2275 lines
82 KiB
Python
2275 lines
82 KiB
Python
import re
|
|
import warnings
|
|
|
|
import numpy as np
|
|
import pytest
|
|
from scipy import stats
|
|
|
|
from sklearn import datasets, svm
|
|
from sklearn.datasets import make_multilabel_classification
|
|
from sklearn.exceptions import UndefinedMetricWarning
|
|
from sklearn.linear_model import LogisticRegression
|
|
from sklearn.metrics import (
|
|
accuracy_score,
|
|
auc,
|
|
average_precision_score,
|
|
coverage_error,
|
|
dcg_score,
|
|
det_curve,
|
|
label_ranking_average_precision_score,
|
|
label_ranking_loss,
|
|
ndcg_score,
|
|
precision_recall_curve,
|
|
roc_auc_score,
|
|
roc_curve,
|
|
top_k_accuracy_score,
|
|
)
|
|
from sklearn.metrics._ranking import _dcg_sample_scores, _ndcg_sample_scores
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.preprocessing import label_binarize
|
|
from sklearn.random_projection import _sparse_random_matrix
|
|
from sklearn.utils._testing import (
|
|
_convert_container,
|
|
assert_allclose,
|
|
assert_almost_equal,
|
|
assert_array_almost_equal,
|
|
assert_array_equal,
|
|
ignore_warnings,
|
|
)
|
|
from sklearn.utils.extmath import softmax
|
|
from sklearn.utils.fixes import CSR_CONTAINERS
|
|
from sklearn.utils.validation import (
|
|
check_array,
|
|
check_consistent_length,
|
|
check_random_state,
|
|
)
|
|
|
|
###############################################################################
|
|
# Utilities for testing
|
|
|
|
CURVE_FUNCS = [
|
|
det_curve,
|
|
precision_recall_curve,
|
|
roc_curve,
|
|
]
|
|
|
|
|
|
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_score = 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_score = y_score[:, 1]
|
|
|
|
y_pred = clf.predict(X[half:])
|
|
y_true = y[half:]
|
|
return y_true, y_pred, y_score
|
|
|
|
|
|
###############################################################################
|
|
# Tests
|
|
|
|
|
|
def _auc(y_true, y_score):
|
|
"""Alternative implementation to check for correctness of
|
|
`roc_auc_score`."""
|
|
pos_label = np.unique(y_true)[1]
|
|
|
|
# Count the number of times positive samples are correctly ranked above
|
|
# negative samples.
|
|
pos = y_score[y_true == pos_label]
|
|
neg = y_score[y_true != pos_label]
|
|
diff_matrix = pos.reshape(1, -1) - neg.reshape(-1, 1)
|
|
n_correct = np.sum(diff_matrix > 0)
|
|
|
|
return n_correct / float(len(pos) * len(neg))
|
|
|
|
|
|
def _average_precision(y_true, y_score):
|
|
"""Alternative implementation to check for correctness of
|
|
`average_precision_score`.
|
|
|
|
Note that this implementation fails on some edge cases.
|
|
For example, for constant predictions e.g. [0.5, 0.5, 0.5],
|
|
y_true = [1, 0, 0] returns an average precision of 0.33...
|
|
but y_true = [0, 0, 1] returns 1.0.
|
|
"""
|
|
pos_label = np.unique(y_true)[1]
|
|
n_pos = np.sum(y_true == pos_label)
|
|
order = np.argsort(y_score)[::-1]
|
|
y_score = y_score[order]
|
|
y_true = y_true[order]
|
|
|
|
score = 0
|
|
for i in range(len(y_score)):
|
|
if y_true[i] == pos_label:
|
|
# Compute precision up to document i
|
|
# i.e, percentage of relevant documents up to document i.
|
|
prec = 0
|
|
for j in range(0, i + 1):
|
|
if y_true[j] == pos_label:
|
|
prec += 1.0
|
|
prec /= i + 1.0
|
|
score += prec
|
|
|
|
return score / n_pos
|
|
|
|
|
|
def _average_precision_slow(y_true, y_score):
|
|
"""A second alternative implementation of average precision that closely
|
|
follows the Wikipedia article's definition (see References). This should
|
|
give identical results as `average_precision_score` for all inputs.
|
|
|
|
References
|
|
----------
|
|
.. [1] `Wikipedia entry for the Average precision
|
|
<https://en.wikipedia.org/wiki/Average_precision>`_
|
|
"""
|
|
precision, recall, threshold = precision_recall_curve(y_true, y_score)
|
|
precision = list(reversed(precision))
|
|
recall = list(reversed(recall))
|
|
average_precision = 0
|
|
for i in range(1, len(precision)):
|
|
average_precision += precision[i] * (recall[i] - recall[i - 1])
|
|
return average_precision
|
|
|
|
|
|
def _partial_roc_auc_score(y_true, y_predict, max_fpr):
|
|
"""Alternative implementation to check for correctness of `roc_auc_score`
|
|
with `max_fpr` set.
|
|
"""
|
|
|
|
def _partial_roc(y_true, y_predict, max_fpr):
|
|
fpr, tpr, _ = roc_curve(y_true, y_predict)
|
|
new_fpr = fpr[fpr <= max_fpr]
|
|
new_fpr = np.append(new_fpr, max_fpr)
|
|
new_tpr = tpr[fpr <= max_fpr]
|
|
idx_out = np.argmax(fpr > max_fpr)
|
|
idx_in = idx_out - 1
|
|
x_interp = [fpr[idx_in], fpr[idx_out]]
|
|
y_interp = [tpr[idx_in], tpr[idx_out]]
|
|
new_tpr = np.append(new_tpr, np.interp(max_fpr, x_interp, y_interp))
|
|
return (new_fpr, new_tpr)
|
|
|
|
new_fpr, new_tpr = _partial_roc(y_true, y_predict, max_fpr)
|
|
partial_auc = auc(new_fpr, new_tpr)
|
|
|
|
# Formula (5) from McClish 1989
|
|
fpr1 = 0
|
|
fpr2 = max_fpr
|
|
min_area = 0.5 * (fpr2 - fpr1) * (fpr2 + fpr1)
|
|
max_area = fpr2 - fpr1
|
|
return 0.5 * (1 + (partial_auc - min_area) / (max_area - min_area))
|
|
|
|
|
|
@pytest.mark.parametrize("drop", [True, False])
|
|
def test_roc_curve(drop):
|
|
# Test Area under Receiver Operating Characteristic (ROC) curve
|
|
y_true, _, y_score = make_prediction(binary=True)
|
|
expected_auc = _auc(y_true, y_score)
|
|
|
|
fpr, tpr, thresholds = roc_curve(y_true, y_score, drop_intermediate=drop)
|
|
roc_auc = auc(fpr, tpr)
|
|
assert_array_almost_equal(roc_auc, expected_auc, decimal=2)
|
|
assert_almost_equal(roc_auc, roc_auc_score(y_true, y_score))
|
|
assert fpr.shape == tpr.shape
|
|
assert fpr.shape == thresholds.shape
|
|
|
|
|
|
def test_roc_curve_end_points():
|
|
# Make sure that roc_curve returns a curve start at 0 and ending and
|
|
# 1 even in corner cases
|
|
rng = np.random.RandomState(0)
|
|
y_true = np.array([0] * 50 + [1] * 50)
|
|
y_pred = rng.randint(3, size=100)
|
|
fpr, tpr, thr = roc_curve(y_true, y_pred, drop_intermediate=True)
|
|
assert fpr[0] == 0
|
|
assert fpr[-1] == 1
|
|
assert fpr.shape == tpr.shape
|
|
assert fpr.shape == thr.shape
|
|
|
|
|
|
def test_roc_returns_consistency():
|
|
# Test whether the returned threshold matches up with tpr
|
|
# make small toy dataset
|
|
y_true, _, y_score = make_prediction(binary=True)
|
|
fpr, tpr, thresholds = roc_curve(y_true, y_score)
|
|
|
|
# use the given thresholds to determine the tpr
|
|
tpr_correct = []
|
|
for t in thresholds:
|
|
tp = np.sum((y_score >= t) & y_true)
|
|
p = np.sum(y_true)
|
|
tpr_correct.append(1.0 * tp / p)
|
|
|
|
# compare tpr and tpr_correct to see if the thresholds' order was correct
|
|
assert_array_almost_equal(tpr, tpr_correct, decimal=2)
|
|
assert fpr.shape == tpr.shape
|
|
assert fpr.shape == thresholds.shape
|
|
|
|
|
|
def test_roc_curve_multi():
|
|
# roc_curve not applicable for multi-class problems
|
|
y_true, _, y_score = make_prediction(binary=False)
|
|
|
|
with pytest.raises(ValueError):
|
|
roc_curve(y_true, y_score)
|
|
|
|
|
|
def test_roc_curve_confidence():
|
|
# roc_curve for confidence scores
|
|
y_true, _, y_score = make_prediction(binary=True)
|
|
|
|
fpr, tpr, thresholds = roc_curve(y_true, y_score - 0.5)
|
|
roc_auc = auc(fpr, tpr)
|
|
assert_array_almost_equal(roc_auc, 0.90, decimal=2)
|
|
assert fpr.shape == tpr.shape
|
|
assert fpr.shape == thresholds.shape
|
|
|
|
|
|
def test_roc_curve_hard():
|
|
# roc_curve for hard decisions
|
|
y_true, pred, y_score = make_prediction(binary=True)
|
|
|
|
# always predict one
|
|
trivial_pred = np.ones(y_true.shape)
|
|
fpr, tpr, thresholds = roc_curve(y_true, trivial_pred)
|
|
roc_auc = auc(fpr, tpr)
|
|
assert_array_almost_equal(roc_auc, 0.50, decimal=2)
|
|
assert fpr.shape == tpr.shape
|
|
assert fpr.shape == thresholds.shape
|
|
|
|
# always predict zero
|
|
trivial_pred = np.zeros(y_true.shape)
|
|
fpr, tpr, thresholds = roc_curve(y_true, trivial_pred)
|
|
roc_auc = auc(fpr, tpr)
|
|
assert_array_almost_equal(roc_auc, 0.50, decimal=2)
|
|
assert fpr.shape == tpr.shape
|
|
assert fpr.shape == thresholds.shape
|
|
|
|
# hard decisions
|
|
fpr, tpr, thresholds = roc_curve(y_true, pred)
|
|
roc_auc = auc(fpr, tpr)
|
|
assert_array_almost_equal(roc_auc, 0.78, decimal=2)
|
|
assert fpr.shape == tpr.shape
|
|
assert fpr.shape == thresholds.shape
|
|
|
|
|
|
def test_roc_curve_one_label():
|
|
y_true = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
|
|
y_pred = [0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
|
|
# assert there are warnings
|
|
expected_message = (
|
|
"No negative samples in y_true, false positive value should be meaningless"
|
|
)
|
|
with pytest.warns(UndefinedMetricWarning, match=expected_message):
|
|
fpr, tpr, thresholds = roc_curve(y_true, y_pred)
|
|
|
|
# all true labels, all fpr should be nan
|
|
assert_array_equal(fpr, np.full(len(thresholds), np.nan))
|
|
assert fpr.shape == tpr.shape
|
|
assert fpr.shape == thresholds.shape
|
|
|
|
# assert there are warnings
|
|
expected_message = (
|
|
"No positive samples in y_true, true positive value should be meaningless"
|
|
)
|
|
with pytest.warns(UndefinedMetricWarning, match=expected_message):
|
|
fpr, tpr, thresholds = roc_curve([1 - x for x in y_true], y_pred)
|
|
# all negative labels, all tpr should be nan
|
|
assert_array_equal(tpr, np.full(len(thresholds), np.nan))
|
|
assert fpr.shape == tpr.shape
|
|
assert fpr.shape == thresholds.shape
|
|
|
|
|
|
def test_roc_curve_toydata():
|
|
# Binary classification
|
|
y_true = [0, 1]
|
|
y_score = [0, 1]
|
|
tpr, fpr, _ = roc_curve(y_true, y_score)
|
|
roc_auc = roc_auc_score(y_true, y_score)
|
|
assert_array_almost_equal(tpr, [0, 0, 1])
|
|
assert_array_almost_equal(fpr, [0, 1, 1])
|
|
assert_almost_equal(roc_auc, 1.0)
|
|
|
|
y_true = [0, 1]
|
|
y_score = [1, 0]
|
|
tpr, fpr, _ = roc_curve(y_true, y_score)
|
|
roc_auc = roc_auc_score(y_true, y_score)
|
|
assert_array_almost_equal(tpr, [0, 1, 1])
|
|
assert_array_almost_equal(fpr, [0, 0, 1])
|
|
assert_almost_equal(roc_auc, 0.0)
|
|
|
|
y_true = [1, 0]
|
|
y_score = [1, 1]
|
|
tpr, fpr, _ = roc_curve(y_true, y_score)
|
|
roc_auc = roc_auc_score(y_true, y_score)
|
|
assert_array_almost_equal(tpr, [0, 1])
|
|
assert_array_almost_equal(fpr, [0, 1])
|
|
assert_almost_equal(roc_auc, 0.5)
|
|
|
|
y_true = [1, 0]
|
|
y_score = [1, 0]
|
|
tpr, fpr, _ = roc_curve(y_true, y_score)
|
|
roc_auc = roc_auc_score(y_true, y_score)
|
|
assert_array_almost_equal(tpr, [0, 0, 1])
|
|
assert_array_almost_equal(fpr, [0, 1, 1])
|
|
assert_almost_equal(roc_auc, 1.0)
|
|
|
|
y_true = [1, 0]
|
|
y_score = [0.5, 0.5]
|
|
tpr, fpr, _ = roc_curve(y_true, y_score)
|
|
roc_auc = roc_auc_score(y_true, y_score)
|
|
assert_array_almost_equal(tpr, [0, 1])
|
|
assert_array_almost_equal(fpr, [0, 1])
|
|
assert_almost_equal(roc_auc, 0.5)
|
|
|
|
y_true = [0, 0]
|
|
y_score = [0.25, 0.75]
|
|
# assert UndefinedMetricWarning because of no positive sample in y_true
|
|
expected_message = (
|
|
"No positive samples in y_true, true positive value should be meaningless"
|
|
)
|
|
with pytest.warns(UndefinedMetricWarning, match=expected_message):
|
|
tpr, fpr, _ = roc_curve(y_true, y_score)
|
|
|
|
with pytest.raises(ValueError):
|
|
roc_auc_score(y_true, y_score)
|
|
assert_array_almost_equal(tpr, [0.0, 0.5, 1.0])
|
|
assert_array_almost_equal(fpr, [np.nan, np.nan, np.nan])
|
|
|
|
y_true = [1, 1]
|
|
y_score = [0.25, 0.75]
|
|
# assert UndefinedMetricWarning because of no negative sample in y_true
|
|
expected_message = (
|
|
"No negative samples in y_true, false positive value should be meaningless"
|
|
)
|
|
with pytest.warns(UndefinedMetricWarning, match=expected_message):
|
|
tpr, fpr, _ = roc_curve(y_true, y_score)
|
|
|
|
with pytest.raises(ValueError):
|
|
roc_auc_score(y_true, y_score)
|
|
assert_array_almost_equal(tpr, [np.nan, np.nan, np.nan])
|
|
assert_array_almost_equal(fpr, [0.0, 0.5, 1.0])
|
|
|
|
# Multi-label classification task
|
|
y_true = np.array([[0, 1], [0, 1]])
|
|
y_score = np.array([[0, 1], [0, 1]])
|
|
with pytest.raises(ValueError):
|
|
roc_auc_score(y_true, y_score, average="macro")
|
|
with pytest.raises(ValueError):
|
|
roc_auc_score(y_true, y_score, average="weighted")
|
|
assert_almost_equal(roc_auc_score(y_true, y_score, average="samples"), 1.0)
|
|
assert_almost_equal(roc_auc_score(y_true, y_score, average="micro"), 1.0)
|
|
|
|
y_true = np.array([[0, 1], [0, 1]])
|
|
y_score = np.array([[0, 1], [1, 0]])
|
|
with pytest.raises(ValueError):
|
|
roc_auc_score(y_true, y_score, average="macro")
|
|
with pytest.raises(ValueError):
|
|
roc_auc_score(y_true, y_score, average="weighted")
|
|
assert_almost_equal(roc_auc_score(y_true, y_score, average="samples"), 0.5)
|
|
assert_almost_equal(roc_auc_score(y_true, y_score, average="micro"), 0.5)
|
|
|
|
y_true = np.array([[1, 0], [0, 1]])
|
|
y_score = np.array([[0, 1], [1, 0]])
|
|
assert_almost_equal(roc_auc_score(y_true, y_score, average="macro"), 0)
|
|
assert_almost_equal(roc_auc_score(y_true, y_score, average="weighted"), 0)
|
|
assert_almost_equal(roc_auc_score(y_true, y_score, average="samples"), 0)
|
|
assert_almost_equal(roc_auc_score(y_true, y_score, average="micro"), 0)
|
|
|
|
y_true = np.array([[1, 0], [0, 1]])
|
|
y_score = np.array([[0.5, 0.5], [0.5, 0.5]])
|
|
assert_almost_equal(roc_auc_score(y_true, y_score, average="macro"), 0.5)
|
|
assert_almost_equal(roc_auc_score(y_true, y_score, average="weighted"), 0.5)
|
|
assert_almost_equal(roc_auc_score(y_true, y_score, average="samples"), 0.5)
|
|
assert_almost_equal(roc_auc_score(y_true, y_score, average="micro"), 0.5)
|
|
|
|
|
|
def test_roc_curve_drop_intermediate():
|
|
# Test that drop_intermediate drops the correct thresholds
|
|
y_true = [0, 0, 0, 0, 1, 1]
|
|
y_score = [0.0, 0.2, 0.5, 0.6, 0.7, 1.0]
|
|
tpr, fpr, thresholds = roc_curve(y_true, y_score, drop_intermediate=True)
|
|
assert_array_almost_equal(thresholds, [np.inf, 1.0, 0.7, 0.0])
|
|
|
|
# Test dropping thresholds with repeating scores
|
|
y_true = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
|
|
y_score = [0.0, 0.1, 0.6, 0.6, 0.7, 0.8, 0.9, 0.6, 0.7, 0.8, 0.9, 0.9, 1.0]
|
|
tpr, fpr, thresholds = roc_curve(y_true, y_score, drop_intermediate=True)
|
|
assert_array_almost_equal(thresholds, [np.inf, 1.0, 0.9, 0.7, 0.6, 0.0])
|
|
|
|
|
|
def test_roc_curve_fpr_tpr_increasing():
|
|
# Ensure that fpr and tpr returned by roc_curve are increasing.
|
|
# Construct an edge case with float y_score and sample_weight
|
|
# when some adjacent values of fpr and tpr are actually the same.
|
|
y_true = [0, 0, 1, 1, 1]
|
|
y_score = [0.1, 0.7, 0.3, 0.4, 0.5]
|
|
sample_weight = np.repeat(0.2, 5)
|
|
fpr, tpr, _ = roc_curve(y_true, y_score, sample_weight=sample_weight)
|
|
assert (np.diff(fpr) < 0).sum() == 0
|
|
assert (np.diff(tpr) < 0).sum() == 0
|
|
|
|
|
|
def test_auc():
|
|
# Test Area Under Curve (AUC) computation
|
|
x = [0, 1]
|
|
y = [0, 1]
|
|
assert_array_almost_equal(auc(x, y), 0.5)
|
|
x = [1, 0]
|
|
y = [0, 1]
|
|
assert_array_almost_equal(auc(x, y), 0.5)
|
|
x = [1, 0, 0]
|
|
y = [0, 1, 1]
|
|
assert_array_almost_equal(auc(x, y), 0.5)
|
|
x = [0, 1]
|
|
y = [1, 1]
|
|
assert_array_almost_equal(auc(x, y), 1)
|
|
x = [0, 0.5, 1]
|
|
y = [0, 0.5, 1]
|
|
assert_array_almost_equal(auc(x, y), 0.5)
|
|
|
|
|
|
def test_auc_errors():
|
|
# Incompatible shapes
|
|
with pytest.raises(ValueError):
|
|
auc([0.0, 0.5, 1.0], [0.1, 0.2])
|
|
|
|
# Too few x values
|
|
with pytest.raises(ValueError):
|
|
auc([0.0], [0.1])
|
|
|
|
# x is not in order
|
|
x = [2, 1, 3, 4]
|
|
y = [5, 6, 7, 8]
|
|
error_message = "x is neither increasing nor decreasing : {}".format(np.array(x))
|
|
with pytest.raises(ValueError, match=re.escape(error_message)):
|
|
auc(x, y)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"y_true, labels",
|
|
[
|
|
(np.array([0, 1, 0, 2]), [0, 1, 2]),
|
|
(np.array([0, 1, 0, 2]), None),
|
|
(["a", "b", "a", "c"], ["a", "b", "c"]),
|
|
(["a", "b", "a", "c"], None),
|
|
],
|
|
)
|
|
def test_multiclass_ovo_roc_auc_toydata(y_true, labels):
|
|
# Tests the one-vs-one multiclass ROC AUC algorithm
|
|
# on a small example, representative of an expected use case.
|
|
y_scores = np.array(
|
|
[[0.1, 0.8, 0.1], [0.3, 0.4, 0.3], [0.35, 0.5, 0.15], [0, 0.2, 0.8]]
|
|
)
|
|
|
|
# Used to compute the expected output.
|
|
# Consider labels 0 and 1:
|
|
# positive label is 0, negative label is 1
|
|
score_01 = roc_auc_score([1, 0, 1], [0.1, 0.3, 0.35])
|
|
# positive label is 1, negative label is 0
|
|
score_10 = roc_auc_score([0, 1, 0], [0.8, 0.4, 0.5])
|
|
average_score_01 = (score_01 + score_10) / 2
|
|
|
|
# Consider labels 0 and 2:
|
|
score_02 = roc_auc_score([1, 1, 0], [0.1, 0.35, 0])
|
|
score_20 = roc_auc_score([0, 0, 1], [0.1, 0.15, 0.8])
|
|
average_score_02 = (score_02 + score_20) / 2
|
|
|
|
# Consider labels 1 and 2:
|
|
score_12 = roc_auc_score([1, 0], [0.4, 0.2])
|
|
score_21 = roc_auc_score([0, 1], [0.3, 0.8])
|
|
average_score_12 = (score_12 + score_21) / 2
|
|
|
|
# Unweighted, one-vs-one multiclass ROC AUC algorithm
|
|
ovo_unweighted_score = (average_score_01 + average_score_02 + average_score_12) / 3
|
|
assert_almost_equal(
|
|
roc_auc_score(y_true, y_scores, labels=labels, multi_class="ovo"),
|
|
ovo_unweighted_score,
|
|
)
|
|
|
|
# Weighted, one-vs-one multiclass ROC AUC algorithm
|
|
# Each term is weighted by the prevalence for the positive label.
|
|
pair_scores = [average_score_01, average_score_02, average_score_12]
|
|
prevalence = [0.75, 0.75, 0.50]
|
|
ovo_weighted_score = np.average(pair_scores, weights=prevalence)
|
|
assert_almost_equal(
|
|
roc_auc_score(
|
|
y_true, y_scores, labels=labels, multi_class="ovo", average="weighted"
|
|
),
|
|
ovo_weighted_score,
|
|
)
|
|
|
|
# Check that average=None raises NotImplemented error
|
|
error_message = "average=None is not implemented for multi_class='ovo'."
|
|
with pytest.raises(NotImplementedError, match=error_message):
|
|
roc_auc_score(y_true, y_scores, labels=labels, multi_class="ovo", average=None)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"y_true, labels",
|
|
[
|
|
(np.array([0, 2, 0, 2]), [0, 1, 2]),
|
|
(np.array(["a", "d", "a", "d"]), ["a", "b", "d"]),
|
|
],
|
|
)
|
|
def test_multiclass_ovo_roc_auc_toydata_binary(y_true, labels):
|
|
# Tests the one-vs-one multiclass ROC AUC algorithm for binary y_true
|
|
#
|
|
# on a small example, representative of an expected use case.
|
|
y_scores = np.array(
|
|
[[0.2, 0.0, 0.8], [0.6, 0.0, 0.4], [0.55, 0.0, 0.45], [0.4, 0.0, 0.6]]
|
|
)
|
|
|
|
# Used to compute the expected output.
|
|
# Consider labels 0 and 1:
|
|
# positive label is 0, negative label is 1
|
|
score_01 = roc_auc_score([1, 0, 1, 0], [0.2, 0.6, 0.55, 0.4])
|
|
# positive label is 1, negative label is 0
|
|
score_10 = roc_auc_score([0, 1, 0, 1], [0.8, 0.4, 0.45, 0.6])
|
|
ovo_score = (score_01 + score_10) / 2
|
|
|
|
assert_almost_equal(
|
|
roc_auc_score(y_true, y_scores, labels=labels, multi_class="ovo"), ovo_score
|
|
)
|
|
|
|
# Weighted, one-vs-one multiclass ROC AUC algorithm
|
|
assert_almost_equal(
|
|
roc_auc_score(
|
|
y_true, y_scores, labels=labels, multi_class="ovo", average="weighted"
|
|
),
|
|
ovo_score,
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"y_true, labels",
|
|
[
|
|
(np.array([0, 1, 2, 2]), None),
|
|
(["a", "b", "c", "c"], None),
|
|
([0, 1, 2, 2], [0, 1, 2]),
|
|
(["a", "b", "c", "c"], ["a", "b", "c"]),
|
|
],
|
|
)
|
|
def test_multiclass_ovr_roc_auc_toydata(y_true, labels):
|
|
# Tests the unweighted, one-vs-rest multiclass ROC AUC algorithm
|
|
# on a small example, representative of an expected use case.
|
|
y_scores = np.array(
|
|
[[1.0, 0.0, 0.0], [0.1, 0.5, 0.4], [0.1, 0.1, 0.8], [0.3, 0.3, 0.4]]
|
|
)
|
|
# Compute the expected result by individually computing the 'one-vs-rest'
|
|
# ROC AUC scores for classes 0, 1, and 2.
|
|
out_0 = roc_auc_score([1, 0, 0, 0], y_scores[:, 0])
|
|
out_1 = roc_auc_score([0, 1, 0, 0], y_scores[:, 1])
|
|
out_2 = roc_auc_score([0, 0, 1, 1], y_scores[:, 2])
|
|
assert_almost_equal(
|
|
roc_auc_score(y_true, y_scores, multi_class="ovr", labels=labels, average=None),
|
|
[out_0, out_1, out_2],
|
|
)
|
|
|
|
# Compute unweighted results (default behaviour is average="macro")
|
|
result_unweighted = (out_0 + out_1 + out_2) / 3.0
|
|
assert_almost_equal(
|
|
roc_auc_score(y_true, y_scores, multi_class="ovr", labels=labels),
|
|
result_unweighted,
|
|
)
|
|
|
|
# Tests the weighted, one-vs-rest multiclass ROC AUC algorithm
|
|
# on the same input (Provost & Domingos, 2000)
|
|
result_weighted = out_0 * 0.25 + out_1 * 0.25 + out_2 * 0.5
|
|
assert_almost_equal(
|
|
roc_auc_score(
|
|
y_true, y_scores, multi_class="ovr", labels=labels, average="weighted"
|
|
),
|
|
result_weighted,
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"multi_class, average",
|
|
[
|
|
("ovr", "macro"),
|
|
("ovr", "micro"),
|
|
("ovo", "macro"),
|
|
],
|
|
)
|
|
def test_perfect_imperfect_chance_multiclass_roc_auc(multi_class, average):
|
|
y_true = np.array([3, 1, 2, 0])
|
|
|
|
# Perfect classifier (from a ranking point of view) has roc_auc_score = 1.0
|
|
y_perfect = [
|
|
[0.0, 0.0, 0.0, 1.0],
|
|
[0.0, 1.0, 0.0, 0.0],
|
|
[0.0, 0.0, 1.0, 0.0],
|
|
[0.75, 0.05, 0.05, 0.15],
|
|
]
|
|
assert_almost_equal(
|
|
roc_auc_score(y_true, y_perfect, multi_class=multi_class, average=average),
|
|
1.0,
|
|
)
|
|
|
|
# Imperfect classifier has roc_auc_score < 1.0
|
|
y_imperfect = [
|
|
[0.0, 0.0, 0.0, 1.0],
|
|
[0.0, 1.0, 0.0, 0.0],
|
|
[0.0, 0.0, 1.0, 0.0],
|
|
[0.0, 0.0, 0.0, 1.0],
|
|
]
|
|
assert (
|
|
roc_auc_score(y_true, y_imperfect, multi_class=multi_class, average=average)
|
|
< 1.0
|
|
)
|
|
|
|
# Chance level classifier has roc_auc_score = 5.0
|
|
y_chance = 0.25 * np.ones((4, 4))
|
|
assert roc_auc_score(
|
|
y_true, y_chance, multi_class=multi_class, average=average
|
|
) == pytest.approx(0.5)
|
|
|
|
|
|
def test_micro_averaged_ovr_roc_auc(global_random_seed):
|
|
seed = global_random_seed
|
|
# Let's generate a set of random predictions and matching true labels such
|
|
# that the predictions are not perfect. To make the problem more interesting,
|
|
# we use an imbalanced class distribution (by using different parameters
|
|
# in the Dirichlet prior (conjugate prior of the multinomial distribution).
|
|
y_pred = stats.dirichlet.rvs([2.0, 1.0, 0.5], size=1000, random_state=seed)
|
|
y_true = np.asarray(
|
|
[
|
|
stats.multinomial.rvs(n=1, p=y_pred_i, random_state=seed).argmax()
|
|
for y_pred_i in y_pred
|
|
]
|
|
)
|
|
y_onehot = label_binarize(y_true, classes=[0, 1, 2])
|
|
fpr, tpr, _ = roc_curve(y_onehot.ravel(), y_pred.ravel())
|
|
roc_auc_by_hand = auc(fpr, tpr)
|
|
roc_auc_auto = roc_auc_score(y_true, y_pred, multi_class="ovr", average="micro")
|
|
assert roc_auc_by_hand == pytest.approx(roc_auc_auto)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"msg, y_true, labels",
|
|
[
|
|
("Parameter 'labels' must be unique", np.array([0, 1, 2, 2]), [0, 2, 0]),
|
|
(
|
|
"Parameter 'labels' must be unique",
|
|
np.array(["a", "b", "c", "c"]),
|
|
["a", "a", "b"],
|
|
),
|
|
(
|
|
(
|
|
"Number of classes in y_true not equal to the number of columns "
|
|
"in 'y_score'"
|
|
),
|
|
np.array([0, 2, 0, 2]),
|
|
None,
|
|
),
|
|
(
|
|
"Parameter 'labels' must be ordered",
|
|
np.array(["a", "b", "c", "c"]),
|
|
["a", "c", "b"],
|
|
),
|
|
(
|
|
(
|
|
"Number of given labels, 2, not equal to the number of columns in "
|
|
"'y_score', 3"
|
|
),
|
|
np.array([0, 1, 2, 2]),
|
|
[0, 1],
|
|
),
|
|
(
|
|
(
|
|
"Number of given labels, 2, not equal to the number of columns in "
|
|
"'y_score', 3"
|
|
),
|
|
np.array(["a", "b", "c", "c"]),
|
|
["a", "b"],
|
|
),
|
|
(
|
|
(
|
|
"Number of given labels, 4, not equal to the number of columns in "
|
|
"'y_score', 3"
|
|
),
|
|
np.array([0, 1, 2, 2]),
|
|
[0, 1, 2, 3],
|
|
),
|
|
(
|
|
(
|
|
"Number of given labels, 4, not equal to the number of columns in "
|
|
"'y_score', 3"
|
|
),
|
|
np.array(["a", "b", "c", "c"]),
|
|
["a", "b", "c", "d"],
|
|
),
|
|
(
|
|
"'y_true' contains labels not in parameter 'labels'",
|
|
np.array(["a", "b", "c", "e"]),
|
|
["a", "b", "c"],
|
|
),
|
|
(
|
|
"'y_true' contains labels not in parameter 'labels'",
|
|
np.array(["a", "b", "c", "d"]),
|
|
["a", "b", "c"],
|
|
),
|
|
(
|
|
"'y_true' contains labels not in parameter 'labels'",
|
|
np.array([0, 1, 2, 3]),
|
|
[0, 1, 2],
|
|
),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("multi_class", ["ovo", "ovr"])
|
|
def test_roc_auc_score_multiclass_labels_error(msg, y_true, labels, multi_class):
|
|
y_scores = np.array(
|
|
[[0.1, 0.8, 0.1], [0.3, 0.4, 0.3], [0.35, 0.5, 0.15], [0, 0.2, 0.8]]
|
|
)
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
roc_auc_score(y_true, y_scores, labels=labels, multi_class=multi_class)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"msg, kwargs",
|
|
[
|
|
(
|
|
(
|
|
r"average must be one of \('macro', 'weighted', None\) for "
|
|
r"multiclass problems"
|
|
),
|
|
{"average": "samples", "multi_class": "ovo"},
|
|
),
|
|
(
|
|
(
|
|
r"average must be one of \('micro', 'macro', 'weighted', None\) for "
|
|
r"multiclass problems"
|
|
),
|
|
{"average": "samples", "multi_class": "ovr"},
|
|
),
|
|
(
|
|
(
|
|
r"sample_weight is not supported for multiclass one-vs-one "
|
|
r"ROC AUC, 'sample_weight' must be None in this case"
|
|
),
|
|
{"multi_class": "ovo", "sample_weight": []},
|
|
),
|
|
(
|
|
(
|
|
r"Partial AUC computation not available in multiclass setting, "
|
|
r"'max_fpr' must be set to `None`, received `max_fpr=0.5` "
|
|
r"instead"
|
|
),
|
|
{"multi_class": "ovo", "max_fpr": 0.5},
|
|
),
|
|
(r"multi_class must be in \('ovo', 'ovr'\)", {}),
|
|
],
|
|
)
|
|
def test_roc_auc_score_multiclass_error(msg, kwargs):
|
|
# Test that roc_auc_score function returns an error when trying
|
|
# to compute multiclass AUC for parameters where an output
|
|
# is not defined.
|
|
rng = check_random_state(404)
|
|
y_score = rng.rand(20, 3)
|
|
y_prob = softmax(y_score)
|
|
y_true = rng.randint(0, 3, size=20)
|
|
with pytest.raises(ValueError, match=msg):
|
|
roc_auc_score(y_true, y_prob, **kwargs)
|
|
|
|
|
|
def test_auc_score_non_binary_class():
|
|
# Test that roc_auc_score function returns an error when trying
|
|
# to compute AUC for non-binary class values.
|
|
rng = check_random_state(404)
|
|
y_pred = rng.rand(10)
|
|
# y_true contains only one class value
|
|
y_true = np.zeros(10, dtype="int")
|
|
err_msg = "ROC AUC score is not defined"
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
roc_auc_score(y_true, y_pred)
|
|
y_true = np.ones(10, dtype="int")
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
roc_auc_score(y_true, y_pred)
|
|
y_true = np.full(10, -1, dtype="int")
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
roc_auc_score(y_true, y_pred)
|
|
|
|
with warnings.catch_warnings(record=True):
|
|
rng = check_random_state(404)
|
|
y_pred = rng.rand(10)
|
|
# y_true contains only one class value
|
|
y_true = np.zeros(10, dtype="int")
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
roc_auc_score(y_true, y_pred)
|
|
y_true = np.ones(10, dtype="int")
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
roc_auc_score(y_true, y_pred)
|
|
y_true = np.full(10, -1, dtype="int")
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
roc_auc_score(y_true, y_pred)
|
|
|
|
|
|
@pytest.mark.parametrize("curve_func", CURVE_FUNCS)
|
|
def test_binary_clf_curve_multiclass_error(curve_func):
|
|
rng = check_random_state(404)
|
|
y_true = rng.randint(0, 3, size=10)
|
|
y_pred = rng.rand(10)
|
|
msg = "multiclass format is not supported"
|
|
with pytest.raises(ValueError, match=msg):
|
|
curve_func(y_true, y_pred)
|
|
|
|
|
|
@pytest.mark.parametrize("curve_func", CURVE_FUNCS)
|
|
def test_binary_clf_curve_implicit_pos_label(curve_func):
|
|
# Check that using string class labels raises an informative
|
|
# error for any supported string dtype:
|
|
msg = (
|
|
"y_true takes value in {'a', 'b'} and pos_label is "
|
|
"not specified: either make y_true take "
|
|
"value in {0, 1} or {-1, 1} or pass pos_label "
|
|
"explicitly."
|
|
)
|
|
with pytest.raises(ValueError, match=msg):
|
|
curve_func(np.array(["a", "b"], dtype="<U1"), [0.0, 1.0])
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
curve_func(np.array(["a", "b"], dtype=object), [0.0, 1.0])
|
|
|
|
# Check that it is possible to use floating point class labels
|
|
# that are interpreted similarly to integer class labels:
|
|
y_pred = [0.0, 1.0, 0.2, 0.42]
|
|
int_curve = curve_func([0, 1, 1, 0], y_pred)
|
|
float_curve = curve_func([0.0, 1.0, 1.0, 0.0], y_pred)
|
|
for int_curve_part, float_curve_part in zip(int_curve, float_curve):
|
|
np.testing.assert_allclose(int_curve_part, float_curve_part)
|
|
|
|
|
|
# TODO(1.7): Update test to check for error when bytes support is removed.
|
|
@ignore_warnings(category=FutureWarning)
|
|
@pytest.mark.parametrize("curve_func", [precision_recall_curve, roc_curve])
|
|
@pytest.mark.parametrize("labels_type", ["list", "array"])
|
|
def test_binary_clf_curve_implicit_bytes_pos_label(curve_func, labels_type):
|
|
# Check that using bytes class labels raises an informative
|
|
# error for any supported string dtype:
|
|
labels = _convert_container([b"a", b"b"], labels_type)
|
|
msg = (
|
|
"y_true takes value in {b'a', b'b'} and pos_label is not "
|
|
"specified: either make y_true take value in {0, 1} or "
|
|
"{-1, 1} or pass pos_label explicitly."
|
|
)
|
|
with pytest.raises(ValueError, match=msg):
|
|
curve_func(labels, [0.0, 1.0])
|
|
|
|
|
|
@pytest.mark.parametrize("curve_func", CURVE_FUNCS)
|
|
def test_binary_clf_curve_zero_sample_weight(curve_func):
|
|
y_true = [0, 0, 1, 1, 1]
|
|
y_score = [0.1, 0.2, 0.3, 0.4, 0.5]
|
|
sample_weight = [1, 1, 1, 0.5, 0]
|
|
|
|
result_1 = curve_func(y_true, y_score, sample_weight=sample_weight)
|
|
result_2 = curve_func(y_true[:-1], y_score[:-1], sample_weight=sample_weight[:-1])
|
|
|
|
for arr_1, arr_2 in zip(result_1, result_2):
|
|
assert_allclose(arr_1, arr_2)
|
|
|
|
|
|
@pytest.mark.parametrize("drop", [True, False])
|
|
def test_precision_recall_curve(drop):
|
|
y_true, _, y_score = make_prediction(binary=True)
|
|
_test_precision_recall_curve(y_true, y_score, drop)
|
|
|
|
# Make sure the first point of the Precision-Recall on the right is:
|
|
# (p=1.0, r=class balance) on a non-balanced dataset [1:]
|
|
p, r, t = precision_recall_curve(y_true[1:], y_score[1:], drop_intermediate=drop)
|
|
assert r[0] == 1.0
|
|
assert p[0] == y_true[1:].mean()
|
|
|
|
# Use {-1, 1} for labels; make sure original labels aren't modified
|
|
y_true[np.where(y_true == 0)] = -1
|
|
y_true_copy = y_true.copy()
|
|
_test_precision_recall_curve(y_true, y_score, drop)
|
|
assert_array_equal(y_true_copy, y_true)
|
|
|
|
labels = [1, 0, 0, 1]
|
|
predict_probas = [1, 2, 3, 4]
|
|
p, r, t = precision_recall_curve(labels, predict_probas, drop_intermediate=drop)
|
|
if drop:
|
|
assert_allclose(p, [0.5, 0.33333333, 1.0, 1.0])
|
|
assert_allclose(r, [1.0, 0.5, 0.5, 0.0])
|
|
assert_allclose(t, [1, 2, 4])
|
|
else:
|
|
assert_allclose(p, [0.5, 0.33333333, 0.5, 1.0, 1.0])
|
|
assert_allclose(r, [1.0, 0.5, 0.5, 0.5, 0.0])
|
|
assert_allclose(t, [1, 2, 3, 4])
|
|
assert p.size == r.size
|
|
assert p.size == t.size + 1
|
|
|
|
|
|
def _test_precision_recall_curve(y_true, y_score, drop):
|
|
# Test Precision-Recall and area under PR curve
|
|
p, r, thresholds = precision_recall_curve(y_true, y_score, drop_intermediate=drop)
|
|
precision_recall_auc = _average_precision_slow(y_true, y_score)
|
|
assert_array_almost_equal(precision_recall_auc, 0.859, 3)
|
|
assert_array_almost_equal(
|
|
precision_recall_auc, average_precision_score(y_true, y_score)
|
|
)
|
|
# `_average_precision` is not very precise in case of 0.5 ties: be tolerant
|
|
assert_almost_equal(
|
|
_average_precision(y_true, y_score), precision_recall_auc, decimal=2
|
|
)
|
|
assert p.size == r.size
|
|
assert p.size == thresholds.size + 1
|
|
# Smoke test in the case of proba having only one value
|
|
p, r, thresholds = precision_recall_curve(
|
|
y_true, np.zeros_like(y_score), drop_intermediate=drop
|
|
)
|
|
assert p.size == r.size
|
|
assert p.size == thresholds.size + 1
|
|
|
|
|
|
@pytest.mark.parametrize("drop", [True, False])
|
|
def test_precision_recall_curve_toydata(drop):
|
|
with np.errstate(all="raise"):
|
|
# Binary classification
|
|
y_true = [0, 1]
|
|
y_score = [0, 1]
|
|
p, r, _ = precision_recall_curve(y_true, y_score, drop_intermediate=drop)
|
|
auc_prc = average_precision_score(y_true, y_score)
|
|
assert_array_almost_equal(p, [0.5, 1, 1])
|
|
assert_array_almost_equal(r, [1, 1, 0])
|
|
assert_almost_equal(auc_prc, 1.0)
|
|
|
|
y_true = [0, 1]
|
|
y_score = [1, 0]
|
|
p, r, _ = precision_recall_curve(y_true, y_score, drop_intermediate=drop)
|
|
auc_prc = average_precision_score(y_true, y_score)
|
|
assert_array_almost_equal(p, [0.5, 0.0, 1.0])
|
|
assert_array_almost_equal(r, [1.0, 0.0, 0.0])
|
|
# Here we are doing a terrible prediction: we are always getting
|
|
# it wrong, hence the average_precision_score is the accuracy at
|
|
# chance: 50%
|
|
assert_almost_equal(auc_prc, 0.5)
|
|
|
|
y_true = [1, 0]
|
|
y_score = [1, 1]
|
|
p, r, _ = precision_recall_curve(y_true, y_score, drop_intermediate=drop)
|
|
auc_prc = average_precision_score(y_true, y_score)
|
|
assert_array_almost_equal(p, [0.5, 1])
|
|
assert_array_almost_equal(r, [1.0, 0])
|
|
assert_almost_equal(auc_prc, 0.5)
|
|
|
|
y_true = [1, 0]
|
|
y_score = [1, 0]
|
|
p, r, _ = precision_recall_curve(y_true, y_score, drop_intermediate=drop)
|
|
auc_prc = average_precision_score(y_true, y_score)
|
|
assert_array_almost_equal(p, [0.5, 1, 1])
|
|
assert_array_almost_equal(r, [1, 1, 0])
|
|
assert_almost_equal(auc_prc, 1.0)
|
|
|
|
y_true = [1, 0]
|
|
y_score = [0.5, 0.5]
|
|
p, r, _ = precision_recall_curve(y_true, y_score, drop_intermediate=drop)
|
|
auc_prc = average_precision_score(y_true, y_score)
|
|
assert_array_almost_equal(p, [0.5, 1])
|
|
assert_array_almost_equal(r, [1, 0.0])
|
|
assert_almost_equal(auc_prc, 0.5)
|
|
|
|
y_true = [0, 0]
|
|
y_score = [0.25, 0.75]
|
|
with pytest.warns(UserWarning, match="No positive class found in y_true"):
|
|
p, r, _ = precision_recall_curve(y_true, y_score, drop_intermediate=drop)
|
|
with pytest.warns(UserWarning, match="No positive class found in y_true"):
|
|
auc_prc = average_precision_score(y_true, y_score)
|
|
assert_allclose(p, [0, 0, 1])
|
|
assert_allclose(r, [1, 1, 0])
|
|
assert_allclose(auc_prc, 0)
|
|
|
|
y_true = [1, 1]
|
|
y_score = [0.25, 0.75]
|
|
p, r, _ = precision_recall_curve(y_true, y_score, drop_intermediate=drop)
|
|
assert_almost_equal(average_precision_score(y_true, y_score), 1.0)
|
|
assert_array_almost_equal(p, [1.0, 1.0, 1.0])
|
|
assert_array_almost_equal(r, [1, 0.5, 0.0])
|
|
|
|
# Multi-label classification task
|
|
y_true = np.array([[0, 1], [0, 1]])
|
|
y_score = np.array([[0, 1], [0, 1]])
|
|
with pytest.warns(UserWarning, match="No positive class found in y_true"):
|
|
assert_allclose(
|
|
average_precision_score(y_true, y_score, average="macro"), 0.5
|
|
)
|
|
with pytest.warns(UserWarning, match="No positive class found in y_true"):
|
|
assert_allclose(
|
|
average_precision_score(y_true, y_score, average="weighted"), 1.0
|
|
)
|
|
assert_allclose(
|
|
average_precision_score(y_true, y_score, average="samples"), 1.0
|
|
)
|
|
assert_allclose(average_precision_score(y_true, y_score, average="micro"), 1.0)
|
|
|
|
y_true = np.array([[0, 1], [0, 1]])
|
|
y_score = np.array([[0, 1], [1, 0]])
|
|
with pytest.warns(UserWarning, match="No positive class found in y_true"):
|
|
assert_allclose(
|
|
average_precision_score(y_true, y_score, average="macro"), 0.5
|
|
)
|
|
with pytest.warns(UserWarning, match="No positive class found in y_true"):
|
|
assert_allclose(
|
|
average_precision_score(y_true, y_score, average="weighted"), 1.0
|
|
)
|
|
assert_allclose(
|
|
average_precision_score(y_true, y_score, average="samples"), 0.75
|
|
)
|
|
assert_allclose(average_precision_score(y_true, y_score, average="micro"), 0.5)
|
|
|
|
y_true = np.array([[1, 0], [0, 1]])
|
|
y_score = np.array([[0, 1], [1, 0]])
|
|
assert_almost_equal(
|
|
average_precision_score(y_true, y_score, average="macro"), 0.5
|
|
)
|
|
assert_almost_equal(
|
|
average_precision_score(y_true, y_score, average="weighted"), 0.5
|
|
)
|
|
assert_almost_equal(
|
|
average_precision_score(y_true, y_score, average="samples"), 0.5
|
|
)
|
|
assert_almost_equal(
|
|
average_precision_score(y_true, y_score, average="micro"), 0.5
|
|
)
|
|
|
|
y_true = np.array([[0, 0], [0, 0]])
|
|
y_score = np.array([[0, 1], [0, 1]])
|
|
with pytest.warns(UserWarning, match="No positive class found in y_true"):
|
|
assert_allclose(
|
|
average_precision_score(y_true, y_score, average="macro"), 0.0
|
|
)
|
|
assert_allclose(
|
|
average_precision_score(y_true, y_score, average="weighted"), 0.0
|
|
)
|
|
with pytest.warns(UserWarning, match="No positive class found in y_true"):
|
|
assert_allclose(
|
|
average_precision_score(y_true, y_score, average="samples"), 0.0
|
|
)
|
|
with pytest.warns(UserWarning, match="No positive class found in y_true"):
|
|
assert_allclose(
|
|
average_precision_score(y_true, y_score, average="micro"), 0.0
|
|
)
|
|
|
|
y_true = np.array([[1, 1], [1, 1]])
|
|
y_score = np.array([[0, 1], [0, 1]])
|
|
assert_allclose(average_precision_score(y_true, y_score, average="macro"), 1.0)
|
|
assert_allclose(
|
|
average_precision_score(y_true, y_score, average="weighted"), 1.0
|
|
)
|
|
assert_allclose(
|
|
average_precision_score(y_true, y_score, average="samples"), 1.0
|
|
)
|
|
assert_allclose(average_precision_score(y_true, y_score, average="micro"), 1.0)
|
|
|
|
y_true = np.array([[1, 0], [0, 1]])
|
|
y_score = np.array([[0.5, 0.5], [0.5, 0.5]])
|
|
assert_almost_equal(
|
|
average_precision_score(y_true, y_score, average="macro"), 0.5
|
|
)
|
|
assert_almost_equal(
|
|
average_precision_score(y_true, y_score, average="weighted"), 0.5
|
|
)
|
|
assert_almost_equal(
|
|
average_precision_score(y_true, y_score, average="samples"), 0.5
|
|
)
|
|
assert_almost_equal(
|
|
average_precision_score(y_true, y_score, average="micro"), 0.5
|
|
)
|
|
|
|
with np.errstate(all="ignore"):
|
|
# if one class is never present weighted should not be NaN
|
|
y_true = np.array([[0, 0], [0, 1]])
|
|
y_score = np.array([[0, 0], [0, 1]])
|
|
with pytest.warns(UserWarning, match="No positive class found in y_true"):
|
|
assert_allclose(
|
|
average_precision_score(y_true, y_score, average="weighted"), 1
|
|
)
|
|
|
|
|
|
def test_precision_recall_curve_drop_intermediate():
|
|
"""Check the behaviour of the `drop_intermediate` parameter."""
|
|
y_true = [0, 0, 0, 0, 1, 1]
|
|
y_score = [0.0, 0.2, 0.5, 0.6, 0.7, 1.0]
|
|
precision, recall, thresholds = precision_recall_curve(
|
|
y_true, y_score, drop_intermediate=True
|
|
)
|
|
assert_allclose(thresholds, [0.0, 0.7, 1.0])
|
|
|
|
# Test dropping thresholds with repeating scores
|
|
y_true = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
|
|
y_score = [0.0, 0.1, 0.6, 0.6, 0.7, 0.8, 0.9, 0.6, 0.7, 0.8, 0.9, 0.9, 1.0]
|
|
precision, recall, thresholds = precision_recall_curve(
|
|
y_true, y_score, drop_intermediate=True
|
|
)
|
|
assert_allclose(thresholds, [0.0, 0.6, 0.7, 0.8, 0.9, 1.0])
|
|
|
|
# Test all false keeps only endpoints
|
|
y_true = [0, 0, 0, 0]
|
|
y_score = [0.0, 0.1, 0.2, 0.3]
|
|
precision, recall, thresholds = precision_recall_curve(
|
|
y_true, y_score, drop_intermediate=True
|
|
)
|
|
assert_allclose(thresholds, [0.0, 0.3])
|
|
|
|
# Test all true keeps all thresholds
|
|
y_true = [1, 1, 1, 1]
|
|
y_score = [0.0, 0.1, 0.2, 0.3]
|
|
precision, recall, thresholds = precision_recall_curve(
|
|
y_true, y_score, drop_intermediate=True
|
|
)
|
|
assert_allclose(thresholds, [0.0, 0.1, 0.2, 0.3])
|
|
|
|
|
|
def test_average_precision_constant_values():
|
|
# Check the average_precision_score of a constant predictor is
|
|
# the TPR
|
|
|
|
# Generate a dataset with 25% of positives
|
|
y_true = np.zeros(100, dtype=int)
|
|
y_true[::4] = 1
|
|
# And a constant score
|
|
y_score = np.ones(100)
|
|
# The precision is then the fraction of positive whatever the recall
|
|
# is, as there is only one threshold:
|
|
assert average_precision_score(y_true, y_score) == 0.25
|
|
|
|
|
|
def test_average_precision_score_binary_pos_label_errors():
|
|
# Raise an error when pos_label is not in binary y_true
|
|
y_true = np.array([0, 1])
|
|
y_pred = np.array([0, 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):
|
|
average_precision_score(y_true, y_pred, pos_label=2)
|
|
|
|
|
|
def test_average_precision_score_multilabel_pos_label_errors():
|
|
# Raise an error for multilabel-indicator y_true with
|
|
# pos_label other than 1
|
|
y_true = np.array([[1, 0], [0, 1], [0, 1], [1, 0]])
|
|
y_pred = np.array([[0.9, 0.1], [0.1, 0.9], [0.8, 0.2], [0.2, 0.8]])
|
|
err_msg = (
|
|
"Parameter pos_label is fixed to 1 for multilabel-indicator y_true. "
|
|
"Do not set pos_label or set pos_label to 1."
|
|
)
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
average_precision_score(y_true, y_pred, pos_label=0)
|
|
|
|
|
|
def test_average_precision_score_multiclass_pos_label_errors():
|
|
# Raise an error for multiclass y_true with pos_label other than 1
|
|
y_true = np.array([0, 1, 2, 0, 1, 2])
|
|
y_pred = np.array(
|
|
[
|
|
[0.5, 0.2, 0.1],
|
|
[0.4, 0.5, 0.3],
|
|
[0.1, 0.2, 0.6],
|
|
[0.2, 0.3, 0.5],
|
|
[0.2, 0.3, 0.5],
|
|
[0.2, 0.3, 0.5],
|
|
]
|
|
)
|
|
err_msg = (
|
|
"Parameter pos_label is fixed to 1 for multiclass y_true. "
|
|
"Do not set pos_label or set pos_label to 1."
|
|
)
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
average_precision_score(y_true, y_pred, pos_label=3)
|
|
|
|
|
|
def test_score_scale_invariance():
|
|
# Test that average_precision_score and roc_auc_score are invariant by
|
|
# the scaling or shifting of probabilities
|
|
# This test was expanded (added scaled_down) in response to github
|
|
# issue #3864 (and others), where overly aggressive rounding was causing
|
|
# problems for users with very small y_score values
|
|
y_true, _, y_score = make_prediction(binary=True)
|
|
|
|
roc_auc = roc_auc_score(y_true, y_score)
|
|
roc_auc_scaled_up = roc_auc_score(y_true, 100 * y_score)
|
|
roc_auc_scaled_down = roc_auc_score(y_true, 1e-6 * y_score)
|
|
roc_auc_shifted = roc_auc_score(y_true, y_score - 10)
|
|
assert roc_auc == roc_auc_scaled_up
|
|
assert roc_auc == roc_auc_scaled_down
|
|
assert roc_auc == roc_auc_shifted
|
|
|
|
pr_auc = average_precision_score(y_true, y_score)
|
|
pr_auc_scaled_up = average_precision_score(y_true, 100 * y_score)
|
|
pr_auc_scaled_down = average_precision_score(y_true, 1e-6 * y_score)
|
|
pr_auc_shifted = average_precision_score(y_true, y_score - 10)
|
|
assert pr_auc == pr_auc_scaled_up
|
|
assert pr_auc == pr_auc_scaled_down
|
|
assert pr_auc == pr_auc_shifted
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"y_true,y_score,expected_fpr,expected_fnr",
|
|
[
|
|
([0, 0, 1], [0, 0.5, 1], [0], [0]),
|
|
([0, 0, 1], [0, 0.25, 0.5], [0], [0]),
|
|
([0, 0, 1], [0.5, 0.75, 1], [0], [0]),
|
|
([0, 0, 1], [0.25, 0.5, 0.75], [0], [0]),
|
|
([0, 1, 0], [0, 0.5, 1], [0.5], [0]),
|
|
([0, 1, 0], [0, 0.25, 0.5], [0.5], [0]),
|
|
([0, 1, 0], [0.5, 0.75, 1], [0.5], [0]),
|
|
([0, 1, 0], [0.25, 0.5, 0.75], [0.5], [0]),
|
|
([0, 1, 1], [0, 0.5, 1], [0.0], [0]),
|
|
([0, 1, 1], [0, 0.25, 0.5], [0], [0]),
|
|
([0, 1, 1], [0.5, 0.75, 1], [0], [0]),
|
|
([0, 1, 1], [0.25, 0.5, 0.75], [0], [0]),
|
|
([1, 0, 0], [0, 0.5, 1], [1, 1, 0.5], [0, 1, 1]),
|
|
([1, 0, 0], [0, 0.25, 0.5], [1, 1, 0.5], [0, 1, 1]),
|
|
([1, 0, 0], [0.5, 0.75, 1], [1, 1, 0.5], [0, 1, 1]),
|
|
([1, 0, 0], [0.25, 0.5, 0.75], [1, 1, 0.5], [0, 1, 1]),
|
|
([1, 0, 1], [0, 0.5, 1], [1, 1, 0], [0, 0.5, 0.5]),
|
|
([1, 0, 1], [0, 0.25, 0.5], [1, 1, 0], [0, 0.5, 0.5]),
|
|
([1, 0, 1], [0.5, 0.75, 1], [1, 1, 0], [0, 0.5, 0.5]),
|
|
([1, 0, 1], [0.25, 0.5, 0.75], [1, 1, 0], [0, 0.5, 0.5]),
|
|
],
|
|
)
|
|
def test_det_curve_toydata(y_true, y_score, expected_fpr, expected_fnr):
|
|
# Check on a batch of small examples.
|
|
fpr, fnr, _ = det_curve(y_true, y_score)
|
|
|
|
assert_allclose(fpr, expected_fpr)
|
|
assert_allclose(fnr, expected_fnr)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"y_true,y_score,expected_fpr,expected_fnr",
|
|
[
|
|
([1, 0], [0.5, 0.5], [1], [0]),
|
|
([0, 1], [0.5, 0.5], [1], [0]),
|
|
([0, 0, 1], [0.25, 0.5, 0.5], [0.5], [0]),
|
|
([0, 1, 0], [0.25, 0.5, 0.5], [0.5], [0]),
|
|
([0, 1, 1], [0.25, 0.5, 0.5], [0], [0]),
|
|
([1, 0, 0], [0.25, 0.5, 0.5], [1], [0]),
|
|
([1, 0, 1], [0.25, 0.5, 0.5], [1], [0]),
|
|
([1, 1, 0], [0.25, 0.5, 0.5], [1], [0]),
|
|
],
|
|
)
|
|
def test_det_curve_tie_handling(y_true, y_score, expected_fpr, expected_fnr):
|
|
fpr, fnr, _ = det_curve(y_true, y_score)
|
|
|
|
assert_allclose(fpr, expected_fpr)
|
|
assert_allclose(fnr, expected_fnr)
|
|
|
|
|
|
def test_det_curve_sanity_check():
|
|
# Exactly duplicated inputs yield the same result.
|
|
assert_allclose(
|
|
det_curve([0, 0, 1], [0, 0.5, 1]),
|
|
det_curve([0, 0, 0, 0, 1, 1], [0, 0, 0.5, 0.5, 1, 1]),
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("y_score", [(0), (0.25), (0.5), (0.75), (1)])
|
|
def test_det_curve_constant_scores(y_score):
|
|
fpr, fnr, threshold = det_curve(
|
|
y_true=[0, 1, 0, 1, 0, 1], y_score=np.full(6, y_score)
|
|
)
|
|
|
|
assert_allclose(fpr, [1])
|
|
assert_allclose(fnr, [0])
|
|
assert_allclose(threshold, [y_score])
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"y_true",
|
|
[
|
|
([0, 0, 0, 0, 0, 1]),
|
|
([0, 0, 0, 0, 1, 1]),
|
|
([0, 0, 0, 1, 1, 1]),
|
|
([0, 0, 1, 1, 1, 1]),
|
|
([0, 1, 1, 1, 1, 1]),
|
|
],
|
|
)
|
|
def test_det_curve_perfect_scores(y_true):
|
|
fpr, fnr, _ = det_curve(y_true=y_true, y_score=y_true)
|
|
|
|
assert_allclose(fpr, [0])
|
|
assert_allclose(fnr, [0])
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"y_true, y_pred, err_msg",
|
|
[
|
|
([0, 1], [0, 0.5, 1], "inconsistent numbers of samples"),
|
|
([0, 1, 1], [0, 0.5], "inconsistent numbers of samples"),
|
|
([0, 0, 0], [0, 0.5, 1], "Only one class present in y_true"),
|
|
([1, 1, 1], [0, 0.5, 1], "Only one class present in y_true"),
|
|
(
|
|
["cancer", "cancer", "not cancer"],
|
|
[0.2, 0.3, 0.8],
|
|
"pos_label is not specified",
|
|
),
|
|
],
|
|
)
|
|
def test_det_curve_bad_input(y_true, y_pred, err_msg):
|
|
# input variables with inconsistent numbers of samples
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
det_curve(y_true, y_pred)
|
|
|
|
|
|
def test_det_curve_pos_label():
|
|
y_true = ["cancer"] * 3 + ["not cancer"] * 7
|
|
y_pred_pos_not_cancer = np.array([0.1, 0.4, 0.6, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.9])
|
|
y_pred_pos_cancer = 1 - y_pred_pos_not_cancer
|
|
|
|
fpr_pos_cancer, fnr_pos_cancer, th_pos_cancer = det_curve(
|
|
y_true,
|
|
y_pred_pos_cancer,
|
|
pos_label="cancer",
|
|
)
|
|
fpr_pos_not_cancer, fnr_pos_not_cancer, th_pos_not_cancer = det_curve(
|
|
y_true,
|
|
y_pred_pos_not_cancer,
|
|
pos_label="not cancer",
|
|
)
|
|
|
|
# check that the first threshold will change depending which label we
|
|
# consider positive
|
|
assert th_pos_cancer[0] == pytest.approx(0.4)
|
|
assert th_pos_not_cancer[0] == pytest.approx(0.2)
|
|
|
|
# check for the symmetry of the fpr and fnr
|
|
assert_allclose(fpr_pos_cancer, fnr_pos_not_cancer[::-1])
|
|
assert_allclose(fnr_pos_cancer, fpr_pos_not_cancer[::-1])
|
|
|
|
|
|
def check_lrap_toy(lrap_score):
|
|
# Check on several small example that it works
|
|
assert_almost_equal(lrap_score([[0, 1]], [[0.25, 0.75]]), 1)
|
|
assert_almost_equal(lrap_score([[0, 1]], [[0.75, 0.25]]), 1 / 2)
|
|
assert_almost_equal(lrap_score([[1, 1]], [[0.75, 0.25]]), 1)
|
|
|
|
assert_almost_equal(lrap_score([[0, 0, 1]], [[0.25, 0.5, 0.75]]), 1)
|
|
assert_almost_equal(lrap_score([[0, 1, 0]], [[0.25, 0.5, 0.75]]), 1 / 2)
|
|
assert_almost_equal(lrap_score([[0, 1, 1]], [[0.25, 0.5, 0.75]]), 1)
|
|
assert_almost_equal(lrap_score([[1, 0, 0]], [[0.25, 0.5, 0.75]]), 1 / 3)
|
|
assert_almost_equal(
|
|
lrap_score([[1, 0, 1]], [[0.25, 0.5, 0.75]]), (2 / 3 + 1 / 1) / 2
|
|
)
|
|
assert_almost_equal(
|
|
lrap_score([[1, 1, 0]], [[0.25, 0.5, 0.75]]), (2 / 3 + 1 / 2) / 2
|
|
)
|
|
|
|
assert_almost_equal(lrap_score([[0, 0, 1]], [[0.75, 0.5, 0.25]]), 1 / 3)
|
|
assert_almost_equal(lrap_score([[0, 1, 0]], [[0.75, 0.5, 0.25]]), 1 / 2)
|
|
assert_almost_equal(
|
|
lrap_score([[0, 1, 1]], [[0.75, 0.5, 0.25]]), (1 / 2 + 2 / 3) / 2
|
|
)
|
|
assert_almost_equal(lrap_score([[1, 0, 0]], [[0.75, 0.5, 0.25]]), 1)
|
|
assert_almost_equal(lrap_score([[1, 0, 1]], [[0.75, 0.5, 0.25]]), (1 + 2 / 3) / 2)
|
|
assert_almost_equal(lrap_score([[1, 1, 0]], [[0.75, 0.5, 0.25]]), 1)
|
|
assert_almost_equal(lrap_score([[1, 1, 1]], [[0.75, 0.5, 0.25]]), 1)
|
|
|
|
assert_almost_equal(lrap_score([[0, 0, 1]], [[0.5, 0.75, 0.25]]), 1 / 3)
|
|
assert_almost_equal(lrap_score([[0, 1, 0]], [[0.5, 0.75, 0.25]]), 1)
|
|
assert_almost_equal(lrap_score([[0, 1, 1]], [[0.5, 0.75, 0.25]]), (1 + 2 / 3) / 2)
|
|
assert_almost_equal(lrap_score([[1, 0, 0]], [[0.5, 0.75, 0.25]]), 1 / 2)
|
|
assert_almost_equal(
|
|
lrap_score([[1, 0, 1]], [[0.5, 0.75, 0.25]]), (1 / 2 + 2 / 3) / 2
|
|
)
|
|
assert_almost_equal(lrap_score([[1, 1, 0]], [[0.5, 0.75, 0.25]]), 1)
|
|
assert_almost_equal(lrap_score([[1, 1, 1]], [[0.5, 0.75, 0.25]]), 1)
|
|
|
|
# Tie handling
|
|
assert_almost_equal(lrap_score([[1, 0]], [[0.5, 0.5]]), 0.5)
|
|
assert_almost_equal(lrap_score([[0, 1]], [[0.5, 0.5]]), 0.5)
|
|
assert_almost_equal(lrap_score([[1, 1]], [[0.5, 0.5]]), 1)
|
|
|
|
assert_almost_equal(lrap_score([[0, 0, 1]], [[0.25, 0.5, 0.5]]), 0.5)
|
|
assert_almost_equal(lrap_score([[0, 1, 0]], [[0.25, 0.5, 0.5]]), 0.5)
|
|
assert_almost_equal(lrap_score([[0, 1, 1]], [[0.25, 0.5, 0.5]]), 1)
|
|
assert_almost_equal(lrap_score([[1, 0, 0]], [[0.25, 0.5, 0.5]]), 1 / 3)
|
|
assert_almost_equal(
|
|
lrap_score([[1, 0, 1]], [[0.25, 0.5, 0.5]]), (2 / 3 + 1 / 2) / 2
|
|
)
|
|
assert_almost_equal(
|
|
lrap_score([[1, 1, 0]], [[0.25, 0.5, 0.5]]), (2 / 3 + 1 / 2) / 2
|
|
)
|
|
assert_almost_equal(lrap_score([[1, 1, 1]], [[0.25, 0.5, 0.5]]), 1)
|
|
|
|
assert_almost_equal(lrap_score([[1, 1, 0]], [[0.5, 0.5, 0.5]]), 2 / 3)
|
|
|
|
assert_almost_equal(lrap_score([[1, 1, 1, 0]], [[0.5, 0.5, 0.5, 0.5]]), 3 / 4)
|
|
|
|
|
|
def check_zero_or_all_relevant_labels(lrap_score):
|
|
random_state = check_random_state(0)
|
|
|
|
for n_labels in range(2, 5):
|
|
y_score = random_state.uniform(size=(1, n_labels))
|
|
y_score_ties = np.zeros_like(y_score)
|
|
|
|
# No relevant labels
|
|
y_true = np.zeros((1, n_labels))
|
|
assert lrap_score(y_true, y_score) == 1.0
|
|
assert lrap_score(y_true, y_score_ties) == 1.0
|
|
|
|
# Only relevant labels
|
|
y_true = np.ones((1, n_labels))
|
|
assert lrap_score(y_true, y_score) == 1.0
|
|
assert lrap_score(y_true, y_score_ties) == 1.0
|
|
|
|
# Degenerate case: only one label
|
|
assert_almost_equal(
|
|
lrap_score([[1], [0], [1], [0]], [[0.5], [0.5], [0.5], [0.5]]), 1.0
|
|
)
|
|
|
|
|
|
def check_lrap_error_raised(lrap_score):
|
|
# Raise value error if not appropriate format
|
|
with pytest.raises(ValueError):
|
|
lrap_score([0, 1, 0], [0.25, 0.3, 0.2])
|
|
with pytest.raises(ValueError):
|
|
lrap_score([0, 1, 2], [[0.25, 0.75, 0.0], [0.7, 0.3, 0.0], [0.8, 0.2, 0.0]])
|
|
with pytest.raises(ValueError):
|
|
lrap_score(
|
|
[(0), (1), (2)], [[0.25, 0.75, 0.0], [0.7, 0.3, 0.0], [0.8, 0.2, 0.0]]
|
|
)
|
|
|
|
# Check that y_true.shape != y_score.shape raise the proper exception
|
|
with pytest.raises(ValueError):
|
|
lrap_score([[0, 1], [0, 1]], [0, 1])
|
|
with pytest.raises(ValueError):
|
|
lrap_score([[0, 1], [0, 1]], [[0, 1]])
|
|
with pytest.raises(ValueError):
|
|
lrap_score([[0, 1], [0, 1]], [[0], [1]])
|
|
with pytest.raises(ValueError):
|
|
lrap_score([[0, 1]], [[0, 1], [0, 1]])
|
|
with pytest.raises(ValueError):
|
|
lrap_score([[0], [1]], [[0, 1], [0, 1]])
|
|
with pytest.raises(ValueError):
|
|
lrap_score([[0, 1], [0, 1]], [[0], [1]])
|
|
|
|
|
|
def check_lrap_only_ties(lrap_score):
|
|
# Check tie handling in score
|
|
# Basic check with only ties and increasing label space
|
|
for n_labels in range(2, 10):
|
|
y_score = np.ones((1, n_labels))
|
|
|
|
# Check for growing number of consecutive relevant
|
|
for n_relevant in range(1, n_labels):
|
|
# Check for a bunch of positions
|
|
for pos in range(n_labels - n_relevant):
|
|
y_true = np.zeros((1, n_labels))
|
|
y_true[0, pos : pos + n_relevant] = 1
|
|
assert_almost_equal(lrap_score(y_true, y_score), n_relevant / n_labels)
|
|
|
|
|
|
def check_lrap_without_tie_and_increasing_score(lrap_score):
|
|
# Check that Label ranking average precision works for various
|
|
# Basic check with increasing label space size and decreasing score
|
|
for n_labels in range(2, 10):
|
|
y_score = n_labels - (np.arange(n_labels).reshape((1, n_labels)) + 1)
|
|
|
|
# First and last
|
|
y_true = np.zeros((1, n_labels))
|
|
y_true[0, 0] = 1
|
|
y_true[0, -1] = 1
|
|
assert_almost_equal(lrap_score(y_true, y_score), (2 / n_labels + 1) / 2)
|
|
|
|
# Check for growing number of consecutive relevant label
|
|
for n_relevant in range(1, n_labels):
|
|
# Check for a bunch of position
|
|
for pos in range(n_labels - n_relevant):
|
|
y_true = np.zeros((1, n_labels))
|
|
y_true[0, pos : pos + n_relevant] = 1
|
|
assert_almost_equal(
|
|
lrap_score(y_true, y_score),
|
|
sum(
|
|
(r + 1) / ((pos + r + 1) * n_relevant)
|
|
for r in range(n_relevant)
|
|
),
|
|
)
|
|
|
|
|
|
def _my_lrap(y_true, y_score):
|
|
"""Simple implementation of label ranking average precision"""
|
|
check_consistent_length(y_true, y_score)
|
|
y_true = check_array(y_true)
|
|
y_score = check_array(y_score)
|
|
n_samples, n_labels = y_true.shape
|
|
score = np.empty((n_samples,))
|
|
for i in range(n_samples):
|
|
# The best rank correspond to 1. Rank higher than 1 are worse.
|
|
# The best inverse ranking correspond to n_labels.
|
|
unique_rank, inv_rank = np.unique(y_score[i], return_inverse=True)
|
|
n_ranks = unique_rank.size
|
|
rank = n_ranks - inv_rank
|
|
|
|
# Rank need to be corrected to take into account ties
|
|
# ex: rank 1 ex aequo means that both label are rank 2.
|
|
corr_rank = np.bincount(rank, minlength=n_ranks + 1).cumsum()
|
|
rank = corr_rank[rank]
|
|
|
|
relevant = y_true[i].nonzero()[0]
|
|
if relevant.size == 0 or relevant.size == n_labels:
|
|
score[i] = 1
|
|
continue
|
|
|
|
score[i] = 0.0
|
|
for label in relevant:
|
|
# Let's count the number of relevant label with better rank
|
|
# (smaller rank).
|
|
n_ranked_above = sum(rank[r] <= rank[label] for r in relevant)
|
|
|
|
# Weight by the rank of the actual label
|
|
score[i] += n_ranked_above / rank[label]
|
|
|
|
score[i] /= relevant.size
|
|
|
|
return score.mean()
|
|
|
|
|
|
def check_alternative_lrap_implementation(
|
|
lrap_score, n_classes=5, n_samples=20, random_state=0
|
|
):
|
|
_, y_true = make_multilabel_classification(
|
|
n_features=1,
|
|
allow_unlabeled=False,
|
|
random_state=random_state,
|
|
n_classes=n_classes,
|
|
n_samples=n_samples,
|
|
)
|
|
|
|
# Score with ties
|
|
y_score = _sparse_random_matrix(
|
|
n_components=y_true.shape[0],
|
|
n_features=y_true.shape[1],
|
|
random_state=random_state,
|
|
)
|
|
|
|
if hasattr(y_score, "toarray"):
|
|
y_score = y_score.toarray()
|
|
score_lrap = label_ranking_average_precision_score(y_true, y_score)
|
|
score_my_lrap = _my_lrap(y_true, y_score)
|
|
assert_almost_equal(score_lrap, score_my_lrap)
|
|
|
|
# Uniform score
|
|
random_state = check_random_state(random_state)
|
|
y_score = random_state.uniform(size=(n_samples, n_classes))
|
|
score_lrap = label_ranking_average_precision_score(y_true, y_score)
|
|
score_my_lrap = _my_lrap(y_true, y_score)
|
|
assert_almost_equal(score_lrap, score_my_lrap)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"check",
|
|
(
|
|
check_lrap_toy,
|
|
check_lrap_without_tie_and_increasing_score,
|
|
check_lrap_only_ties,
|
|
check_zero_or_all_relevant_labels,
|
|
),
|
|
)
|
|
@pytest.mark.parametrize("func", (label_ranking_average_precision_score, _my_lrap))
|
|
def test_label_ranking_avp(check, func):
|
|
check(func)
|
|
|
|
|
|
def test_lrap_error_raised():
|
|
check_lrap_error_raised(label_ranking_average_precision_score)
|
|
|
|
|
|
@pytest.mark.parametrize("n_samples", (1, 2, 8, 20))
|
|
@pytest.mark.parametrize("n_classes", (2, 5, 10))
|
|
@pytest.mark.parametrize("random_state", range(1))
|
|
def test_alternative_lrap_implementation(n_samples, n_classes, random_state):
|
|
check_alternative_lrap_implementation(
|
|
label_ranking_average_precision_score, n_classes, n_samples, random_state
|
|
)
|
|
|
|
|
|
def test_lrap_sample_weighting_zero_labels():
|
|
# Degenerate sample labeling (e.g., zero labels for a sample) is a valid
|
|
# special case for lrap (the sample is considered to achieve perfect
|
|
# precision), but this case is not tested in test_common.
|
|
# For these test samples, the APs are 0.5, 0.75, and 1.0 (default for zero
|
|
# labels).
|
|
y_true = np.array([[1, 0, 0, 0], [1, 0, 0, 1], [0, 0, 0, 0]], dtype=bool)
|
|
y_score = np.array(
|
|
[[0.3, 0.4, 0.2, 0.1], [0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1]]
|
|
)
|
|
samplewise_lraps = np.array([0.5, 0.75, 1.0])
|
|
sample_weight = np.array([1.0, 1.0, 0.0])
|
|
|
|
assert_almost_equal(
|
|
label_ranking_average_precision_score(
|
|
y_true, y_score, sample_weight=sample_weight
|
|
),
|
|
np.sum(sample_weight * samplewise_lraps) / np.sum(sample_weight),
|
|
)
|
|
|
|
|
|
def test_coverage_error():
|
|
# Toy case
|
|
assert_almost_equal(coverage_error([[0, 1]], [[0.25, 0.75]]), 1)
|
|
assert_almost_equal(coverage_error([[0, 1]], [[0.75, 0.25]]), 2)
|
|
assert_almost_equal(coverage_error([[1, 1]], [[0.75, 0.25]]), 2)
|
|
assert_almost_equal(coverage_error([[0, 0]], [[0.75, 0.25]]), 0)
|
|
|
|
assert_almost_equal(coverage_error([[0, 0, 0]], [[0.25, 0.5, 0.75]]), 0)
|
|
assert_almost_equal(coverage_error([[0, 0, 1]], [[0.25, 0.5, 0.75]]), 1)
|
|
assert_almost_equal(coverage_error([[0, 1, 0]], [[0.25, 0.5, 0.75]]), 2)
|
|
assert_almost_equal(coverage_error([[0, 1, 1]], [[0.25, 0.5, 0.75]]), 2)
|
|
assert_almost_equal(coverage_error([[1, 0, 0]], [[0.25, 0.5, 0.75]]), 3)
|
|
assert_almost_equal(coverage_error([[1, 0, 1]], [[0.25, 0.5, 0.75]]), 3)
|
|
assert_almost_equal(coverage_error([[1, 1, 0]], [[0.25, 0.5, 0.75]]), 3)
|
|
assert_almost_equal(coverage_error([[1, 1, 1]], [[0.25, 0.5, 0.75]]), 3)
|
|
|
|
assert_almost_equal(coverage_error([[0, 0, 0]], [[0.75, 0.5, 0.25]]), 0)
|
|
assert_almost_equal(coverage_error([[0, 0, 1]], [[0.75, 0.5, 0.25]]), 3)
|
|
assert_almost_equal(coverage_error([[0, 1, 0]], [[0.75, 0.5, 0.25]]), 2)
|
|
assert_almost_equal(coverage_error([[0, 1, 1]], [[0.75, 0.5, 0.25]]), 3)
|
|
assert_almost_equal(coverage_error([[1, 0, 0]], [[0.75, 0.5, 0.25]]), 1)
|
|
assert_almost_equal(coverage_error([[1, 0, 1]], [[0.75, 0.5, 0.25]]), 3)
|
|
assert_almost_equal(coverage_error([[1, 1, 0]], [[0.75, 0.5, 0.25]]), 2)
|
|
assert_almost_equal(coverage_error([[1, 1, 1]], [[0.75, 0.5, 0.25]]), 3)
|
|
|
|
assert_almost_equal(coverage_error([[0, 0, 0]], [[0.5, 0.75, 0.25]]), 0)
|
|
assert_almost_equal(coverage_error([[0, 0, 1]], [[0.5, 0.75, 0.25]]), 3)
|
|
assert_almost_equal(coverage_error([[0, 1, 0]], [[0.5, 0.75, 0.25]]), 1)
|
|
assert_almost_equal(coverage_error([[0, 1, 1]], [[0.5, 0.75, 0.25]]), 3)
|
|
assert_almost_equal(coverage_error([[1, 0, 0]], [[0.5, 0.75, 0.25]]), 2)
|
|
assert_almost_equal(coverage_error([[1, 0, 1]], [[0.5, 0.75, 0.25]]), 3)
|
|
assert_almost_equal(coverage_error([[1, 1, 0]], [[0.5, 0.75, 0.25]]), 2)
|
|
assert_almost_equal(coverage_error([[1, 1, 1]], [[0.5, 0.75, 0.25]]), 3)
|
|
|
|
# Non trivial case
|
|
assert_almost_equal(
|
|
coverage_error([[0, 1, 0], [1, 1, 0]], [[0.1, 10.0, -3], [0, 1, 3]]),
|
|
(1 + 3) / 2.0,
|
|
)
|
|
|
|
assert_almost_equal(
|
|
coverage_error(
|
|
[[0, 1, 0], [1, 1, 0], [0, 1, 1]], [[0.1, 10, -3], [0, 1, 3], [0, 2, 0]]
|
|
),
|
|
(1 + 3 + 3) / 3.0,
|
|
)
|
|
|
|
assert_almost_equal(
|
|
coverage_error(
|
|
[[0, 1, 0], [1, 1, 0], [0, 1, 1]], [[0.1, 10, -3], [3, 1, 3], [0, 2, 0]]
|
|
),
|
|
(1 + 3 + 3) / 3.0,
|
|
)
|
|
|
|
|
|
def test_coverage_tie_handling():
|
|
assert_almost_equal(coverage_error([[0, 0]], [[0.5, 0.5]]), 0)
|
|
assert_almost_equal(coverage_error([[1, 0]], [[0.5, 0.5]]), 2)
|
|
assert_almost_equal(coverage_error([[0, 1]], [[0.5, 0.5]]), 2)
|
|
assert_almost_equal(coverage_error([[1, 1]], [[0.5, 0.5]]), 2)
|
|
|
|
assert_almost_equal(coverage_error([[0, 0, 0]], [[0.25, 0.5, 0.5]]), 0)
|
|
assert_almost_equal(coverage_error([[0, 0, 1]], [[0.25, 0.5, 0.5]]), 2)
|
|
assert_almost_equal(coverage_error([[0, 1, 0]], [[0.25, 0.5, 0.5]]), 2)
|
|
assert_almost_equal(coverage_error([[0, 1, 1]], [[0.25, 0.5, 0.5]]), 2)
|
|
assert_almost_equal(coverage_error([[1, 0, 0]], [[0.25, 0.5, 0.5]]), 3)
|
|
assert_almost_equal(coverage_error([[1, 0, 1]], [[0.25, 0.5, 0.5]]), 3)
|
|
assert_almost_equal(coverage_error([[1, 1, 0]], [[0.25, 0.5, 0.5]]), 3)
|
|
assert_almost_equal(coverage_error([[1, 1, 1]], [[0.25, 0.5, 0.5]]), 3)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"y_true, y_score",
|
|
[
|
|
([1, 0, 1], [0.25, 0.5, 0.5]),
|
|
([1, 0, 1], [[0.25, 0.5, 0.5]]),
|
|
([[1, 0, 1]], [0.25, 0.5, 0.5]),
|
|
],
|
|
)
|
|
def test_coverage_1d_error_message(y_true, y_score):
|
|
# Non-regression test for:
|
|
# https://github.com/scikit-learn/scikit-learn/issues/23368
|
|
with pytest.raises(ValueError, match=r"Expected 2D array, got 1D array instead"):
|
|
coverage_error(y_true, y_score)
|
|
|
|
|
|
def test_label_ranking_loss():
|
|
assert_almost_equal(label_ranking_loss([[0, 1]], [[0.25, 0.75]]), 0)
|
|
assert_almost_equal(label_ranking_loss([[0, 1]], [[0.75, 0.25]]), 1)
|
|
|
|
assert_almost_equal(label_ranking_loss([[0, 0, 1]], [[0.25, 0.5, 0.75]]), 0)
|
|
assert_almost_equal(label_ranking_loss([[0, 1, 0]], [[0.25, 0.5, 0.75]]), 1 / 2)
|
|
assert_almost_equal(label_ranking_loss([[0, 1, 1]], [[0.25, 0.5, 0.75]]), 0)
|
|
assert_almost_equal(label_ranking_loss([[1, 0, 0]], [[0.25, 0.5, 0.75]]), 2 / 2)
|
|
assert_almost_equal(label_ranking_loss([[1, 0, 1]], [[0.25, 0.5, 0.75]]), 1 / 2)
|
|
assert_almost_equal(label_ranking_loss([[1, 1, 0]], [[0.25, 0.5, 0.75]]), 2 / 2)
|
|
|
|
# Undefined metrics - the ranking doesn't matter
|
|
assert_almost_equal(label_ranking_loss([[0, 0]], [[0.75, 0.25]]), 0)
|
|
assert_almost_equal(label_ranking_loss([[1, 1]], [[0.75, 0.25]]), 0)
|
|
assert_almost_equal(label_ranking_loss([[0, 0]], [[0.5, 0.5]]), 0)
|
|
assert_almost_equal(label_ranking_loss([[1, 1]], [[0.5, 0.5]]), 0)
|
|
|
|
assert_almost_equal(label_ranking_loss([[0, 0, 0]], [[0.5, 0.75, 0.25]]), 0)
|
|
assert_almost_equal(label_ranking_loss([[1, 1, 1]], [[0.5, 0.75, 0.25]]), 0)
|
|
assert_almost_equal(label_ranking_loss([[0, 0, 0]], [[0.25, 0.5, 0.5]]), 0)
|
|
assert_almost_equal(label_ranking_loss([[1, 1, 1]], [[0.25, 0.5, 0.5]]), 0)
|
|
|
|
# Non trivial case
|
|
assert_almost_equal(
|
|
label_ranking_loss([[0, 1, 0], [1, 1, 0]], [[0.1, 10.0, -3], [0, 1, 3]]),
|
|
(0 + 2 / 2) / 2.0,
|
|
)
|
|
|
|
assert_almost_equal(
|
|
label_ranking_loss(
|
|
[[0, 1, 0], [1, 1, 0], [0, 1, 1]], [[0.1, 10, -3], [0, 1, 3], [0, 2, 0]]
|
|
),
|
|
(0 + 2 / 2 + 1 / 2) / 3.0,
|
|
)
|
|
|
|
assert_almost_equal(
|
|
label_ranking_loss(
|
|
[[0, 1, 0], [1, 1, 0], [0, 1, 1]], [[0.1, 10, -3], [3, 1, 3], [0, 2, 0]]
|
|
),
|
|
(0 + 2 / 2 + 1 / 2) / 3.0,
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
|
def test_label_ranking_loss_sparse(csr_container):
|
|
assert_almost_equal(
|
|
label_ranking_loss(
|
|
csr_container(np.array([[0, 1, 0], [1, 1, 0]])), [[0.1, 10, -3], [3, 1, 3]]
|
|
),
|
|
(0 + 2 / 2) / 2.0,
|
|
)
|
|
|
|
|
|
def test_ranking_appropriate_input_shape():
|
|
# Check that y_true.shape != y_score.shape raise the proper exception
|
|
with pytest.raises(ValueError):
|
|
label_ranking_loss([[0, 1], [0, 1]], [0, 1])
|
|
with pytest.raises(ValueError):
|
|
label_ranking_loss([[0, 1], [0, 1]], [[0, 1]])
|
|
with pytest.raises(ValueError):
|
|
label_ranking_loss([[0, 1], [0, 1]], [[0], [1]])
|
|
with pytest.raises(ValueError):
|
|
label_ranking_loss([[0, 1]], [[0, 1], [0, 1]])
|
|
with pytest.raises(ValueError):
|
|
label_ranking_loss([[0], [1]], [[0, 1], [0, 1]])
|
|
with pytest.raises(ValueError):
|
|
label_ranking_loss([[0, 1], [0, 1]], [[0], [1]])
|
|
|
|
|
|
def test_ranking_loss_ties_handling():
|
|
# Tie handling
|
|
assert_almost_equal(label_ranking_loss([[1, 0]], [[0.5, 0.5]]), 1)
|
|
assert_almost_equal(label_ranking_loss([[0, 1]], [[0.5, 0.5]]), 1)
|
|
assert_almost_equal(label_ranking_loss([[0, 0, 1]], [[0.25, 0.5, 0.5]]), 1 / 2)
|
|
assert_almost_equal(label_ranking_loss([[0, 1, 0]], [[0.25, 0.5, 0.5]]), 1 / 2)
|
|
assert_almost_equal(label_ranking_loss([[0, 1, 1]], [[0.25, 0.5, 0.5]]), 0)
|
|
assert_almost_equal(label_ranking_loss([[1, 0, 0]], [[0.25, 0.5, 0.5]]), 1)
|
|
assert_almost_equal(label_ranking_loss([[1, 0, 1]], [[0.25, 0.5, 0.5]]), 1)
|
|
assert_almost_equal(label_ranking_loss([[1, 1, 0]], [[0.25, 0.5, 0.5]]), 1)
|
|
|
|
|
|
def test_dcg_score():
|
|
_, y_true = make_multilabel_classification(random_state=0, n_classes=10)
|
|
y_score = -y_true + 1
|
|
_test_dcg_score_for(y_true, y_score)
|
|
y_true, y_score = np.random.RandomState(0).random_sample((2, 100, 10))
|
|
_test_dcg_score_for(y_true, y_score)
|
|
|
|
|
|
def _test_dcg_score_for(y_true, y_score):
|
|
discount = np.log2(np.arange(y_true.shape[1]) + 2)
|
|
ideal = _dcg_sample_scores(y_true, y_true)
|
|
score = _dcg_sample_scores(y_true, y_score)
|
|
assert (score <= ideal).all()
|
|
assert (_dcg_sample_scores(y_true, y_true, k=5) <= ideal).all()
|
|
assert ideal.shape == (y_true.shape[0],)
|
|
assert score.shape == (y_true.shape[0],)
|
|
assert ideal == pytest.approx((np.sort(y_true)[:, ::-1] / discount).sum(axis=1))
|
|
|
|
|
|
def test_dcg_ties():
|
|
y_true = np.asarray([np.arange(5)])
|
|
y_score = np.zeros(y_true.shape)
|
|
dcg = _dcg_sample_scores(y_true, y_score)
|
|
dcg_ignore_ties = _dcg_sample_scores(y_true, y_score, ignore_ties=True)
|
|
discounts = 1 / np.log2(np.arange(2, 7))
|
|
assert dcg == pytest.approx([discounts.sum() * y_true.mean()])
|
|
assert dcg_ignore_ties == pytest.approx([(discounts * y_true[:, ::-1]).sum()])
|
|
y_score[0, 3:] = 1
|
|
dcg = _dcg_sample_scores(y_true, y_score)
|
|
dcg_ignore_ties = _dcg_sample_scores(y_true, y_score, ignore_ties=True)
|
|
assert dcg_ignore_ties == pytest.approx([(discounts * y_true[:, ::-1]).sum()])
|
|
assert dcg == pytest.approx(
|
|
[
|
|
discounts[:2].sum() * y_true[0, 3:].mean()
|
|
+ discounts[2:].sum() * y_true[0, :3].mean()
|
|
]
|
|
)
|
|
|
|
|
|
def test_ndcg_ignore_ties_with_k():
|
|
a = np.arange(12).reshape((2, 6))
|
|
assert ndcg_score(a, a, k=3, ignore_ties=True) == pytest.approx(
|
|
ndcg_score(a, a, k=3, ignore_ties=True)
|
|
)
|
|
|
|
|
|
def test_ndcg_negative_ndarray_error():
|
|
"""Check `ndcg_score` exception when `y_true` contains negative values."""
|
|
y_true = np.array([[-0.89, -0.53, -0.47, 0.39, 0.56]])
|
|
y_score = np.array([[0.07, 0.31, 0.75, 0.33, 0.27]])
|
|
expected_message = "ndcg_score should not be used on negative y_true values"
|
|
with pytest.raises(ValueError, match=expected_message):
|
|
ndcg_score(y_true, y_score)
|
|
|
|
|
|
def test_ndcg_invariant():
|
|
y_true = np.arange(70).reshape(7, 10)
|
|
y_score = y_true + np.random.RandomState(0).uniform(-0.2, 0.2, size=y_true.shape)
|
|
ndcg = ndcg_score(y_true, y_score)
|
|
ndcg_no_ties = ndcg_score(y_true, y_score, ignore_ties=True)
|
|
assert ndcg == pytest.approx(ndcg_no_ties)
|
|
assert ndcg == pytest.approx(1.0)
|
|
y_score += 1000
|
|
assert ndcg_score(y_true, y_score) == pytest.approx(1.0)
|
|
|
|
|
|
@pytest.mark.parametrize("ignore_ties", [True, False])
|
|
def test_ndcg_toy_examples(ignore_ties):
|
|
y_true = 3 * np.eye(7)[:5]
|
|
y_score = np.tile(np.arange(6, -1, -1), (5, 1))
|
|
y_score_noisy = y_score + np.random.RandomState(0).uniform(
|
|
-0.2, 0.2, size=y_score.shape
|
|
)
|
|
assert _dcg_sample_scores(
|
|
y_true, y_score, ignore_ties=ignore_ties
|
|
) == pytest.approx(3 / np.log2(np.arange(2, 7)))
|
|
assert _dcg_sample_scores(
|
|
y_true, y_score_noisy, ignore_ties=ignore_ties
|
|
) == pytest.approx(3 / np.log2(np.arange(2, 7)))
|
|
assert _ndcg_sample_scores(
|
|
y_true, y_score, ignore_ties=ignore_ties
|
|
) == pytest.approx(1 / np.log2(np.arange(2, 7)))
|
|
assert _dcg_sample_scores(
|
|
y_true, y_score, log_base=10, ignore_ties=ignore_ties
|
|
) == pytest.approx(3 / np.log10(np.arange(2, 7)))
|
|
assert ndcg_score(y_true, y_score, ignore_ties=ignore_ties) == pytest.approx(
|
|
(1 / np.log2(np.arange(2, 7))).mean()
|
|
)
|
|
assert dcg_score(y_true, y_score, ignore_ties=ignore_ties) == pytest.approx(
|
|
(3 / np.log2(np.arange(2, 7))).mean()
|
|
)
|
|
y_true = 3 * np.ones((5, 7))
|
|
expected_dcg_score = (3 / np.log2(np.arange(2, 9))).sum()
|
|
assert _dcg_sample_scores(
|
|
y_true, y_score, ignore_ties=ignore_ties
|
|
) == pytest.approx(expected_dcg_score * np.ones(5))
|
|
assert _ndcg_sample_scores(
|
|
y_true, y_score, ignore_ties=ignore_ties
|
|
) == pytest.approx(np.ones(5))
|
|
assert dcg_score(y_true, y_score, ignore_ties=ignore_ties) == pytest.approx(
|
|
expected_dcg_score
|
|
)
|
|
assert ndcg_score(y_true, y_score, ignore_ties=ignore_ties) == pytest.approx(1.0)
|
|
|
|
|
|
def test_ndcg_error_single_document():
|
|
"""Check that we raise an informative error message when trying to
|
|
compute NDCG with a single document."""
|
|
err_msg = (
|
|
"Computing NDCG is only meaningful when there is more than 1 document. "
|
|
"Got 1 instead."
|
|
)
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
ndcg_score([[1]], [[1]])
|
|
|
|
|
|
def test_ndcg_score():
|
|
_, y_true = make_multilabel_classification(random_state=0, n_classes=10)
|
|
y_score = -y_true + 1
|
|
_test_ndcg_score_for(y_true, y_score)
|
|
y_true, y_score = np.random.RandomState(0).random_sample((2, 100, 10))
|
|
_test_ndcg_score_for(y_true, y_score)
|
|
|
|
|
|
def _test_ndcg_score_for(y_true, y_score):
|
|
ideal = _ndcg_sample_scores(y_true, y_true)
|
|
score = _ndcg_sample_scores(y_true, y_score)
|
|
assert (score <= ideal).all()
|
|
all_zero = (y_true == 0).all(axis=1)
|
|
assert ideal[~all_zero] == pytest.approx(np.ones((~all_zero).sum()))
|
|
assert ideal[all_zero] == pytest.approx(np.zeros(all_zero.sum()))
|
|
assert score[~all_zero] == pytest.approx(
|
|
_dcg_sample_scores(y_true, y_score)[~all_zero]
|
|
/ _dcg_sample_scores(y_true, y_true)[~all_zero]
|
|
)
|
|
assert score[all_zero] == pytest.approx(np.zeros(all_zero.sum()))
|
|
assert ideal.shape == (y_true.shape[0],)
|
|
assert score.shape == (y_true.shape[0],)
|
|
|
|
|
|
def test_partial_roc_auc_score():
|
|
# Check `roc_auc_score` for max_fpr != `None`
|
|
y_true = np.array([0, 0, 1, 1])
|
|
assert roc_auc_score(y_true, y_true, max_fpr=1) == 1
|
|
assert roc_auc_score(y_true, y_true, max_fpr=0.001) == 1
|
|
with pytest.raises(ValueError):
|
|
assert roc_auc_score(y_true, y_true, max_fpr=-0.1)
|
|
with pytest.raises(ValueError):
|
|
assert roc_auc_score(y_true, y_true, max_fpr=1.1)
|
|
with pytest.raises(ValueError):
|
|
assert roc_auc_score(y_true, y_true, max_fpr=0)
|
|
|
|
y_scores = np.array([0.1, 0, 0.1, 0.01])
|
|
roc_auc_with_max_fpr_one = roc_auc_score(y_true, y_scores, max_fpr=1)
|
|
unconstrained_roc_auc = roc_auc_score(y_true, y_scores)
|
|
assert roc_auc_with_max_fpr_one == unconstrained_roc_auc
|
|
assert roc_auc_score(y_true, y_scores, max_fpr=0.3) == 0.5
|
|
|
|
y_true, y_pred, _ = make_prediction(binary=True)
|
|
for max_fpr in np.linspace(1e-4, 1, 5):
|
|
assert_almost_equal(
|
|
roc_auc_score(y_true, y_pred, max_fpr=max_fpr),
|
|
_partial_roc_auc_score(y_true, y_pred, max_fpr),
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"y_true, k, true_score",
|
|
[
|
|
([0, 1, 2, 3], 1, 0.25),
|
|
([0, 1, 2, 3], 2, 0.5),
|
|
([0, 1, 2, 3], 3, 0.75),
|
|
],
|
|
)
|
|
def test_top_k_accuracy_score(y_true, k, true_score):
|
|
y_score = np.array(
|
|
[
|
|
[0.4, 0.3, 0.2, 0.1],
|
|
[0.1, 0.3, 0.4, 0.2],
|
|
[0.4, 0.1, 0.2, 0.3],
|
|
[0.3, 0.2, 0.4, 0.1],
|
|
]
|
|
)
|
|
score = top_k_accuracy_score(y_true, y_score, k=k)
|
|
assert score == pytest.approx(true_score)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"y_score, k, true_score",
|
|
[
|
|
(np.array([-1, -1, 1, 1]), 1, 1),
|
|
(np.array([-1, 1, -1, 1]), 1, 0.5),
|
|
(np.array([-1, 1, -1, 1]), 2, 1),
|
|
(np.array([0.2, 0.2, 0.7, 0.7]), 1, 1),
|
|
(np.array([0.2, 0.7, 0.2, 0.7]), 1, 0.5),
|
|
(np.array([0.2, 0.7, 0.2, 0.7]), 2, 1),
|
|
],
|
|
)
|
|
def test_top_k_accuracy_score_binary(y_score, k, true_score):
|
|
y_true = [0, 0, 1, 1]
|
|
|
|
threshold = 0.5 if y_score.min() >= 0 and y_score.max() <= 1 else 0
|
|
y_pred = (y_score > threshold).astype(np.int64) if k == 1 else y_true
|
|
|
|
score = top_k_accuracy_score(y_true, y_score, k=k)
|
|
score_acc = accuracy_score(y_true, y_pred)
|
|
|
|
assert score == score_acc == pytest.approx(true_score)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"y_true, true_score, labels",
|
|
[
|
|
(np.array([0, 1, 1, 2]), 0.75, [0, 1, 2, 3]),
|
|
(np.array([0, 1, 1, 1]), 0.5, [0, 1, 2, 3]),
|
|
(np.array([1, 1, 1, 1]), 0.5, [0, 1, 2, 3]),
|
|
(np.array(["a", "e", "e", "a"]), 0.75, ["a", "b", "d", "e"]),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("labels_as_ndarray", [True, False])
|
|
def test_top_k_accuracy_score_multiclass_with_labels(
|
|
y_true, true_score, labels, labels_as_ndarray
|
|
):
|
|
"""Test when labels and y_score are multiclass."""
|
|
if labels_as_ndarray:
|
|
labels = np.asarray(labels)
|
|
y_score = np.array(
|
|
[
|
|
[0.4, 0.3, 0.2, 0.1],
|
|
[0.1, 0.3, 0.4, 0.2],
|
|
[0.4, 0.1, 0.2, 0.3],
|
|
[0.3, 0.2, 0.4, 0.1],
|
|
]
|
|
)
|
|
|
|
score = top_k_accuracy_score(y_true, y_score, k=2, labels=labels)
|
|
assert score == pytest.approx(true_score)
|
|
|
|
|
|
def test_top_k_accuracy_score_increasing():
|
|
# Make sure increasing k leads to a higher score
|
|
X, y = datasets.make_classification(
|
|
n_classes=10, n_samples=1000, n_informative=10, random_state=0
|
|
)
|
|
|
|
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
|
|
|
|
clf = LogisticRegression(random_state=0)
|
|
clf.fit(X_train, y_train)
|
|
|
|
for X, y in zip((X_train, X_test), (y_train, y_test)):
|
|
scores = [
|
|
top_k_accuracy_score(y, clf.predict_proba(X), k=k) for k in range(2, 10)
|
|
]
|
|
|
|
assert np.all(np.diff(scores) > 0)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"y_true, k, true_score",
|
|
[
|
|
([0, 1, 2, 3], 1, 0.25),
|
|
([0, 1, 2, 3], 2, 0.5),
|
|
([0, 1, 2, 3], 3, 1),
|
|
],
|
|
)
|
|
def test_top_k_accuracy_score_ties(y_true, k, true_score):
|
|
# Make sure highest indices labels are chosen first in case of ties
|
|
y_score = np.array(
|
|
[
|
|
[5, 5, 7, 0],
|
|
[1, 5, 5, 5],
|
|
[0, 0, 3, 3],
|
|
[1, 1, 1, 1],
|
|
]
|
|
)
|
|
assert top_k_accuracy_score(y_true, y_score, k=k) == pytest.approx(true_score)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"y_true, k",
|
|
[
|
|
([0, 1, 2, 3], 4),
|
|
([0, 1, 2, 3], 5),
|
|
],
|
|
)
|
|
def test_top_k_accuracy_score_warning(y_true, k):
|
|
y_score = np.array(
|
|
[
|
|
[0.4, 0.3, 0.2, 0.1],
|
|
[0.1, 0.4, 0.3, 0.2],
|
|
[0.2, 0.1, 0.4, 0.3],
|
|
[0.3, 0.2, 0.1, 0.4],
|
|
]
|
|
)
|
|
expected_message = (
|
|
r"'k' \(\d+\) greater than or equal to 'n_classes' \(\d+\) will result in a "
|
|
"perfect score and is therefore meaningless."
|
|
)
|
|
with pytest.warns(UndefinedMetricWarning, match=expected_message):
|
|
score = top_k_accuracy_score(y_true, y_score, k=k)
|
|
assert score == 1
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"y_true, y_score, labels, msg",
|
|
[
|
|
(
|
|
[0, 0.57, 1, 2],
|
|
[
|
|
[0.2, 0.1, 0.7],
|
|
[0.4, 0.3, 0.3],
|
|
[0.3, 0.4, 0.3],
|
|
[0.4, 0.5, 0.1],
|
|
],
|
|
None,
|
|
"y type must be 'binary' or 'multiclass', got 'continuous'",
|
|
),
|
|
(
|
|
[0, 1, 2, 3],
|
|
[
|
|
[0.2, 0.1, 0.7],
|
|
[0.4, 0.3, 0.3],
|
|
[0.3, 0.4, 0.3],
|
|
[0.4, 0.5, 0.1],
|
|
],
|
|
None,
|
|
r"Number of classes in 'y_true' \(4\) not equal to the number of "
|
|
r"classes in 'y_score' \(3\).",
|
|
),
|
|
(
|
|
["c", "c", "a", "b"],
|
|
[
|
|
[0.2, 0.1, 0.7],
|
|
[0.4, 0.3, 0.3],
|
|
[0.3, 0.4, 0.3],
|
|
[0.4, 0.5, 0.1],
|
|
],
|
|
["a", "b", "c", "c"],
|
|
"Parameter 'labels' must be unique.",
|
|
),
|
|
(
|
|
["c", "c", "a", "b"],
|
|
[
|
|
[0.2, 0.1, 0.7],
|
|
[0.4, 0.3, 0.3],
|
|
[0.3, 0.4, 0.3],
|
|
[0.4, 0.5, 0.1],
|
|
],
|
|
["a", "c", "b"],
|
|
"Parameter 'labels' must be ordered.",
|
|
),
|
|
(
|
|
[0, 0, 1, 2],
|
|
[
|
|
[0.2, 0.1, 0.7],
|
|
[0.4, 0.3, 0.3],
|
|
[0.3, 0.4, 0.3],
|
|
[0.4, 0.5, 0.1],
|
|
],
|
|
[0, 1, 2, 3],
|
|
r"Number of given labels \(4\) not equal to the number of classes in "
|
|
r"'y_score' \(3\).",
|
|
),
|
|
(
|
|
[0, 0, 1, 2],
|
|
[
|
|
[0.2, 0.1, 0.7],
|
|
[0.4, 0.3, 0.3],
|
|
[0.3, 0.4, 0.3],
|
|
[0.4, 0.5, 0.1],
|
|
],
|
|
[0, 1, 3],
|
|
"'y_true' contains labels not in parameter 'labels'.",
|
|
),
|
|
(
|
|
[0, 1],
|
|
[[0.5, 0.2, 0.2], [0.3, 0.4, 0.2]],
|
|
None,
|
|
(
|
|
"`y_true` is binary while y_score is 2d with 3 classes. If"
|
|
" `y_true` does not contain all the labels, `labels` must be provided"
|
|
),
|
|
),
|
|
],
|
|
)
|
|
def test_top_k_accuracy_score_error(y_true, y_score, labels, msg):
|
|
with pytest.raises(ValueError, match=msg):
|
|
top_k_accuracy_score(y_true, y_score, k=2, labels=labels)
|
|
|
|
|
|
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
|
|
def test_label_ranking_avg_precision_score_should_allow_csr_matrix_for_y_true_input(
|
|
csr_container,
|
|
):
|
|
# Test that label_ranking_avg_precision_score accept sparse y_true.
|
|
# Non-regression test for #22575
|
|
y_true = csr_container([[1, 0, 0], [0, 0, 1]])
|
|
y_score = np.array([[0.5, 0.9, 0.6], [0, 0, 1]])
|
|
result = label_ranking_average_precision_score(y_true, y_score)
|
|
assert result == pytest.approx(2 / 3)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"metric", [average_precision_score, det_curve, precision_recall_curve, roc_curve]
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"classes", [(False, True), (0, 1), (0.0, 1.0), ("zero", "one")]
|
|
)
|
|
def test_ranking_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)
|
|
y_proba = rng.rand(n_samples)
|
|
result = metric(y_true, y_proba, pos_label=pos_label)
|
|
if isinstance(result, float):
|
|
assert not np.isnan(result)
|
|
else:
|
|
metric_1, metric_2, thresholds = result
|
|
assert not np.isnan(metric_1).any()
|
|
assert not np.isnan(metric_2).any()
|
|
assert not np.isnan(thresholds).any()
|
|
|
|
|
|
def test_roc_curve_with_probablity_estimates(global_random_seed):
|
|
"""Check that thresholds do not exceed 1.0 when `y_score` is a probability
|
|
estimate.
|
|
|
|
Non-regression test for:
|
|
https://github.com/scikit-learn/scikit-learn/issues/26193
|
|
"""
|
|
rng = np.random.RandomState(global_random_seed)
|
|
y_true = rng.randint(0, 2, size=10)
|
|
y_score = rng.rand(10)
|
|
_, _, thresholds = roc_curve(y_true, y_score)
|
|
assert np.isinf(thresholds[0])
|
|
|
|
|
|
# TODO(1.7): remove
|
|
def test_precision_recall_curve_deprecation_warning():
|
|
"""Check the message for future deprecation."""
|
|
# Check precision_recall_curve function
|
|
y_true, _, y_score = make_prediction(binary=True)
|
|
|
|
warn_msg = "probas_pred was deprecated in version 1.5"
|
|
with pytest.warns(FutureWarning, match=warn_msg):
|
|
precision_recall_curve(
|
|
y_true,
|
|
probas_pred=y_score,
|
|
)
|
|
|
|
error_msg = "`probas_pred` and `y_score` cannot be both specified"
|
|
with pytest.raises(ValueError, match=error_msg):
|
|
precision_recall_curve(
|
|
y_true,
|
|
probas_pred=y_score,
|
|
y_score=y_score,
|
|
)
|