Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/scikit_learn-1.4.dev0-py3.8-linux-x86_64.egg/sklearn/__init__.py: 68%
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1"""
2The :mod:`sklearn` module includes functions to configure global settings and
3get information about the working environment.
4"""
6# Machine learning module for Python
7# ==================================
8#
9# sklearn is a Python module integrating classical machine
10# learning algorithms in the tightly-knit world of scientific Python
11# packages (numpy, scipy, matplotlib).
12#
13# It aims to provide simple and efficient solutions to learning problems
14# that are accessible to everybody and reusable in various contexts:
15# machine-learning as a versatile tool for science and engineering.
16#
17# See http://scikit-learn.org for complete documentation.
19import logging
20import os
21import random
22import sys
24from ._config import config_context, get_config, set_config
26logger = logging.getLogger(__name__)
29# PEP0440 compatible formatted version, see:
30# https://www.python.org/dev/peps/pep-0440/
31#
32# Generic release markers:
33# X.Y.0 # For first release after an increment in Y
34# X.Y.Z # For bugfix releases
35#
36# Admissible pre-release markers:
37# X.Y.ZaN # Alpha release
38# X.Y.ZbN # Beta release
39# X.Y.ZrcN # Release Candidate
40# X.Y.Z # Final release
41#
42# Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
43# 'X.Y.dev0' is the canonical version of 'X.Y.dev'
44#
45__version__ = "1.4.dev0"
48# On OSX, we can get a runtime error due to multiple OpenMP libraries loaded
49# simultaneously. This can happen for instance when calling BLAS inside a
50# prange. Setting the following environment variable allows multiple OpenMP
51# libraries to be loaded. It should not degrade performances since we manually
52# take care of potential over-subcription performance issues, in sections of
53# the code where nested OpenMP loops can happen, by dynamically reconfiguring
54# the inner OpenMP runtime to temporarily disable it while under the scope of
55# the outer OpenMP parallel section.
56os.environ.setdefault("KMP_DUPLICATE_LIB_OK", "True")
58# Workaround issue discovered in intel-openmp 2019.5:
59# https://github.com/ContinuumIO/anaconda-issues/issues/11294
60os.environ.setdefault("KMP_INIT_AT_FORK", "FALSE")
62try:
63 # This variable is injected in the __builtins__ by the build
64 # process. It is used to enable importing subpackages of sklearn when
65 # the binaries are not built
66 # mypy error: Cannot determine type of '__SKLEARN_SETUP__'
67 __SKLEARN_SETUP__ # type: ignore
68except NameError:
69 __SKLEARN_SETUP__ = False
71if __SKLEARN_SETUP__:
72 sys.stderr.write("Partial import of sklearn during the build process.\n")
73 # We are not importing the rest of scikit-learn during the build
74 # process, as it may not be compiled yet
75else:
76 # `_distributor_init` allows distributors to run custom init code.
77 # For instance, for the Windows wheel, this is used to pre-load the
78 # vcomp shared library runtime for OpenMP embedded in the sklearn/.libs
79 # sub-folder.
80 # It is necessary to do this prior to importing show_versions as the
81 # later is linked to the OpenMP runtime to make it possible to introspect
82 # it and importing it first would fail if the OpenMP dll cannot be found.
83 from . import (
84 __check_build, # noqa: F401
85 _distributor_init, # noqa: F401
86 )
87 from .base import clone
88 from .utils._show_versions import show_versions
90 __all__ = [
91 "calibration",
92 "cluster",
93 "covariance",
94 "cross_decomposition",
95 "datasets",
96 "decomposition",
97 "dummy",
98 "ensemble",
99 "exceptions",
100 "experimental",
101 "externals",
102 "feature_extraction",
103 "feature_selection",
104 "gaussian_process",
105 "inspection",
106 "isotonic",
107 "kernel_approximation",
108 "kernel_ridge",
109 "linear_model",
110 "manifold",
111 "metrics",
112 "mixture",
113 "model_selection",
114 "multiclass",
115 "multioutput",
116 "naive_bayes",
117 "neighbors",
118 "neural_network",
119 "pipeline",
120 "preprocessing",
121 "random_projection",
122 "semi_supervised",
123 "svm",
124 "tree",
125 "discriminant_analysis",
126 "impute",
127 "compose",
128 # Non-modules:
129 "clone",
130 "get_config",
131 "set_config",
132 "config_context",
133 "show_versions",
134 ]
137def setup_module(module):
138 """Fixture for the tests to assure globally controllable seeding of RNGs"""
140 import numpy as np
142 # Check if a random seed exists in the environment, if not create one.
143 _random_seed = os.environ.get("SKLEARN_SEED", None)
144 if _random_seed is None:
145 _random_seed = np.random.uniform() * np.iinfo(np.int32).max
146 _random_seed = int(_random_seed)
147 print("I: Seeding RNGs with %r" % _random_seed)
148 np.random.seed(_random_seed)
149 random.seed(_random_seed)