Uploaded Test files

venv/Lib/site-packages/sklearn/utils/fixes.py

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+"""Compatibility fixes for older version of python, numpy and scipy
+
+If you add content to this file, please give the version of the package
+at which the fixe is no longer needed.
+"""
+# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
+#          Gael Varoquaux <gael.varoquaux@normalesup.org>
+#          Fabian Pedregosa <fpedregosa@acm.org>
+#          Lars Buitinck
+#
+# License: BSD 3 clause
+
+from distutils.version import LooseVersion
+
+import numpy as np
+import scipy.sparse as sp
+import scipy
+import scipy.stats
+from scipy.sparse.linalg import lsqr as sparse_lsqr  # noqa
+from numpy.ma import MaskedArray as _MaskedArray  # TODO: remove in 0.25
+
+from .deprecation import deprecated
+
+try:
+    from pkg_resources import parse_version  # type: ignore
+except ImportError:
+    # setuptools not installed
+    parse_version = LooseVersion  # type: ignore
+
+
+np_version = parse_version(np.__version__)
+sp_version = parse_version(scipy.__version__)
+
+
+if sp_version >= parse_version('1.4'):
+    from scipy.sparse.linalg import lobpcg
+else:
+    # Backport of lobpcg functionality from scipy 1.4.0, can be removed
+    # once support for sp_version < parse_version('1.4') is dropped
+    # mypy error: Name 'lobpcg' already defined (possibly by an import)
+    from ..externals._lobpcg import lobpcg  # type: ignore  # noqa
+
+
+def _object_dtype_isnan(X):
+    return X != X
+
+
+# TODO: replace by copy=False, when only scipy > 1.1 is supported.
+def _astype_copy_false(X):
+    """Returns the copy=False parameter for
+    {ndarray, csr_matrix, csc_matrix}.astype when possible,
+    otherwise don't specify
+    """
+    if sp_version >= parse_version('1.1') or not sp.issparse(X):
+        return {'copy': False}
+    else:
+        return {}
+
+
+def _joblib_parallel_args(**kwargs):
+    """Set joblib.Parallel arguments in a compatible way for 0.11 and 0.12+
+
+    For joblib 0.11 this maps both ``prefer`` and ``require`` parameters to
+    a specific ``backend``.
+
+    Parameters
+    ----------
+
+    prefer : str in {'processes', 'threads'} or None
+        Soft hint to choose the default backend if no specific backend
+        was selected with the parallel_backend context manager.
+
+    require : 'sharedmem' or None
+        Hard condstraint to select the backend. If set to 'sharedmem',
+        the selected backend will be single-host and thread-based even
+        if the user asked for a non-thread based backend with
+        parallel_backend.
+
+    See joblib.Parallel documentation for more details
+    """
+    import joblib
+
+    if parse_version(joblib.__version__) >= parse_version('0.12'):
+        return kwargs
+
+    extra_args = set(kwargs.keys()).difference({'prefer', 'require'})
+    if extra_args:
+        raise NotImplementedError('unhandled arguments %s with joblib %s'
+                                  % (list(extra_args), joblib.__version__))
+    args = {}
+    if 'prefer' in kwargs:
+        prefer = kwargs['prefer']
+        if prefer not in ['threads', 'processes', None]:
+            raise ValueError('prefer=%s is not supported' % prefer)
+        args['backend'] = {'threads': 'threading',
+                           'processes': 'multiprocessing',
+                           None: None}[prefer]
+
+    if 'require' in kwargs:
+        require = kwargs['require']
+        if require not in [None, 'sharedmem']:
+            raise ValueError('require=%s is not supported' % require)
+        if require == 'sharedmem':
+            args['backend'] = 'threading'
+    return args
+
+
+class loguniform(scipy.stats.reciprocal):
+    """A class supporting log-uniform random variables.
+
+    Parameters
+    ----------
+    low : float
+        The minimum value
+    high : float
+        The maximum value
+
+    Methods
+    -------
+    rvs(self, size=None, random_state=None)
+        Generate log-uniform random variables
+
+    The most useful method for Scikit-learn usage is highlighted here.
+    For a full list, see
+    `scipy.stats.reciprocal
+    <https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.reciprocal.html>`_.
+    This list includes all functions of ``scipy.stats`` continuous
+    distributions such as ``pdf``.
+
+    Notes
+    -----
+    This class generates values between ``low`` and ``high`` or
+
+        low <= loguniform(low, high).rvs() <= high
+
+    The logarithmic probability density function (PDF) is uniform. When
+    ``x`` is a uniformly distributed random variable between 0 and 1, ``10**x``
+    are random variales that are equally likely to be returned.
+
+    This class is an alias to ``scipy.stats.reciprocal``, which uses the
+    reciprocal distribution:
+    https://en.wikipedia.org/wiki/Reciprocal_distribution
+
+    Examples
+    --------
+
+    >>> from sklearn.utils.fixes import loguniform
+    >>> rv = loguniform(1e-3, 1e1)
+    >>> rvs = rv.rvs(random_state=42, size=1000)
+    >>> rvs.min()  # doctest: +SKIP
+    0.0010435856341129003
+    >>> rvs.max()  # doctest: +SKIP
+    9.97403052786026
+    """
+
+
+@deprecated(
+    'MaskedArray is deprecated in version 0.23 and will be removed in version '
+    '0.25. Use numpy.ma.MaskedArray instead.'
+)
+class MaskedArray(_MaskedArray):
+    pass  # TODO: remove in 0.25