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[MRG] Add sample_weight parameter to cohen_kappa_score (resubmitted from #7569) #8335

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Merged
TomDLT merged 1 commit into from
Feb 10, 2017
Merged

[MRG] Add sample_weight parameter to cohen_kappa_score (resubmitted from #7569) #8335

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3 changes: 3 additions & 0 deletions doc/whats_new.rst
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Expand Up @@ -144,6 +144,9 @@ Enhancements
- Added ability to use sparse matrices in :func:`feature_selection.f_regression`
with ``center=True``. :issue:`8065` by :user:`Daniel LeJeune <acadiansith>`.

- Add ``sample_weight`` parameter to :func:`metrics.cohen_kappa_score` by
Victor Poughon.

Bug fixes
.........

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8 changes: 6 additions & 2 deletions sklearn/metrics/classification.py
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Expand Up @@ -275,7 +275,7 @@ def confusion_matrix(y_true, y_pred, labels=None, sample_weight=None):
return CM


def cohen_kappa_score(y1, y2, labels=None, weights=None):
def cohen_kappa_score(y1, y2, labels=None, weights=None, sample_weight=None):
"""Cohen's kappa: a statistic that measures inter-annotator agreement.

This function computes Cohen's kappa [1]_, a score that expresses the level
Expand Down Expand Up @@ -311,6 +311,9 @@ class labels [2]_.
List of weighting type to calculate the score. None means no weighted;
"linear" means linear weighted; "quadratic" means quadratic weighted.

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

Returns
-------
kappa : float
Expand All @@ -328,7 +331,8 @@ class labels [2]_.
.. [3] `Wikipedia entry for the Cohen's kappa.
<https://en.wikipedia.org/wiki/Cohen%27s_kappa>`_
"""
confusion = confusion_matrix(y1, y2, labels=labels)
confusion = confusion_matrix(y1, y2, labels=labels,
sample_weight=sample_weight)
n_classes = confusion.shape[0]
sum0 = np.sum(confusion, axis=0)
sum1 = np.sum(confusion, axis=1)
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1 change: 0 additions & 1 deletion sklearn/metrics/tests/test_common.py
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Expand Up @@ -369,7 +369,6 @@

# No Sample weight support
METRICS_WITHOUT_SAMPLE_WEIGHT = [
"cohen_kappa_score",
"confusion_matrix", # Left this one here because the tests in this file do
# not work for confusion_matrix, as its output is a
# matrix instead of a number. Testing of
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