X = np.random.randn(14, 2)
y = [0, 0, 1, 1, 2, 3, 3, 0, 1, 2, 3, 0, 2, 3]
ovr = OneVsRestClassifier(SGDClassifier())
ovr.partial_fit(X[:7], y[:7], np.unique(y))
ovr.partial_fit(X[7:], y[7:])
pred = ovr.predict(X)
ovr1 = OneVsRestClassifier(SGDClassifier())
pred1 = ovr1.fit(X, y).predict(X)

In [46]: np.mean(pred==y)
Out[46]: 0.35714285714285715

In [47]: np.mean(pred1==y)
Out[47]: 0.5

ovr = OneVsRestClassifier(SGDClassifier())
X, y = iris.data, iris.target
ovr.partial_fit(X[:60], y[:60], np.unique(y))
ovr.partial_fit(X[60:], y[60:])
In [54]: pred = ovr.predict(X)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-54-48d087b7f7f3> in <module>()
----> 1 pred = ovr.predict(X)

/home/kaichogami/codes/development_scikit-learn/scikit-learn/sklearn/multiclass.py in predict(self, X)
    307             indicator = sp.csc_matrix((data, indices, indptr),
    308                                       shape=(n_samples, len(self.estimators_)))
--> 309             return self.label_binarizer_.inverse_transform(indicator)
    310 
    311     def predict_proba(self, X):

/home/kaichogami/codes/development_scikit-learn/scikit-learn/sklearn/preprocessing/label.pyc in inverse_transform(self, Y, threshold)
    369         else:
    370             y_inv = _inverse_binarize_thresholding(Y, self.y_type_,
--> 371                                                    self.classes_, threshold)
    372 
    373         if self.sparse_input_:

/home/kaichogami/codes/development_scikit-learn/scikit-learn/sklearn/preprocessing/label.pyc in _inverse_binarize_thresholding(y, output_type, classes, threshold)
    582     if output_type == "binary" and y.ndim == 2 and y.shape[1] > 2:
    583         raise ValueError("output_type='binary', but y.shape = {0}".
--> 584                          format(y.shape))
    585 
    586     if output_type != "binary" and y.shape[1] != len(classes):

ValueError: output_type='binary', but y.shape = (150, 3)

ovr.partial_fit(X[:20], y[:20], np.unique(y))
/home/kaichogami/codes/development_scikit-learn/scikit-learn/sklearn/multiclass.py in partial_fit(self, X, y, classes)
    253         self.label_binarizer_ = LabelBinarizer(sparse_output=True)
    254         Y = self.label_binarizer_.fit_transform(y)
--> 255         Y = Y.tocsc()
    256         Y = Y.T.toarray()
    257         if len(self.label_binarizer_.classes_) == 2:

AttributeError: 'numpy.ndarray' object has no attribute 'toc