1772 lines
68 KiB
Python
1772 lines
68 KiB
Python
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import re
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import pytest
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import numpy as np
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import warnings
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from scipy.sparse import csr_matrix
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from sklearn import datasets
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from sklearn import svm
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from sklearn.utils.extmath import softmax
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from sklearn.datasets import make_multilabel_classification
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from sklearn.random_projection import _sparse_random_matrix
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from sklearn.utils.validation import check_array, check_consistent_length
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from sklearn.utils.validation import check_random_state
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from sklearn.utils._testing import assert_allclose
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from sklearn.utils._testing import assert_almost_equal
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from sklearn.utils._testing import assert_array_equal
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from sklearn.utils._testing import assert_array_almost_equal
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from sklearn.utils._testing import assert_warns
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from sklearn.metrics import accuracy_score
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from sklearn.metrics import auc
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from sklearn.metrics import average_precision_score
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from sklearn.metrics import coverage_error
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from sklearn.metrics import det_curve
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from sklearn.metrics import label_ranking_average_precision_score
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from sklearn.metrics import precision_recall_curve
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from sklearn.metrics import label_ranking_loss
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from sklearn.metrics import roc_auc_score
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from sklearn.metrics import roc_curve
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from sklearn.metrics._ranking import _ndcg_sample_scores, _dcg_sample_scores
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from sklearn.metrics import ndcg_score, dcg_score
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from sklearn.metrics import top_k_accuracy_score
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from sklearn.exceptions import UndefinedMetricWarning
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LogisticRegression
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###############################################################################
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# Utilities for testing
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def make_prediction(dataset=None, binary=False):
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"""Make some classification predictions on a toy dataset using a SVC
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If binary is True restrict to a binary classification problem instead of a
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multiclass classification problem
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"""
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if dataset is None:
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# import some data to play with
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dataset = datasets.load_iris()
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X = dataset.data
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y = dataset.target
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if binary:
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# restrict to a binary classification task
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X, y = X[y < 2], y[y < 2]
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n_samples, n_features = X.shape
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p = np.arange(n_samples)
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rng = check_random_state(37)
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rng.shuffle(p)
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X, y = X[p], y[p]
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half = int(n_samples / 2)
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# add noisy features to make the problem harder and avoid perfect results
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rng = np.random.RandomState(0)
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X = np.c_[X, rng.randn(n_samples, 200 * n_features)]
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# run classifier, get class probabilities and label predictions
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clf = svm.SVC(kernel='linear', probability=True, random_state=0)
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probas_pred = clf.fit(X[:half], y[:half]).predict_proba(X[half:])
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if binary:
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# only interested in probabilities of the positive case
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# XXX: do we really want a special API for the binary case?
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probas_pred = probas_pred[:, 1]
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y_pred = clf.predict(X[half:])
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y_true = y[half:]
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return y_true, y_pred, probas_pred
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###############################################################################
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# Tests
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def _auc(y_true, y_score):
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"""Alternative implementation to check for correctness of
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`roc_auc_score`."""
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pos_label = np.unique(y_true)[1]
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# Count the number of times positive samples are correctly ranked above
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# negative samples.
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pos = y_score[y_true == pos_label]
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neg = y_score[y_true != pos_label]
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diff_matrix = pos.reshape(1, -1) - neg.reshape(-1, 1)
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n_correct = np.sum(diff_matrix > 0)
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return n_correct / float(len(pos) * len(neg))
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def _average_precision(y_true, y_score):
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"""Alternative implementation to check for correctness of
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`average_precision_score`.
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Note that this implementation fails on some edge cases.
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For example, for constant predictions e.g. [0.5, 0.5, 0.5],
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y_true = [1, 0, 0] returns an average precision of 0.33...
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but y_true = [0, 0, 1] returns 1.0.
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"""
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pos_label = np.unique(y_true)[1]
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n_pos = np.sum(y_true == pos_label)
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order = np.argsort(y_score)[::-1]
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y_score = y_score[order]
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y_true = y_true[order]
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score = 0
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for i in range(len(y_score)):
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if y_true[i] == pos_label:
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# Compute precision up to document i
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# i.e, percentage of relevant documents up to document i.
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prec = 0
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for j in range(0, i + 1):
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if y_true[j] == pos_label:
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prec += 1.0
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prec /= (i + 1.0)
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score += prec
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return score / n_pos
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def _average_precision_slow(y_true, y_score):
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"""A second alternative implementation of average precision that closely
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follows the Wikipedia article's definition (see References). This should
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give identical results as `average_precision_score` for all inputs.
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References
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----------
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.. [1] `Wikipedia entry for the Average precision
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<https://en.wikipedia.org/wiki/Average_precision>`_
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"""
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precision, recall, threshold = precision_recall_curve(y_true, y_score)
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precision = list(reversed(precision))
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recall = list(reversed(recall))
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average_precision = 0
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for i in range(1, len(precision)):
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average_precision += precision[i] * (recall[i] - recall[i - 1])
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return average_precision
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def _partial_roc_auc_score(y_true, y_predict, max_fpr):
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"""Alternative implementation to check for correctness of `roc_auc_score`
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with `max_fpr` set.
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"""
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def _partial_roc(y_true, y_predict, max_fpr):
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fpr, tpr, _ = roc_curve(y_true, y_predict)
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new_fpr = fpr[fpr <= max_fpr]
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new_fpr = np.append(new_fpr, max_fpr)
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new_tpr = tpr[fpr <= max_fpr]
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idx_out = np.argmax(fpr > max_fpr)
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idx_in = idx_out - 1
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x_interp = [fpr[idx_in], fpr[idx_out]]
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y_interp = [tpr[idx_in], tpr[idx_out]]
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new_tpr = np.append(new_tpr, np.interp(max_fpr, x_interp, y_interp))
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return (new_fpr, new_tpr)
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new_fpr, new_tpr = _partial_roc(y_true, y_predict, max_fpr)
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partial_auc = auc(new_fpr, new_tpr)
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# Formula (5) from McClish 1989
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fpr1 = 0
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fpr2 = max_fpr
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min_area = 0.5 * (fpr2 - fpr1) * (fpr2 + fpr1)
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max_area = fpr2 - fpr1
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return 0.5 * (1 + (partial_auc - min_area) / (max_area - min_area))
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@pytest.mark.parametrize('drop', [True, False])
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def test_roc_curve(drop):
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# Test Area under Receiver Operating Characteristic (ROC) curve
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y_true, _, probas_pred = make_prediction(binary=True)
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expected_auc = _auc(y_true, probas_pred)
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fpr, tpr, thresholds = roc_curve(y_true, probas_pred,
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drop_intermediate=drop)
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roc_auc = auc(fpr, tpr)
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assert_array_almost_equal(roc_auc, expected_auc, decimal=2)
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assert_almost_equal(roc_auc, roc_auc_score(y_true, probas_pred))
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assert fpr.shape == tpr.shape
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assert fpr.shape == thresholds.shape
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def test_roc_curve_end_points():
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# Make sure that roc_curve returns a curve start at 0 and ending and
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# 1 even in corner cases
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rng = np.random.RandomState(0)
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y_true = np.array([0] * 50 + [1] * 50)
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y_pred = rng.randint(3, size=100)
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fpr, tpr, thr = roc_curve(y_true, y_pred, drop_intermediate=True)
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assert fpr[0] == 0
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assert fpr[-1] == 1
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assert fpr.shape == tpr.shape
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assert fpr.shape == thr.shape
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def test_roc_returns_consistency():
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# Test whether the returned threshold matches up with tpr
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# make small toy dataset
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y_true, _, probas_pred = make_prediction(binary=True)
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fpr, tpr, thresholds = roc_curve(y_true, probas_pred)
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# use the given thresholds to determine the tpr
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tpr_correct = []
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for t in thresholds:
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tp = np.sum((probas_pred >= t) & y_true)
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p = np.sum(y_true)
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tpr_correct.append(1.0 * tp / p)
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# compare tpr and tpr_correct to see if the thresholds' order was correct
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assert_array_almost_equal(tpr, tpr_correct, decimal=2)
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assert fpr.shape == tpr.shape
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assert fpr.shape == thresholds.shape
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def test_roc_curve_multi():
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# roc_curve not applicable for multi-class problems
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y_true, _, probas_pred = make_prediction(binary=False)
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with pytest.raises(ValueError):
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roc_curve(y_true, probas_pred)
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def test_roc_curve_confidence():
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# roc_curve for confidence scores
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y_true, _, probas_pred = make_prediction(binary=True)
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fpr, tpr, thresholds = roc_curve(y_true, probas_pred - 0.5)
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roc_auc = auc(fpr, tpr)
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assert_array_almost_equal(roc_auc, 0.90, decimal=2)
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assert fpr.shape == tpr.shape
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assert fpr.shape == thresholds.shape
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def test_roc_curve_hard():
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# roc_curve for hard decisions
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y_true, pred, probas_pred = make_prediction(binary=True)
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# always predict one
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trivial_pred = np.ones(y_true.shape)
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fpr, tpr, thresholds = roc_curve(y_true, trivial_pred)
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roc_auc = auc(fpr, tpr)
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assert_array_almost_equal(roc_auc, 0.50, decimal=2)
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assert fpr.shape == tpr.shape
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assert fpr.shape == thresholds.shape
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# always predict zero
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trivial_pred = np.zeros(y_true.shape)
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fpr, tpr, thresholds = roc_curve(y_true, trivial_pred)
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roc_auc = auc(fpr, tpr)
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assert_array_almost_equal(roc_auc, 0.50, decimal=2)
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assert fpr.shape == tpr.shape
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assert fpr.shape == thresholds.shape
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# hard decisions
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fpr, tpr, thresholds = roc_curve(y_true, pred)
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roc_auc = auc(fpr, tpr)
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assert_array_almost_equal(roc_auc, 0.78, decimal=2)
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assert fpr.shape == tpr.shape
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assert fpr.shape == thresholds.shape
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def test_roc_curve_one_label():
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y_true = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
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y_pred = [0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
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# assert there are warnings
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w = UndefinedMetricWarning
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fpr, tpr, thresholds = assert_warns(w, roc_curve, y_true, y_pred)
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# all true labels, all fpr should be nan
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assert_array_equal(fpr, np.full(len(thresholds), np.nan))
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assert fpr.shape == tpr.shape
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assert fpr.shape == thresholds.shape
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# assert there are warnings
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fpr, tpr, thresholds = assert_warns(w, roc_curve,
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[1 - x for x in y_true],
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y_pred)
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# all negative labels, all tpr should be nan
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assert_array_equal(tpr, np.full(len(thresholds), np.nan))
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assert fpr.shape == tpr.shape
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assert fpr.shape == thresholds.shape
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def test_roc_curve_toydata():
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# Binary classification
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y_true = [0, 1]
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y_score = [0, 1]
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tpr, fpr, _ = roc_curve(y_true, y_score)
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roc_auc = roc_auc_score(y_true, y_score)
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assert_array_almost_equal(tpr, [0, 0, 1])
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assert_array_almost_equal(fpr, [0, 1, 1])
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assert_almost_equal(roc_auc, 1.)
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y_true = [0, 1]
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y_score = [1, 0]
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tpr, fpr, _ = roc_curve(y_true, y_score)
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roc_auc = roc_auc_score(y_true, y_score)
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assert_array_almost_equal(tpr, [0, 1, 1])
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assert_array_almost_equal(fpr, [0, 0, 1])
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assert_almost_equal(roc_auc, 0.)
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y_true = [1, 0]
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y_score = [1, 1]
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tpr, fpr, _ = roc_curve(y_true, y_score)
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roc_auc = roc_auc_score(y_true, y_score)
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assert_array_almost_equal(tpr, [0, 1])
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assert_array_almost_equal(fpr, [0, 1])
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assert_almost_equal(roc_auc, 0.5)
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y_true = [1, 0]
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y_score = [1, 0]
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tpr, fpr, _ = roc_curve(y_true, y_score)
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roc_auc = roc_auc_score(y_true, y_score)
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assert_array_almost_equal(tpr, [0, 0, 1])
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assert_array_almost_equal(fpr, [0, 1, 1])
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assert_almost_equal(roc_auc, 1.)
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y_true = [1, 0]
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y_score = [0.5, 0.5]
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tpr, fpr, _ = roc_curve(y_true, y_score)
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roc_auc = roc_auc_score(y_true, y_score)
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assert_array_almost_equal(tpr, [0, 1])
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assert_array_almost_equal(fpr, [0, 1])
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assert_almost_equal(roc_auc, .5)
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y_true = [0, 0]
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y_score = [0.25, 0.75]
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# assert UndefinedMetricWarning because of no positive sample in y_true
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tpr, fpr, _ = assert_warns(UndefinedMetricWarning, roc_curve, y_true,
|
||
|
|
y_score)
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
roc_auc_score(y_true, y_score)
|
||
|
|
assert_array_almost_equal(tpr, [0., 0.5, 1.])
|
||
|
|
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
|
||
|
|
tpr, fpr, _ = assert_warns(UndefinedMetricWarning, 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., 0.5, 1.])
|
||
|
|
|
||
|
|
# 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.)
|
||
|
|
assert_almost_equal(roc_auc_score(y_true, y_score, average="micro"), 1.)
|
||
|
|
|
||
|
|
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"), .5)
|
||
|
|
assert_almost_equal(roc_auc_score(y_true, y_score, average="weighted"), .5)
|
||
|
|
assert_almost_equal(roc_auc_score(y_true, y_score, average="samples"), .5)
|
||
|
|
assert_almost_equal(roc_auc_score(y_true, y_score, average="micro"), .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., 0.2, 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, [2., 1., 0.7, 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., 0.1, 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,
|
||
|
|
[2.0, 1.0, 0.9, 0.7, 0.6, 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)
|
||
|
|
|
||
|
|
|
||
|
|
@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])
|
||
|
|
result_unweighted = (out_0 + out_1 + out_2) / 3.
|
||
|
|
|
||
|
|
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(
|
||
|
|
"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 \('macro', 'weighted'\) for "
|
||
|
|
r"multiclass problems"), {"average": "samples", "multi_class": "ovo"}),
|
||
|
|
((r"average must be one of \('macro', 'weighted'\) for "
|
||
|
|
r"multiclass problems"), {"average": "micro", "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='ovp' is not supported for multiclass ROC AUC, "
|
||
|
|
r"multi_class must be in \('ovo', 'ovr'\)"),
|
||
|
|
{"multi_class": "ovp"}),
|
||
|
|
(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)
|
||
|
|
|
||
|
|
|
||
|
|
def test_binary_clf_curve_multiclass_error():
|
||
|
|
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):
|
||
|
|
precision_recall_curve(y_true, y_pred)
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
roc_curve(y_true, y_pred)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("curve_func", [
|
||
|
|
precision_recall_curve,
|
||
|
|
roc_curve,
|
||
|
|
])
|
||
|
|
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):
|
||
|
|
roc_curve(np.array(["a", "b"], dtype='<U1'), [0., 1.])
|
||
|
|
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
roc_curve(np.array(["a", "b"], dtype=object), [0., 1.])
|
||
|
|
|
||
|
|
# The error message is slightly different for bytes-encoded
|
||
|
|
# class labels, but otherwise the behavior is the same:
|
||
|
|
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):
|
||
|
|
roc_curve(np.array([b"a", b"b"], dtype='<S1'), [0., 1.])
|
||
|
|
|
||
|
|
# Check that it is possible to use floating point class labels
|
||
|
|
# that are interpreted similarly to integer class labels:
|
||
|
|
y_pred = [0., 1., 0.2, 0.42]
|
||
|
|
int_curve = roc_curve([0, 1, 1, 0], y_pred)
|
||
|
|
float_curve = roc_curve([0., 1., 1., 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)
|
||
|
|
|
||
|
|
|
||
|
|
def test_precision_recall_curve():
|
||
|
|
y_true, _, probas_pred = make_prediction(binary=True)
|
||
|
|
_test_precision_recall_curve(y_true, probas_pred)
|
||
|
|
|
||
|
|
# 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, probas_pred)
|
||
|
|
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)
|
||
|
|
assert_array_almost_equal(p, np.array([0.5, 0.33333333, 0.5, 1., 1.]))
|
||
|
|
assert_array_almost_equal(r, np.array([1., 0.5, 0.5, 0.5, 0.]))
|
||
|
|
assert_array_almost_equal(t, np.array([1, 2, 3, 4]))
|
||
|
|
assert p.size == r.size
|
||
|
|
assert p.size == t.size + 1
|
||
|
|
|
||
|
|
|
||
|
|
def _test_precision_recall_curve(y_true, probas_pred):
|
||
|
|
# Test Precision-Recall and aread under PR curve
|
||
|
|
p, r, thresholds = precision_recall_curve(y_true, probas_pred)
|
||
|
|
precision_recall_auc = _average_precision_slow(y_true, probas_pred)
|
||
|
|
assert_array_almost_equal(precision_recall_auc, 0.859, 3)
|
||
|
|
assert_array_almost_equal(precision_recall_auc,
|
||
|
|
average_precision_score(y_true, probas_pred))
|
||
|
|
# `_average_precision` is not very precise in case of 0.5 ties: be tolerant
|
||
|
|
assert_almost_equal(_average_precision(y_true, probas_pred),
|
||
|
|
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(probas_pred))
|
||
|
|
assert p.size == r.size
|
||
|
|
assert p.size == thresholds.size + 1
|
||
|
|
|
||
|
|
|
||
|
|
def test_precision_recall_curve_errors():
|
||
|
|
# Contains non-binary labels
|
||
|
|
with pytest.raises(ValueError):
|
||
|
|
precision_recall_curve([0, 1, 2], [[0.0], [1.0], [1.0]])
|
||
|
|
|
||
|
|
|
||
|
|
def test_precision_recall_curve_toydata():
|
||
|
|
with np.errstate(all="raise"):
|
||
|
|
# Binary classification
|
||
|
|
y_true = [0, 1]
|
||
|
|
y_score = [0, 1]
|
||
|
|
p, r, _ = precision_recall_curve(y_true, y_score)
|
||
|
|
auc_prc = average_precision_score(y_true, y_score)
|
||
|
|
assert_array_almost_equal(p, [1, 1])
|
||
|
|
assert_array_almost_equal(r, [1, 0])
|
||
|
|
assert_almost_equal(auc_prc, 1.)
|
||
|
|
|
||
|
|
y_true = [0, 1]
|
||
|
|
y_score = [1, 0]
|
||
|
|
p, r, _ = precision_recall_curve(y_true, y_score)
|
||
|
|
auc_prc = average_precision_score(y_true, y_score)
|
||
|
|
assert_array_almost_equal(p, [0.5, 0., 1.])
|
||
|
|
assert_array_almost_equal(r, [1., 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)
|
||
|
|
auc_prc = average_precision_score(y_true, y_score)
|
||
|
|
assert_array_almost_equal(p, [0.5, 1])
|
||
|
|
assert_array_almost_equal(r, [1., 0])
|
||
|
|
assert_almost_equal(auc_prc, .5)
|
||
|
|
|
||
|
|
y_true = [1, 0]
|
||
|
|
y_score = [1, 0]
|
||
|
|
p, r, _ = precision_recall_curve(y_true, y_score)
|
||
|
|
auc_prc = average_precision_score(y_true, y_score)
|
||
|
|
assert_array_almost_equal(p, [1, 1])
|
||
|
|
assert_array_almost_equal(r, [1, 0])
|
||
|
|
assert_almost_equal(auc_prc, 1.)
|
||
|
|
|
||
|
|
y_true = [1, 0]
|
||
|
|
y_score = [0.5, 0.5]
|
||
|
|
p, r, _ = precision_recall_curve(y_true, y_score)
|
||
|
|
auc_prc = average_precision_score(y_true, y_score)
|
||
|
|
assert_array_almost_equal(p, [0.5, 1])
|
||
|
|
assert_array_almost_equal(r, [1, 0.])
|
||
|
|
assert_almost_equal(auc_prc, .5)
|
||
|
|
|
||
|
|
y_true = [0, 0]
|
||
|
|
y_score = [0.25, 0.75]
|
||
|
|
with pytest.raises(Exception):
|
||
|
|
precision_recall_curve(y_true, y_score)
|
||
|
|
with pytest.raises(Exception):
|
||
|
|
average_precision_score(y_true, y_score)
|
||
|
|
|
||
|
|
y_true = [1, 1]
|
||
|
|
y_score = [0.25, 0.75]
|
||
|
|
p, r, _ = precision_recall_curve(y_true, y_score)
|
||
|
|
assert_almost_equal(average_precision_score(y_true, y_score), 1.)
|
||
|
|
assert_array_almost_equal(p, [1., 1., 1.])
|
||
|
|
assert_array_almost_equal(r, [1, 0.5, 0.])
|
||
|
|
|
||
|
|
# Multi-label classification task
|
||
|
|
y_true = np.array([[0, 1], [0, 1]])
|
||
|
|
y_score = np.array([[0, 1], [0, 1]])
|
||
|
|
with pytest.raises(Exception):
|
||
|
|
average_precision_score(y_true, y_score, average="macro")
|
||
|
|
with pytest.raises(Exception):
|
||
|
|
average_precision_score(y_true, y_score, average="weighted")
|
||
|
|
assert_almost_equal(average_precision_score(y_true, y_score,
|
||
|
|
average="samples"), 1.)
|
||
|
|
assert_almost_equal(average_precision_score(y_true, y_score,
|
||
|
|
average="micro"), 1.)
|
||
|
|
|
||
|
|
y_true = np.array([[0, 1], [0, 1]])
|
||
|
|
y_score = np.array([[0, 1], [1, 0]])
|
||
|
|
with pytest.raises(Exception):
|
||
|
|
average_precision_score(y_true, y_score, average="macro")
|
||
|
|
with pytest.raises(Exception):
|
||
|
|
average_precision_score(y_true, y_score, average="weighted")
|
||
|
|
assert_almost_equal(average_precision_score(y_true, y_score,
|
||
|
|
average="samples"), 0.75)
|
||
|
|
assert_almost_equal(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([[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]])
|
||
|
|
assert_almost_equal(average_precision_score(y_true, y_score,
|
||
|
|
average="weighted"), 1)
|
||
|
|
|
||
|
|
|
||
|
|
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) == .25
|
||
|
|
|
||
|
|
|
||
|
|
def test_average_precision_score_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)
|
||
|
|
# 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_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, _, probas_pred = make_prediction(binary=True)
|
||
|
|
|
||
|
|
roc_auc = roc_auc_score(y_true, probas_pred)
|
||
|
|
roc_auc_scaled_up = roc_auc_score(y_true, 100 * probas_pred)
|
||
|
|
roc_auc_scaled_down = roc_auc_score(y_true, 1e-6 * probas_pred)
|
||
|
|
roc_auc_shifted = roc_auc_score(y_true, probas_pred - 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, probas_pred)
|
||
|
|
pr_auc_scaled_up = average_precision_score(y_true, 100 * probas_pred)
|
||
|
|
pr_auc_scaled_down = average_precision_score(y_true, 1e-6 * probas_pred)
|
||
|
|
pr_auc_shifted = average_precision_score(y_true, probas_pred - 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.
|
||
|
|
assert lrap_score(y_true, y_score_ties) == 1.
|
||
|
|
|
||
|
|
# Only relevant labels
|
||
|
|
y_true = np.ones((1, n_labels))
|
||
|
|
assert lrap_score(y_true, y_score) == 1.
|
||
|
|
assert lrap_score(y_true, y_score_ties) == 1.
|
||
|
|
|
||
|
|
# Degenerate case: only one label
|
||
|
|
assert_almost_equal(lrap_score([[1], [0], [1], [0]],
|
||
|
|
[[0.5], [0.5], [0.5], [0.5]]), 1.)
|
||
|
|
|
||
|
|
|
||
|
|
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.
|
||
|
|
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 trival case
|
||
|
|
assert_almost_equal(coverage_error([[0, 1, 0], [1, 1, 0]],
|
||
|
|
[[0.1, 10., -3], [0, 1, 3]]),
|
||
|
|
(1 + 3) / 2.)
|
||
|
|
|
||
|
|
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.)
|
||
|
|
|
||
|
|
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.)
|
||
|
|
|
||
|
|
|
||
|
|
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)
|
||
|
|
|
||
|
|
|
||
|
|
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 trival case
|
||
|
|
assert_almost_equal(label_ranking_loss([[0, 1, 0], [1, 1, 0]],
|
||
|
|
[[0.1, 10., -3], [0, 1, 3]]),
|
||
|
|
(0 + 2 / 2) / 2.)
|
||
|
|
|
||
|
|
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.)
|
||
|
|
|
||
|
|
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.)
|
||
|
|
|
||
|
|
# Sparse csr matrices
|
||
|
|
assert_almost_equal(label_ranking_loss(
|
||
|
|
csr_matrix(np.array([[0, 1, 0], [1, 1, 0]])),
|
||
|
|
[[0.1, 10, -3], [3, 1, 3]]),
|
||
|
|
(0 + 2 / 2) / 2.)
|
||
|
|
|
||
|
|
|
||
|
|
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_invariant():
|
||
|
|
y_true = np.arange(70).reshape(7, 10)
|
||
|
|
y_score = y_true + np.random.RandomState(0).uniform(
|
||
|
|
-.2, .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.)
|
||
|
|
y_score += 1000
|
||
|
|
assert ndcg_score(y_true, y_score) == pytest.approx(1.)
|
||
|
|
|
||
|
|
|
||
|
|
@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(
|
||
|
|
-.2, .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.)
|
||
|
|
|
||
|
|
|
||
|
|
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([.2, .2, .7, .7]), 1, 1),
|
||
|
|
(np.array([.2, .7, .2, .7]), 1, 0.5),
|
||
|
|
(np.array([.2, .7, .2, .7]), 2, 1),
|
||
|
|
])
|
||
|
|
def test_top_k_accuracy_score_binary(y_score, k, true_score):
|
||
|
|
y_true = [0, 0, 1, 1]
|
||
|
|
|
||
|
|
threshold = .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],
|
||
|
|
])
|
||
|
|
w = UndefinedMetricWarning
|
||
|
|
score = assert_warns(w, top_k_accuracy_score, y_true, y_score, k=k)
|
||
|
|
assert score == 1
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('y_true, labels, msg', [
|
||
|
|
(
|
||
|
|
[0, .57, 1, 2],
|
||
|
|
None,
|
||
|
|
"y type must be 'binary' or 'multiclass', got 'continuous'"
|
||
|
|
),
|
||
|
|
(
|
||
|
|
[0, 1, 2, 3],
|
||
|
|
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'],
|
||
|
|
['a', 'b', 'c', 'c'],
|
||
|
|
"Parameter 'labels' must be unique."
|
||
|
|
),
|
||
|
|
(
|
||
|
|
['c', 'c', 'a', 'b'],
|
||
|
|
['a', 'c', 'b'],
|
||
|
|
"Parameter 'labels' must be ordered."
|
||
|
|
),
|
||
|
|
(
|
||
|
|
[0, 0, 1, 2],
|
||
|
|
[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, 1, 3],
|
||
|
|
"'y_true' contains labels not in parameter 'labels'."
|
||
|
|
),
|
||
|
|
])
|
||
|
|
def test_top_k_accuracy_score_error(y_true, labels, msg):
|
||
|
|
y_score = np.array([
|
||
|
|
[0.2, 0.1, 0.7],
|
||
|
|
[0.4, 0.3, 0.3],
|
||
|
|
[0.3, 0.4, 0.3],
|
||
|
|
[0.4, 0.5, 0.1],
|
||
|
|
])
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
top_k_accuracy_score(y_true, y_score, k=2, labels=labels)
|