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dashboard/flask-server/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 fix 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 functools import update_wrapper
+import functools
+
+import sklearn
+import numpy as np
+import scipy
+import scipy.stats
+import threadpoolctl
+from .._config import config_context, get_config
+from ..externals._packaging.version import parse as parse_version
+
+
+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
+
+try:
+    from scipy.optimize._linesearch import line_search_wolfe2, line_search_wolfe1
+except ImportError:  # SciPy < 1.8
+    from scipy.optimize.linesearch import line_search_wolfe2, line_search_wolfe1  # type: ignore  # noqa
+
+
+def _object_dtype_isnan(X):
+    return X != X
+
+
+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 variables 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
+    """
+
+
+# remove when https://github.com/joblib/joblib/issues/1071 is fixed
+def delayed(function):
+    """Decorator used to capture the arguments of a function."""
+
+    @functools.wraps(function)
+    def delayed_function(*args, **kwargs):
+        return _FuncWrapper(function), args, kwargs
+
+    return delayed_function
+
+
+class _FuncWrapper:
+    """ "Load the global configuration before calling the function."""
+
+    def __init__(self, function):
+        self.function = function
+        self.config = get_config()
+        update_wrapper(self, self.function)
+
+    def __call__(self, *args, **kwargs):
+        with config_context(**self.config):
+            return self.function(*args, **kwargs)
+
+
+# Rename the `method` kwarg to `interpolation` for NumPy < 1.22, because
+# `interpolation` kwarg was deprecated in favor of `method` in NumPy >= 1.22.
+def _percentile(a, q, *, method="linear", **kwargs):
+    return np.percentile(a, q, interpolation=method, **kwargs)
+
+
+if np_version < parse_version("1.22"):
+    percentile = _percentile
+else:  # >= 1.22
+    from numpy import percentile  # type: ignore  # noqa
+
+
+# compatibility fix for threadpoolctl >= 3.0.0
+# since version 3 it's possible to setup a global threadpool controller to avoid
+# looping through all loaded shared libraries each time.
+# the global controller is created during the first call to threadpoolctl.
+def _get_threadpool_controller():
+    if not hasattr(threadpoolctl, "ThreadpoolController"):
+        return None
+
+    if not hasattr(sklearn, "_sklearn_threadpool_controller"):
+        sklearn._sklearn_threadpool_controller = threadpoolctl.ThreadpoolController()
+
+    return sklearn._sklearn_threadpool_controller
+
+
+def threadpool_limits(limits=None, user_api=None):
+    controller = _get_threadpool_controller()
+    if controller is not None:
+        return controller.limit(limits=limits, user_api=user_api)
+    else:
+        return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
+
+
+threadpool_limits.__doc__ = threadpoolctl.threadpool_limits.__doc__
+
+
+def threadpool_info():
+    controller = _get_threadpool_controller()
+    if controller is not None:
+        return controller.info()
+    else:
+        return threadpoolctl.threadpool_info()
+
+
+threadpool_info.__doc__ = threadpoolctl.threadpool_info.__doc__