Sorry, I think my previous message was a little bit ambiguous.
What I would try is:
1) Unpickle the original pickle file in Python 2
2) Pickle it via joblib
3) Load it in Python 3
(I think you only did step 3), right? Sorry for the confusion).
I also just saw a related SO post that might be very helpful:
http://stackoverflow.com/questions/11305790/pickle-incompatability-of-numpy-arrays-between-python-2-and-3
<http://stackoverflow.com/questions/11305790/pickle-incompatability-of-numpy-arrays-between-python-2-and-3>
Best,
Sebastian
> On Jan 22, 2015, at 5:10 PM, jni.s...@gmail.com wrote:
>
> Hi Sebastian,
>
> Thanks for the response, but actually joblib doesn't work either:
>
> In [1]: from sklearn.externals import joblib
>
> In [2]: rf = joblib.load('rf-1.joblib')
> ---------------------------------------------------------------------------
> error Traceback (most recent call last)
> <ipython-input-3-2c47f0ec1d5b> in <module>()
> ----> 1 rf = joblib.load('rf-1.joblib')
>
> /Users/nuneziglesiasj/anaconda/envs/py3k-gala/lib/python3.3/site-packages/sklearn/externals/joblib/numpy_pickle.py
> in load(filename, mmap_mode)
> 417 'ignoring mmap_mode "%(mmap_mode)s"
> flag passed'
> 418 % locals(), Warning, stacklevel=2)
> --> 419 unpickler = ZipNumpyUnpickler(filename,
> file_handle=file_handle)
> 420 else:
> 421 unpickler = NumpyUnpickler(filename,
> file_handle=file_handle,
>
> /Users/nuneziglesiasj/anaconda/envs/py3k-gala/lib/python3.3/site-packages/sklearn/externals/joblib/numpy_pickle.py
> in __init__(self, filename, file_handle)
> 306 NumpyUnpickler.__init__(self, filename,
> 307 file_handle,
> --> 308 mmap_mode=None)
> 309
> 310 def _open_pickle(self, file_handle):
>
> /Users/nuneziglesiasj/anaconda/envs/py3k-gala/lib/python3.3/site-packages/sklearn/externals/joblib/numpy_pickle.py
> in __init__(self, filename, file_handle, mmap_mode)
> 264 self._dirname = os.path.dirname(filename)
> 265 self.mmap_mode = mmap_mode
> --> 266 self.file_handle = self._open_pickle(file_handle)
> 267 Unpickler.__init__(self, self.file_handle)
> 268 try:
>
> /Users/nuneziglesiasj/anaconda/envs/py3k-gala/lib/python3.3/site-packages/sklearn/externals/joblib/numpy_pickle.py
> in _open_pickle(self, file_handle)
> 309
> 310 def _open_pickle(self, file_handle):
> --> 311 return BytesIO(read_zfile(file_handle))
> 312
> 313
>
> /Users/nuneziglesiasj/anaconda/envs/py3k-gala/lib/python3.3/site-packages/sklearn/externals/joblib/numpy_pickle.py
> in read_zfile(file_handle)
> 66 # We use the known length of the data to tell Zlib the size of the
> 67 # buffer to allocate.
> ---> 68 data = zlib.decompress(file_handle.read(), 15, length)
> 69 assert len(data) == length, (
> 70 "Incorrect data length while decompressing %s."
>
> error: Error -3 while decompressing data: incorrect header check
>
>
> The very same commands work fine in Py2:
>
> In [1]: from sklearn.externals import joblib
>
> In [2]: rf1 = joblib.load('rf-1.joblib')
>
> In [3]:
>
>
> Is this unexpected?
>
>
>
>
> On Fri, Jan 23, 2015 at 1:57 AM, Sebastian Raschka <se.rasc...@gmail.com
> <mailto:se.rasc...@gmail.com>> wrote:
>
> Hi, Juan,
>
> It's been some time, but I remember that I had similar issues. I think it has
> to do with the numpy arrays that specifically cause problems in pickle.
> (http://bugs.python.org/issue6784)
>
> You could try to use joblib (which should also be more efficient):
>
> >>> from sklearn.externals import joblib
> >>> joblib.dump(clf, 'filename.pkl')
> >>> clf = joblib.load('filename.pkl')
>
> (http://scikit-learn.org/stable/modules/model_persistence.html)
>
>
> Best,
> Sebastian
>
> > On Jan 22, 2015, at 8:50 AM, jni.s...@gmail.com wrote:
> >
> > Hi all,
> >
> > I'm working on a project that depends on sklearn. I've been up test
> > coverage (which includes saving a RandomForest, so far using joblib
> > serialization), and now I wanted to make the project Python 3-compatible.
> > However, the final roadblock is the sharing of RF objects: I can't load the
> > Python 2-serialized RFs with Python 3 tests. Of course, the test outcome
> > depends on the exact RF that was created a while back. Is there any way
> > around this?
> >
> > Thanks!
> >
> > Juan.
> >
> >
> > ------------------------------------------------------------------------------
> >
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>
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GigeNET is offering a free month of service with a new server in Ashburn.
Choose from 2 high performing configs, both with 100TB of bandwidth.
Higher redundancy.Lower latency.Increased capacity.Completely compliant.
http://p.sf.net/sfu/gigenet
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