scikit multilabel classification: ValueError: bad input shape

Question

I beieve SGDClassifier() with loss='log' supports Multilabel classification and I do not have to use OneVsRestClassifier. Check this

Now, my dataset is quite big and I am using HashingVectorizer and passing result as input to SGDClassifier. My target has 42048 features.

When I run this, as follows:

clf.partial_fit(X_train_batch, y)

I get: ValueError: bad input shape (300000, 42048).

I have also used classes as the parameter as follows, but still same problem.

clf.partial_fit(X_train_batch, y, classes=np.arange(42048))

In the documentation of SGDClassifier, it says y : numpy array of shape [n_samples]

Answer 1

No, SGDClassifier does not do multilabel classification -- it does multiclass classification, which is a different problem, although both are solved using a one-vs-all problem reduction.

Then, neither SGD nor OneVsRestClassifier.fit will accept a sparse matrix for y. The former wants an array of labels, as you've already found out. The latter wants, for multilabel purposes, a list of lists of labels, e.g.

y = [[1], [2, 3], [1, 3]]

to denote that X[0] has label 1, X[1] has labels {2,3} and X[2] has labels {1,3}.