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[MRG+1] Deprecating the use of size_threshold parameter in manhattan_distances #9295

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Merged
merged 4 commits into from
Jul 13, 2017
Merged

[MRG+1] Deprecating the use of size_threshold parameter in manhattan_distances #9295

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dc13fd2
deprecating size_threshold in manhattan_distances
pravarmahajan Jul 7, 2017
de451cb
fixing a minor pep8 error
pravarmahajan Jul 7, 2017
9ddcf82
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn…
pravarmahajan Jul 13, 2017
49a49f1
version number for deprecation
pravarmahajan Jul 13, 2017
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7 changes: 6 additions & 1 deletion sklearn/metrics/pairwise.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@

import itertools
from functools import partial
import warnings

import numpy as np
from scipy.spatial import distance
Expand Down Expand Up @@ -467,7 +468,7 @@ def pairwise_distances_argmin(X, Y, axis=1, metric="euclidean",


def manhattan_distances(X, Y=None, sum_over_features=True,
size_threshold=5e8):
size_threshold=None):
""" Compute the L1 distances between the vectors in X and Y.

With sum_over_features equal to False it returns the componentwise
Expand Down Expand Up @@ -520,6 +521,10 @@ def manhattan_distances(X, Y=None, sum_over_features=True,
array([[ 1., 1.],
[ 1., 1.]]...)
"""
if size_threshold is not None:
warnings.warn('Use of the "size_threshold" is deprecated '
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can you please explicitly mention the version in which it is deprecated (0.19) and the one in which it will be removed (0.21)

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Sure, will do it.

'in 0.19 and it will be removed version '
'0.21 of scikit-learn', DeprecationWarning)
X, Y = check_pairwise_arrays(X, Y)

if issparse(X) or issparse(Y):
Expand Down
9 changes: 5 additions & 4 deletions sklearn/metrics/tests/test_pairwise.py
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Expand Up @@ -12,6 +12,7 @@
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raises_regexp
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import ignore_warnings

from sklearn.externals.six import iteritems
Expand Down Expand Up @@ -74,10 +75,10 @@ def test_pairwise_distances():
assert_equal(S.shape[0], X.shape[0])
assert_equal(S.shape[1], Y.shape[0])
assert_array_almost_equal(S, S2)
# Low-level function for manhattan can divide in blocks to avoid
# using too much memory during the broadcasting
S3 = manhattan_distances(X, Y, size_threshold=10)
assert_array_almost_equal(S, S3)
# Using size_threshold argument should raise
# a deprecation warning
assert_warns(DeprecationWarning,
manhattan_distances, X, Y, size_threshold=10)
# Test cosine as a string metric versus cosine callable
# The string "cosine" uses sklearn.metric,
# while the function cosine is scipy.spatial
Expand Down