From 2d5788f8bea56afa22653e62270afa30f776e9ce Mon Sep 17 00:00:00 2001
From: Olivier Grisel <olivier.grisel@ensta.org>
Date: Mon, 1 Nov 2010 17:24:15 +0100
Subject: [PATCH] cosmits
---
scikits/learn/grid_search.py | 45 ++++++++++++++++++------------------
1 file changed, 22 insertions(+), 23 deletions(-)
diff --git a/scikits/learn/grid_search.py b/scikits/learn/grid_search.py
index 6c434561df..5524f09c85 100644
--- a/scikits/learn/grid_search.py
+++ b/scikits/learn/grid_search.py
@@ -28,26 +28,26 @@ except:
def iter_grid(param_grid):
- """ Generators on the combination of the various parameter lists given.
+ """Generators on the combination of the various parameter lists given
- Parameters
- -----------
- kwargs: keyword arguments, lists
- Each keyword argument must be a list of values that should
- be explored.
-
- Returns
- --------
- params: dictionary
- Dictionnary with the input parameters taking the various
- values succesively.
-
- Examples
- ---------
- >>> from scikits.learn.grid_search import iter_grid
- >>> param_grid = {'a':[1, 2], 'b':[True, False]}
- >>> list(iter_grid(param_grid))
- [{'a': 1, 'b': True}, {'a': 1, 'b': False}, {'a': 2, 'b': True}, {'a': 2, 'b': False}]
+ Parameters
+ -----------
+ kwargs: keyword arguments, lists
+ Each keyword argument must be a list of values that should
+ be explored.
+
+ Returns
+ --------
+ params: dictionary
+ Dictionnary with the input parameters taking the various
+ values succesively.
+
+ Examples
+ ---------
+ >>> from scikits.learn.grid_search import iter_grid
+ >>> param_grid = {'a':[1, 2], 'b':[True, False]}
+ >>> list(iter_grid(param_grid))
+ [{'a': 1, 'b': True}, {'a': 1, 'b': False}, {'a': 2, 'b': True}, {'a': 2, 'b': False}]
"""
if hasattr(param_grid, 'has_key'):
@@ -64,6 +64,7 @@ def iter_grid(param_grid):
def fit_grid_point(X, y, base_clf, clf_params, cv, loss_func, iid,
**fit_params):
"""Run fit on one set of parameters
+
Returns the score and the instance of the classifier
"""
# update parameters of the classifier after a copy of its base structure
@@ -97,10 +98,8 @@ def fit_grid_point(X, y, base_clf, clf_params, cv, loss_func, iid,
return score, clf
-################################################################################
class GridSearchCV(BaseEstimator):
- """
- Grid search on the parameters of a classifier.
+ """Grid search on the parameters of a classifier
Important members are fit, predict.
@@ -178,6 +177,7 @@ class GridSearchCV(BaseEstimator):
def fit(self, X, y, refit=True, cv=None, **kw):
"""Run fit with all sets of parameters
+
Returns the best classifier
Parameters
@@ -230,7 +230,6 @@ class GridSearchCV(BaseEstimator):
return self
-
def score(self, X, y=None):
# This method is overridden during the fit if the best estimator
# found has a score function.
--
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