make the pipeline / grid_search object nicer to introspect in tests

scikits/learn/feature_extraction/tests/test_text.py

@@ -277,10 +277,12 @@ def test_dense_vectorizer_pipeline_grid_selection():
     pred = grid_search.fit(list(train_data), y_train).predict(list(test_data))
     assert_array_equal(pred, y_test)
 
-    # on this toy dataset bigram representation yields higher predictive
-    # accurracy
-    # TODO: unstable test...
-    # assert_equal(grid_search.best_estimator.steps[0][1].analyzer.max_n, 2)
+    # on this toy dataset bigram representation which is used in the last of the
+    # grid_search is considered the best estimator since they all converge to
+    # 100% accurracy models
+    assert_equal(grid_search.best_score, 1.0)
+    best_vectorizer = grid_search.best_estimator.named_steps['vect']
+    assert_equal(best_vectorizer.analyzer.max_n, 2)
 
 
 def test_pickle():

scikits/learn/grid_search.py

@@ -62,6 +62,7 @@ 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
@@ -209,7 +210,7 @@ class GridSearchCV(BaseEstimator):
                     for clf_params in grid)
 
         # Out is a list of pairs: score, estimator
-        best_estimator = max(out)[1] # get maximum score
+        self.best_score, best_estimator = max(out) # get maximum score
 
         if refit:
             # fit the best estimator using the entire dataset

scikits/learn/pipeline.py

@@ -92,10 +92,10 @@ class Pipeline(BaseEstimator):
             fit/transform) that are chained, in the order in which
             they are chained, with the last object an estimator.
         """
-        self._named_steps = dict(steps)
+        self.named_steps = dict(steps)
         names, estimators = zip(*steps)
         self.steps = steps
-        assert len(self._named_steps) == len(steps), ("Names provided are "
+        assert len(self.named_steps) == len(steps), ("Names provided are "
             "not unique: %s" % names)
         transforms = estimators[:-1]
         estimator = estimators[-1]
@@ -115,8 +115,8 @@ class Pipeline(BaseEstimator):
         if not deep:
             return super(Pipeline, self)._get_params(deep=False)
         else:
-            out = self._named_steps.copy()
-            for name, step in self._named_steps.iteritems():
+            out = self.named_steps.copy()
+            for name, step in self.named_steps.iteritems():
                 for key, value in step._get_params(deep=True).iteritems():
                     out['%s__%s' % (name, key)] = value
             return out

scikits/learn/tests/test_pipeline.py

@@ -56,7 +56,7 @@ def test_pipeline_init():
 
     # Test clone
     pipe2 = clone(pipe)
-    assert_false(pipe._named_steps['svc'] is pipe2._named_steps['svc'])
+    assert_false(pipe.named_steps['svc'] is pipe2.named_steps['svc'])
 
     # Check that appart from estimators, the parameters are the same
     params = pipe._get_params()