move no da objects into utils.py

Author: Slasnista

README.md

@@ -136,6 +136,8 @@ The contributors to this library are:
 * [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/)
 * [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation)
 * [Léo Gautheron](https://github.com/aje) (GPU implementation)
+* [Nathalie Gayraud]()
+* [Stanislas Chambon](https://slasnista.github.io/)
 
 This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages):
 

ot/da.py

@@ -10,14 +10,13 @@
 # License: MIT License
 
 import numpy as np
-import warnings
 
 from .bregman import sinkhorn
 from .lp import emd
 from .utils import unif, dist, kernel
+from .utils import deprecated, BaseEstimator
 from .optim import cg
 from .optim import gcg
-from .deprecation import deprecated
 
 
 def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10,
@@ -936,139 +935,6 @@ def predict(self, x):
             print("Warning, model not fitted yet, returning None")
             return None
 
-##############################################################################
-# proposal
-##############################################################################
-
-
-# adapted from sklearn
-
-class BaseEstimator(object):
-    """Base class for all estimators in scikit-learn
-    Notes
-    -----
-    All estimators should specify all the parameters that can be set
-    at the class level in their ``__init__`` as explicit keyword
-    arguments (no ``*args`` or ``**kwargs``).
-    """
-
-    @classmethod
-    def _get_param_names(cls):
-        """Get parameter names for the estimator"""
-        try:
-            from inspect import signature
-        except ImportError:
-            from .externals.funcsigs import signature
-        # fetch the constructor or the original constructor before
-        # deprecation wrapping if any
-        init = getattr(cls.__init__, 'deprecated_original', cls.__init__)
-        if init is object.__init__:
-            # No explicit constructor to introspect
-            return []
-
-        # introspect the constructor arguments to find the model parameters
-        # to represent
-        init_signature = signature(init)
-        # Consider the constructor parameters excluding 'self'
-        parameters = [p for p in init_signature.parameters.values()
-                      if p.name != 'self' and p.kind != p.VAR_KEYWORD]
-        for p in parameters:
-            if p.kind == p.VAR_POSITIONAL:
-                raise RuntimeError("scikit-learn estimators should always "
-                                   "specify their parameters in the signature"
-                                   " of their __init__ (no varargs)."
-                                   " %s with constructor %s doesn't "
-                                   " follow this convention."
-                                   % (cls, init_signature))
-        # Extract and sort argument names excluding 'self'
-        return sorted([p.name for p in parameters])
-
-    def get_params(self, deep=True):
-        """Get parameters for this estimator.
-
-        Parameters
-        ----------
-        deep : boolean, optional
-            If True, will return the parameters for this estimator and
-            contained subobjects that are estimators.
-
-        Returns
-        -------
-        params : mapping of string to any
-            Parameter names mapped to their values.
-        """
-        out = dict()
-        for key in self._get_param_names():
-            # We need deprecation warnings to always be on in order to
-            # catch deprecated param values.
-            # This is set in utils/__init__.py but it gets overwritten
-            # when running under python3 somehow.
-            warnings.simplefilter("always", DeprecationWarning)
-            try:
-                with warnings.catch_warnings(record=True) as w:
-                    value = getattr(self, key, None)
-                if len(w) and w[0].category == DeprecationWarning:
-                    # if the parameter is deprecated, don't show it
-                    continue
-            finally:
-                warnings.filters.pop(0)
-
-            # XXX: should we rather test if instance of estimator?
-            if deep and hasattr(value, 'get_params'):
-                deep_items = value.get_params().items()
-                out.update((key + '__' + k, val) for k, val in deep_items)
-            out[key] = value
-        return out
-
-    def set_params(self, **params):
-        """Set the parameters of this estimator.
-
-        The method works on simple estimators as well as on nested objects
-        (such as pipelines). The latter have parameters of the form
-        ``<component>__<parameter>`` so that it's possible to update each
-        component of a nested object.
-
-        Returns
-        -------
-        self
-        """
-        if not params:
-            # Simple optimisation to gain speed (inspect is slow)
-            return self
-        valid_params = self.get_params(deep=True)
-        # for key, value in iteritems(params):
-        for key, value in params.items():
-            split = key.split('__', 1)
-            if len(split) > 1:
-                # nested objects case
-                name, sub_name = split
-                if name not in valid_params:
-                    raise ValueError('Invalid parameter %s for estimator %s. '
-                                     'Check the list of available parameters '
-                                     'with `estimator.get_params().keys()`.' %
-                                     (name, self))
-                sub_object = valid_params[name]
-                sub_object.set_params(**{sub_name: value})
-            else:
-                # simple objects case
-                if key not in valid_params:
-                    raise ValueError('Invalid parameter %s for estimator %s. '
-                                     'Check the list of available parameters '
-                                     'with `estimator.get_params().keys()`.' %
-                                     (key, self.__class__.__name__))
-                setattr(self, key, value)
-        return self
-
-    def __repr__(self):
-        from sklearn.base import _pprint
-        class_name = self.__class__.__name__
-        return '%s(%s)' % (class_name, _pprint(self.get_params(deep=False),
-                                               offset=len(class_name),),)
-
-    # __getstate__ and __setstate__ are omitted because they only contain
-    # conditionals that are not satisfied by our objects (e.g.,
-    # ``if type(self).__module__.startswith('sklearn.')``.
-
 
 def distribution_estimation_uniform(X):
     """estimates a uniform distribution from an array of samples X