Using KNeighborsClassifier as a base learner for AdaBoostClassifier

gistfile1.txt

Running following code:

clf = AdaBoostClassifier(n_estimators=100, base_estimator=KNeighborsClassifier()); clf.fit(x,y)


I get the following traceback:

TypeError                                 Traceback (most recent call last)
/usr/local/lib/python2.7/dist-packages/IPython/utils/py3compat.pyc in execfile(fname, *where)
    176             else:
    177                 filename = fname
--> 178             __builtin__.execfile(filename, *where)

/home/vdesai/Dropbox/currentDocs/Kaggle/scikitComp/code/ensemble/train_adaboost.py in <module>()
     49 
     50 if __name__=="__main__":
---> 51     main()

/home/vdesai/Dropbox/currentDocs/Kaggle/scikitComp/code/ensemble/train_adaboost.py in main()
     24     #clf = AdaBoostClassifier(n_estimators=100, base_estimator=SVC(probability=True));
     25     clf = AdaBoostClassifier(n_estimators=100, base_estimator=KNeighborsClassifier());
---> 26     clf.fit(x,y)
     27     tup = ('adaboost', x, clf)
     28     data_io.save_model(tup, 'adaboost.pickle')

/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/weight_boosting.pyc in fit(self, X, y, sample_weight)
    387                     "algorithm='SAMME' instead.")
    388 
--> 389         return super(AdaBoostClassifier, self).fit(X, y, sample_weight)
    390 
    391     def _boost(self, iboost, X, y, sample_weight):

/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/weight_boosting.pyc in fit(self, X, y, sample_weight)
    123                 iboost,
    124                 X, y,
--> 125                 sample_weight)
    126 
    127             # Early termination

/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/weight_boosting.pyc in _boost(self, iboost, X, y, sample_weight)
    425         """
    426         if self.algorithm == 'SAMME.R':
--> 427             return self._boost_real(iboost, X, y, sample_weight)
    428 
    429         else:  # elif self.algorithm == "SAMME":

/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/weight_boosting.pyc in _boost_real(self, iboost, X, y, sample_weight)
    439             pass
    440 
--> 441         estimator.fit(X, y, sample_weight=sample_weight)
    442 
    443         y_predict_proba = estimator.predict_proba(X)

TypeError: fit() got an unexpected keyword argument 'sample_weight'