On 04/17/2012 04:38 PM, Olivier Grisel wrote:
> Le 17 avril 2012 05:32, Dimitrios Pritsos<[email protected]> a écrit :
>> Hello again
>>
>> I am trying to run an OneClassSVM() test:
>>
>> import sklearn.svm.sparse as sp_svm
>>
>> ocsvm = sp_svm.OneClassSVM(nu=0.5, kernel='linear')
>>
>> ocsvm.fit( ssp.csr_matrix(train_X, shape=train_X.shape, dtype=np.float64) )
>>
>>
>> and I am getting the following message:
>>
>> File
>> "/home/dimitrios/Development_Workspace/webgenreidentification/src/experiments_lowbow.py",
>> line 147, in evaluate
>> ocsvm.fit( ssp.csr_matrix(train_X, shape=train_X.shape,
>> dtype=np.float64) ) #, train_Y)
>> File
>> "/usr/local/lib/python2.6/dist-packages/sklearn/svm/sparse/classes.py", line
>> 175, in fit
>> X, [], sample_weight=sample_weight)
>> File "/usr/local/lib/python2.6/dist-packages/sklearn/svm/sparse/base.py",
>> line 22, in fit
>> return super(SparseBaseLibSVM, self).fit(X, y, sample_weight)
>> File "/usr/local/lib/python2.6/dist-packages/sklearn/svm/base.py", line
>> 150, in fit
>> fit(X, y, sample_weight)
>> File "/usr/local/lib/python2.6/dist-packages/sklearn/svm/base.py", line
>> 263, in _sparse_fit
>> % (X.shape, y.shape))
>> ValueError: X and y have incompatible shapes: (180, 255) vs (0,)
>> Note: Sparse matrices cannot be indexed w/boolean masks (use `indices=True`
>> in CV).
> It seems that sparse.OneClassSVM has been broken by the last
> refactoring: there is no `y` hence for unsupervised models so the
> error message does not make sense.
>
> Anyway sklearn.svm.sparse.OneClassSVM should just be a backward compat
> estimator. sklearn.svm.OneClassSVM should work with both dense and
> sparse data now. Can you confirm that you don't have the issue with
> sklearn.svm.OneClassSVM ?
>
> If so we might just remove the sklearn.svm.sparse.OneClassSVM
> implementation and create an alias to sklearn.svm.OneClassSVM for the
> backward compat instead.
>
Unfortunately, sklearn.svm.OneClassSVM returns the same error !?
Regards,
Dimitrios
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