Merge pull request #3 from aldder/sequentialfeatureselection_earlystop

fix rasbt#886 (comment) a…

Author: aldder

mlxtend/feature_selection/sequential_feature_selector.py

@@ -210,7 +210,7 @@ def __init__(self, estimator, k_features=1,
         if not isinstance(early_stop_rounds, int) or early_stop_rounds < 0:
             raise ValueError('Number of early stopping round should be '
                              'an integer value greater than or equal to 0.'
-                             'Got %d' % early_stop_rounds)
+                             'Got %s' % early_stop_rounds)
 
         self.early_stop_rounds = early_stop_rounds
 

mlxtend/feature_selection/tests/test_sequential_feature_selector.py

@@ -978,7 +978,7 @@ def test_custom_feature_names():
                n_jobs=1)
 
     sfs1 = sfs1.fit(X, y, custom_feature_names=(
-          'sepal length', 'sepal width', 'petal length', 'petal width'))
+        'sepal length', 'sepal width', 'petal length', 'petal width'))
     assert sfs1.k_feature_idx_ == (1, 3)
     assert sfs1.k_feature_names_ == ('sepal width', 'petal width')
     assert sfs1.subsets_[2]['feature_names'] == ('sepal width',
@@ -1000,13 +1000,12 @@ def test_run_forward_earlystop():
               k_features='best',
               forward=True,
               floating=False,
-              early_stop=True,
               early_stop_rounds=esr,
               verbose=0)
     sfs.fit(X_iris_with_noise, y_iris)
     assert len(sfs.subsets_) < X_iris_with_noise.shape[1]
-    assert all([sfs.subsets_[list(sfs.subsets_)[-esr-1]]['avg_score']
-               >= sfs.subsets_[i]['avg_score'] for i in sfs.subsets_.keys()])
+    assert all([sfs.k_score_ >= sfs.subsets_[i]['avg_score']
+               for i in sfs.subsets_])
 
 
 def test_run_backward_earlystop():
@@ -1024,10 +1023,9 @@ def test_run_backward_earlystop():
               k_features='best',
               forward=False,
               floating=False,
-              early_stop=True,
               early_stop_rounds=esr,
               verbose=0)
     sfs.fit(X_iris_with_noise, y_iris)
     assert len(sfs.subsets_) > 1
-    assert all([sfs.subsets_[list(sfs.subsets_)[-esr-1]]['avg_score']
-               >= sfs.subsets_[i]['avg_score'] for i in sfs.subsets_.keys()])
+    assert all([sfs.k_score_ >= sfs.subsets_[i]['avg_score']
+               for i in sfs.subsets_])