Adds fit_params support for StackingClassifier

mlxtend/classifier/stacking_classification.py

@@ -77,8 +77,8 @@ def __init__(self, classifiers, meta_classifier,
         self.verbose = verbose
         self.use_features_in_secondary = use_features_in_secondary
 
-    def fit(self, X, y):
-        """ Fit ensemble classifers and the meta-classifier.
+    def fit(self, X, y, **fit_params):
+        """Fit ensemble classifers and the meta-classifier.
 
         Parameters
         ----------
@@ -87,18 +87,26 @@ def fit(self, X, y):
             n_features is the number of features.
         y : array-like, shape = [n_samples] or [n_samples, n_outputs]
             Target values.
+        fit_params : dict, optional
+            Parameters to pass to the fit methods of the classifiers and
+            meta_classifier.
 
         Returns
         -------
         self : object
 
         """
         self.clfs_ = [clone(clf) for clf in self.classifiers]
+        self.named_clfs_ = {key: value for key, value in
+                            _name_estimators(self.clfs_)}
         self.meta_clf_ = clone(self.meta_classifier)
+        self.named_meta_clf_ = {'meta-%s' % key: value for key, value in
+                                _name_estimators([self.meta_clf_])}
+
         if self.verbose > 0:
             print("Fitting %d classifiers..." % (len(self.classifiers)))
 
-        for clf in self.clfs_:
+        for name, clf in six.iteritems(self.named_clfs_):
 
             if self.verbose > 0:
                 i = self.clfs_.index(clf) + 1
@@ -112,14 +120,27 @@ def fit(self, X, y):
             if self.verbose > 1:
                 print(_name_estimators((clf,))[0][1])
 
-            clf.fit(X, y)
+            # Extract fit_params for clf
+            clf_fit_params = {}
+            for key, value in six.iteritems(fit_params):
+                if name in key and 'meta-' not in key:
+                    clf_fit_params[key.replace(name+'__', '')] = value
+
+            clf.fit(X, y, **clf_fit_params)
 
         meta_features = self._predict_meta_features(X)
+        # Extract fit_params for meta_clf_
+        meta_clf_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
 
         if not self.use_features_in_secondary:
-            self.meta_clf_.fit(meta_features, y)
+            self.meta_clf_.fit(meta_features, y, **meta_clf_fit_params)
         else:
-            self.meta_clf_.fit(np.hstack((X, meta_features)), y)
+            self.meta_clf_.fit(np.hstack((X, meta_features)), y,
+                               **meta_clf_fit_params)
 
         return self