10000 updated tests · scikit-learn/scikit-learn@50fbd04 · GitHub
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varunagrawalvarunagrawal
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updated tests
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‎sklearn/metrics/ranking.py

Lines changed: 0 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -412,7 +412,6 @@ def precision_recall_curve(y_true, probas_pred, pos_label=None,
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sample_weight=sample_weight)
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precision = tps / (tps + fps)
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# recall = tps / tps[-1]
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recall = np.ones(tps.size) if tps[-1] == 0 else tps / tps[-1]
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# stop when full recall attained

‎sklearn/metrics/tests/test_ranking.py

Lines changed: 17 additions & 42 deletions
Original file line numberDiff line numberDiff line change
@@ -536,10 +536,11 @@ def test_precision_recall_curve_toydata():
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assert_array_almost_equal(r, [1, 0.])
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assert_almost_equal(auc_prc, .75)
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# y_true = [0, 0]
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# y_score = [0.25, 0.75]
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# assert_raises(Exception, precision_recall_curve, y_true, y_score)
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# assert_raises(Exception, average_precision_score, y_true, y_score)
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y_true = [0, 0]
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y_score = [0.25, 0.75]
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p, r, _ = precision_recall_curve(y_true, y_score)
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assert_array_equal(p, np.array([0.0, 1.0]))
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assert_array_equal(r, np.array([1.0, 0.0]))
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y_true = [1, 1]
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y_score = [0.25, 0.75]
@@ -549,24 +550,21 @@ def test_precision_recall_curve_toydata():
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assert_array_almost_equal(r, [1, 0.5, 0.])
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# Multi-label classification task
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# y_true = np.array([[0, 1], [0, 1]])
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# y_score = np.array([[0, 1], [0, 1]])
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# assert_raises(Exception, average_precision_score, y_true, y_score,
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# average="macro")
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# assert_raises(Exception, average_precision_score, y_true, y_score,
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# average="weighted")
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# assert_almost_equal(average_precision_score(y_true, y_score,
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# average="samples"), 1.)
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# assert_almost_equal(average_precision_score(y_true, y_score,
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# average="micro"), 1.)
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#
553+
y_true = np.array([[0, 1], [0, 1]])
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y_score = np.array([[0, 1], [0, 1]])
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assert_almost_equal(average_precision_score(y_true, y_score,
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average="macro"), 0.75)
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assert_almost_equal(average_precision_score(y_true, y_score,
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average="weighted"), 1.0)
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assert_almost_equal(average_precision_score(y_true, y_score,
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average="samples"), 1.)
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assert_almost_equal(average_precision_score(y_true, y_score,
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average="micro"), 1.)
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y_true = np.array([[0, 1], [0, 1]])
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y_score = np.array([[0, 1], [1, 0]])
566-
# assert_raises(Exception, average_precision_score, y_true, y_score,
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# average="macro")
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# assert_raises(Exception, average_precision_score, y_true, y_score,
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# average="weighted")
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assert_almost_equal(average_precision_score(y_true, y_score, average="macro"), 0.75)
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assert_almost_equal(average_precision_score(y_true, y_score, average="weighted"), 1.0)
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assert_almost_equal(average_precision_score(y_true, y_score,
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average="samples"), 0.625)
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assert_almost_equal(average_precision_score(y_true, y_score,
@@ -980,26 +978,3 @@ def test_ranking_loss_ties_handling():
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assert_almost_equal(label_ranking_loss([[1, 0, 1]], [[0.25, 0.5, 0.5]]), 1)
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assert_almost_equal(label_ranking_loss([[1, 1, 0]], [[0.25, 0.5, 0.5]]), 1)
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984-
def test_precision_recall_curve_all_negatives():
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"""
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Test edge case for `precision_recall_curve`
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if all the ground truth labels are negative.
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Precision values should not be `nan`.
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"""
990-
y_true = [0 for _ in range(10)]
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probas_pred = [np.random.rand() for _ in range(10)]
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_, recall, _ = precision_recall_curve(y_true, probas_pred)
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assert_not_equal(recall[0], np.nan)
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def test_precision_recall_curve_all_positives():
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"""
998 5E5D -
Test edge case for `precision_recall_curve`
999-
if all the ground truth labels are positive.
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"""
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y_true = [1 for _ in range(10)]
1002-
probas_pred = [np.random.rand() for _ in range(10)]
1003-
precision, _, _ = precision_recall_curve(y_true, probas_pred)
1004-
1005-
assert_array_equal(precision, [1.0 for _ in range(len(precision))])

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