1, 1, 1, 0, 0, 0]),
	"y_pred": np.array([0, 0, 0, 1, 1, 1]),
	},
	"negative_likelihood_ratio ill-defined and being set to nan",
	),
	# When `tp + fn == 0` both ratios are undefined
	(
	{
	"y_true": np.array([0, 0, 0, 0, 0, 0]),
	"y_pred": np.array([1, 1, 1, 0, 0, 0]),
	},
	"no samples of the positive class were present in the testing set",
	),
	],
	)
	def test_likelihood_ratios_warnings(params, warn_msg):
	# likelihood_ratios must raise warnings when at
	# least one of the ratios is ill-defined.

	with pytest.warns(UserWarning, match=warn_msg):
	class_likelihood_ratios(**params)


	@pytest.mark.parametrize(
	"params, err_msg",
	[
	(
	{
	"y_true": np.array([0, 1, 0, 1, 0]),
	"y_pred": np.array([1, 1, 0, 0, 2]),
	},
	(
	"class_likelihood_ratios only supports binary classification "
	"problems, got targets of type: multiclass"
	),
	),
	],
	)
	def test_likelihood_ratios_errors(params, err_msg):
	# likelihood_ratios must raise error when attempting
	# non-binary classes to avoid Simpson's paradox
	with pytest.raises(ValueError, match=err_msg):
	class_likelihood_ratios(**params)


	def test_likelihood_ratios():
	# Build confusion matrix with tn=9, fp=8, fn=1, tp=2,
	# sensitivity=2/3, specificity=9/17, prevalence=3/20,
	# LR+=34/24, LR-=17/27
	y_true = np.array([1] * 3 + [0] * 17)
	y_pred = np.array([1] * 2 + [0] * 10 + [1] * 8)

	pos, neg = class_likelihood_ratios(y_true, y_pred)
	assert_allclose(pos, 34 / 24)
	assert_allclose(neg, 17 / 27)

	# Build limit case with y_pred = y_true
	pos, neg = class_likelihood_ratios(y_true, y_true)
	assert_array_equal(pos, np.nan * 2)
	assert_allclose(neg, np.zeros(2), rtol=1e-12)

	# Ignore last 5 samples to get tn=9, fp=3, fn=1, tp=2,
	# sensitivity=2/3, specificity=9/12, prevalence=3/20,
	# LR+=24/9, LR-=12/27
	sample_weight = np.array([1.0] * 15 + [0.0] * 5)
	pos, neg = class_likelihood_ratios(y_true, y_pred, sample_weight=sample_weight)
	assert_allclose(pos, 24 / 9)
	assert_allclose(neg, 12 / 27)


	def test_cohen_kappa():
	# These label vectors reproduce the contingency matrix from Artstein and
	# Poesio (2008), Table 1: np.array([[20, 20], [10, 50]]).
	y1 = np.array([0] * 40 + [1] * 60)
	y2 = np.array([0] * 20 + [1] * 20 + [0] * 10 + [1] * 50)
	kappa = cohen_kappa_score(y1, y2)
	assert_almost_equal(kappa, 0.348, decimal=3)
	assert kappa == cohen_kappa_score(y2, y1)

	# Add spurious labels and ignore them.
	y1 = np.append(y1, [2] * 4)
	y2 = np.append(y2, [2] * 4)
	assert cohen_kappa_score(y1, y2, labels=[0, 1]) == kappa

	assert_almost_equal(cohen_kappa_score(y1, y1), 1.0)

	# Multiclass example: Artstein and Poesio, Table 4.
	y1 = np.array([0] * 46 + [1] * 44 + [2] * 10)
	y2 = np.array([0] * 52 + [1] * 32 + [2] * 16)
	assert_almost_equal(cohen_kappa_score(y1, y2), 0.8013, decimal=4)

	# Weighting example: none, linear, quadratic.
	y1 = np.array([0] * 46 + [1] * 44 + [2] * 10)
	y2 = np.array([0] * 50 + [1] * 40 + [2] * 10)
	assert_almost_equal(cohen_kappa_score(y1, y2), 0.9315, decimal=4)
	assert_almost_equal(cohen_kappa_score(y1, y2, weights="linear"), 0.9412, decimal=4)
	assert_almost_equal(
	cohen_kappa_score(y1, y2, weights="quadratic"), 0.9541, decimal=4
	)


	@pytest.mark.parametrize("zero_division", [0, 1, np.nan])
	@pytest.mark.parametrize("y_true, y_pred", [([0], [0])])
	@pytest.mark.parametrize(
	"metric",
	[
	f1_score,
	partial(fbeta_score, beta=1),
	precision_score,
	recall_score,
	],
	)
	def test_zero_division_nan_no_warning(metric, y_true, y_pred, zero_division):
	"""Check the behaviour of `zero_division` when setting to 0, 1 or np.nan.
	No warnings should be raised.
	"""
	with warnings.catch_warnings():
	warnings.simplefilter("error")
	result = metric(y_true, y_pred, zero_division=zero_division)

	if np.isnan(zero_division):
	assert np.isnan(result)
	else:
	assert result == zero_division


	@pytest.mark.parametrize("y_true, y_pred", [([0], [0])])
	@pytest.mark.parametrize(
	"metric",
	[
	f1_score,
	partial(fbeta_score, beta=1),
	precision_score,
	recall_score,
	],
	)
	def test_zero_division_nan_warning(metric, y_true, y_pred):
	"""Check the behaviour of `zero_division` when setting to "warn".
	A `UndefinedMetricWarning` should be raised.
	"""
	with pytest.warns(UndefinedMetricWarning):
	result = metric(y_true, y_pred, zero_division="warn")
	assert result == 0.0


	def test_matthews_corrcoef_against_numpy_corrcoef():
	rng = np.random.RandomState(0)
	y_true = rng.randint(0, 2, size=20)
	y_pred = rng.randint(0, 2, size=20)

	assert_almost_equal(
	matthews_corrcoef(y_true, y_pred), np.corrcoef(y_true, y_pred)[0, 1], 10
	)


	def test_matthews_corrcoef_against_jurman():
	# Check that the multiclass matthews_corrcoef agrees with the definition
	# presented in Jurman, Riccadonna, Furlanello, (2012). A Comparison of MCC
	# and CEN Error Measures in MultiClass Prediction
	rng = np.random.RandomState(0)
	y_true = rng.randint(0, 2, size=20)
	y_pred = rng.randint(0, 2, size=20)
	sample_weight = rng.rand(20)

	C = confusion_matrix(y_true, y_pred, sample_weight=sample_weight)
	N = len(C)
	cov_ytyp = sum(
	[
	C[k, k] * C[m, l] - C[l, k] * C[k, m]
	for k in range(N)
	for m in range(N)
	for l in range(N)
	]
	)
	cov_ytyt = sum(
	[
	C[:, k].sum()
	* np.sum([C[g, f] for f in range(N) for g in range(N) if f != k])
	for k in range(N)
	]
	)
	cov_ypyp = np.sum(
	[
	C[k, :].sum()
	* np.sum([C[f, g] for f in range(N) for g in range(N) if f != k])
	for k in range(N)
	]
	)
	mcc_jurman = cov_ytyp / np.sqrt(cov_ytyt * cov_ypyp)
	mcc_ours = matthews_corrcoef(y_true, y_pred, sample_weight=sample_weight)

	assert_almost_equal(mcc_ours, mcc_jurman, 10)


	def test_matthews_corrcoef():
	rng = np.random.RandomState(0)
	y_true = ["a" if i == 0 else "b" for i in rng.randint(0, 2, size=20)]

	# corrcoef of same vectors must be 1
	assert_almost_equal(matthews_corrcoef(y_true, y_true), 1.0)

	# corrcoef, when the two vectors are opposites of each other, should be -1
	y_true_inv = ["b" if i == "a" else "a" for i in y_true]
	assert_almost_equal(matthews_corrcoef(y_true, y_true_inv), -1)

	y_true_inv2 = label_binarize(y_true, classes=["a", "b"])
	y_true_inv2 = np.where(y_true_inv2, "a", "b")
	assert_almost_equal(matthews_corrcoef(y_true, y_true_inv2), -1)

	# For the zero vector case, the corrcoef cannot be calculated and should
	# output 0
	assert_almost_equal(matthews_corrcoef([0, 0, 0, 0], [0, 0, 0, 0]), 0.0)

	# And also for any other vector with 0 variance
	assert_almost_equal(matthews_corrcoef(y_true, ["a"] * len(y_true)), 0.0)

	# These two vectors have 0 correlation and hence mcc should be 0
	y_1 = [1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1]
	y_2 = [1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1]
	assert_almost_equal(matthews_corrcoef(y_1, y_2), 0.0)

	# Check that sample weight is able to selectively exclude
	mask = [1] * 10 + [0] * 10
	# Now the first half of the vector elements are alone given a weight of 1
	# and hence the mcc will not be a perfect 0 as in the previous case
	with pytest.raises(AssertionError):
	assert_almost_equal(matthews_corrcoef(y_1, y_2, sample_weight=mask), 0.0)


	def test_matthews_corrcoef_multiclass():
	rng = np.random.RandomState(0)
	ord_a = ord("a")
	n_classes = 4
	y_true = [chr(ord_a + i) for i in rng.randint(0, n_classes, size=20)]

	# corrcoef of same vectors must be 1
	assert_almost_equal(matthews_corrcoef(y_true, y_true), 1.0)

	# with multiclass > 2 it is not possible to achieve -1
	y_true = [0, 0, 1, 1, 2, 2]
	y_pred_bad = [2, 2, 0, 0, 1, 1]
	assert_almost_equal(matthews_corrcoef(y_true, y_pred_bad), -0.5)

	# Maximizing false positives and negatives minimizes the MCC
	# The minimum will be different for depending on the input
	y_true = [0, 0, 1, 1, 2, 2]
	y_pred_min = [1, 1, 0, 0, 0, 0]
	assert_almost_equal(matthews_corrcoef(y_true, y_pred_min), -12 / np.sqrt(24 * 16))

	# Zero variance will result in an mcc of zero
	y_true = [0, 1, 2]
	y_pred = [3, 3, 3]
	assert_almost_equal(matthews_corrcoef(y_true, y_pred), 0.0)

	# Also for ground truth with zero variance
	y_true = [3, 3, 3]
	y_pred = [0, 1, 2]
	assert_almost_equal(matthews_corrcoef(y_true, y_pred), 0.0)

	# These two vectors have 0 correlation and hence mcc should be 0
	y_1 = [0, 1, 2, 0, 1, 2, 0, 1, 2]
	y_2 = [1, 1, 1, 2, 2, 2, 0, 0, 0]
	assert_almost_equal(matthews_corrcoef(y_1, y_2), 0.0)

	# We can test that binary assumptions hold using the multiclass computation
	# by masking the weight of samples not in the first two classes

	# Masking the last label should let us get an MCC of -1
	y_true = [0, 0, 1, 1, 2]
	y_pred = [1, 1, 0, 0, 2]
	sample_weight = [1, 1, 1, 1, 0]
	assert_almost_equal(
	matthews_corrcoef(y_true, y_pred, sample_weight=sample_weight), -1
	)

	# For the zero vector case, the corrcoef cannot be calculated and should
	# output 0
	y_true = [0, 0, 1, 2]
	y_pred = [0, 0, 1, 2]
	sample_weight = [1, 1, 0, 0]
	assert_almost_equal(
	matthews_corrcoef(y_true, y_pred, sample_weight=sample_weight), 0.0
	)


	@pytest.mark.parametrize("n_points", [100, 10000])
	def test_matthews_corrcoef_overflow(n_points):
	# https://github.com/scikit-learn/scikit-learn/issues/9622
	rng = np.random.RandomState(20170906)

	def mcc_safe(y_true, y_pred):
	conf_matrix = confusion_matrix(y_true, y_pred)
	true_pos = conf_matrix[1, 1]
	false_pos = conf_matrix[1, 0]
	false_neg = conf_matrix[0, 1]
	n_points = len(y_true)
	pos_rate = (true_pos + false_neg) / n_points
	activity = (true_pos + false_pos) / n_points
	mcc_numerator = true_pos / n_points - pos_rate * activity
	mcc_denominator = activity * pos_rate * (1 - activity) * (1 - pos_rate)
	return mcc_numerator / np.sqrt(mcc_denominator)

	def random_ys(n_points): # binary
	x_true = rng.random_sample(n_points)
	x_pred = x_true + 0.2 * (rng.random_sample(n_points) - 0.5)
	y_true = x_true > 0.5
	y_pred = x_pred > 0.5
	return y_true, y_pred

	arr = np.repeat([0.0, 1.0], n_points) # binary
	assert_almost_equal(matthews_corrcoef(arr, arr), 1.0)
	arr = np.repeat([0.0, 1.0, 2.0], n_points) # multiclass
	assert_almost_equal(matthews_corrcoef(arr, arr), 1.0)

	y_true, y_pred = random_ys(n_points)
	assert_almost_equal(matthews_corrcoef(y_true, y_true), 1.0)
	assert_almost_equal(matthews_corrcoef(y_true, y_pred), mcc_safe(y_true, y_pred))


	def test_precision_recall_f1_score_multiclass():
	# Test Precision Recall and F1 Score for multiclass classification task
	y_true, y_pred, _ = make_prediction(binary=False)

	# compute scores with default labels introspection
	p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average=None)
	assert_array_almost_equal(p, [0.83, 0.33, 0.42], 2)
	assert_array_almost_equal(r, [0.79, 0.09, 0.90], 2)
	assert_array_almost_equal(f, [0.81, 0.15, 0.57], 2)
	assert_array_equal(s, [24, 31, 20])

	# averaging tests
	ps = precision_score(y_true, y_pred, pos_label=1, average="micro")
	assert_array_almost_equal(ps, 0.53, 2)

	rs = recall_score(y_true, y_pred, average="micro")
	assert_array_almost_equal(rs, 0.53, 2)

	fs = f1_score(y_true, y_pred, average="micro")
	assert_array_almost_equal(fs, 0.53, 2)

	ps = precision_score(y_true, y_pred, average="macro")
	assert_array_almost_equal(ps, 0.53, 2)

	rs = recall_score(y_true, y_pred, average="macro")
	assert_array_almost_equal(rs, 0.60, 2)

	fs = f1_score(y_true, y_pred, average="macro")
	assert_array_almost_equal(fs, 0.51, 2)

	ps = precision_score(y_true, y_pred, average="weighted")
	assert_array_almost_equal(ps, 0.51, 2)

	rs = recall_score(y_true, y_pred, average="weighted")
	assert_array_almost_equal(rs, 0.53, 2)

	fs = f1_score(y_true, y_pred, average="weighted")
	assert_array_almost_equal(fs, 0.47, 2)

	with pytest.raises(ValueError):
	precision_score(y_true, y_pred, average="samples")
	with pytest.raises(ValueError):
	recall_score(y_true, y_pred, average="samples")
	with pytest.raises(ValueError):
	f1_score(y_true, y_pred, average="samples")
	with pytest.raises(ValueError):
	fbeta_score(y_true, y_pred, average="samples", beta=0.5)

	# same prediction but with and explicit label ordering
	p, r, f, s = precision_recall_fscore_support(
	y_true, y_pred, labels=[0, 2, 1], average=None
	)
	assert_array_almost_equal(p, [0.83, 0.41, 0.33], 2)
	assert_array_almost_equal(r, [0.79, 0.90, 0.10], 2)
	assert_array_almost_equal(f, [0.81, 0.57, 0.15], 2)
	assert_array_equal(s, [24, 20, 31])


	@pytest.mark.parametrize("average", ["samples", "micro", "macro", "weighted", None])
	def test_precision_refcall_f1_score_multilabel_unordered_labels(average):
	# test that labels need not be sorted in the multilabel case
	y_true = np.array([[1, 1, 0, 0]])
	y_pred = np.array([[0, 0, 1, 1]])
	p, r, f, s = precision_recall_fscore_support(
	y_true, y_pred, labels=[3, 0, 1, 2], warn_for=[], average=average
	)
	assert_array_equal(p, 0)
	assert_array_equal(r, 0)
	assert_array_equal(f, 0)
	if average is None:
	assert_array_equal(s, [0, 1, 1, 0])


	def test_precision_recall_f1_score_binary_averaged():
	y_true = np.array([0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1])
	y_pred = np.array([1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1])

	# compute scores with default labels introspection
	ps, rs, fs, _ = precision_recall_fscore_support(y_true, y_pred, average=None)
	p, r, f, _ = precision_recall_fscore_support(y_true, y_pred, average="macro")
	assert p == np.mean(ps)
	assert r == np.mean(rs)
	assert f == np.mean(fs)
	p, r, f, _ = precision_recall_fscore_support(y_true, y_pred, average="weighted")
	support = np.bincount(y_true)
	assert p == np.average(ps, weights=support)
	assert r == np.average(rs, weights=support)
	assert f == np.average(fs, weights=support)


	def test_zero_precision_recall():
	# Check that pathological cases do not bring NaNs

	old_error_settings = np.seterr(all="raise")

	try:
	y_true = np.array([0, 1, 2, 0, 1, 2])
	y_pred = np.array([2, 0, 1, 1, 2, 0])

	assert_almost_equal(precision_score(y_true, y_pred, average="macro"), 0.0, 2)
	assert_almost_equal(recall_score(y_true, y_pred, average="macro"), 0.0, 2)
	assert_almost_equal(f1_score(y_true, y_pred, average="macro"), 0.0, 2)

	finally:
	np.seterr(**old_error_settings)


	def test_confusion_matrix_multiclass_subset_labels():
	# Test confusion matrix - multi-class case with subset of labels
	y_true, y_pred, _ = make_prediction(binary=False)

	# compute confusion matrix with only first two labels considered
	cm = confusion_matrix(y_true, y_pred, labels=[0, 1])
	assert_array_equal(cm, [[19, 4], [4, 3]])

	# compute confusion matrix with explicit label ordering for only subset
	# of labels
	cm = confusion_matrix(y_true, y_pred, labels=[2, 1])
	assert_array_equal(cm, [[18, 2], [24, 3]])

	# a label not in y_true should result in zeros for that row/column
	extra_label = np.max(y_true) + 1
	cm = confusion_matrix(y_true, y_pred, labels=[2, extra_label])
	assert_array_equal(cm, [[18, 0], [0, 0]])


	@pytest.mark.parametrize(
	"labels, err_msg",
	[
	([], "'labels' should contains at least one label."),
	([3, 4], "At least one label specified must be in y_true"),
	],
	ids=["empty list", "unknown labels"],
	)
	def test_confusion_matrix_error(labels, err_msg):
	y_true, y_pred, _ = make_prediction(binary=False)
	with pytest.raises(ValueError, match=err_msg):
	confusion_matrix(y_true, y_pred, labels=labels)


	@pytest.mark.parametrize(
	"labels", (None, [0, 1], [0, 1, 2]), ids=["None", "binary", "multiclass"]
	)
	def test_confusion_matrix_on_zero_length_input(labels):
	expected_n_classes = len(labels) if labels else 0
	expected = np.zeros((expected_n_classes, expected_n_classes), dtype=int)
	cm = confusion_matrix([], [], labels=labels)
	assert_array_equal(cm, expected)


	def test_confusion_matrix_dtype():
	y = [0, 1, 1]
	weight = np.ones(len(y))
	# confusion_matrix returns int64 by default
	cm = confusion_matrix(y, y)
	assert cm.dtype == np.int64
	# The dtype of confusion_matrix is always 64 bit
	for dtype in [np.bool_, np.int32, np.uint64]:
	cm = confusion_matrix(y, y, sample_weight=weight.astype(dtype, copy=False))
	assert cm.dtype == np.int64
	for dtype in [np.float32, np.float64, None, object]:
	cm = confusion_matrix(y, y, sample_weight=weight.astype(dtype, copy=False))
	assert cm.dtype == np.float64

	# np.iinfo(np.uint32).max should be accumulated correctly
	weight = np.full(len(y), 4294967295, dtype=np.uint32)
	cm = confusion_matrix(y, y, sample_weight=weight)
	assert cm[0, 0] == 4294967295
	assert cm[1, 1] == 8589934590

	# np.iinfo(np.int64).max should cause an overflow
	weight = np.full(len(y), 9223372036854775807, dtype=np.int64)
	cm = confusion_matrix(y, y, sample_weight=weight)
	assert cm[0, 0] == 9223372036854775807
	assert cm[1, 1] == -2


	@pytest.mark.parametrize("dtype", ["Int64", "Float64", "boolean"])
	def test_confusion_matrix_pandas_nullable(dtype):
	"""Checks that confusion_matrix works with pandas nullable dtypes.

	Non-regression test for gh-25635.
	"""
	pd = pytest.importorskip("pandas")

	y_ndarray = np.array([1, 0, 0, 1, 0, 1, 1, 0, 1])
	y_true = pd.Series(y_ndarray, dtype=dtype)
	y_predicted = pd.Series([0, 0, 1, 1, 0, 1, 1, 1, 1], dtype="int64")

	output = confusion_matrix(y_true, y_predicted)
	expected_output = confusion_matrix(y_ndarray, y_predicted)

	assert_array_equal(output, expected_output)


	def test_classification_report_multiclass():
	# Test performance report
	iris = datasets.load_iris()
	y_true, y_pred, _ = make_prediction(dataset=iris, binary=False)

	# print classification report with class names
	expected_report = """\
	precision recall f1-score support

	setosa 0.83 0.79 0.81 24
	versicolor 0.33 0.10 0.15 31
	virginica 0.42 0.90 0.57 20

	accuracy 0.53 75
	macro avg 0.53 0.60 0.51 75
	weighted avg 0.51 0.53 0.47 75
	"""
	report = classification_report(
	y_true,
	y_pred,
	labels=np.arange(len(iris.target_names)),
	target_names=iris.target_names,
	)
	assert report == expected_report


	def test_classification_report_multiclass_balanced():
	y_true, y_pred = [0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]

	expected_report = """\
	precision recall f1-score support

	0 0.33 0.33 0.33 3
	1 0.33 0.33 0.33 3
	2 0.33 0.33 0.33 3

	accuracy 0.33 9
	macro avg 0.33 0.33 0.33 9
	weighted avg 0.33 0.33 0.33 9
	"""
	report = classification_report(y_true, y_pred)
	assert report == expected_report


	def test_classification_report_multiclass_with_label_detection():
	iris = datasets.load_iris()
	y_true, y_pred, _ = make_prediction(dataset=iris, binary=False)

	# print classification report with label detection
	expected_report = """\
	precision recall f1-score support

	0 0.83 0.79 0.81 24
	1 0.33 0.10 0.15 31
	2 0.42 0.90 0.57 20

	accuracy 0.53 75
	macro avg 0.53 0.60 0.51 75
	weighted avg 0.51 0.53 0.47 75
	"""
	report = classification_report(y_true, y_pred)
	assert report == expected_report


	def test_classification_report_multiclass_with_digits():
	# Test performance report with added digits in floating point values
	iris = datasets.load_iris()
	y_true, y_pred, _ = make_prediction(dataset=iris, binary=False)

	# print classification report with class names
	expected_report = """\
	precision recall f1-score support

	setosa 0.82609 0.79167 0.80851 24
	versicolor 0.33333 0.09677 0.15000 31
	virginica 0.41860 0.90000 0.57143 20

	accuracy 0.53333 75
	macro avg 0.52601 0.59615 0.50998 75
	weighted avg 0.51375 0.53333 0.47310 75
	"""
	report = classification_report(
	y_true,
	y_pred,
	labels=np.arange(len(iris.target_names)),
	target_names=iris.target_names,
	digits=5,
	)
	assert report == expected_report


	def test_classification_report_multiclass_with_string_label():
	y_true, y_pred, _ = make_prediction(binary=False)

	y_true = np.array(["blue", "green", "red"])[y_true]
	y_pred = np.array(["blue", "green", "red"])[y_pred]

	expected_report = """\
	precision recall f1-score support

	blue 0.83 0.79 0.81 24
	green 0.33 0.10 0.15 31
	red 0.42 0.90 0.57 20

	accuracy 0.53 75
	macro avg 0.53 0.60 0.51 75
	weighted avg 0.51 0.53 0.47 75
	"""
	report = classification_report(y_true, y_pred)
	assert report == expected_report

	expected_report = """\
	precision recall f1-score support

	a 0.83 0.79 0.81 24
	b 0.33 0.10 0.15 31
	c 0.42 0.90 0.57 20

	accuracy 0.53 75
	macro avg 0.53 0.60 0.51 75
	weighted avg 0.51 0.53 0.47 75
	"""
	report = classification_report(y_true, y_pred, target_names=["a", "b", "c"])
	assert report == expected_report


	def test_classification_report_multiclass_with_unicode_label():
	y_true, y_pred, _ = make_prediction(binary=False)

	labels = np.array(["blue\xa2", "green\xa2", "red\xa2"])
	y_true = labels[y_true]
	y_pred = labels[y_pred]

	expected_report = """\
	precision recall f1-score support

	blue\xa2 0.83 0.79 0.81 24
	green\xa2 0.33 0.10 0.15 31
	red\xa2 0.42 0.90 0.57 20

	accuracy 0.53 75
	macro avg 0.53 0.60 0.51 75
	weighted avg 0.51 0.53 0.47 75
	"""
	report = classification_report(y_true, y_pred)
	assert report == expected_report


	def test_classification_report_multiclass_with_long_string_label():
	y_true, y_pred, _ = make_prediction(binary=False)

	labels = np.array(["blue", "green" * 5, "red"])
	y_true = labels[y_true]
	y_pred = labels[y_pred]

	expected_report = """\
	precision recall f1-score support

	blue 0.83 0.79 0.81 24
	greengreengreengreengreen 0.33 0.10 0.15 31
	red 0.42 0.90 0.57 20

	accuracy 0.53 75
	macro avg 0.53 0.60 0.51 75
	weighted avg 0.51 0.53 0.47 75
	"""

	report = classification_report(y_true, y_pred)
	assert report == expected_report


	def test_classification_report_labels_target_names_unequal_length():
	y_true = [0, 0, 2, 0, 0]
	y_pred = [0, 2, 2, 0, 0]
	target_names = ["class 0", "class 1", "class 2"]

	msg = "labels size, 2, does not match size of target_names, 3"
	with pytest.warns(UserWarning, match=msg):
	classification_report(y_true, y_pred, labels=[0, 2], target_names=target_names)


	def test_classification_report_no_labels_target_names_unequal_length():
	y_true = [0, 0, 2, 0, 0]
	y_pred = [0, 2, 2, 0, 0]
	target_names = ["class 0", "class 1", "class 2"]

	err_msg = (
	"Number of classes, 2, does not "
	"match size of target_names, 3. "
	"Try specifying the labels parameter"
	)
	with pytest.raises(ValueError, match=err_msg):
	classification_report(y_true, y_pred, target_names=target_names)


	@pytest.mark.filterwarnings(r"ignore::sklearn.exceptions.UndefinedMetricWarning")
	def test_multilabel_classification_report():
	n_classes = 4
	n_samples = 50

	_, y_true = make_multilabel_classification(
	n_features=1, n_samples=n_samples, n_classes=n_classes, random_state=0
	)

	_, y_pred = make_multilabel_classification(
	n_features=1, n_samples=n_samples, n_classes=n_classes, random_state=1
	)

	expected_report = """\
	precision recall f1-score support

	0 0.50 0.67 0.57 24
	1 0.51 0.74 0.61 27
	2 0.29 0.08 0.12 26
	3 0.52 0.56 0.54 27

	micro avg 0.50 0.51 0.50 104
	macro avg 0.45 0.51 0.46 104
	weighted avg 0.45 0.51 0.46 104
	samples avg 0.46 0.42 0.40 104
	"""

	report = classification_report(y_true, y_pred)
	assert report == expected_report


	def test_multilabel_zero_one_loss_subset():
	# Dense label indicator matrix format
	y1 = np.array([[0, 1, 1], [1, 0, 1]])
	y2 = np.array([[0, 0, 1], [1, 0, 1]])

	assert zero_one_loss(y1, y2) == 0.5
	assert zero_one_loss(y1, y1) == 0
	assert zero_one_loss(y2, y2) == 0
	assert zero_one_loss(y2, np.logical_not(y2)) == 1
	assert zero_one_loss(y1, np.logical_not(y1)) == 1
	assert zero_one_loss(y1, np.zeros(y1.shape)) == 1
	assert zero_one_loss(y2, np.zeros(y1.shape)) == 1


	def test_multilabel_hamming_loss():
	# Dense label indicator matrix format
	y1 = np.array([[0, 1, 1], [1, 0, 1]])
	y2 = np.array([[0, 0, 1], [1, 0, 1]])
	w = np.array([1, 3])

	assert hamming_loss(y1, y2) == 1 / 6
	assert hamming_loss(y1, y1) == 0
	assert hamming_loss(y2, y2) == 0
	assert hamming_loss(y2, 1 - y2) == 1
	assert hamming_loss(y1, 1 - y1) == 1
	assert hamming_loss(y1, np.zeros(y1.shape)) == 4 / 6
	assert hamming_loss(y2, np.zeros(y1.shape)) == 0.5
	assert hamming_loss(y1, y2, sample_weight=w) == 1.0 / 12
	assert hamming_loss(y1, 1 - y2, sample_weight=w) == 11.0 / 12
	assert hamming_loss(y1, np.zeros_like(y1), sample_weight=w) == 2.0 / 3
	# sp_hamming only works with 1-D arrays
	assert hamming_loss(y1[0], y2[0]) == sp_hamming(y1[0], y2[0])


	def test_jaccard_score_validation():
	y_true = np.array([0, 1, 0, 1, 1])
	y_pred = np.array([0, 1, 0, 1, 1])
	err_msg = r"pos_label=2 is not a valid label. It should be one of \[0, 1\]"
	with pytest.raises(ValueError, match=err_msg):
	jaccard_score(y_true, y_pred, average="binary", pos_label=2)

	y_true = np.array([[0, 1, 1], [1, 0, 0]])
	y_pred = np.array([[1, 1, 1], [1, 0, 1]])
	msg1 = (
	r"Target is multilabel-indicator but average='binary'. "
	r"Please choose another average setting, one of \[None, "
	r"'micro', 'macro', 'weighted', 'samples'\]."
	)
	with pytest.raises(ValueError, match=msg1):
	jaccard_score(y_true, y_pred, average="binary", pos_label=-1)

	y_true = np.array([0, 1, 1, 0, 2])
	y_pred = np.array([1, 1, 1, 1, 0])
	msg2 = (
	r"Target is multiclass but average='binary'. Please choose "
	r"another average setting, one of \[None, 'micro', 'macro', "
	r"'weighted'\]."
	)
	with pytest.raises(ValueError, match=msg2):
	jaccard_score(y_true, y_pred, average="binary")
	msg3 = "Samplewise metrics are not available outside of multilabel classification."
	with pytest.raises(ValueError, match=msg3):
	jaccard_score(y_true, y_pred, average="samples")

	msg = (
	r"Note that pos_label \(set to 3\) is ignored when "
	r"average != 'binary' \(got 'micro'\). You may use "
	r"labels=\[pos_label\] to specify a single positive "
	"class."
	)
	with pytest.warns(UserWarning, match=msg):
	jaccard_score(y_true, y_pred, average="micro", pos_label=3)


	def test_multilabel_jaccard_score(recwarn):
	# Dense label indicator matrix format
	y1 = np.array([[0, 1, 1], [1, 0, 1]])
	y2 = np.array([[0, 0, 1], [1, 0, 1]])

	# size(y1 \inter y2) = [1, 2]
	# size(y1 \union y2) = [2, 2]

	assert jaccard_score(y1, y2, average="samples") == 0.75
	assert jaccard_score(y1, y1, average="samples") == 1
	assert jaccard_score(y2, y2, average="samples") == 1
	assert jaccard_score(y2, np.logical_not(y2), average="samples") == 0
	assert jaccard_score(y1, np.logical_not(y1), average="samples") == 0
	assert jaccard_score(y1, np.zeros(y1.shape), average="samples") == 0
	assert jaccard_score(y2, np.zeros(y1.shape), average="samples") == 0

	y_true = np.array([[0, 1, 1], [1, 0, 0]])
	y_pred = np.array([[1, 1, 1], [1, 0, 1]])
	# average='macro'
	assert_almost_equal(jaccard_score(y_true, y_pred, average="macro"), 2.0 / 3)
	# average='micro'
	assert_almost_equal(jaccard_score(y_true, y_pred, average="micro"), 3.0 / 5)
	# average='samples'
	assert_almost_equal(jaccard_score(y_true, y_pred, average="samples"), 7.0 / 12)
	assert_almost_equal(
	jaccard_score(y_true, y_pred, average="samples", labels=[0, 2]), 1.0 / 2
	)
	assert_almost_equal(
	jaccard_score(y_true, y_pred, average="samples", labels=[1, 2]), 1.0 / 2
	)
	# average=None
	assert_array_equal(
	jaccard_score(y_true, y_pred, average=None), np.array([1.0 / 2, 1.0, 1.0 / 2])
	)

	y_true = np.array([[0, 1, 1], [1, 0, 1]])
	y_pred = np.array([[1, 1, 1], [1, 0, 1]])
	assert_almost_equal(jaccard_score(y_true, y_pred, average="macro"), 5.0 / 6)
	# average='weighted'
	assert_almost_equal(jaccard_score(y_true, y_pred, average="weighted"), 7.0 / 8)

	msg2 = "Got 4 > 2"
	with pytest.raises(ValueError, match=msg2):
	jaccard_score(y_true, y_pred, labels=[4], average="macro")
	msg3 = "Got -1 < 0"
	with pytest.raises(ValueError, match=msg3):
	jaccard_score(y_true, y_pred, labels=[-1], average="macro")

	msg = (
	"Jaccard is ill-defined and being set to 0.0 in labels "
	"with no true or predicted samples."
	)

	with pytest.warns(UndefinedMetricWarning, match=msg):
	assert (
	jaccard_score(np.array([[0, 1]]), np.array([[0, 1]]), average="macro")
	== 0.5
	)

	msg = (
	"Jaccard is ill-defined and being set to 0.0 in samples "
	"with no true or predicted labels."
	)

	with pytest.warns(UndefinedMetricWarning, match=msg):
	assert (
	jaccard_score(
	np.array([[0, 0], [1, 1]]),
	np.array([[0, 0], [1, 1]]),
	average="samples",
	)
	== 0.5
	)

	assert not list(recwarn)


	def test_multiclass_jaccard_score(recwarn):
	y_true = ["ant", "ant", "cat", "cat", "ant", "cat", "bird", "bird"]
	y_pred = ["cat", "ant", "cat", "cat", "ant", "bird", "bird", "cat"]
	labels = ["ant", "bird", "cat"]
	lb = LabelBinarizer()
	lb.fit(labels)
	y_true_bin = lb.transform(y_true)
	y_pred_bin = lb.transform(y_pred)
	multi_jaccard_score = partial(jaccard_score, y_true, y_pred)
	bin_jaccard_score = partial(jaccard_score, y_true_bin, y_pred_bin)
	multi_labels_list = [
	["ant", "bird"],
	["ant", "cat"],
	["cat", "bird"],
	["ant"],
	["bird"],
	["cat"],
	None,
	]
	bin_labels_list = [[0, 1], [0, 2], [2, 1], [0], [1], [2], None]

	# other than average='samples'/'none-samples', test everything else here
	for average in ("macro", "weighted", "micro", None):
	for m_label, b_label in zip(multi_labels_list, bin_labels_list):
	assert_almost_equal(
	multi_jaccard_score(average=average, labels=m_label),
	bin_jaccard_score(average=average, labels=b_label),
	)

	y_true = np.array([[0, 0], [0, 0], [0, 0]])
	y_pred = np.array([[0, 0], [0, 0], [0, 0]])
	with ignore_warnings():
	assert jaccard_score(y_true, y_pred, average="weighted") == 0

	assert not list(recwarn)


	def test_average_binary_jaccard_score(recwarn):
	# tp=0, fp=0, fn=1, tn=0
	assert jaccard_score([1], [0], average="binary") == 0.0
	# tp=0, fp=0, fn=0, tn=1
	msg = (
	"Jaccard is ill-defined and being set to 0.0 due to "
	"no true or predicted samples"
	)
	with pytest.warns(UndefinedMetricWarning, match=msg):
	assert jaccard_score([0, 0], [0, 0], average="binary") == 0.0

	# tp=1, fp=0, fn=0, tn=0 (pos_label=0)
	assert jaccard_score([0], [0], pos_label=0, average="binary") == 1.0
	y_true = np.array([1, 0, 1, 1, 0])
	y_pred = np.array([1, 0, 1, 1, 1])
	assert_almost_equal(jaccard_score(y_true, y_pred, average="binary"), 3.0 / 4)
	assert_almost_equal(
	jaccard_score(y_true, y_pred, average="binary", pos_label=0), 1.0 / 2
	)

	assert not list(recwarn)


	def test_jaccard_score_zero_division_warning():
	# check that we raised a warning with default behavior if a zero division
	# happens
	y_true = np.array([[1, 0, 1], [0, 0, 0]])
	y_pred = np.array([[0, 0, 0], [0, 0, 0]])
	msg = (
	"Jaccard is ill-defined and being set to 0.0 in "
	"samples with no true or predicted labels."
	" Use `zero_division` parameter to control this behavior."
	)
	with pytest.warns(UndefinedMetricWarning, match=msg):
	score = jaccard_score(y_true, y_pred, average="samples", zero_division="warn")
	assert score == pytest.approx(0.0)


	@pytest.mark.parametrize("zero_division, expected_score", [(0, 0), (1, 0.5)])
	def test_jaccard_score_zero_division_set_value(zero_division, expected_score):
	# check that we don't issue warning by passing the zero_division parameter
	y_true = np.array([[1, 0, 1], [0, 0, 0]])
	y_pred = np.array([[0, 0, 0], [0, 0, 0]])
	with warnings.catch_warnings():
	warnings.simplefilter("error", UndefinedMetricWarning)
	score = jaccard_score(
	y_true, y_pred, average="samples", zero_division=zero_division
	)
	assert score == pytest.approx(expected_score)


	@pytest.mark.filterwarnings(r"ignore::sklearn.exceptions.UndefinedMetricWarning")
	def test_precision_recall_f1_score_multilabel_1():
	# Test precision_recall_f1_score on a crafted multilabel example
	# First crafted example

	y_true = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 1]])
	y_pred = np.array([[0, 1, 0, 0], [0, 1, 0, 0], [1, 0, 1, 0]])

	p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average=None)

	# tp = [0, 1, 1, 0]
	# fn = [1, 0, 0, 1]
	# fp = [1, 1, 0, 0]
	# Check per class

	assert_array_almost_equal(p, [0.0, 0.5, 1.0, 0.0], 2)
	assert_array_almost_equal(r, [0.0, 1.0, 1.0, 0.0], 2)
	assert_array_almost_equal(f, [0.0, 1 / 1.5, 1, 0.0], 2)
	assert_array_almost_equal(s, [1, 1, 1, 1], 2)

	f2 = fbeta_score(y_true, y_pred, beta=2, average=None)
	support = s
	assert_array_almost_equal(f2, [0, 0.83, 1, 0], 2)

	# Check macro
	p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="macro")
	assert_almost_equal(p, 1.5 / 4)
	assert_almost_equal(r, 0.5)
	assert_almost_equal(f, 2.5 / 1.5 * 0.25)
	assert s is None
	assert_almost_equal(
	fbeta_score(y_true, y_pred, beta=2, average="macro"), np.mean(f2)
	)

	# Check micro
	p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="micro")
	assert_almost_equal(p, 0.5)
	assert_almost_equal(r, 0.5)
	assert_almost_equal(f, 0.5)
	assert s is None
	assert_almost_equal(
	fbeta_score(y_true, y_pred, beta=2, average="micro"),
	(1 + 4) * p * r / (4 * p + r),
	)

	# Check weighted
	p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="weighted")
	assert_almost_equal(p, 1.5 / 4)
	assert_almost_equal(r, 0.5)
	assert_almost_equal(f, 2.5 / 1.5 * 0.25)
	assert s is None
	assert_almost_equal(
	fbeta_score(y_true, y_pred, beta=2, average="weighted"),
	np.average(f2, weights=support),
	)
	# Check samples
	# \|h(x_i) inter y_i \| = [0, 1, 1]
	# \|y_i\| = [1, 1, 2]
	# \|h(x_i)\| = [1, 1, 2]
	p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="samples")
	assert_almost_equal(p, 0.5)
	assert_almost_equal(r, 0.5)
	assert_almost_equal(f, 0.5)
	assert s is None
	assert_almost_equal(fbeta_score(y_true, y_pred, beta=2, average="samples"), 0.5)


	@pytest.mark.filterwarnings(r"ignore::sklearn.exceptions.UndefinedMetricWarning")
	def test_precision_recall_f1_score_multilabel_2():
	# Test precision_recall_f1_score on a crafted multilabel example 2
	# Second crafted example
	y_true = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 1, 1, 0]])
	y_pred = np.array([[0, 0, 0, 1], [0, 0, 0, 1], [1, 1, 0, 0]])

	# tp = [ 0. 1. 0. 0.]
	# fp = [ 1. 0. 0. 2.]
	# fn = [ 1. 1. 1. 0.]

	p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average=None)
	assert_array_almost_equal(p, [0.0, 1.0, 0.0, 0.0], 2)
	assert_array_almost_equal(r, [0.0, 0.5, 0.0, 0.0], 2)
	assert_array_almost_equal(f, [0.0, 0.66, 0.0, 0.0], 2)
	assert_array_almost_equal(s, [1, 2, 1, 0], 2)

	f2 = fbeta_score(y_true, y_pred, beta=2, average=None)
	support = s
	assert_array_almost_equal(f2, [0, 0.55, 0, 0], 2)

	p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="micro")
	assert_almost_equal(p, 0.25)
	assert_almost_equal(r, 0.25)
	assert_almost_equal(f, 2 * 0.25 * 0.25 / 0.5)
	assert s is None
	assert_almost_equal(
	fbeta_score(y_true, y_pred, beta=2, average="micro"),
	(1 + 4) * p * r / (4 * p + r),
	)

	p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="macro")
	assert_almost_equal(p, 0.25)
	assert_almost_equal(r, 0.125)
	assert_almost_equal(f, 2 / 12)
	assert s is None
	assert_almost_equal(
	fbeta_score(y_true, y_pred, beta=2, average="macro"), np.mean(f2)
	)

	p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="weighted")
	assert_almost_equal(p, 2 / 4)
	assert_almost_equal(r, 1 / 4)
	assert_almost_equal(f, 2 / 3 * 2 / 4)
	assert s is None
	assert_almost_equal(
	fbeta_score(y_true, y_pred, beta=2, average="weighted"),
	np.average(f2, weights=support),
	)

	p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="samples")
	# Check samples
	# \|h(x_i) inter y_i \| = [0, 0, 1]
	# \|y_i\| = [1, 1, 2]
	# \|h(x_i)\| = [1, 1, 2]

	assert_almost_equal(p, 1 / 6)
	assert_almost_equal(r, 1 / 6)
	assert_almost_equal(f, 2 / 4 * 1 / 3)
	assert s is None
	assert_almost_equal(
	fbeta_score(y_true, y_pred, beta=2, average="samples"), 0.1666, 2
	)


	@pytest.mark.filterwarnings(r"ignore::sklearn.exceptions.UndefinedMetricWarning")
	@pytest.mark.parametrize(
	"zero_division, zero_division_expected",
	[("warn", 0), (0, 0), (1, 1), (np.nan, np.nan)],
	)
	def test_precision_recall_f1_score_with_an_empty_prediction(
	zero_division, zero_division_expected
	):
	y_true = np.array([[0, 1, 0, 0], [1, 0, 0, 0], [0, 1, 1, 0]])
	y_pred = np.array([[0, 0, 0, 0], [0, 0, 0, 1], [0, 1, 1, 0]])

	# true_pos = [ 0. 1. 1. 0.]
	# false_pos = [ 0. 0. 0. 1.]
	# false_neg = [ 1. 1. 0. 0.]

	p, r, f, s = precision_recall_fscore_support(
	y_true, y_pred, average=None, zero_division=zero_division
	)

	assert_array_almost_equal(p, [zero_division_expected, 1.0, 1.0, 0.0], 2)
	assert_array_almost_equal(r, [0.0, 0.5, 1.0, zero_division_expected], 2)
	expected_f = 0
	assert_array_almost_equal(f, [expected_f, 1 / 1.5, 1, expected_f], 2)
	assert_array_almost_equal(s, [1, 2, 1, 0], 2)

	f2 = fbeta_score(y_true, y_pred, beta=2, average=None, zero_division=zero_division)
	support = s
	assert_array_almost_equal(f2, [expected_f, 0.55, 1, expected_f], 2)

	p, r, f, s = precision_recall_fscore_support(
	y_true, y_pred, average="macro", zero_division=zero_division
	)

	value_to_sum = 0 if np.isnan(zero_division_expected) else zero_division_expected
	values_to_average = 3 + (not np.isnan(zero_division_expected))

	assert_almost_equal(p, (2 + value_to_sum) / values_to_average)
	assert_almost_equal(r, (1.5 + value_to_sum) / values_to_average)
	expected_f = (2 / 3 + 1) / 4
	assert_almost_equal(f, expected_f)
	assert s is None
	assert_almost_equal(
	fbeta_score(
	y_true,
	y_pred,
	beta=2,
	average="macro",
	zero_division=zero_division,
	),
	_nanaverage(f2, weights=None),
	)

	p, r, f, s = precision_recall_fscore_support(
	y_true, y_pred, average="micro", zero_division=zero_division
	)
	assert_almost_equal(p, 2 / 3)
	assert_almost_equal(r, 0.5)
	assert_almost_equal(f, 2 / 3 / (2 / 3 + 0.5))
	assert s is None
	assert_almost_equal(
	fbeta_score(
	y_true, y_pred, beta=2, average="micro", zero_division=zero_division
	),
	(1 + 4) * p * r / (4 * p + r),
	)

	p, r, f, s = precision_recall_fscore_support(
	y_true, y_pred, average="weighted", zero_division=zero_division
	)
	assert_almost_equal(p, 3 / 4 if zero_division_expected == 0 else 1.0)
	assert_almost_equal(r, 0.5)
	values_to_average = 4
	assert_almost_equal(f, (2 * 2 / 3 + 1) / values_to_average)
	assert s is None
	assert_almost_equal(
	fbeta_score(
	y_true, y_pred, beta=2, average="weighted", zero_division=zero_division
	),
	_nanaverage(f2, weights=support),
	)

	p, r, f, s = precision_recall_fscore_support(y_true, y_pred, average="samples")
	# \|h(x_i) inter y_i \| = [0, 0, 2]
	# \|y_i\| = [1, 1, 2]
	# \|h(x_i)\| = [0, 1, 2]
	assert_almost_equal(p, 1 / 3)
	assert_almost_equal(r, 1 / 3)
	assert_almost_equal(f, 1 / 3)
	assert s is None
	expected_result = 0.333
	assert_almost_equal(
	fbeta_score(
	y_true, y_pred, beta=2, average="samples", zero_division=zero_division
	),
	expected_result,
	2,
	)


	@pytest.mark.parametrize("beta", [1])
	@pytest.mark.parametrize("average", ["macro", "micro", "weighted", "samples"])
	@pytest.mark.parametrize("zero_division", [0, 1, np.nan])
	def test_precision_recall_f1_no_labels(beta, average, zero_division):
	y_true = np.zeros((20, 3))
	y_pred = np.zeros_like(y_true)

	with warnings.catch_warnings():
	warnings.simplefilter("error")

	p, r, f, s = precision_recall_fscore_support(
	y_true,
	y_pred,
	average=average,
	beta=beta,
	zero_division=zero_division,
	)
	fbeta = fbeta_score(
	y_true,
	y_pred,
	beta=beta,
	average=average,
	zero_division=zero_division,
	)
	assert s is None

	# if zero_division = nan, check that all metrics are nan and exit
	if np.isnan(zero_division):
	for metric in [p, r, f, fbeta]:
	assert np.isnan(metric)
	return

	zero_division = float(zero_division)
	assert_almost_equal(p, zero_division)
	assert_almost_equal(r, zero_division)
	assert_almost_equal(f, zero_division)

	assert_almost_equal(fbeta, float(zero_division))


	@pytest.mark.parametrize("average", ["macro", "micro", "weighted", "samples"])
	def test_precision_recall_f1_no_labels_check_warnings(average):
	y_true = np.zeros((20, 3))
	y_pred = np.zeros_like(y_true)

	func = precision_recall_fscore_support
	with pytest.warns(UndefinedMetricWarning):
	p, r, f, s = func(y_true, y_pred, average=average, beta=1.0)

	assert_almost_equal(p, 0)
	assert_almost_equal(r, 0)
	assert_almost_equal(f, 0)
	assert s is None

	with pytest.warns(UndefinedMetricWarning):
	fbeta = fbeta_score(y_true, y_pred, average=average, beta=1.0)

	assert_almost_equal(fbeta, 0)


	@pytest.mark.parametrize("zero_division", [0, 1, np.nan])
	def test_precision_recall_f1_no_labels_average_none(zero_division):
	y_true = np.zeros((20, 3))
	y_pred = np.zeros_like(y_true)

	# tp = [0, 0, 0]
	# fn = [0, 0, 0]
	# fp = [0, 0, 0]
	# support = [0, 0, 0]
	# \|y_hat_i inter y_i \| = [0, 0, 0]
	# \|y_i\| = [0, 0, 0]
	# \|y_hat_i\| = [0, 0, 0]

	with warnings.catch_warnings():
	warnings.simplefilter("error")

	p, r, f, s = precision_recall_fscore_support(
	y_true,
	y_pred,
	average=None,
	beta=1.0,
	zero_division=zero_division,
	)
	fbeta = fbeta_score(
	y_true, y_pred, beta=1.0, average=None, zero_division=zero_division
	)

	zero_division = np.float64(zero_division)
	assert_array_almost_equal(p, [zero_division, zero_division, zero_division], 2)
	assert_array_almost_equal(r, [zero_division, zero_division, zero_division], 2)
	assert_array_almost_equal(f, [zero_division, zero_division, zero_division], 2)
	assert_array_almost_equal(s, [0, 0, 0], 2)

	assert_array_almost_equal(fbeta, [zero_division, zero_division, zero_division], 2)


	def test_precision_recall_f1_no_labels_average_none_warn():
	y_true = np.zeros((20, 3))
	y_pred = np.zeros_like(y_true)

	# tp = [0, 0, 0]
	# fn = [0, 0, 0]
	# fp = [0, 0, 0]
	# support = [0, 0, 0]
	# \|y_hat_i inter y_i \| = [0, 0, 0]
	# \|y_i\| = [0, 0, 0]
	# \|y_hat_i\| = [0, 0, 0]

	with pytest.warns(UndefinedMetricWarning):
	p, r, f, s = precision_recall_fscore_support(
	y_true, y_pred, average=None, beta=1
	)

	assert_array_almost_equal(p, [0, 0, 0], 2)
	assert_array_almost_equal(r, [0, 0, 0], 2)
	assert_array_almost_equal(f, [0, 0, 0], 2)
	assert_array_almost_equal(s, [0, 0, 0], 2)

	with pytest.warns(UndefinedMetricWarning):
	fbeta = fbeta_score(y_true, y_pred, beta=1, average=None)

	assert_array_almost_equal(fbeta, [0, 0, 0], 2)


	def test_prf_warnings():
	# average of per-label scores
	f, w = precision_recall_fscore_support, UndefinedMetricWarning
	for average in [None, "weighted", "macro"]:
	msg = (
	"Precision is ill-defined and "
	"being set to 0.0 in labels with no predicted samples."
	" Use `zero_division` parameter to control"
	" this behavior."
	)
	with pytest.warns(w, match=msg):
	f([0, 1, 2], [1, 1, 2], average=average)

	msg = (
	"Recall is ill-defined and "
	"being set to 0.0 in labels with no true samples."
	" Use `zero_division` parameter to control"
	" this behavior."
	)
	with pytest.warns(w, match=msg):
	f([1, 1, 2], [0, 1, 2], average=average)

	# average of per-sample scores
	msg = (
	"Precision is ill-defined and "
	"being set to 0.0 in samples with no predicted labels."
	" Use `zero_division` parameter to control"
	" this behavior."
	)
	with pytest.warns(w, match=msg):
	f(np.array([[1, 0], [1, 0]]), np.array([[1, 0], [0, 0]]), average="samples")

	msg = (
	"Recall is ill-defined and "
	"being set to 0.0 in samples with no true labels."
	" Use `zero_division` parameter to control"
	" this behavior."
	)
	with pytest.warns(w, match=msg):
	f(np.array([[1, 0], [0, 0]]), np.array([[1, 0], [1, 0]]), average="samples")

	# single score: micro-average
	msg = (
	"Precision is ill-defined and "
	"being set to 0.0 due to no predicted samples."
	" Use `zero_division` parameter to control"
	" this behavior."
	)
	with pytest.warns(w, match=msg):
	f(np.array([[1, 1], [1, 1]]), np.array([[0, 0], [0, 0]]), average="micro")

	msg = (
	"Recall is ill-defined and "
	"being set to 0.0 due to no true samples."
	" Use `zero_division` parameter to control"
	" this behavior."
	)
	with pytest.warns(w, match=msg):
	f(np.array([[0, 0], [0, 0]]), np.array([[1, 1], [1, 1]]), average="micro")

	# single positive label
	msg = (
	"Precision is ill-defined and "
	"being set to 0.0 due to no predicted samples."
	" Use `zero_division` parameter to control"
	" this behavior."
	)
	with pytest.warns(w, match=msg):
	f([1, 1], [-1, -1], average="binary")

	msg = (
	"Recall is ill-defined and "
	"being set to 0.0 due to no true samples."
	" Use `zero_division` parameter to control"
	" this behavior."
	)
	with pytest.warns(w, match=msg):
	f([-1, -1], [1, 1], average="binary")

	with warnings.catch_warnings(record=True) as record:
	warnings.simplefilter("always")
	precision_recall_fscore_support([0, 0], [0, 0], average="binary")
	msg = (
	"F-score is ill-defined and being set to 0.0 due to no true nor "
	"predicted samples. Use `zero_division` parameter to control this"
	" behavior."
	)
	assert str(record.pop().message) == msg
	msg = (
	"Recall is ill-defined and "
	"being set to 0.0 due to no true samples."
	" Use `zero_division` parameter to control"
	" this behavior."
	)
	assert str(record.pop().message) == msg
	msg = (
	"Precision is ill-defined and "
	"being set to 0.0 due to no predicted samples."
	" Use `zero_division` parameter to control"
	" this behavior."
	)
	assert str(record.pop().message) == msg


	@pytest.mark.parametrize("zero_division", [0, 1, np.nan])
	def test_prf_no_warnings_if_zero_division_set(zero_division):
	with warnings.catch_warnings():
	warnings.simplefilter("error")

	# average of per-label scores
	for average in [None, "weighted", "macro"]:
	precision_recall_fscore_support(
	[0, 1, 2], [1, 1, 2], average=average, zero_division=zero_division
	)

	precision_recall_fscore_support(
	[1, 1, 2], [0, 1, 2], average=average, zero_division=zero_division
	)

	# average of per-sample scores
	precision_recall_fscore_support(
	np.array([[1, 0], [1, 0]]),
	np.array([[1, 0], [0, 0]]),
	average="samples",
	zero_division=zero_division,
	)

	precision_recall_fscore_support(
	np.array([[1, 0], [0, 0]]),
	np.array([[1, 0], [1, 0]]),
	average="samples",
	zero_division=zero_division,
	)

	# single score: micro-average
	precision_recall_fscore_support(
	np.array([[1, 1], [1, 1]]),
	np.array([[0, 0], [0, 0]]),
	average="micro",
	zero_division=zero_division,
	)

	precision_recall_fscore_support(
	np.array([[0, 0], [0, 0]]),
	np.array([[1, 1], [1, 1]]),
	average="micro",
	zero_division=zero_division,
	)

	# single positive label
	precision_recall_fscore_support(
	[1, 1], [-1, -1], average="binary", zero_division=zero_division
	)

	precision_recall_fscore_support(
	[-1, -1], [1, 1], average="binary", zero_division=zero_division
	)

	with warnings.catch_warnings(record=True) as record:
	warnings.simplefilter("always")
	precision_recall_fscore_support(
	[0, 0], [0, 0], average="binary", zero_division=zero_division
	)
	assert len(record) == 0


	@pytest.mark.parametrize("zero_division", ["warn", 0, 1, np.nan])
	def test_recall_warnings(zero_division):
	with warnings.catch_warnings():
	warnings.simplefilter("error")

	recall_score(
	np.array([[1, 1], [1, 1]]),
	np.array([[0, 0], [0, 0]]),
	average="micro",
	zero_division=zero_division,
	)

	with warnings.catch_warnings(record=True) as record:
	warnings.simplefilter("always")
	recall_score(
	np.array([[0, 0], [0, 0]]),
	np.array([[1, 1], [1, 1]]),
	average="micro",
	zero_division=zero_division,
	)
	if zero_division == "warn":
	assert (
	str(record.pop().message) == "Recall is ill-defined and "
	"being set to 0.0 due to no true samples."
	" Use `zero_division` parameter to control"
	" this behavior."
	)
	else:
	assert len(record) == 0

	recall_score([0, 0], [0, 0])
	if zero_division == "warn":
	assert (
	str(record.pop().message) == "Recall is ill-defined and "
	"being set to 0.0 due to no true samples."
	" Use `zero_division` parameter to control"
	" this behavior."
	)


	@pytest.mark.parametrize("zero_division", ["warn", 0, 1, np.nan])
	def test_precision_warnings(zero_division):
	with warnings.catch_warnings(record=True) as record:
	warnings.simplefilter("always")
	precision_score(
	np.array([[1, 1], [1, 1]]),
	np.array([[0, 0], [0, 0]]),
	average="micro",
	zero_division=zero_division,
	)
	if zero_division == "warn":
	assert (
	str(record.pop().message) == "Precision is ill-defined and "
	"being set to 0.0 due to no predicted samples."
	" Use `zero_division` parameter to control"
	" this behavior."
	)
	else:
	assert len(record) == 0

	precision_score([0, 0], [0, 0])
	if zero_division == "warn":
	assert (
	str(record.pop().message) == "Precision is ill-defined and "
	"being set to 0.0 due to no predicted samples."
	" Use `zero_division` parameter to control"
	" this behavior."
	)

	with warnings.catch_warnings():
	warnings.simplefilter("error")

	precision_score(
	np.array([[0, 0], [0, 0]]),
	np.array([[1, 1], [1, 1]]),
	average="micro",
	zero_division=zero_division,
	)


	@pytest.mark.parametrize("zero_division", ["warn", 0, 1, np.nan])
	def test_fscore_warnings(zero_division):
	with warnings.catch_warnings(record=True) as record:
	warnings.simplefilter("always")

	for score in [f1_score, partial(fbeta_score, beta=2)]:
	score(
	np.array([[1, 1], [1, 1]]),
	np.array([[0, 0], [0, 0]]),
	average="micro",
	zero_division=zero_division,
	)
	assert len(record) == 0

	score(
	np.array([[0, 0], [0, 0]]),
	np.array([[1, 1], [1, 1]]),
	average="micro",
	zero_division=zero_division,
	)
	assert len(record) == 0

	score(
	np.array([[0, 0], [0, 0]]),
	np.array([[0, 0], [0, 0]]),
	average="micro",
	zero_division=zero_division,
	)
	if zero_division == "warn":
	assert (
	str(record.pop().message) == "F-score is ill-defined and "
	"being set to 0.0 due to no true nor predicted "
	"samples. Use `zero_division` parameter to "
	"control this behavior."
	)
	else:
	assert len(record) == 0


	def test_prf_average_binary_data_non_binary():
	# Error if user does not explicitly set non-binary average mode
	y_true_mc = [1, 2, 3, 3]
	y_pred_mc = [1, 2, 3, 1]
	msg_mc = (
	r"Target is multiclass but average='binary'. Please "
	r"choose another average setting, one of \["
	r"None, 'micro', 'macro', 'weighted'\]."
	)
	y_true_ind = np.array([[0, 1, 1], [1, 0, 0], [0, 0, 1]])
	y_pred_ind = np.array([[0, 1, 0], [1, 0, 0], [0, 0, 1]])
	msg_ind = (
	r"Target is multilabel-indicator but average='binary'. Please "
	r"choose another average setting, one of \["
	r"None, 'micro', 'macro', 'weighted', 'samples'\]."
	)

	for y_true, y_pred, msg in [
	(y_true_mc, y_pred_mc, msg_mc),
	(y_true_ind, y_pred_ind, msg_ind),
	]:
	for metric in [
	precision_score,
	recall_score,
	f1_score,
	partial(fbeta_score, beta=2),
	]:
	with pytest.raises(ValueError, match=msg):
	metric(y_true, y_pred)


	def test__check_targets():
	# Check that _check_targets correctly merges target types, squeezes
	# output and fails if input lengths differ.
	IND = "multilabel-indicator"
	MC = "multiclass"
	BIN = "binary"
	CNT = "continuous"
	MMC = "multiclass-multioutput"
	MCN = "continuous-multioutput"
	# all of length 3
	EXAMPLES = [
	(IND, np.array([[0, 1, 1], [1, 0, 0], [0, 0, 1]])),
	# must not be considered binary
	(IND, np.array([[0, 1], [1, 0], [1, 1]])),
	(MC, [2, 3, 1]),
	(BIN, [0, 1, 1]),
	(CNT, [0.0, 1.5, 1.0]),
	(MC, np.array([[2], [3], [1]])),
	(BIN, np.array([[0], [1], [1]])),
	(CNT, np.array([[0.0], [1.5], [1.0]])),
	(MMC, np.array([[0, 2], [1, 3], [2, 3]])),
	(MCN, np.array([[0.5, 2.0], [1.1, 3.0], [2.0, 3.0]])),
	]
	# expected type given input types, or None for error
	# (types will be tried in either order)
	EXPECTED = {
	(IND, IND): IND,
	(MC, MC): MC,
	(BIN, BIN): BIN,
	(MC, IND): None,
	(BIN, IND): None,
	(BIN, MC): MC,
	# Disallowed types
	(CNT, CNT): None,
	(MMC, MMC): None,
	(MCN, MCN): None,
	(IND, CNT): None,
	(MC, CNT): None,
	(BIN, CNT): None,
	(MMC, CNT): None,
	(MCN, CNT): None,
	(IND, MMC): None,
	(MC, MMC): None,
	(BIN, MMC): None,
	(MCN, MMC): None,
	(IND, MCN): None,
	(MC, MCN): None,
	(BIN, MCN): None,
	}

	for (type1, y1), (type2, y2) in product(EXAMPLES, repeat=2):
	try:
	expected = EXPECTED[type1, type2]
	except KeyError:
	expected = EXPECTED[type2, type1]
	if expected is None:
	with pytest.raises(ValueError):
	_check_targets(y1, y2)

	if type1 != type2:
	err_msg = (
	"Classification metrics can't handle a mix "
	"of {0} and {1} targets".format(type1, type2)
	)
	with pytest.raises(ValueError, match=err_msg):
	_check_targets(y1, y2)

	else:
	if type1 not in (BIN, MC, IND):
	err_msg = "{0} is not supported".format(type1)
	with pytest.raises(ValueError, match=err_msg):
	_check_targets(y1, y2)

	else:
	merged_type, y1out, y2out = _check_targets(y1, y2)
	assert merged_type == expected
	if merged_type.startswith("multilabel"):
	assert y1out.format == "csr"
	assert y2out.format == "csr"
	else:
	assert_array_equal(y1out, np.squeeze(y1))
	assert_array_equal(y2out, np.squeeze(y2))
	with pytest.raises(ValueError):
	_check_targets(y1[:-1], y2)

	# Make sure seq of seq is not supported
	y1 = [(1, 2), (0, 2, 3)]
	y2 = [(2,), (0, 2)]
	msg = (
	"You appear to be using a legacy multi-label data representation. "
	"Sequence of sequences are no longer supported; use a binary array"
	" or sparse matrix instead - the MultiLabelBinarizer"
	" transformer can convert to this format."
	)
	with pytest.raises(ValueError, match=msg):
	_check_targets(y1, y2)


	def test__check_targets_multiclass_with_both_y_true_and_y_pred_binary():
	# https://github.com/scikit-learn/scikit-learn/issues/8098
	y_true = [0, 1]
	y_pred = [0, -1]
	assert _check_targets(y_true, y_pred)[0] == "multiclass"


	def test_hinge_loss_binary():
	y_true = np.array([-1, 1, 1, -1])
	pred_decision = np.array([-8.5, 0.5, 1.5, -0.3])
	assert hinge_loss(y_true, pred_decision) == 1.2 / 4

	y_true = np.array([0, 2, 2, 0])
	pred_decision = np.array([-8.5, 0.5, 1.5, -0.3])
	assert hinge_loss(y_true, pred_decision) == 1.2 / 4


	def test_hinge_loss_multiclass():
	pred_decision = np.array(
	[
	[+0.36, -0.17, -0.58, -0.99],
	[-0.54, -0.37, -0.48, -0.58],
	[-1.45, -0.58, -0.38, -0.17],
	[-0.54, -0.38, -0.48, -0.58],
	[-2.36, -0.79, -0.27, +0.24],
	[-1.45, -0.58, -0.38, -0.17],
	]
	)
	y_true = np.array([0, 1, 2, 1, 3, 2])
	dummy_losses = np.array(
	[
	1 - pred_decision[0][0] + pred_decision[0][1],
	1 - pred_decision[1][1] + pred_decision[1][2],
	1 - pred_decision[2][2] + pred_decision[2][3],
	1 - pred_decision[3][1] + pred_decision[3][2],
	1 - pred_decision[4][3] + pred_decision[4][2],
	1 - pred_decision[5][2] + pred_decision[5][3],
	]
	)
	np.clip(dummy_losses, 0, None, out=dummy_losses)
	dummy_hinge_loss = np.mean(dummy_losses)
	assert hinge_loss(y_true, pred_decision) == dummy_hinge_loss


	def test_hinge_loss_multiclass_missing_labels_with_labels_none():
	y_true = np.array([0, 1, 2, 2])
	pred_decision = np.array(
	[
	[+1.27, 0.034, -0.68, -1.40],
	[-1.45, -0.58, -0.38, -0.17],
	[-2.36, -0.79, -0.27, +0.24],
	[-2.36, -0.79, -0.27, +0.24],
	]
	)
	error_message = (
	"Please include all labels in y_true or pass labels as third argument"
	)
	with pytest.raises(ValueError, match=error_message):
	hinge_loss(y_true, pred_decision)


	def test_hinge_loss_multiclass_no_consistent_pred_decision_shape():
	# test for inconsistency between multiclass problem and pred_decision
	# argument
	y_true = np.array([2, 1, 0, 1, 0, 1, 1])
	pred_decision = np.array([0, 1, 2, 1, 0, 2, 1])
	error_message = (
	"The shape of pred_decision cannot be 1d array"
	"with a multiclass target. pred_decision shape "
	"must be (n_samples, n_classes), that is "
	"(7, 3). Got: (7,)"
	)
	with pytest.raises(ValueError, match=re.escape(error_message)):
	hinge_loss(y_true=y_true, pred_decision=pred_decision)

	# test for inconsistency between pred_decision shape and labels number
	pred_decision = np.array([[0, 1], [0, 1], [0, 1], [0, 1], [2, 0], [0, 1], [1, 0]])
	labels = [0, 1, 2]
	error_message = (
	"The shape of pred_decision is not "
	"consistent with the number of classes. "
	"With a multiclass target, pred_decision "
	"shape must be (n_samples, n_classes), that is "
	"(7, 3). Got: (7, 2)"
	)
	with pytest.raises(ValueError, match=re.escape(error_message)):
	hinge_loss(y_true=y_true, pred_decision=pred_decision, labels=labels)


	def test_hinge_loss_multiclass_with_missing_labels():
	pred_decision = np.array(
	[
	[+0.36, -0.17, -0.58, -0.99],
	[-0.55, -0.38, -0.48, -0.58],
	[-1.45, -0.58, -0.38, -0.17],
	[-0.55, -0.38, -0.48, -0.58],
	[-1.45, -0.58, -0.38, -0.17],
	]
	)
	y_true = np.array([0, 1, 2, 1, 2])
	labels = np.array([0, 1, 2, 3])
	dummy_losses = np.array(
	[
	1 - pred_decision[0][0] + pred_decision[0][1],
	1 - pred_decision[1][1] + pred_decision[1][2],
	1 - pred_decision[2][2] + pred_decision[2][3],
	1 - pred_decision[3][1] + pred_decision[3][2],
	1 - pred_decision[4][2] + pred_decision[4][3],
	]
	)
	np.clip(dummy_losses, 0, None, out=dummy_losses)
	dummy_hinge_loss = np.mean(dummy_losses)
	assert hinge_loss(y_true, pred_decision, labels=labels) == dummy_hinge_loss


	def test_hinge_loss_multiclass_missing_labels_only_two_unq_in_y_true():
	# non-regression test for:
	# https://github.com/scikit-learn/scikit-learn/issues/17630
	# check that we can compute the hinge loss when providing an array
	# with labels allowing to not have all labels in y_true
	pred_decision = np.array(
	[
	[+0.36, -0.17, -0.58],
	[-0.15, -0.58, -0.48],
	[-1.45, -0.58, -0.38],
	[-0.55, -0.78, -0.42],
	[-1.45, -0.58, -0.38],
	]
	)
	y_true = np.array([0, 2, 2, 0, 2])
	labels = np.array([0, 1, 2])
	dummy_losses = np.array(
	[
	1 - pred_decision[0][0] + pred_decision[0][1],
	1 - pred_decision[1][2] + pred_decision[1][0],
	1 - pred_decision[2][2] + pred_decision[2][1],
	1 - pred_decision[3][0] + pred_decision[3][2],
	1 - pred_decision[4][2] + pred_decision[4][1],
	]
	)
	np.clip(dummy_losses, 0, None, out=dummy_losses)
	dummy_hinge_loss = np.mean(dummy_losses)
	assert_almost_equal(
	hinge_loss(y_true, pred_decision, labels=labels), dummy_hinge_loss
	)


	def test_hinge_loss_multiclass_invariance_lists():
	# Currently, invariance of string and integer labels cannot be tested
	# in common invariance tests because invariance tests for multiclass
	# decision functions is not implemented yet.
	y_true = ["blue", "green", "red", "green", "white", "red"]
	pred_decision = [
	[+0.36, -0.17, -0.58, -0.99],
	[-0.55, -0.38, -0.48, -0.58],
	[-1.45, -0.58, -0.38, -0.17],
	[-0.55, -0.38, -0.48, -0.58],
	[-2.36, -0.79, -0.27, +0.24],
	[-1.45, -0.58, -0.38, -0.17],
	]
	dummy_losses = np.array(
	[
	1 - pred_decision[0][0] + pred_decision[0][1],
	1 - pred_decision[1][1] + pred_decision[1][2],
	1 - pred_decision[2][2] + pred_decision[2][3],
	1 - pred_decision[3][1] + pred_decision[3][2],
	1 - pred_decision[4][3] + pred_decision[4][2],
	1 - pred_decision[5][2] + pred_decision[5][3],
	]
	)
	np.clip(dummy_losses, 0, None, out=dummy_losses)
	dummy_hinge_loss = np.mean(dummy_losses)
	assert hinge_loss(y_true, pred_decision) == dummy_hinge_loss


	def test_log_loss():
	# binary case with symbolic labels ("no" < "yes")
	y_true = ["no", "no", "no", "yes", "yes", "yes"]
	y_pred = np.array(
	[[0.5, 0.5], [0.1, 0.9], [0.01, 0.99], [0.9, 0.1], [0.75, 0.25], [0.001, 0.999]]
	)
	loss = log_loss(y_true, y_pred)
	loss_true = -np.mean(bernoulli.logpmf(np.array(y_true) == "yes", y_pred[:, 1]))
	assert_allclose(loss, loss_true)

	# multiclass case; adapted from http://bit.ly/RJJHWA
	y_true = [1, 0, 2]
	y_pred = [[0.2, 0.7, 0.1], [0.6, 0.2, 0.2], [0.6, 0.1, 0.3]]
	loss = log_loss(y_true, y_pred, normalize=True)
	assert_allclose(loss, 0.6904911)

	# check that we got all the shapes and axes right
	# by doubling the length of y_true and y_pred
	y_true *= 2
	y_pred *= 2
	loss = log_loss(y_true, y_pred, normalize=False)
	assert_allclose(loss, 0.6904911 * 6)

	# raise error if number of classes are not equal.
	y_true = [1, 0, 2]
	y_pred = [[0.3, 0.7], [0.6, 0.4], [0.4, 0.6]]
	with pytest.raises(ValueError):
	log_loss(y_true, y_pred)

	# case when y_true is a string array object
	y_true = ["ham", "spam", "spam", "ham"]
	y_pred = [[0.3, 0.7], [0.6, 0.4], [0.4, 0.6], [0.7, 0.3]]
	loss = log_loss(y_true, y_pred)
	assert_allclose(loss, 0.7469410)

	# test labels option

	y_true = [2, 2]
	y_pred = [[0.2, 0.8], [0.6, 0.4]]
	y_score = np.array([[0.1, 0.9], [0.1, 0.9]])
	error_str = (
	r"y_true contains only one label \(2\). Please provide "
	r"the true labels explicitly through the labels argument."
	)
	with pytest.raises(ValueError, match=error_str):
	log_loss(y_true, y_pred)

	y_pred = [[0.2, 0.8], [0.6, 0.4], [0.7, 0.3]]
	error_str = r"Found input variables with inconsistent numbers of samples: \[3, 2\]"
	with pytest.raises(ValueError, match=error_str):
	log_loss(y_true, y_pred)

	# works when the labels argument is used

	true_log_loss = -np.mean(np.log(y_score[:, 1]))
	calculated_log_loss = log_loss(y_true, y_score, labels=[1, 2])
	assert_allclose(calculated_log_loss, true_log_loss)

	# ensure labels work when len(np.unique(y_true)) != y_pred.shape[1]
	y_true = [1, 2, 2]
	y_score2 = [[0.7, 0.1, 0.2], [0.2, 0.7, 0.1], [0.1, 0.7, 0.2]]
	loss = log_loss(y_true, y_score2, labels=[1, 2, 3])
	assert_allclose(loss, -np.log(0.7))


	@pytest.mark.parametrize("dtype", [np.float64, np.float32, np.float16])
	def test_log_loss_eps(dtype):
	"""Check the behaviour internal eps that changes depending on the input dtype.

	Non-regression test for:
	https://github.com/scikit-learn/scikit-learn/issues/24315
	"""
	y_true = np.array([0, 1], dtype=dtype)
	y_pred = np.array([1, 0], dtype=dtype)

	loss = log_loss(y_true, y_pred)
	assert np.isfinite(loss)


	@pytest.mark.parametrize("dtype", [np.float64, np.float32, np.float16])
	def test_log_loss_not_probabilities_warning(dtype):
	"""Check that log_loss raises a warning when y_pred values don't sum to 1."""
	y_true = np.array([0, 1, 1, 0])
	y_pred = np.array([[0.2, 0.7], [0.6, 0.3], [0.4, 0.7], [0.8, 0.3]], dtype=dtype)

	with pytest.warns(UserWarning, match="The y_pred values do not sum to one."):
	log_loss(y_true, y_pred)


	@pytest.mark.parametrize(
	"y_true, y_pred",
	[
	([0, 1, 0], [0, 1, 0]),
	([0, 1, 0], [[1, 0], [0, 1], [1, 0]]),
	([0, 1, 2], [[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
	],
	)
	def test_log_loss_perfect_predictions(y_true, y_pred):
	"""Check that log_loss returns 0 for perfect predictions."""
	# Because of the clipping, the result is not exactly 0
	assert log_loss(y_true, y_pred) == pytest.approx(0)


	def test_log_loss_pandas_input():
	# case when input is a pandas series and dataframe gh-5715
	y_tr = np.array(["ham", "spam", "spam", "ham"])
	y_pr = np.array([[0.3, 0.7], [0.6, 0.4], [0.4, 0.6], [0.7, 0.3]])
	types = [(MockDataFrame, MockDataFrame)]
	try:
	from pandas import DataFrame, Series

	types.append((Series, DataFrame))
	except ImportError:
	pass
	for TrueInputType, PredInputType in types:
	# y_pred dataframe, y_true series
	y_true, y_pred = TrueInputType(y_tr), PredInputType(y_pr)
	loss = log_loss(y_true, y_pred)
	assert_allclose(loss, 0.7469410)


	def test_brier_score_loss():
	# Check brier_score_loss function
	y_true = np.array([0, 1, 1, 0, 1, 1])
	y_pred = np.array([0.1, 0.8, 0.9, 0.3, 1.0, 0.95])
	true_score = linalg.norm(y_true - y_pred) ** 2 / len(y_true)

	assert_almost_equal(brier_score_loss(y_true, y_true), 0.0)
	assert_almost_equal(brier_score_loss(y_true, y_pred), true_score)
	assert_almost_equal(brier_score_loss(1.0 + y_true, y_pred), true_score)
	assert_almost_equal(brier_score_loss(2 * y_true - 1, y_pred), true_score)
	with pytest.raises(ValueError):
	brier_score_loss(y_true, y_pred[1:])
	with pytest.raises(ValueError):
	brier_score_loss(y_true, y_pred + 1.0)
	with pytest.raises(ValueError):
	brier_score_loss(y_true, y_pred - 1.0)

	# ensure to raise an error for multiclass y_true
	y_true = np.array([0, 1, 2, 0])
	y_pred = np.array([0.8, 0.6, 0.4, 0.2])
	error_message = (
	"Only binary classification is supported. The type of the target is multiclass"
	)

	with pytest.raises(ValueError, match=error_message):
	brier_score_loss(y_true, y_pred)

	# calculate correctly when there's only one class in y_true
	assert_almost_equal(brier_score_loss([-1], [0.4]), 0.16)
	assert_almost_equal(brier_score_loss([0], [0.4]), 0.16)
	assert_almost_equal(brier_score_loss([1], [0.4]), 0.36)
	assert_almost_equal(brier_score_loss(["foo"], [0.4], pos_label="bar"), 0.16)
	assert_almost_equal(brier_score_loss(["foo"], [0.4], pos_label="foo"), 0.36)


	def test_balanced_accuracy_score_unseen():
	msg = "y_pred contains classes not in y_true"
	with pytest.warns(UserWarning, match=msg):
	balanced_accuracy_score([0, 0, 0], [0, 0, 1])


	@pytest.mark.parametrize(
	"y_true,y_pred",
	[
	(["a", "b", "a", "b"], ["a", "a", "a", "b"]),
	(["a", "b", "c", "b"], ["a", "a", "a", "b"]),
	(["a", "a", "a", "b"], ["a", "b", "c", "b"]),
	],
	)
	def test_balanced_accuracy_score(y_true, y_pred):
	macro_recall = recall_score(
	y_true, y_pred, average="macro", labels=np.unique(y_true)
	)
	with ignore_warnings():
	# Warnings are tested in test_balanced_accuracy_score_unseen
	balanced = balanced_accuracy_score(y_true, y_pred)
	assert balanced == pytest.approx(macro_recall)
	adjusted = balanced_accuracy_score(y_true, y_pred, adjusted=True)
	chance = balanced_accuracy_score(y_true, np.full_like(y_true, y_true[0]))
	assert adjusted == (balanced - chance) / (1 - chance)


	@pytest.mark.parametrize(
	"metric",
	[
	jaccard_score,
	f1_score,
	partial(fbeta_score, beta=0.5),
	precision_recall_fscore_support,
	precision_score,
	recall_score,
	brier_score_loss,
	],
	)
	@pytest.mark.parametrize(
	"classes", [(False, True), (0, 1), (0.0, 1.0), ("zero", "one")]
	)
	def test_classification_metric_pos_label_types(metric, classes):
	"""Check that the metric works with different types of `pos_label`.

	We can expect `pos_label` to be a bool, an integer, a float, a string.
	No error should be raised for those types.
	"""
	rng = np.random.RandomState(42)
	n_samples, pos_label = 10, classes[-1]
	y_true = rng.choice(classes, size=n_samples, replace=True)
	if metric is brier_score_loss:
	# brier score loss requires probabilities
	y_pred = rng.uniform(size=n_samples)
	else:
	y_pred = y_true.copy()
	result = metric(y_true, y_pred, pos_label=pos_label)
	assert not np.any(np.isnan(result))


	@pytest.mark.parametrize(
	"y_true, y_pred, expected_score",
	[
	(np.array([0, 1]), np.array([1, 0]), 0.0),
	(np.array([0, 1]), np.array([0, 1]), 1.0),
	(np.array([0, 1]), np.array([0, 0]), 0.0),
	(np.array([0, 0]), np.array([0, 0]), 1.0),
	],
	)
	def test_f1_for_small_binary_inputs_with_zero_division(y_true, y_pred, expected_score):
	"""Check the behaviour of `zero_division` for f1-score.

	Non-regression test for:
	https://github.com/scikit-learn/scikit-learn/issues/26965
	"""
	assert f1_score(y_true, y_pred, zero_division=1.0) == pytest.approx(expected_score)


	@pytest.mark.parametrize(
	"scoring",
	[
	make_scorer(f1_score, zero_division=np.nan),
	make_scorer(fbeta_score, beta=2, zero_division=np.nan),
	make_scorer(precision_score, zero_division=np.nan),
	make_scorer(recall_score, zero_division=np.nan),
	],
	)
	def test_classification_metric_division_by_zero_nan_validaton(scoring):
	"""Check that we validate `np.nan` properly for classification metrics.

	With `n_jobs=2` in cross-validation, the `np.nan` used for the singleton will be
	different in the sub-process and we should not use the `is` operator but
	`math.isnan`.

	Non-regression test for:
	https://github.com/scikit-learn/scikit-learn/issues/27563
	"""
	X, y = datasets.make_classification(random_state=0)
	classifier = DecisionTreeClassifier(max_depth=3, random_state=0).fit(X, y)
	cross_val_score(classifier, X, y, scoring=scoring, n_jobs=2, error_score="raise")


	# TODO(1.7): remove
	def test_brier_score_loss_deprecation_warning():
	"""Check the message for future deprecation."""
	# Check brier_score_loss function
	y_true = np.array([0, 1, 1, 0, 1, 1])
	y_pred = np.array([0.1, 0.8, 0.9, 0.3, 1.0, 0.95])

	warn_msg = "y_prob was deprecated in version 1.5"
	with pytest.warns(FutureWarning, match=warn_msg):
	brier_score_loss(
	y_true,
	y_prob=y_pred,
	)

	error_msg = "`y_prob` and `y_proba` cannot be both specified"
	with pytest.raises(ValueError, match=error_msg):
	brier_score_loss(
	y_true,
	y_prob=y_pred,
	y_proba=y_pred,
	)


	def test_d2_log_loss_score():
	y_true = [0, 0, 0, 1, 1, 1]
	y_true_string = ["no", "no", "no", "yes", "yes", "yes"]
	y_pred = np.array(
	[
	[0.5, 0.5],
	[0.9, 0.1],
	[0.4, 0.6],
	[0.6, 0.4],
	[0.35, 0.65],
	[0.01, 0.99],
	]
	)
	y_pred_null = np.array(
	[
	[0.5, 0.5],
	[0.5, 0.5],
	[0.5, 0.5],
	[0.5, 0.5],
	[0.5, 0.5],
	[0.5, 0.5],
	]
	)
	d2_score = d2_log_loss_score(y_true=y_true, y_pred=y_pred)
	log_likelihood = log_loss(y_true=y_true, y_pred=y_pred, normalize=False)
	log_likelihood_null = log_loss(y_true=y_true, y_pred=y_pred_null, normalize=False)
	d2_score_true = 1 - log_likelihood / log_likelihood_null
	assert d2_score == pytest.approx(d2_score_true)

	# check that using sample weight also gives the correct d2 score
	sample_weight = np.array([2, 1, 3, 4, 3, 1])
	y_pred_null[:, 0] = sample_weight[:3].sum() / sample_weight.sum()
	y_pred_null[:, 1] = sample_weight[3:].sum() / sample_weight.sum()
	d2_score = d2_log_loss_score(
	y_true=y_true, y_pred=y_pred, sample_weight=sample_weight
	)
	log_likelihood = log_loss(
	y_true=y_true,
	y_pred=y_pred,
	sample_weight=sample_weight,
	normalize=False,
	)
	log_likelihood_null = log_loss(
	y_true=y_true,
	y_pred=y_pred_null,
	sample_weight=sample_weight,
	normalize=False,
	)
	d2_score_true = 1 - log_likelihood / log_likelihood_null
	assert d2_score == pytest.approx(d2_score_true)

	# check if good predictions give a relatively higher value for the d2 score
	y_pred = np.array(
	[
	[0.9, 0.1],
	[0.8, 0.2],
	[0.9, 0.1],
	[0.1, 0.9],
	[0.2, 0.8],
	[0.1, 0.9],
	]
	)
	d2_score = d2_log_loss_score(y_true, y_pred)
	assert 0.5 < d2_score < 1.0
	# check that a similar value is obtained for string labels
	d2_score_string = d2_log_loss_score(y_true_string, y_pred)
	assert d2_score_string == pytest.approx(d2_score)

	# check if poor predictions gives a relatively low value for the d2 score
	y_pred = np.array(
	[
	[0.5, 0.5],
	[0.1, 0.9],
	[0.1, 0.9],
	[0.9, 0.1],
	[0.75, 0.25],
	[0.1, 0.9],
	]
	)
	d2_score = d2_log_loss_score(y_true, y_pred)
	assert d2_score < 0
	# check that a similar value is obtained for string labels
	d2_score_string = d2_log_loss_score(y_true_string, y_pred)
	assert d2_score_string == pytest.approx(d2_score)

	# check if simply using the average of the classes as the predictions
	# gives a d2 score of 0
	y_true = [0, 0, 0, 1, 1, 1]
	y_pred = np.array(
	[
	[0.5, 0.5],
	[0.5, 0.5],
	[0.5, 0.5],
	[0.5, 0.5],
	[0.5, 0.5],
	[0.5, 0.5],
	]
	)
	d2_score = d2_log_loss_score(y_true, y_pred)
	assert d2_score == 0
	d2_score_string = d2_log_loss_score(y_true_string, y_pred)
	assert d2_score_string == 0

	# check if simply using the average of the classes as the predictions
	# gives a d2 score of 0 when the positive class has a higher proportion
	y_true = [0, 1, 1, 1]
	y_true_string = ["no", "yes", "yes", "yes"]
	y_pred = np.array([[0.25, 0.75], [0.25, 0.75], [0.25, 0.75], [0.25, 0.75]])
	d2_score = d2_log_loss_score(y_true, y_pred)
	assert d2_score == 0
	d2_score_string = d2_log_loss_score(y_true_string, y_pred)
	assert d2_score_string == 0
	sample_weight = [2, 2, 2, 2]
	d2_score_with_sample_weight = d2_log_loss_score(
	y_true, y_pred, sample_weight=sample_weight
	)
	assert d2_score_with_sample_weight == 0

	# check that the d2 scores seem correct when more than 2
	# labels are specified
	y_true = ["high", "high", "low", "neutral"]
	sample_weight = [1.4, 0.6, 0.8, 0.2]

	y_pred = np.array(
	[
	[0.8, 0.1, 0.1],
	[0.8, 0.1, 0.1],
	[0.1, 0.8, 0.1],
	[0.1, 0.1, 0.8],
	]
	)
	d2_score = d2_log_loss_score(y_true, y_pred)
	assert 0.5 < d2_score < 1.0
	d2_score = d2_log_loss_score(y_true, y_pred, sample_weight=sample_weight)
	assert 0.5 < d2_score < 1.0

	y_pred = np.array(
	[
	[0.2, 0.5, 0.3],
	[0.1, 0.7, 0.2],
	[0.1, 0.1, 0.8],
	[0.2, 0.7, 0.1],
	]
	)
	d2_score = d2_log_loss_score(y_true, y_pred)
	assert d2_score < 0
	d2_score = d2_log_loss_score(y_true, y_pred, sample_weight=sample_weight)
	assert d2_score < 0


	def test_d2_log_loss_score_raises():
	"""Test that d2_log_loss_score raises the appropriate errors on
	invalid inputs."""
	y_true = [0, 1, 2]
	y_pred = [[0.2, 0.8], [0.5, 0.5], [0.4, 0.6]]
	err = "contain different number of classes"
	with pytest.raises(ValueError, match=err):
	d2_log_loss_score(y_true, y_pred)

	# check error if the number of classes in labels do not match the number
	# of classes in y_pred.
	y_true = ["a", "b", "c"]
	y_pred = [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]
	labels = [0, 1, 2]
	err = "number of classes in labels is different"
	with pytest.raises(ValueError, match=err):
	d2_log_loss_score(y_true, y_pred, labels=labels)

	# check error if y_true and y_pred do not have equal lengths
	y_true = [0, 1, 2]
	y_pred = [[0.5, 0.5, 0.5], [0.6, 0.3, 0.1]]
	err = "inconsistent numbers of samples"
	with pytest.raises(ValueError, match=err):
	d2_log_loss_score(y_true, y_pred)

	# check warning for samples < 2
	y_true = [1]
	y_pred = [[0.5, 0.5]]
	err = "score is not well-defined"
	with pytest.warns(UndefinedMetricWarning, match=err):
	d2_log_loss_score(y_true, y_pred)

	# check error when y_true only has 1 label
	y_true = [1, 1, 1]
	y_pred = [[0.5, 0.5], [0.5, 0.5], [0.5, 5]]
	err = "y_true contains only one label"
	with pytest.raises(ValueError, match=err):
	d2_log_loss_score(y_true, y_pred)

	# check error when y_true only has 1 label and labels also has
	# only 1 label
	y_true = [1, 1, 1]
	labels = [1]
	y_pred = [[0.5, 0.5], [0.5, 0.5], [0.5, 5]]
	err = "The labels array needs to contain at least two"
	with pytest.raises(ValueError, match=err):
	d2_log_loss_score(y_true, y_pred, labels=labels)