From 27206b381d3b50bc5182f17503ad4bff71a696b5 Mon Sep 17 00:00:00 2001
From: James Bourbeau <jrbourbeau@gmail.com>
Date: Thu, 21 Sep 2017 23:53:05 -0500
Subject: [PATCH] Adds fit_param test for StackingClassifier

---
 mlxtend/classifier/stacking_classification.py |  8 +++----
 .../tests/test_stacking_classifier.py         | 23 +++++++++++++++++++
 2 files changed, 27 insertions(+), 4 deletions(-)

diff --git a/mlxtend/classifier/stacking_classification.py b/mlxtend/classifier/stacking_classification.py
index 35dfec80e..5057b4c08 100644
--- a/mlxtend/classifier/stacking_classification.py
+++ b/mlxtend/classifier/stacking_classification.py
@@ -130,17 +130,17 @@ def fit(self, X, y, **fit_params):
 
         meta_features = self._predict_meta_features(X)
         # Extract fit_params for meta_clf_
-        meta_clf_fit_params = {}
+        meta_fit_params = {}
         meta_clf_name = list(self.named_meta_clf_.keys())[0]
         for key, value in six.iteritems(fit_params):
             if meta_clf_name in key and 'meta-' in meta_clf_name:
-                meta_clf_fit_params[key.replace(meta_clf_name+'__', '')] = value
+                meta_fit_params[key.replace(meta_clf_name+'__', '')] = value
 
         if not self.use_features_in_secondary:
-            self.meta_clf_.fit(meta_features, y, **meta_clf_fit_params)
+            self.meta_clf_.fit(meta_features, y, **meta_fit_params)
         else:
             self.meta_clf_.fit(np.hstack((X, meta_features)), y,
-                               **meta_clf_fit_params)
+                               **meta_fit_params)
 
         return self
 
diff --git a/mlxtend/classifier/tests/test_stacking_classifier.py b/mlxtend/classifier/tests/test_stacking_classifier.py
index ca25d05b2..92cfa87d6 100644
--- a/mlxtend/classifier/tests/test_stacking_classifier.py
+++ b/mlxtend/classifier/tests/test_stacking_classifier.py
@@ -79,6 +79,29 @@ def test_StackingClassifier_proba_concat_1():
     assert scores_mean == 0.93, scores_mean
 
 
+def test_StackingClassifier_fit_params():
+    np.random.seed(123)
+    meta = LogisticRegression()
+    clf1 = RandomForestClassifier()
+    clf2 = GaussianNB()
+    sclf = StackingClassifier(classifiers=[clf1, clf2],
+                              meta_classifier=meta)
+    n_samples = X.shape[0]
+    fit_params = {
+        'randomforestclassifier__sample_weight': np.ones(n_samples),
+        'meta-logisticregression__sample_weight': np.arange(n_samples)
+        }
+
+    scores = cross_val_score(sclf,
+                             X,
+                             y,
+                             cv=5,
+                             scoring='accuracy',
+                             fit_params=fit_params)
+    scores_mean = (round(scores.mean(), 2))
+    assert scores_mean == 0.95
+
+
 def test_StackingClassifier_avg_vs_concat():
     np.random.seed(123)
     lr1 = LogisticRegression()