In your code, 'document' is just a string, not a feature vector. You should
use the same Vectorizer that you used to train the classifier to begin with.
Trained classifier objects are generally not compatible across versions.
You should retrain the classifier using the new version (and who knows, you
might even get a better classifier!). Although your particular error looks
like a problem with your installation. Try reinstalling?
-Ken
On Mon, Jan 14, 2013 at 8:22 PM, Ark <[email protected]> wrote:
>
> > Could you please provide a minimum code sample to reproduce and open an
> > issue on github.
>
> Following the minimalistic code to reproduce the issue (assuming the
> classifier is already trained and saved). I will open the issue on github
> for
> the same.
>
> -------------------------------------------------------------
> from sklearn.externals import joblib
>
>
> # Train the classifier on a dataset of text
> # documents with
> # OneVsRestClassifier(SGDClassifier(loss=log, n_iter=35))
>
> # classifier object dumped using joblib.dump,
> # without compression, for later use.
>
>
> classifier = joblib.load("classifier.joblib")
>
> with open("file") as f:
> document = f.read()
>
> predict=classifier.predict(document)
>
> --------------------------------------------------------------
>
>
> > That would be great.
> > Which version of sklearn are you using?
> I am on scikit 0.12.1
>
> >
> > 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.
>
> I am using OneVsRest so as to analyze the binary classifiers for each
> category.
> I also tried downloading the 0.13 version from source and installing it.
> This
> time I see a different error. The steps to reproduce for version 0.13 in
> ipython
> are as follows:
>
> $ ipython
> Python 2.6.6 (r266:84292, Jun 18 2012, 14:18:47)
> Type "copyright", "credits" or "license" for more information.
>
> IPython 0.13 -- An enhanced Interactive Python.
> ? -> Introduction and overview of IPython's features.
> %quickref -> Quick reference.
> help -> Python's own help system.
> object? -> Details about 'object', use 'object??' for extra details.
>
> In [1]: import sklearn
>
> In [2]: from sklearn.externals import joblib
>
> In [3]: clf = joblib.load("/home/n7/classifier.joblib")
> ---------------------------------------------------------------------------
> ImportError Traceback (most recent call last)
> <ipython-input-3-999ca461c6f0> in <module>()
> ----> 1 clf = joblib.load("/home/n7/classifier.joblib")
>
> /home/n7/env/lib/python2.6/site-
> packages/sklearn/externals/joblib/numpy_pickle.pyc in load(filename,
> mmap_mode)
> 416
> 417 try:
> --> 418 obj = unpickler.load()
> 419 finally:
> 420 if hasattr(unpickler, 'file_handle'):
>
> /usr/lib64/python2.6/pickle.pyc in load(self)
> 856 while 1:
> 857 key = read(1)
> --> 858 dispatch[key](self)
> 859 except _Stop, stopinst:
> 860 return stopinst.value
>
> /usr/lib64/python2.6/pickle.pyc in load_global(self)
> 1088 module = self.readline()[:-1]
> 1089 name = self.readline()[:-1]
> -> 1090 klass = self.find_class(module, name)
> 1091 self.append(klass)
> 1092 dispatch[GLOBAL] = load_global
>
> /usr/lib64/python2.6/pickle.pyc in find_class(self, module, name)
> 1122 def find_class(self, module, name):
> 1123 # Subclasses may override this
> -> 1124 __import__(module)
> 1125 mod = sys.modules[module]
> 1126 klass = getattr(mod, name)
>
> /home/n7/env/lib/python2.6/site-packages/sklearn/linear_model/__init__.py
> in
> <module>()
> 22 from .ridge import Ridge, RidgeCV, RidgeClassifier,
> RidgeClassifierCV, \
> 23 ridge_regression
> ---> 24 from .logistic import LogisticRegression
> 25 from .omp import orthogonal_mp, orthogonal_mp_gram,
> OrthogonalMatchingPursuit
> 26 from .perceptron import Perceptron
>
> /home/n7/env/lib/python2.6/site-packages/sklearn/linear_model/logistic.py
> in
> <module>()
> 4 from ..feature_selection.selector_mixin import SelectorMixin
> 5 from ..svm.base import BaseLibLinear
> ----> 6 from ..svm.liblinear import csr_predict_prob_wrap,
> predict_prob_wrap
> 7
> 8
>
> ImportError: cannot import name csr_predict_prob_wrap
>
>
>
>
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