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Commit edda6eef authored by Roman Yurchak's avatar Roman Yurchak
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More fixes for joblib

parent b93fcc47
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.circleci/config.yml
View file @ edda6eef
...@@ -27,8 +27,8 @@ jobs: ...@@ -27,8 +27,8 @@ jobs:
# download scipy package from https://github.com/iodide-project/pyodide/pull/211 # download scipy package from https://github.com/iodide-project/pyodide/pull/211
mkdir -p build mkdir -p build
wget -q -O build/scipy.js https://1463-122663163-gh.circle-artifacts.com/0/home/circleci/repo/build/scipy.js wget -q -O build/scipy.js https://1535-122663163-gh.circle-artifacts.com/0/home/circleci/repo/build/scipy.js
wget -q -O build/scipy.data https://1463-122663163-gh.circle-artifacts.com/0/home/circleci/repo/build/scipy.data wget -q -O build/scipy.data https://1535-122663163-gh.circle-artifacts.com/0/home/circleci/repo/build/scipy.data
ccache -z ccache -z
make make
......
packages/scikit-learn/meta.yaml
View file @ edda6eef
...@@ -7,8 +7,8 @@ source: ...@@ -7,8 +7,8 @@ source:
sha256: 97d1d971f8ec257011e64b7d655df68081dd3097322690afa1a71a1d755f8c18 sha256: 97d1d971f8ec257011e64b7d655df68081dd3097322690afa1a71a1d755f8c18
patches: patches:
#- patches/unvendor-joblib.patch
- patches/use-site-joblib.patch - patches/use-site-joblib.patch
- patches/support-joblib-011.patch
build: build:
cflags: -Wno-implicit-function-declaration cflags: -Wno-implicit-function-declaration
...@@ -21,9 +21,39 @@ requirements: ...@@ -21,9 +21,39 @@ requirements:
test: test:
imports: imports:
- sklearn - sklearn
- sklearn.calibration
- sklearn.cluster - sklearn.cluster
- sklearn.compose - sklearn.compose
- sklearn.covariance - sklearn.covariance
- sklearn.cross_decomposition - sklearn.cross_decomposition
- sklearn.datasets - sklearn.datasets
- sklearn.decomposition - sklearn.decomposition
- sklearn.discriminant_analysis
- sklearn.dummy
- sklearn.ensemble
- sklearn.exceptions
- sklearn.externals
- sklearn.feature_extraction
- sklearn.feature_selection
- sklearn.gaussian_process
- sklearn.impute
- sklearn.isotonic
- sklearn.kernel_approximation
- sklearn.kernel_ridge
- sklearn.linear_model
- sklearn.manifold
- sklearn.metrics
- sklearn.mixture
- sklearn.model_selection
- sklearn.multiclass
- sklearn.multioutput
- sklearn.naive_bayes
- sklearn.neighbors
- sklearn.neural_network
- sklearn.pipeline
- sklearn.preprocessing
- sklearn.random_projection
- sklearn.semi_supervised
- sklearn.svm
- sklearn.tree
- sklearn.utils
packages/scikit-learn/patches/support-joblib-011.patch 0 → 100644
View file @ edda6eef
commit 09954a12bdcef544e940a9b4f661dd7210e2357d
Author: Joel Nothman <joel.nothman@gmail.com>
Date: Wed Nov 7 08:10:23 2018 +1100
BLD we should ensure continued support for joblib 0.11 (#12350)
diff --git a/sklearn/ensemble/forest.py b/sklearn/ensemble/forest.py
index 542f7ca80..e54f131a4 100644
--- a/sklearn/ensemble/forest.py
+++ b/sklearn/ensemble/forest.py
@@ -50,7 +50,6 @@ import numpy as np
from scipy.sparse import issparse
from scipy.sparse import hstack as sparse_hstack
-
from ..base import ClassifierMixin, RegressorMixin
from ..utils import Parallel, delayed
from ..externals import six
@@ -62,7 +61,7 @@ from ..tree._tree import DTYPE, DOUBLE
from ..utils import check_random_state, check_array, compute_sample_weight
from ..exceptions import DataConversionWarning, NotFittedError
from .base import BaseEnsemble, _partition_estimators
-from ..utils.fixes import parallel_helper
+from ..utils.fixes import parallel_helper, _joblib_parallel_args
from ..utils.multiclass import check_classification_targets
from ..utils.validation import check_is_fitted
@@ -175,7 +174,7 @@ class BaseForest(six.with_metaclass(ABCMeta, BaseEnsemble)):
"""
X = self._validate_X_predict(X)
results = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
- prefer="threads")(
+ **_joblib_parallel_args(prefer="threads"))(
delayed(parallel_helper)(tree, 'apply', X, check_input=False)
for tree in self.estimators_)
@@ -206,7 +205,7 @@ class BaseForest(six.with_metaclass(ABCMeta, BaseEnsemble)):
"""
X = self._validate_X_predict(X)
indicators = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
- prefer="threads")(
+ **_joblib_parallel_args(prefer='threads'))(
delayed(parallel_helper)(tree, 'decision_path', X,
check_input=False)
for tree in self.estimators_)
@@ -324,11 +323,11 @@ class BaseForest(six.with_metaclass(ABCMeta, BaseEnsemble)):
# Parallel loop: we prefer the threading backend as the Cython code
# for fitting the trees is internally releasing the Python GIL
# making threading more efficient than multiprocessing in
- # that case. However, we respect any parallel_backend contexts set
- # at a higher level, since correctness does not rely on using
- # threads.
+ # that case. However, for joblib 0.12+ we respect any
+ # parallel_backend contexts set at a higher level,
+ # since correctness does not rely on using threads.
trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
- prefer="threads")(
+ **_joblib_parallel_args(prefer='threads'))(
delayed(_parallel_build_trees)(
t, self, X, y, sample_weight, i, len(trees),
verbose=self.verbose, class_weight=self.class_weight)
@@ -375,7 +374,7 @@ class BaseForest(six.with_metaclass(ABCMeta, BaseEnsemble)):
check_is_fitted(self, 'estimators_')
all_importances = Parallel(n_jobs=self.n_jobs,
- prefer="threads")(
+ **_joblib_parallel_args(prefer='threads'))(
delayed(getattr)(tree, 'feature_importances_')
for tree in self.estimators_)
@@ -591,7 +590,8 @@ class ForestClassifier(six.with_metaclass(ABCMeta, BaseForest,
all_proba = [np.zeros((X.shape[0], j), dtype=np.float64)
for j in np.atleast_1d(self.n_classes_)]
lock = threading.Lock()
- Parallel(n_jobs=n_jobs, verbose=self.verbose, require="sharedmem")(
+ Parallel(n_jobs=n_jobs, verbose=self.verbose,
+ **_joblib_parallel_args(require="sharedmem"))(
delayed(_accumulate_prediction)(e.predict_proba, X, all_proba,
lock)
for e in self.estimators_)
@@ -699,7 +699,8 @@ class ForestRegressor(six.with_metaclass(ABCMeta, BaseForest, RegressorMixin)):
# Parallel loop
lock = threading.Lock()
- Parallel(n_jobs=n_jobs, verbose=self.verbose, require="sharedmem")(
+ Parallel(n_jobs=n_jobs, verbose=self.verbose,
+ **_joblib_parallel_args(require="sharedmem"))(
delayed(_accumulate_prediction)(e.predict, X, [y_hat], lock)
for e in self.estimators_)
diff --git a/sklearn/ensemble/tests/test_forest.py b/sklearn/ensemble/tests/test_forest.py
index f12f6f886..c08e5cc32 100644
--- a/sklearn/ensemble/tests/test_forest.py
+++ b/sklearn/ensemble/tests/test_forest.py
@@ -10,6 +10,7 @@ Testing for the forest module (sklearn.ensemble.forest).
import pickle
from collections import defaultdict
+from distutils.version import LooseVersion
import itertools
from itertools import combinations
from itertools import product
@@ -21,6 +22,7 @@ from scipy.sparse import coo_matrix
import pytest
+from sklearn.utils import _joblib
from sklearn.utils import parallel_backend
from sklearn.utils import register_parallel_backend
try:
@@ -1287,6 +1289,8 @@ class MyBackend(LokyBackend):
register_parallel_backend('testing', MyBackend)
+@pytest.mark.skipif(_joblib.__version__ < LooseVersion('0.12'),
+ reason='tests not yet supported in joblib <0.12')
def test_backend_respected():
clf = RandomForestClassifier(n_estimators=10, n_jobs=2)
diff --git a/sklearn/linear_model/coordinate_descent.py b/sklearn/linear_model/coordinate_descent.py
index 2d0723944..4d0ded532 100644
--- a/sklearn/linear_model/coordinate_descent.py
+++ b/sklearn/linear_model/coordinate_descent.py
@@ -22,6 +22,7 @@ from ..utils import Parallel, delayed, effective_n_jobs
from ..externals import six
from ..externals.six.moves import xrange
from ..utils.extmath import safe_sparse_dot
+from ..utils.fixes import _joblib_parallel_args
from ..utils.validation import check_is_fitted
from ..utils.validation import column_or_1d
from ..exceptions import ConvergenceWarning
@@ -1203,7 +1204,7 @@ class LinearModelCV(six.with_metaclass(ABCMeta, LinearModel)):
for this_l1_ratio, this_alphas in zip(l1_ratios, alphas)
for train, test in folds)
mse_paths = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
- prefer="threads")(jobs)
+ **_joblib_parallel_args(prefer="threads"))(jobs)
mse_paths = np.reshape(mse_paths, (n_l1_ratio, len(folds), -1))
mean_mse = np.mean(mse_paths, axis=1)
self.mse_path_ = np.squeeze(np.rollaxis(mse_paths, 2, 1))
diff --git a/sklearn/linear_model/logistic.py b/sklearn/linear_model/logistic.py
index 01a4f78ab..091c13c1f 100644
--- a/sklearn/linear_model/logistic.py
+++ b/sklearn/linear_model/logistic.py
@@ -33,6 +33,7 @@ from ..exceptions import (NotFittedError, ConvergenceWarning,
ChangedBehaviorWarning)
from ..utils.multiclass import check_classification_targets
from ..utils import Parallel, delayed, effective_n_jobs
+from ..utils.fixes import _joblib_parallel_args
from ..model_selection import check_cv
from ..externals import six
from ..metrics import get_scorer
@@ -1346,7 +1347,7 @@ class LogisticRegression(BaseEstimator, LinearClassifierMixin,
else:
prefer = 'processes'
fold_coefs_ = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
- prefer=prefer)(
+ **_joblib_parallel_args(prefer=prefer))(
path_func(X, y, pos_class=class_, Cs=[self.C],
fit_intercept=self.fit_intercept, tol=self.tol,
verbose=self.verbose, solver=solver,
@@ -1775,7 +1776,7 @@ class LogisticRegressionCV(LogisticRegression, BaseEstimator,
else:
prefer = 'processes'
fold_coefs_ = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
- prefer=prefer)(
+ **_joblib_parallel_args(prefer=prefer))(
path_func(X, y, train, test, pos_class=label, Cs=self.Cs,
fit_intercept=self.fit_intercept, penalty=self.penalty,
dual=self.dual, solver=solver, tol=self.tol,
diff --git a/sklearn/linear_model/stochastic_gradient.py b/sklearn/linear_model/stochastic_gradient.py
index 5e253003a..236bc10a9 100644
--- a/sklearn/linear_model/stochastic_gradient.py
+++ b/sklearn/linear_model/stochastic_gradient.py
@@ -33,6 +33,7 @@ from .sgd_fast import SquaredLoss
from .sgd_fast import Huber
from .sgd_fast import EpsilonInsensitive
from .sgd_fast import SquaredEpsilonInsensitive
+from ..utils.fixes import _joblib_parallel_args
LEARNING_RATE_TYPES = {"constant": 1, "optimal": 2, "invscaling": 3,
"adaptive": 4, "pa1": 5, "pa2": 6}
@@ -613,8 +614,8 @@ class BaseSGDClassifier(six.with_metaclass(ABCMeta, BaseSGD,
strategy is called OVA: One Versus All.
"""
# Use joblib to fit OvA in parallel.
- result = Parallel(n_jobs=self.n_jobs, prefer="threads",
- verbose=self.verbose)(
+ result = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
+ **_joblib_parallel_args(require="sharedmem"))(
delayed(fit_binary)(self, i, X, y, alpha, C, learning_rate,
max_iter, self._expanded_class_weight[i],
1., sample_weight)
diff --git a/sklearn/linear_model/tests/test_sgd.py b/sklearn/linear_model/tests/test_sgd.py
index 15e29ce4d..7ad29e2c0 100644
--- a/sklearn/linear_model/tests/test_sgd.py
+++ b/sklearn/linear_model/tests/test_sgd.py
@@ -1,4 +1,5 @@
+from distutils.version import LooseVersion
import pickle
import unittest
import pytest
@@ -28,6 +29,13 @@ from sklearn.preprocessing import StandardScaler
from sklearn.exceptions import ConvergenceWarning
from sklearn.model_selection import train_test_split
from sklearn.linear_model import sgd_fast
+from sklearn.model_selection import RandomizedSearchCV
+from sklearn.utils import _joblib
+
+
+# 0.23. warning about tol not having its correct default value.
+pytestmark = pytest.mark.filterwarnings(
+ "ignore:max_iter and tol parameters have been")
class SparseSGDClassifier(SGDClassifier):
@@ -1471,3 +1479,31 @@ def test_gradient_squared_epsilon_insensitive():
(2.0, 2.2, -0.2), (-2.0, 1.0, -5.8)
]
_test_gradient_common(loss, cases)
+
+
+def test_multi_thread_multi_class_and_early_stopping():
+ # This is a non-regression test for a bad interaction between
+ # early stopping internal attribute and thread-based parallelism.
+ clf = SGDClassifier(alpha=1e-3, tol=1e-3, max_iter=1000,
+ early_stopping=True, n_iter_no_change=100,
+ random_state=0, n_jobs=2)
+ clf.fit(iris.data, iris.target)
+ assert clf.n_iter_ > clf.n_iter_no_change
+ assert clf.n_iter_ < clf.n_iter_no_change + 20
+ assert clf.score(iris.data, iris.target) > 0.8
+
+
+def test_multi_core_gridsearch_and_early_stopping():
+ # This is a non-regression test for a bad interaction between
+ # early stopping internal attribute and process-based multi-core
+ # parallelism.
+ param_grid = {
+ 'alpha': np.logspace(-4, 4, 9),
+ 'n_iter_no_change': [5, 10, 50],
+ }
+ clf = SGDClassifier(tol=1e-3, max_iter=1000, early_stopping=True,
+ random_state=0)
+ search = RandomizedSearchCV(clf, param_grid, n_iter=10, cv=5, n_jobs=2,
+ random_state=0)
+ search.fit(iris.data, iris.target)
+ assert search.best_score_ > 0.8
diff --git a/sklearn/utils/fixes.py b/sklearn/utils/fixes.py
index 070afbdbb..24554fe68 100644
--- a/sklearn/utils/fixes.py
+++ b/sklearn/utils/fixes.py
@@ -14,6 +14,8 @@ import os
import errno
import sys
+from distutils.version import LooseVersion
+
import numpy as np
import scipy.sparse as sp
import scipy
@@ -332,3 +334,51 @@ except ImportError: # python <3.3
from collections import Iterable as _Iterable # noqa
from collections import Mapping as _Mapping # noqa
from collections import Sized as _Sized # noqa
+
+
+def _joblib_parallel_args(**kwargs):
+ """Set joblib.Parallel arguments in a compatible way for 0.11 and 0.12+
+
+ For joblib 0.11 this maps both ``prefer`` and ``require`` parameters to
+ a specific ``backend``.
+
+ Parameters
+ ----------
+
+ prefer : str in {'processes', 'threads'} or None
+ Soft hint to choose the default backend if no specific backend
+ was selected with the parallel_backend context manager.
+
+ require : 'sharedmem' or None
+ Hard condstraint to select the backend. If set to 'sharedmem',
+ the selected backend will be single-host and thread-based even
+ if the user asked for a non-thread based backend with
+ parallel_backend.
+
+ See joblib.Parallel documentation for more details
+ """
+ from . import _joblib
+
+ if _joblib.__version__ >= LooseVersion('0.12'):
+ return kwargs
+
+ extra_args = set(kwargs.keys()).difference({'prefer', 'require'})
+ if extra_args:
+ raise NotImplementedError('unhandled arguments %s with joblib %s'
+ % (list(extra_args), _joblib.__version__))
+ args = {}
+ if 'prefer' in kwargs:
+ prefer = kwargs['prefer']
+ if prefer not in ['threads', 'processes', None]:
+ raise ValueError('prefer=%s is not supported' % prefer)
+ args['backend'] = {'threads': 'threading',
+ 'processes': 'multiprocessing',
+ None: None}[prefer]
+
+ if 'require' in kwargs:
+ require = kwargs['require']
+ if require not in [None, 'sharedmem']:
+ raise ValueError('require=%s is not supported' % require)
+ if require == 'sharedmem':
+ args['backend'] = 'threading'
+ return args
diff --git a/sklearn/utils/tests/test_fixes.py b/sklearn/utils/tests/test_fixes.py
index 92f954439..5b7b960fa 100644
--- a/sklearn/utils/tests/test_fixes.py
+++ b/sklearn/utils/tests/test_fixes.py
@@ -16,6 +16,7 @@ from sklearn.utils.fixes import divide
from sklearn.utils.fixes import MaskedArray
from sklearn.utils.fixes import nanmedian
from sklearn.utils.fixes import nanpercentile
+from sklearn.utils.fixes import _joblib_parallel_args
def test_divide():
@@ -57,3 +58,33 @@ def test_nanmedian(axis, expected_median):
def test_nanpercentile(a, q, expected_percentile):
percentile = nanpercentile(a, q)
assert_allclose(percentile, expected_percentile)
+
+
+@pytest.mark.parametrize('joblib_version', ('0.11', '0.12.0'))
+def test_joblib_parallel_args(monkeypatch, joblib_version):
+ import sklearn.utils._joblib
+ monkeypatch.setattr(sklearn.utils._joblib, '__version__', joblib_version)
+
+ if joblib_version == '0.12.0':
+ # arguments are simply passed through
+ assert _joblib_parallel_args(prefer='threads') == {'prefer': 'threads'}
+ assert _joblib_parallel_args(prefer='processes', require=None) == {
+ 'prefer': 'processes', 'require': None}
+ assert _joblib_parallel_args(non_existing=1) == {'non_existing': 1}
+ elif joblib_version == '0.11':
+ # arguments are mapped to the corresponding backend
+ assert _joblib_parallel_args(prefer='threads') == {
+ 'backend': 'threading'}
+ assert _joblib_parallel_args(prefer='processes') == {
+ 'backend': 'multiprocessing'}
+ with pytest.raises(ValueError):
+ _joblib_parallel_args(prefer='invalid')
+ assert _joblib_parallel_args(
+ prefer='processes', require='sharedmem') == {
+ 'backend': 'threading'}
+ with pytest.raises(ValueError):
+ _joblib_parallel_args(require='invalid')
+ with pytest.raises(NotImplementedError):
+ _joblib_parallel_args(verbose=True)
+ else:
+ raise ValueError
packages/scikit-learn/patches/use-site-joblib.patch
View file @ edda6eef
commit 16cf9dc5f79533a121a421b095b6e7ef9ee76e9c commit 8778fe57c37a275fc36959e9bade234bd9bbe88f
Author: Roman Yurchak <rth.yurchak@pm.me> Author: Roman Yurchak <rth.yurchak@pm.me>
Date: Thu Oct 25 16:56:54 2018 +0200 Date: Thu Oct 25 16:56:54 2018 +0200
Use site joblib Use site joblib
diff --git a/sklearn/datasets/california_housing.py b/sklearn/datasets/california_housing.py
index 76cb27dad..98cb34f6e 100644
--- a/sklearn/datasets/california_housing.py
+++ b/sklearn/datasets/california_housing.py
@@ -33,7 +33,7 @@ from .base import _fetch_remote
from .base import _pkl_filepath
from .base import RemoteFileMetadata
from ..utils import Bunch
-from ..externals import joblib
+from ..utils import _joblib as _joblib
# The original data can be found at:
# http://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.tgz
diff --git a/sklearn/datasets/covtype.py b/sklearn/datasets/covtype.py
index a08f61f02..4ac93f93f 100644
--- a/sklearn/datasets/covtype.py
+++ b/sklearn/datasets/covtype.py
@@ -27,7 +27,7 @@ from .base import RemoteFileMetadata
from ..utils import Bunch
from .base import _pkl_filepath
from ..utils.fixes import makedirs
-from ..externals import joblib
+from ..utils import _joblib as joblib
from ..utils import check_random_state
# The original data can be found in:
diff --git a/sklearn/datasets/kddcup99.py b/sklearn/datasets/kddcup99.py
index c8ed0e308..3aa6ebb35 100644
--- a/sklearn/datasets/kddcup99.py
+++ b/sklearn/datasets/kddcup99.py
@@ -22,7 +22,8 @@ from .base import _fetch_remote
from .base import get_data_home
from .base import RemoteFileMetadata
from ..utils import Bunch
-from ..externals import joblib, six
+from ..externals import six
+from ..utils import _joblib as joblib
from ..utils import check_random_state
from ..utils import shuffle as shuffle_method
diff --git a/sklearn/datasets/olivetti_faces.py b/sklearn/datasets/olivetti_faces.py
index 74915c6c6..ba5bfecfb 100644
--- a/sklearn/datasets/olivetti_faces.py
+++ b/sklearn/datasets/olivetti_faces.py
@@ -24,7 +24,7 @@ from .base import _fetch_remote
from .base import RemoteFileMetadata
from .base import _pkl_filepath
from ..utils import check_random_state, Bunch
-from ..externals import joblib
+from ..utils import _joblib as joblib
# The original data can be found at:
# http://cs.nyu.edu/~roweis/data/olivettifaces.mat
diff --git a/sklearn/datasets/rcv1.py b/sklearn/datasets/rcv1.py
index 7890d7e18..ea22fb076 100644
--- a/sklearn/datasets/rcv1.py
+++ b/sklearn/datasets/rcv1.py
@@ -22,7 +22,7 @@ from .base import _pkl_filepath
from .base import _fetch_remote
from .base import RemoteFileMetadata
from ..utils.fixes import makedirs
-from ..externals import joblib
+from ..utils import _joblib as joblib
from .svmlight_format import load_svmlight_files
from ..utils import shuffle as shuffle_
from ..utils import Bunch
diff --git a/sklearn/datasets/species_distributions.py b/sklearn/datasets/species_distributions.py diff --git a/sklearn/datasets/species_distributions.py b/sklearn/datasets/species_distributions.py
index 6d8acddcc..8191048d7 100644 index 6d8acddcc..8191048d7 100644
--- a/sklearn/datasets/species_distributions.py --- a/sklearn/datasets/species_distributions.py
...@@ -17,17 +83,48 @@ index 6d8acddcc..8191048d7 100644 ...@@ -17,17 +83,48 @@ index 6d8acddcc..8191048d7 100644
PY3_OR_LATER = sys.version_info[0] >= 3 PY3_OR_LATER = sys.version_info[0] >= 3
diff --git a/sklearn/datasets/twenty_newsgroups.py b/sklearn/datasets/twenty_newsgroups.py
index 8df908a2e..a2440222a 100644
--- a/sklearn/datasets/twenty_newsgroups.py
+++ b/sklearn/datasets/twenty_newsgroups.py
@@ -45,7 +45,7 @@ from ..utils import check_random_state, Bunch
from ..utils import deprecated
from ..feature_extraction.text import CountVectorizer
from ..preprocessing import normalize
-from ..externals import joblib
+from ..utils import _joblib as joblib
logger = logging.getLogger(__name__)
diff --git a/sklearn/ensemble/base.py b/sklearn/ensemble/base.py
index 321031892..2a516619b 100644
--- a/sklearn/ensemble/base.py
+++ b/sklearn/ensemble/base.py
@@ -13,9 +13,11 @@ from ..base import BaseEstimator
from ..base import MetaEstimatorMixin
from ..utils import check_random_state
from ..externals import six
-from ..externals.joblib import effective_n_jobs
+from ..utils import _joblib
from abc import ABCMeta, abstractmethod
+effective_n_jobs = _joblib.effective_n_jobs
+
MAX_RAND_SEED = np.iinfo(np.int32).max
diff --git a/sklearn/ensemble/tests/test_forest.py b/sklearn/ensemble/tests/test_forest.py diff --git a/sklearn/ensemble/tests/test_forest.py b/sklearn/ensemble/tests/test_forest.py
index d7586c286..d1168cdcf 100644 index d7586c286..f12f6f886 100644
--- a/sklearn/ensemble/tests/test_forest.py --- a/sklearn/ensemble/tests/test_forest.py
+++ b/sklearn/ensemble/tests/test_forest.py +++ b/sklearn/ensemble/tests/test_forest.py
@@ -23,7 +23,11 @@ import pytest @@ -23,7 +23,12 @@ import pytest
from sklearn.utils import parallel_backend from sklearn.utils import parallel_backend
from sklearn.utils import register_parallel_backend from sklearn.utils import register_parallel_backend
-from sklearn.externals.joblib.parallel import LokyBackend -from sklearn.externals.joblib.parallel import LokyBackend
+try: +try:
+ from sklearn.externals.joblib.parallel import LokyBackend + from sklearn.utils import _joblib
+ LokyBackend = _joblib.parallel.LokyBackend
+except ImportError: +except ImportError:
+ LokyBackend = object + LokyBackend = object
+ +
...@@ -76,6 +173,19 @@ index bffd43cc1..df4c96893 100644 ...@@ -76,6 +173,19 @@ index bffd43cc1..df4c96893 100644
from sklearn.utils import Parallel, delayed, Memory, parallel_backend from sklearn.utils import Parallel, delayed, Memory, parallel_backend
if os.environ.get('SKLEARN_SITE_JOBLIB', False): if os.environ.get('SKLEARN_SITE_JOBLIB', False):
diff --git a/sklearn/utils/_joblib.py b/sklearn/utils/_joblib.py
index e1c39a401..9c4e815f7 100644
--- a/sklearn/utils/_joblib.py
+++ b/sklearn/utils/_joblib.py
@@ -5,7 +5,7 @@ import os as _os
import warnings as _warnings
# An environment variable to use the site joblib
-if _os.environ.get('SKLEARN_SITE_JOBLIB', False):
+if True:
with _warnings.catch_warnings():
_warnings.simplefilter("ignore")
# joblib imports may raise DeprecationWarning on certain Python
diff --git a/sklearn/utils/testing.py b/sklearn/utils/testing.py diff --git a/sklearn/utils/testing.py b/sklearn/utils/testing.py
index 75b378961..b81b9ab58 100644 index 75b378961..b81b9ab58 100644
--- a/sklearn/utils/testing.py --- a/sklearn/utils/testing.py
......
test/packages/test_scikit-learn.py
View file @ edda6eef
from textwrap import dedent import pytest
def test_scikit_learn(selenium_standalone):
def test_scikit_learn(selenium_standalone, request):
selenium = selenium_standalone selenium = selenium_standalone
if selenium.browser == 'chrome':
request.applymarker(pytest.mark.xfail(
run=False, reason='chrome not supported'))
# no automatic depedency resolution for now # no automatic depedency resolution for now
selenium.load_package(["numpy", "joblib"]) selenium.load_package(["numpy", "joblib"])
selenium.load_package("scipy") selenium.load_package("scipy")
...@@ -20,59 +24,3 @@ def test_scikit_learn(selenium_standalone): ...@@ -20,59 +24,3 @@ def test_scikit_learn(selenium_standalone):
print(estimator.predict(X)) print(estimator.predict(X))
estimator.score(X, y) estimator.score(X, y)
""") > 0 """) > 0
print(selenium.logs)
def test_import(selenium_standalone):
selenium = selenium_standalone
# no automatic depedency resolution for now
selenium.load_package(["numpy", "joblib"])
selenium.load_package("scipy")
selenium.load_package("scikit-learn")
cmd = dedent("""
import sklearn
import sklearn.calibration
import sklearn.calibration
import sklearn.cluster
import sklearn.compose
import sklearn.covariance
import sklearn.cross_decomposition
import sklearn.datasets
import sklearn.decomposition
import sklearn.discriminant_analysis
import sklearn.dummy
import sklearn.ensemble
import sklearn.exceptions
import sklearn.externals
import sklearn.feature_extraction
import sklearn.feature_selection
import sklearn.gaussian_process
import sklearn.impute
import sklearn.isotonic
import sklearn.kernel_approximation
import sklearn.kernel_ridge
import sklearn.linear_model
import sklearn.manifold
import sklearn.metrics
import sklearn.mixture
import sklearn.model_selection
import sklearn.multiclass
import sklearn.multioutput
import sklearn.naive_bayes
import sklearn.neighbors
import sklearn.neural_network
import sklearn.pipeline
import sklearn.preprocessing
import sklearn.random_projection
import sklearn.semi_supervised
import sklearn.svm
import sklearn.tree
import sklearn.utils
""").splitlines()
for line in cmd:
try:
selenium.run(line)
print(f'{line} -- OK')
except:
print(f'Error: {line} failed')
print(selenium.logs)
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