Scikit-learn: 机器学习库 - 主模块

import sys
import logging
import os
import random


from ._config import get_config, set_config, config_context

logger = logging.getLogger(__name__)


# PEP0440 compatible formatted version, see:
# https://www.python.org/dev/peps/pep-0440/
#
# Generic release markers:
#   X.Y.0   # For first release after an increment in Y
#   X.Y.Z   # For bugfix releases
#
# Admissible pre-release markers:
#   X.Y.ZaN   # Alpha release
#   X.Y.ZbN   # Beta release
#   X.Y.ZrcN  # Release Candidate
#   X.Y.Z     # Final release
#
# Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
# 'X.Y.dev0' is the canonical version of 'X.Y.dev'
#
__version__ = '1.0.2'


# On OSX, we can get a runtime error due to multiple OpenMP libraries loaded
# simultaneously. This can happen for instance when calling BLAS inside a
# prange. Setting the following environment variable allows multiple OpenMP
# libraries to be loaded. It should not degrade performances since we manually
# take care of potential over-subcription performance issues, in sections of
# the code where nested OpenMP loops can happen, by dynamically reconfiguring
# the inner OpenMP runtime to temporarily disable it while under the scope of
# the outer OpenMP parallel section.
os.environ.setdefault('KMP_DUPLICATE_LIB_OK', 'True')

# Workaround issue discovered in intel-openmp 2019.5:
# https://github.com/ContinuumIO/anaconda-issues/issues/11294
os.environ.setdefault('KMP_INIT_AT_FORK', 'FALSE')

try:
    # This variable is injected in the __builtins__ by the build
    # process. It is used to enable importing subpackages of sklearn when
    # the binaries are not built
    # mypy error: Cannot determine type of '__SKLEARN_SETUP__'
    __SKLEARN_SETUP__  # type: ignore
except NameError:
    __SKLEARN_SETUP__ = False

if __SKLEARN_SETUP__:
    sys.stderr.write('Partial import of sklearn during the build process.\n')
    # We are not importing the rest of scikit-learn during the build
    # process, as it may not be compiled yet
else:
    # `_distributor_init` allows distributors to run custom init code.
    # For instance, for the Windows wheel, this is used to pre-load the
    # vcomp shared library runtime for OpenMP embedded in the sklearn/.libs
    # sub-folder.
    # It is necessary to do this prior to importing show_versions as the
    # later is linked to the OpenMP runtime to make it possible to introspect
    # it and importing it first would fail if the OpenMP dll cannot be found.
    from . import _distributor_init  # noqa: F401
    from . import __check_build  # noqa: F401
    from .base import clone
    from .utils._show_versions import show_versions

    __all__ = [
        'calibration',
        'cluster',
        'covariance',
        'cross_decomposition',
        'datasets',
        'decomposition',
        'dummy',
        'ensemble',
        'exceptions',
        'experimental',
        'externals',
        'feature_extraction',
        'feature_selection',
        'gaussian_process',
        'inspection',
        'isotonic',
        'kernel_approximation',
        'kernel_ridge',
        'linear_model',
        'manifold',
        'metrics',
        'mixture',
        'model_selection',
        'multiclass',
        'multioutput',
        'naive_bayes',
        'neighbors',
        'neural_network',
        'pipeline',
        'preprocessing',
        'random_projection',
        'semi_supervised',
        'svm',
        'tree',
        'discriminant_analysis',
        'impute',
        'compose',
        # Non-modules:
        'clone',
        'get_config',
        'set_config',
        'config_context',
        'show_versions',
    ]


def setup_module(module):
    '''Fixture for the tests to assure globally controllable seeding of RNGs'''

    import numpy as np

    # Check if a random seed exists in the environment, if not create one.
    _random_seed = os.environ.get('SKLEARN_SEED', None)
    if _random_seed is None:
        _random_seed = np.random.uniform() * np.iinfo(np.int32).max
    _random_seed = int(_random_seed)
    print('I: Seeding RNGs with %r' % _random_seed)
    np.random.seed(_random_seed)
    random.seed(_random_seed)

解释以上代码内容：这是 scikit-learn 的主要模块，提供了许多机器学习算法和工具。其中包括聚类、分类、回归、降维、模型选择、预处理等多个子模块。此外，还有一些辅助工具，如版本展示、克隆、配置等。代码中还包括了控制随机数生成器种子的功能，以便测试时可以控制随机性。