86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
"""
|
|
Machine learning module for Python
|
|
==================================
|
|
|
|
sklearn is a Python module integrating classical machine
|
|
learning algorithms in the tightly-knit world of scientific Python
|
|
packages (numpy, scipy, matplotlib).
|
|
|
|
It aims to provide simple and efficient solutions to learning problems
|
|
that are accessible to everybody and reusable in various contexts:
|
|
machine-learning as a versatile tool for science and engineering.
|
|
|
|
See http://scikit-learn.org for complete documentation.
|
|
"""
|
|
import sys
|
|
import re
|
|
import warnings
|
|
|
|
|
|
# Make sure that DeprecationWarning within this package always gets printed
|
|
warnings.filterwarnings('always', category=DeprecationWarning,
|
|
module='^{0}\.'.format(re.escape(__name__)))
|
|
|
|
# PEP0440 compatible formatted version, see:
|
|
# https://www.python.org/dev/peps/pep-0440/
|
|
#
|
|
# Generic release markers:
|
|
# X.Y
|
|
# X.Y.Z # For bugfix releases
|
|
#
|
|
# Admissible pre-release markers:
|
|
# X.YaN # Alpha release
|
|
# X.YbN # Beta release
|
|
# X.YrcN # Release Candidate
|
|
# X.Y # Final release
|
|
#
|
|
# Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
|
|
#
|
|
__version__ = '0.16.dev'
|
|
|
|
|
|
try:
|
|
# This variable is injected in the __builtins__ by the build
|
|
# process. It used to enable importing subpackages of sklearn when
|
|
# the binaries are not built
|
|
__SKLEARN_SETUP__
|
|
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 the scikit during the build
|
|
# process, as it may not be compiled yet
|
|
else:
|
|
from . import __check_build
|
|
from .base import clone
|
|
__check_build # avoid flakes unused variable error
|
|
|
|
__all__ = ['cluster', 'covariance', 'cross_decomposition',
|
|
'cross_validation', 'datasets', 'decomposition', 'dummy',
|
|
'ensemble', 'externals', 'feature_extraction',
|
|
'feature_selection', 'gaussian_process', 'grid_search', 'hmm',
|
|
'isotonic', 'kernel_approximation', 'lda', 'learning_curve',
|
|
'linear_model', 'manifold', 'metrics', 'mixture', 'multiclass',
|
|
'naive_bayes', 'neighbors', 'neural_network', 'pipeline',
|
|
'preprocessing', 'qda', 'random_projection', 'semi_supervised',
|
|
'svm', 'tree',
|
|
# Non-modules:
|
|
'clone']
|
|
|
|
|
|
def setup_module(module):
|
|
"""Fixture for the tests to assure globally controllable seeding of RNGs"""
|
|
import os
|
|
import numpy as np
|
|
import random
|
|
|
|
# It could have been provided in the environment
|
|
_random_seed = os.environ.get('SKLEARN_SEED', None)
|
|
if _random_seed is None:
|
|
_random_seed = np.random.uniform() * (2 ** 31 - 1)
|
|
_random_seed = int(_random_seed)
|
|
print("I: Seeding RNGs with %r" % _random_seed)
|
|
np.random.seed(_random_seed)
|
|
random.seed(_random_seed)
|