Uploaded Test files

venv/Lib/site-packages/sklearn/__init__.py

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+"""
+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 logging
+import os
+
+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
+#   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__ = '0.23.2'
+
+
+# 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 os
+    import numpy as np
+    import random
+
+    # 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)