65 lines
2.2 KiB
Python
65 lines
2.2 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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__version__ = '0.16-git'
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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='^{0}\.'.format(re.escape(__name__)))
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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 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 the scikit 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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__all__ = ['cross_validation', 'cluster', 'covariance',
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'datasets', 'decomposition', 'feature_extraction',
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'feature_selection', 'semi_supervised',
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'gaussian_process', 'grid_search', 'hmm', 'lda', 'linear_model',
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'metrics', 'mixture', 'naive_bayes', 'neighbors', 'pipeline',
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'preprocessing', 'qda', 'svm', 'clone',
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'cross_decomposition', 'isotonic']
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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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"""
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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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