Adds fit_params to StackingRegressor fit method + adds test

Author: jrbourbeau

mlxtend/classifier/stacking_classification.py

@@ -88,8 +88,8 @@ def fit(self, X, y, **fit_params):
         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.
+            Parameters to pass to the fit methods of `classifiers` and
+            `meta_classifier`.
 
         Returns
         -------

mlxtend/regressor/stacking_regression.py

@@ -65,7 +65,7 @@ def __init__(self, regressors, meta_regressor, verbose=0):
                                      _name_estimators([meta_regressor])}
         self.verbose = verbose
 
-    def fit(self, X, y):
+    def fit(self, X, y, **fit_params):
         """Learn weight coefficients from training data for each regressor.
 
         Parameters
@@ -75,18 +75,25 @@ def fit(self, X, y):
             n_features is the number of features.
         y : array-like, shape = [n_samples] or [n_samples, n_targets]
             Target values.
+        fit_params : dict, optional
+            Parameters to pass to the fit methods of `regressors` and
+            `meta_regressor`.
 
         Returns
         -------
         self : object
 
         """
         self.regr_ = [clone(regr) for regr in self.regressors]
+        self.named_regr_ = {key: value for key, value in
+                            _name_estimators(self.regr_)}
         self.meta_regr_ = clone(self.meta_regressor)
+        self.named_meta_regr_ = {'meta-%s' % key: value for key, value in
+                                 _name_estimators([self.meta_regr_])}
         if self.verbose > 0:
             print("Fitting %d regressors..." % (len(self.regressors)))
 
-        for regr in self.regr_:
+        for name, regr in six.iteritems(self.named_regr_):
 
             if self.verbose > 0:
                 i = self.regr_.index(regr) + 1
@@ -100,10 +107,23 @@ def fit(self, X, y):
             if self.verbose > 1:
                 print(_name_estimators((regr,))[0][1])
 
-            regr.fit(X, y)
+            # Extract fit_params for regr
+            regr_fit_params = {}
+            for key, value in six.iteritems(fit_params):
+                if name in key and 'meta-' not in key:
+                    regr_fit_params[key.replace(name+'__', '')] = value
+
+            regr.fit(X, y, **regr_fit_params)
 
         meta_features = self._predict_meta_features(X)
-        self.meta_regr_.fit(meta_features, y)
+        # Extract fit_params for meta_regr_
+        meta_fit_params = {}
+        meta_regr_name = list(self.named_meta_regr_.keys())[0]
+        for key, value in six.iteritems(fit_params):
+            if meta_regr_name in key and 'meta-' in meta_regr_name:
+                meta_fit_params[key.replace(meta_regr_name+'__', '')] = value
+        self.meta_regr_.fit(meta_features, y, **meta_fit_params)
+
         return self
 
     @property

mlxtend/regressor/tests/test_stacking_regression.py

@@ -11,7 +11,7 @@
 import numpy as np
 from numpy.testing import assert_almost_equal
 from nose.tools import raises
-from sklearn.model_selection import GridSearchCV
+from sklearn.model_selection import GridSearchCV, cross_val_score
 
 # Generating a sample dataset
 np.random.seed(1)
@@ -108,6 +108,23 @@ def test_gridsearch_numerate_regr():
     assert best == got
 
 
+def test_StackingRegressor_fit_params():
+    lr = LinearRegression()
+    svr_lin = SVR(kernel='linear')
+    ridge = Ridge(random_state=1)
+    svr_rbf = SVR(kernel='rbf')
+    stregr = StackingRegressor(regressors=[svr_lin, lr, ridge],
+                               meta_regressor=svr_rbf)
+
+    fit_params = {'ridge__sample_weight': np.ones(X1.shape[0]),
+                  'svr__sample_weight': np.ones(X1.shape[0]),
+                  'meta-svr__sample_weight': np.ones(X1.shape[0])}
+
+    scores = cross_val_score(stregr, X1, y, cv=5, fit_params=fit_params)
+    scores_mean = (round(scores.mean(), 1))
+    assert scores_mean == 0.1
+
+
 def test_get_coeff():
     lr = LinearRegression()
     svr_lin = SVR(kernel='linear')