scikit-learn mlxtend带有sample_weights的EnsembleVoteClassifier

Question

我正在尝试根据mlxtend documentation

拟合EnsembleVoteClassifier

对于普通的grid.fit我可以使用fit_params来设置sample_weight，但是使用VotingClassifier它不起作用。怎么解决这个问题？

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})