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[MRG+1] Add sample_weight parameter to metrics.jaccard_similarity_score #3497

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[MRG+1] Add sample_weight parameter to metrics.jaccard_similarity_score #3497

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c0225ab
Add sample_weight parameter to metrics.jaccard_similarity_score
jatinshah Jul 29, 2014
6a2c60c
Correcting for PR comments
jatinshah Jul 29, 2014
a459d93
Added What's New
jatinshah Jul 29, 2014
8eb264d
Merge remote-tracking branch 'upstream/master'
jatinshah Jul 29, 2014
8f8c607
Sample weight invariance tests check for almost equal
jatinshah Jul 30, 2014
5c16096
Correcting for PR comments
jatinshah Jul 30, 2014
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4 changes: 4 additions & 0 deletions doc/whats_new.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,10 @@ Enhancements
descent for :class:`linear_model.Lasso`, :class:`linear_model.ElasticNet`
and related. By `Manoj Kumar`_.

- Add ``sample_weight`` parameter to `metrics.jaccard_similarity_score`.
By `Jatin Shah`.



Documentation improvements
..........................
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14 changes: 11 additions & 3 deletions sklearn/metrics/classification.py
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Expand Up @@ -15,6 +15,7 @@
# Lars Buitinck <[email protected]>
# Joel Nothman <[email protected]>
# Noel Dawe <[email protected]>
# Jatin Shah <[email protected]>
# License: BSD 3 clause

from __future__ import division
Expand Down Expand Up @@ -237,7 +238,8 @@ def confusion_matrix(y_true, y_pred, labels=None):
return CM


def jaccard_similarity_score(y_true, y_pred, normalize=True):
def jaccard_similarity_score(y_true, y_pred, normalize=True,
sample_weight=None):
"""Jaccard similarity coefficient score

The Jaccard index [1], or Jaccard similarity coefficient, defined as
Expand All @@ -258,6 +260,9 @@ def jaccard_similarity_score(y_true, y_pred, normalize=True):
over the sample set. Otherwise, return the average of Jaccard
similarity coefficient.

sample_weight : array-like of shape = [n_samples], optional
Sample weights.

Returns
-------
score : float
Expand Down Expand Up @@ -340,9 +345,12 @@ def jaccard_similarity_score(y_true, y_pred, normalize=True):
score = y_true == y_pred

if normalize:
return np.mean(score)
return np.average(score, weights=sample_weight)
else:
return np.sum(score)
if sample_weight is not None:
return np.dot(score, sample_weight)
else:
return np.sum(score)


def matthews_corrcoef(y_true, y_pred):
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9 changes: 4 additions & 5 deletions sklearn/metrics/tests/test_common.py
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Expand Up @@ -315,7 +315,6 @@
"confusion_matrix",
"hamming_loss",
"hinge_loss",
"jaccard_similarity_score", "unnormalized_jaccard_similarity_score",
"log_loss",
"matthews_corrcoef_score",
]
Expand Down Expand Up @@ -904,10 +903,10 @@ def check_sample_weight_invariance(name, metric, y1, y2):

# check that unit weights gives the same score as no weight
unweighted_score = metric(y1, y2, sample_weight=None)
assert_equal(
assert_almost_equal(
unweighted_score,
metric(y1, y2, sample_weight=np.ones(shape=len(y1))),
msg="For %s sample_weight=None is not equivalent to "
err_msg="For %s sample_weight=None is not equivalent to "
"sample_weight=ones" % name)

# check that the weighted and unweighted scores are unequal
Expand All @@ -920,9 +919,9 @@ def check_sample_weight_invariance(name, metric, y1, y2):
# check that sample_weight can be a list
weighted_score_list = metric(y1, y2,
sample_weight=sample_weight.tolist())
assert_equal(
assert_almost_equal(
weighted_score, weighted_score_list,
msg="Weighted scores for array and list sample_weight input are "
err_msg="Weighted scores for array and list sample_weight input are "
"not equal (%f != %f) for %s" % (
weighted_score, weighted_score_list, name))

Expand Down