content: """
  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.
  # 'X.Y.dev0' is the canonical version of 'X.Y.dev'
  #
  __version__ = '0.17.dev0'
  
  
  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__ = ['calibration', 'cluster', 'covariance', 'cross_decomposition',
                 'cross_validation', 'datasets', 'decomposition', 'dummy',
                 'ensemble', 'externals', 'feature_extraction',
                 'feature_selection', 'gaussian_process', 'grid_search',
                 'isotonic', 'kernel_approximation', 'kernel_ridge',
                 '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)
  