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Fix parallel backend neighbors #12172

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Merged
merged 4 commits into from
Sep 27, 2018
Merged

Fix parallel backend neighbors #12172

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6dd446f
FIX issue #12171 parallel_backend in neighbors
tomMoral Sep 26, 2018
6cfc09d
TST add a non-regression test for parallel backends in neighbors
tomMoral Sep 26, 2018
42d03ca
CLN pep8
tomMoral Sep 26, 2018
b9c3fcb
DOC add whats_new entry
tomMoral Sep 27, 2018
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5 changes: 5 additions & 0 deletions doc/whats_new/v0.20.rst
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Expand Up @@ -23,6 +23,11 @@ enhancements to features released in 0.20.0.
those estimators as part of parallel parameter search or cross-validation.
:issue:`12122` by :user:`Olivier Grisel <ogrisel>`.

- |Fix| force the parallelism backend to :code:`threading` for
:class:`neighbors.KDTree` and :class:`neighbors.BallTree` in Python 2.7 to
avoid pickling errors caused by the serialization of their methods.
:issue:`12171` by :user:`Thomas Moreau <tomMoral>`

.. _changes_0_20:

Version 0.20.0
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4 changes: 3 additions & 1 deletion sklearn/neighbors/base.py
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Expand Up @@ -9,6 +9,7 @@
from functools import partial
from distutils.version import LooseVersion

import sys
import warnings
from abc import ABCMeta, abstractmethod

Expand Down Expand Up @@ -429,7 +430,8 @@ class from an array representing our data set and ask who's
raise ValueError(
"%s does not work with sparse matrices. Densify the data, "
"or set algorithm='brute'" % self._fit_method)
if LooseVersion(joblib_version) < LooseVersion('0.12'):
if (sys.version_info < (3,) or
LooseVersion(joblib_version) < LooseVersion('0.12')):
# Deal with change of API in joblib
delayed_query = delayed(self._tree.query,
check_pickle=False)
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21 changes: 21 additions & 0 deletions sklearn/neighbors/tests/test_neighbors.py
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Expand Up @@ -27,6 +27,8 @@
from sklearn.utils.testing import ignore_warnings
from sklearn.utils.validation import check_random_state

from sklearn.externals.joblib import parallel_backend

rng = np.random.RandomState(0)
# load and shuffle iris dataset
iris = datasets.load_iris()
Expand Down Expand Up @@ -1316,6 +1318,25 @@ def test_same_radius_neighbors_parallel(algorithm):
assert_array_almost_equal(graph, graph_parallel)


@pytest.mark.parametrize('backend', ['loky', 'multiprocessing', 'threading'])
@pytest.mark.parametrize('algorithm', ALGORITHMS)
def test_knn_forcing_backend(backend, algorithm):
# Non-regression test which ensure the knn methods are properly working
# even when forcing the global joblib backend.
with parallel_backend(backend):
X, y = datasets.make_classification(n_samples=30, n_features=5,
n_redundant=0, random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y)

clf = neighbors.KNeighborsClassifier(n_neighbors=3,
algorithm=algorithm,
n_jobs=3)
clf.fit(X_train, y_train)
clf.predict(X_test)
clf.kneighbors(X_test)
clf.kneighbors_graph(X_test, mode='distance').toarray()


def test_dtype_convert():
classifier = neighbors.KNeighborsClassifier(n_neighbors=1)
CLASSES = 15
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