title: scikit-learn mlxtend EnsembleVoteClassifier with sample_weights
body: <p>I am trying to fit an EnsembleVoteClassifier according to <a href="https://rasbt.github.io/mlxtend/user_guide/classifier/EnsembleVoteClassifier/" rel="nofollow noreferrer">mlxtend documentation</a></p>
  
  <p>For normal grid.fit I can use fit_params to set sample_weight, but with the VotingClassifier it does not work. How can this be solved?</p>
  
  <pre><code>from sklearn import datasets
  iris = datasets.load_iris()
  X, y = iris.data[:, :], iris.target
  from sklearn.model_selection import GridSearchCV
  from sklearn.linear_model import LogisticRegression
  from sklearn.naive_bayes import GaussianNB 
  from sklearn.ensemble import RandomForestClassifier
  from mlxtend.classifier import EnsembleVoteClassifier
  from sklearn.pipeline import Pipeline
  from mlxtend.feature_selection import SequentialFeatureSelector
  clf1 = LogisticRegression(random_state=1)
  clf2 = RandomForestClassifier(random_state=1)
  clf3 = GaussianNB()
  '''Creating a feature-selection-classifier pipeline'''
  sfs1 = SequentialFeatureSelector(clf1, 
                                   k_features=4,
                                   forward=True, 
                                   floating=False, 
                                   scoring='accuracy',
                                   verbose=0,
                                   cv=0)
  clf1_pipe = Pipeline([('sfs', sfs1),
                        ('logreg', clf1)])
  eclf = EnsembleVoteClassifier(clfs=[clf1_pipe, clf2, clf3], 
                                voting='soft')
  params = {'pipeline__sfs__k_features': [1, 2, 3],
            'pipeline__logreg__C': [1.0, 100.0],
            'randomforestclassifier__n_estimators': [20, 200]}
  grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5)
  sample_weights = [1] * len(iris.target)
  grid.fit(iris.data, iris.target,**{'pipeline__logreg__sample_weight': sample_weights})
  </code></pre>