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Move comment on relationship between labels and pos_label to docstring
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@jnothman
jnothman committed Sep 1, 2016
1 parent 367d73f commit 2b11962
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Showing 2 changed files with 17 additions and 6 deletions.
19 changes: 16 additions & 3 deletions sklearn/metrics/classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -624,6 +624,9 @@ def f1_score(y_true, y_pred, labels=None, pos_label=1, average='binary',
pos_label : str or int, 1 by default
The class to report if ``average='binary'`` and the data is binary.
If the data are multiclass or multilabel, this will be ignored;
setting `labels=[pos_label]` and `average != 'binary'` will report
scores for that label only.
average : string, [None, 'binary' (default), 'micro', 'macro', 'samples', \
'weighted']
Expand Down Expand Up @@ -724,6 +727,9 @@ def fbeta_score(y_true, y_pred, beta, labels=None, pos_label=1,
pos_label : str or int, 1 by default
The class to report if ``average='binary'`` and the data is binary.
If the data are multiclass or multilabel, this will be ignored;
setting `labels=[pos_label]` and `average != 'binary'` will report
scores for that label only.
average : string, [None, 'binary' (default), 'micro', 'macro', 'samples', \
'weighted']
Expand Down Expand Up @@ -894,6 +900,9 @@ def precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None,
pos_label : str or int, 1 by default
The class to report if ``average='binary'`` and the data is binary.
If the data are multiclass or multilabel, this will be ignored;
setting `labels=[pos_label]` and `average != 'binary'` will report
scores for that label only.
average : string, [None (default), 'binary', 'micro', 'macro', 'samples', \
'weighted']
Expand Down Expand Up @@ -1002,9 +1011,7 @@ def precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None,
labels = [pos_label]
else:
raise ValueError("Target is %s but average='binary'. Please "
"choose another average setting. You may then "
"use labels=[pos_label] to specify a single "
"positive class." % y_type)
"choose another average setting." % y_type)
elif pos_label not in (None, 1):
warnings.warn("Note that pos_label (set to %r) is ignored when "
"average != 'binary' (got %r). You may use "
Expand Down Expand Up @@ -1164,6 +1171,9 @@ def precision_score(y_true, y_pred, labels=None, pos_label=1,
pos_label : str or int, 1 by default
The class to report if ``average='binary'`` and the data is binary.
If the data are multiclass or multilabel, this will be ignored;
setting `labels=[pos_label]` and `average != 'binary'` will report
scores for that label only.
average : string, [None, 'binary' (default), 'micro', 'macro', 'samples', \
'weighted']
Expand Down Expand Up @@ -1260,6 +1270,9 @@ def recall_score(y_true, y_pred, labels=None, pos_label=1, average='binary',
pos_label : str or int, 1 by default
The class to report if ``average='binary'`` and the data is binary.
If the data are multiclass or multilabel, this will be ignored;
setting `labels=[pos_label]` and `average != 'binary'` will report
scores for that label only.
average : string, [None, 'binary' (default), 'micro', 'macro', 'samples', \
'weighted']
Expand Down
4 changes: 1 addition & 3 deletions sklearn/metrics/tests/test_classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1159,9 +1159,7 @@ def test_prf_average_binary_data_non_binary():
partial(fbeta_score, beta=2)]:
assert_raise_message(ValueError,
"Target is %s but average='binary'. Please "
"choose another average setting. You may "
"then use labels=[pos_label] to specify a "
"single positive class." % y_type,
"choose another average setting." % y_type,
metric, y_true, y_pred)


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