8000 [MRG] Bugfix for precision_recall_curve when all labels are negative by varunagrawal · Pull Request #14621 · scikit-learn/scikit-learn · GitHub
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[MRG] Bugfix for precision_recall_curve when all labels are negative #14621

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[MRG] Bugfix for precision_recall_curve when all labels are negative #14621

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2 changes: 1 addition & 1 deletion sklearn/metrics/_ranking.py
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Original file line number Diff line number Diff line change
Expand Up @@ -678,7 +678,7 @@ def precision_recall_curve(y_true, probas_pred, *, pos_label=None,

precision = tps / (tps + fps)
precision[np.isnan(precision)] = 0
recall = tps / tps[-1]
recall = np.ones(tps.size) if tps[-1] == 0 else tps / tps[-1]

# stop when full recall attained
# and reverse the outputs so recall is decreasing
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37 changes: 37 additions & 0 deletions sklearn/metrics/tests/test_ranking.py
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Original file line number Diff line number Diff line change
Expand Up @@ -807,6 +807,11 @@ def test_precision_recall_curve_toydata():
precision_recall_curve(y_true, y_score)
with pytest.raises(Exception):
average_precision_score(y_true, y_score)
# p, r, _ = precision_recall_curve(y_true, y_score)
# auc_prc = average_precision_score(y_true, y_score)
# assert_array_almost_equal(p, [0, 1])
# assert_array_almost_equal(r, [1, 0.])
# assert_almost_equal(auc_prc, 0.)

y_true = [1, 1]
y_score = [0.25, 0.75]
Expand All @@ -822,6 +827,10 @@ def test_precision_recall_curve_toydata():
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="macro"), 0.5)
# assert_almost_equal(average_precision_score(y_true, y_score,
# average="weighted"), 1.)
assert_almost_equal(average_precision_score(y_true, y_score,
average="samples"), 1.)
assert_almost_equal(average_precision_score(y_true, y_score,
Expand All @@ -833,6 +842,10 @@ def test_precision_recall_curve_toydata():
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="macro"), 0.5)
# assert_almost_equal(average_precision_score(y_true, y_score,
# average="weighted"), 1.)
assert_almost_equal(average_precision_score(y_true, y_score,
average="samples"), 0.75)
assert_almost_equal(average_precision_score(y_true, y_score,
Expand Down Expand Up @@ -860,12 +873,36 @@ def test_precision_recall_curve_toydata():
assert_almost_equal(average_precision_score(y_true, y_score,
average="micro"), 0.5)

<<<<<<< HEAD
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)
=======
y_true = np.array([[0, 0], [0, 0]])
y_score = np.array([[0, 1], [0, 1]])
assert_almost_equal(average_precision_score(y_true, y_score,
average="macro"), 0.)
assert_almost_equal(average_precision_score(y_true, y_score,
average="weighted"), 0.)
assert_almost_equal(average_precision_score(y_true, y_score,
average="samples"), 0.)
assert_almost_equal(average_precision_score(y_true, y_score,
average="micro"), 0.)

y_true = np.array([[1, 1], [1, 1]])
y_score = np.array([[0, 1], [0, 1]])
assert_almost_equal(average_precision_score(y_true, y_score,
average="macro"), 1.)
assert_almost_equal(average_precision_score(y_true, y_score,
average="weighted"), 1.)
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.)
>>>>>>> fixed bug for precision recall curve when all labels are negative


def test_average_precision_constant_values():
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