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Implemented step-function interpolation for average_precision_score #6065

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Implemented step-function interpolation for average_precision_score #6065

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9 changes: 7 additions & 2 deletions sklearn/metrics/ranking.py
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Original file line number Diff line number Diff line change
Expand Up @@ -109,6 +109,10 @@ def average_precision_score(y_true, y_score, average="macro",
"""Compute average precision (AP) from prediction scores

This score corresponds to the area under the precision-recall curve.
Interpolation between operating points is by horizontal sections.
For all recall values r not corresponding to an operating point, the
precision p is equal to the precision p' at the first operating point
(r', p') such that r' > r.

Note: this implementation is restricted to the binary classification task
or multilabel classification task.
Expand Down Expand Up @@ -166,13 +170,14 @@ def average_precision_score(y_true, y_score, average="macro",
>>> y_true = np.array([0, 0, 1, 1])
>>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
>>> average_precision_score(y_true, y_scores) # doctest: +ELLIPSIS
0.79...
0.83...

"""
def _binary_average_precision(y_true, y_score, sample_weight=None):
precision, recall, thresholds = precision_recall_curve(
y_true, y_score, sample_weight=sample_weight)
return auc(recall, precision)
return sum([(recall[i] - recall[i+1]) * precision[i]
for i in xrange(len(recall) - 1)])

return _average_binary_score(_binary_average_precision, y_true, y_score,
average, sample_weight=sample_weight)
Expand Down