97 lines
3.3 KiB
Python
97 lines
3.3 KiB
Python
"""
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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 re
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import warnings
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import logging
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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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logger.addHandler(logging.StreamHandler())
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logger.setLevel(logging.INFO)
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# Make sure that DeprecationWarning within this package always gets printed
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warnings.filterwarnings('always', category=DeprecationWarning,
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module=r'^{0}\.'.format(re.escape(__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__ = '0.21.dev0'
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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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__SKLEARN_SETUP__
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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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from . import __check_build
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from .base import clone
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from .utils._show_versions import show_versions
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__check_build # avoid flakes unused variable error
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__all__ = ['calibration', 'cluster', 'covariance', 'cross_decomposition',
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'datasets', 'decomposition', 'dummy', 'ensemble', 'exceptions',
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'externals', 'feature_extraction', 'feature_selection',
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'gaussian_process', 'isotonic', 'kernel_approximation',
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'kernel_ridge', 'linear_model', 'manifold', 'metrics',
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'mixture', '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 os
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import numpy as np
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import random
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# It could have been provided in the environment
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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() * (2 ** 31 - 1)
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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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