8000 FIX Fix recall in multilabel classification when true labels are all negative by varunagrawal · Pull Request #19085 · scikit-learn/scikit-learn · GitHub
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FIX Fix recall in multilabel classification when true labels are all negative #19085

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Merged
merged 11 commits into from
Mar 25, 2022
Merged

FIX Fix recall in multilabel classification when true labels are all negative #19085

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f082b66
add fix for all negative labels in multilabel classification
varunagrawal Dec 31, 2020
5a8939c
Merge branch 'main' into pr-curve-bugfix
varunagrawal Apr 2, 2021
e6e3c1c
Merge branch 'main' into pr-curve-bugfix
varunagrawal Aug 21, 2021
e0d17b4
update changelog
varunagrawal Aug 21, 2021
6619c82
linting
varunagrawal Aug 21, 2021
a134bd2
black formatting
varunagrawal Aug 21, 2021
722706a
Merge branch 'master' into pr/varunagrawal/19085
jeremiedbb Mar 24, 2022
d921a2e
address comments + add warning and comments
jeremiedbb Mar 24, 2022
3dd7785
cln
jeremiedbb Mar 24, 2022
92296ed
address comments
jeremiedbb Mar 24, 2022
20ad4d0
assert warnings
jeremiedbb Mar 25, 2022
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3 changes: 3 additions & 0 deletions doc/whats_new/v1.1.rst
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Expand Up @@ -702,6 +702,9 @@ Changelog
- |Fix| Fixed a bug in :func:`metrics.normalized_mutual_info_score` which could return
unbounded values. :pr:`22635` by :user:`Jérémie du Boisberranger <jeremiedbb>`.

- |Fix| Fixes `precision_recall_curve` and `average_precision_score` when true labels
are all negative. :pr:`19085` by :user:`Varun Agrawal <varunagrawal>`.

:mod:`sklearn.model_selection`
..............................

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18 changes: 14 additions & 4 deletions sklearn/metrics/_ranking.py
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8000
Original file line number Diff line number Diff line change
Expand Up @@ -865,15 +865,25 @@ def precision_recall_curve(y_true, probas_pred, *, pos_label=None, sample_weight
y_true, probas_pred, pos_label=pos_label, sample_weight=sample_weight
)

precision = tps / (tps + fps)
precision[np.isnan(precision)] = 0
recall = tps / tps[-1]
ps = tps + fps
precision = np.divide(tps, ps, where=(ps != 0))

# When no positive label in y_true, recall is set to 1 for all thresholds
# tps[-1] == 0 <=> y_true == all negative labels
if tps[-1] == 0:
warnings.warn(
"No positive class found in y_true, "
"recall is set to one for all thresholds."
)
recall = np.ones_like(tps)
else:
recall = tps / tps[-1]

# stop when full recall attained
# and reverse the outputs so recall is decreasing
last_ind = tps.searchsorted(tps[-1])
sl = slice(last_ind, None, -1)
return np.r_[precision[sl], 1], np.r_[recall[sl], 0], thresholds[sl]
return np.hstack((precision[sl], 1)), np.hstack((recall[sl], 0)), thresholds[sl]


def roc_curve(
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83 changes: 60 additions & 23 deletions sklearn/metrics/tests/test_ranking.py
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Expand Up @@ -915,10 +915,13 @@ def test_precision_recall_curve_toydata():

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)
with pytest.warns(UserWarning, match="No positive class found in y_true"):
p, r, _ = precision_recall_curve(y_true, y_score)
with pytest.warns(UserWarning, match="No positive class found in y_true"):
auc_prc = average_precision_score(y_true, y_score)
assert_allclose(p, [0, 1])
assert_allclose(r, [1, 0])
assert_allclose(auc_prc, 0)

y_true = [1, 1]
y_score = [0.25, 0.75]
Expand All @@ -930,29 +933,33 @@ def test_precision_recall_curve_toydata():
# 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(
with pytest.warns(UserWarning, match="No positive class found in y_true"):
assert_allclose(
average_precision_score(y_true, y_score, average="macro"), 0.5
)
with pytest.warns(UserWarning, match="No positive class found in y_true"):
assert_allclose(
average_precision_score(y_true, y_score, average="weighted"), 1.0
)
assert_allclose(
average_precision_score(y_true, y_score, average="samples"), 1.0
)
assert_almost_equal(
average_precision_score(y_true, y_score, average="micro"), 1.0
)
assert_allclose(average_precision_score(y_true, y_score, average="micro"), 1.0)

y_true = np.array([[0, 1], [0, 1]])
y_score = np.array([[0, 1], [1, 0]])
with pytest.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(
with pytest.warns(UserWarning, match="No positive class found in y_true"):
assert_allclose(
average_precision_score(y_true, y_score, average="macro"), 0.5
)
with pytest.warns(UserWarning, match="No positive class found in y_true"):
assert_allclose(
average_precision_score(y_true, y_score, average="weighted"), 1.0
)
assert_allclose(
average_precision_score(y_true, y_score, average="samples"), 0.75
)
assert_almost_equal(
average_precision_score(y_true, y_score, average="micro"), 0.5
)
assert_allclose(average_precision_score(y_true, y_score, average="micro"), 0.5)

y_true = np.array([[1, 0], [0, 1]])
y_score = np.array([[0, 1], [1, 0]])
Expand All @@ -969,6 +976,35 @@ def test_precision_recall_curve_toydata():
average_precision_score(y_true, y_score, average="micro"), 0.5
)

y_true = np.array([[0, 0], [0, 0]])
y_score = np.array([[0, 1], [0, 1]])
with pytest.warns(UserWarning, match="No positive class found in y_true"):
assert_allclose(
average_precision_score(y_true, y_score, average="macro"), 0.0
)
assert_allclose(
average_precision_score(y_true, y_score, average="weighted"), 0.0
)
with pytest.warns(UserWarning, match="No positive class found in y_true"):
assert_allclose(
average_precision_score(y_true, y_score, average="samples"), 0.0
)
with pytest.warns(UserWarning, match="No positive class found in y_true"):
assert_allclose(
average_precision_score(y_true, y_score, average="micro"), 0.0
)

y_true = np.array([[1, 1], [1, 1]])
y_score = np.array([[0, 1], [0, 1]])
assert_allclose(average_precision_score(y_true, y_score, average="macro"), 1.0)
assert_allclose(
average_precision_score(y_true, y_score, average="weighted"), 1.0
)
assert_allclose(
average_precision_score(y_true, y_score, average="samples"), 1.0
)
assert_allclose(average_precision_score(y_true, y_score, average="micro"), 1.0)

y_true = np.array([[1, 0], [0, 1]])
y_score = np.array([[0.5, 0.5], [0.5, 0.5]])
assert_almost_equal(
Expand All @@ -988,9 +1024,10 @@ def test_precision_recall_curve_toydata():
# 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
)
with pytest.warns(UserWarning, match="No positive class found in y_true"):
assert_allclose(
average_precision_score(y_true, y_score, average="weighted"), 1
)


def test_average_precision_constant_values():
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0