How to set class weights for OneVsRestClassifier in scikit-learn? -


i need svm working multilabel classifier, decided use onevsrestclassifier wrapper. problem arises training set becomes highly unbalanced: given class there more negative examples positive. solved class_weight parameter, if use in classifier wrapped in onevsrestclassifier, error:

from sklearn.svm import linearsvc sklearn.multiclass import onevsrestclassifier  weights = {'ham': 1, 'eggs': 2} svm = onevsrestclassifier(linearsvc(class_weight=weights))  x = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 0]] y = [['ham'], [], ['eggs', 'spam'], ['spam'], ['eggs']]  svm.fit(x, y) 
traceback (most recent call last):   file "", line 1, in    file "/usr/local/lib/python2.7/site-packages/sklearn/multiclass.py", line 197, in fit     n_jobs=self.n_jobs)   file "/usr/local/lib/python2.7/site-packages/sklearn/multiclass.py", line 87, in fit_ovr     in range(y.shape[1]))   file "/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 514, in __call__     self.dispatch(function, args, kwargs)   file "/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 311, in dispatch     job = immediateapply(func, args, kwargs)   file "/usr/local/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 135, in __init__     self.results = func(*args, **kwargs)   file "/usr/local/lib/python2.7/site-packages/sklearn/multiclass.py", line 56, in _fit_binary     estimator.fit(x, y)   file "/usr/local/lib/python2.7/site-packages/sklearn/svm/base.py", line 681, in fit     self.classes_, y)   file "/usr/local/lib/python2.7/site-packages/sklearn/utils/class_weight.py", line 49, in compute_class_weight     if classes[i] != c: indexerror: index 2 out of bounds axis 0 size 2

the problem linearsvc expects binary class [0, 1]. giving weights non-binary classes ('ham', 'egg' or [0,1,2]) fails. can use 'auto' weights instead, automatically "balances" classes choosing appropriate weights. work multiclass onevsrest classifier.

svm = onevsrestclassifier(linearsvc(class_weight='auto'))  x = [[1, 2], [3, 4], [5, 4]] y = [0,1,2]  svm.fit(x, y)

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