forked from 170010011/fr
113 lines
4.4 KiB
Python
113 lines
4.4 KiB
Python
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"""
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Machine learning module for Python
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==================================
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sklearn is a Python module integrating classical machine
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learning algorithms in the tightly-knit world of scientific Python
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packages (numpy, scipy, matplotlib).
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It aims to provide simple and efficient solutions to learning problems
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that are accessible to everybody and reusable in various contexts:
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machine-learning as a versatile tool for science and engineering.
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See http://scikit-learn.org for complete documentation.
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"""
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import sys
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import logging
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import os
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import random
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from ._config import get_config, set_config, config_context
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logger = logging.getLogger(__name__)
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# PEP0440 compatible formatted version, see:
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# https://www.python.org/dev/peps/pep-0440/
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#
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# Generic release markers:
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# X.Y
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# X.Y.Z # For bugfix releases
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#
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# Admissible pre-release markers:
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# X.YaN # Alpha release
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# X.YbN # Beta release
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# X.YrcN # Release Candidate
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# X.Y # Final release
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#
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# Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
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# 'X.Y.dev0' is the canonical version of 'X.Y.dev'
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#
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__version__ = '0.24.1'
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# On OSX, we can get a runtime error due to multiple OpenMP libraries loaded
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# simultaneously. This can happen for instance when calling BLAS inside a
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# prange. Setting the following environment variable allows multiple OpenMP
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# libraries to be loaded. It should not degrade performances since we manually
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# take care of potential over-subcription performance issues, in sections of
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# the code where nested OpenMP loops can happen, by dynamically reconfiguring
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# the inner OpenMP runtime to temporarily disable it while under the scope of
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# the outer OpenMP parallel section.
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os.environ.setdefault("KMP_DUPLICATE_LIB_OK", "True")
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# Workaround issue discovered in intel-openmp 2019.5:
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# https://github.com/ContinuumIO/anaconda-issues/issues/11294
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os.environ.setdefault("KMP_INIT_AT_FORK", "FALSE")
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try:
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# This variable is injected in the __builtins__ by the build
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# process. It is used to enable importing subpackages of sklearn when
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# the binaries are not built
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# mypy error: Cannot determine type of '__SKLEARN_SETUP__'
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__SKLEARN_SETUP__ # type: ignore
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except NameError:
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__SKLEARN_SETUP__ = False
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if __SKLEARN_SETUP__:
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sys.stderr.write('Partial import of sklearn during the build process.\n')
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# We are not importing the rest of scikit-learn during the build
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# process, as it may not be compiled yet
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else:
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# `_distributor_init` allows distributors to run custom init code.
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# For instance, for the Windows wheel, this is used to pre-load the
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# vcomp shared library runtime for OpenMP embedded in the sklearn/.libs
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# sub-folder.
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# It is necessary to do this prior to importing show_versions as the
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# later is linked to the OpenMP runtime to make it possible to introspect
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# it and importing it first would fail if the OpenMP dll cannot be found.
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from . import _distributor_init # noqa: F401
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from . import __check_build # noqa: F401
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from .base import clone
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from .utils._show_versions import show_versions
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__all__ = ['calibration', 'cluster', 'covariance', 'cross_decomposition',
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'datasets', 'decomposition', 'dummy', 'ensemble', 'exceptions',
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'experimental', 'externals', 'feature_extraction',
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'feature_selection', 'gaussian_process', 'inspection',
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'isotonic', 'kernel_approximation', 'kernel_ridge',
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'linear_model', 'manifold', 'metrics', 'mixture',
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'model_selection', 'multiclass', 'multioutput',
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'naive_bayes', 'neighbors', 'neural_network', 'pipeline',
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'preprocessing', 'random_projection', 'semi_supervised',
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'svm', 'tree', 'discriminant_analysis', 'impute', 'compose',
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# Non-modules:
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'clone', 'get_config', 'set_config', 'config_context',
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'show_versions']
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def setup_module(module):
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"""Fixture for the tests to assure globally controllable seeding of RNGs"""
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import numpy as np
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# Check if a random seed exists in the environment, if not create one.
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_random_seed = os.environ.get('SKLEARN_SEED', None)
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if _random_seed is None:
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_random_seed = np.random.uniform() * np.iinfo(np.int32).max
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_random_seed = int(_random_seed)
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print("I: Seeding RNGs with %r" % _random_seed)
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np.random.seed(_random_seed)
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random.seed(_random_seed)
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