[ext/sklearn] updated parameter_search (fixes a undetected test failu…

…re in doctest) (markovmodel#829)

pyemma/_ext/sklearn/__init__.py

@@ -1,19 +0,0 @@
-
-# This file is part of PyEMMA.
-#
-# Copyright (c) 2015, 2014 Computational Molecular Biology Group, Freie Universitaet Berlin (GER)
-#
-# PyEMMA is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-
-__author__ = 'noe'

pyemma/_ext/sklearn/base.py

@@ -1,21 +1,3 @@
-
-# This file is part of PyEMMA.
-#
-# Copyright (c) 2015, 2014 Computational Molecular Biology Group, Freie Universitaet Berlin (GER)
-#
-# PyEMMA is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-
 """
 --------------------------------------------------------------------------------------------
 Extracted from skikit-learn to ensure basic compatibility

pyemma/_ext/sklearn/parameter_search.py

@@ -1,21 +1,3 @@
-
-# This file is part of PyEMMA.
-#
-# Copyright (c) 2015, 2014 Computational Molecular Biology Group, Freie Universitaet Berlin (GER)
-#
-# PyEMMA is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Lesser General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-
 """
 --------------------------------------------------------------------------------------------
 Extracted from skikit-learn to ensure basic compatibility
@@ -43,20 +25,30 @@
 from functools import reduce
 import operator
 from six.moves import zip
+import numpy as np
+
+
 
 class ParameterGrid(object):
     """Grid of parameters with a discrete number of values for each.
+
     Can be used to iterate over parameter value combinations with the
     Python built-in function iter.
+
+    Read more in the :ref:`User Guide <grid_search>`.
+
     Parameters
     ----------
     param_grid : dict of string to sequence, or sequence of such
         The parameter grid to explore, as a dictionary mapping estimator
         parameters to sequences of allowed values.
+
         An empty dict signifies default parameters.
+
         A sequence of dicts signifies a sequence of grids to search, and is
         useful to avoid exploring parameter combinations that make no sense
         or have no effect. See the examples below.
+
     Examples
     --------
     >>> from sklearn.grid_search import ParameterGrid
@@ -65,11 +57,15 @@ class ParameterGrid(object):
     ...    [{'a': 1, 'b': True}, {'a': 1, 'b': False},
     ...     {'a': 2, 'b': True}, {'a': 2, 'b': False}])
     True
+
     >>> grid = [{'kernel': ['linear']}, {'kernel': ['rbf'], 'gamma': [1, 10]}]
     >>> list(ParameterGrid(grid)) == [{'kernel': 'linear'},
     ...                               {'kernel': 'rbf', 'gamma': 1},
     ...                               {'kernel': 'rbf', 'gamma': 10}]
     True
+    >>> ParameterGrid(grid)[1] == {'kernel': 'rbf', 'gamma': 1}
+    True
+
     See also
     --------
     :class:`GridSearchCV`:
@@ -85,6 +81,7 @@ def __init__(self, param_grid):
 
     def __iter__(self):
         """Iterate over the points in the grid.
+
         Returns
         -------
         params : iterator over dict of string to any
@@ -97,14 +94,55 @@ def __iter__(self):
             if not items:
                 yield {}
             else:
-                keys, values = list(zip(*items))
+                keys, values = zip(*items)
                 for v in product(*values):
-                    params = dict(list(zip(keys, v)))
+                    params = dict(zip(keys, v))
                     yield params
 
     def __len__(self):
         """Number of points on the grid."""
         # Product function that can handle iterables (np.product can't).
         product = partial(reduce, operator.mul)
-        return sum(product(len(v) for v in list(p.values())) if p else 1
-                   for p in self.param_grid)
+        return sum(product(len(v) for v in p.values()) if p else 1
+                   for p in self.param_grid)
+
+    def __getitem__(self, ind):
+        """Get the parameters that would be ``ind``th in iteration
+
+        Parameters
+        ----------
+        ind : int
+            The iteration index
+
+        Returns
+        -------
+        params : dict of string to any
+            Equal to list(self)[ind]
+        """
+        # This is used to make discrete sampling without replacement memory
+        # efficient.
+        for sub_grid in self.param_grid:
+            # XXX: could memoize information used here
+            if not sub_grid:
+                if ind == 0:
+                    return {}
+                else:
+                    ind -= 1
+                    continue
+
+            # Reverse so most frequent cycling parameter comes first
+            keys, values_lists = zip(*sorted(sub_grid.items())[::-1])
+            sizes = [len(v_list) for v_list in values_lists]
+            total = np.product(sizes)
+
+            if ind >= total:
+                # Try the next grid
+                ind -= total
+            else:
+                out = {}
+                for key, v_list, n in zip(keys, values_lists, sizes):
+                    ind, offset = divmod(ind, n)
+                    out[key] = v_list[offset]
+                return out
+
+        raise IndexError('ParameterGrid index out of range')