Hi Ark
Thanks for reporting the issue.
Could you please provide a minimum code sample to reproduce and open an
issue on github.
That would be great.
Which version of sklearn are you using?
Also, are you aware that you don't need the OneVsRestClassifier for
multi-class support in SGDClassifier?
SGDClassifier has multi-class support on its own.
If you didn't know this, it would be good if you could point us to the
docs that gave you the impression
you needed OneVsRestClassifier - this seems to be a common misconception.
Best,
Andy
On 01/11/2013 01:19 AM, Ark wrote:
> Hello,
> I see an issue with predict in case of predicting a text document. [I
> load
> an already trained classifier (OneVsRest(SGDClassifier(loss=log))) using
> joblib.load].
> Thanks.
>
>
> In [1]: import sklearn
>
> In [2]: from sklearn.externals import joblib
>
> In [4]: clf = joblib.load("classifier.joblib")
>
> In [6]: with open("topredict.txt") as f:
> ...: em = f.read()
>
> In [7]: clf.predict
> Out[7]:
> <bound method OneVsRestClassifier.predict of
> OneVsRestClassifier(estimator=SGDClassifier(alpha=1e-05, class_weight=None,
> epsilon=0.1, eta0=0.0,
> fit_intercept=True, learning_rate='optimal', loss='log', n_iter=35,
> n_jobs=-1, penalty='l2', power_t=0.5, rho=0.85, seed=0,
> shuffle=True, verbose=0, warm_start=False))>
>
> In [8]: clf.predict(em)
> ---------------------------------------------------------------------------
> AttributeError Traceback (most recent call last)
> <ipython-input-20-3c92945e466e> in <module>()
> ----> 1 clf.predict(em)
>
> /home/n/env/lib/python2.6/site-packages/sklearn/multiclass.pyc in
> predict(self,
> X)
> 180 self._check_is_fitted()
> 181
> --> 182 return predict_ovr(self.estimators_, self.label_binarizer_, X)
> 183
> 184 @property
>
> /home/n/env/lib/python2.6/site-packages/sklearn/multiclass.pyc in
> predict_ovr(estimators, label_binarizer, X)
> 79 def predict_ovr(estimators, label_binarizer, X):
> 80 """Make predictions using the one-vs-the-rest strategy."""
> ---> 81 Y = np.array([_predict_binary(e, X) for e in estimators])
> 82 e = estimators[0]
> 83 thresh = 0 if hasattr(e, "decision_function") and
> is_classifier(e)
> else .5
>
> /home/n7/env/lib/python2.6/site-packages/sklearn/multiclass.pyc in
> _predict_binary(estimator, X)
> 54 else:
> 55 # probabilities of the positive class
> ---> 56 return estimator.predict_proba(X)[:, 1]
> 57
> 58
>
> AttributeError: 'list' object has no attribute 'predict_proba'
>
> In [9]:
>
>
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