Joel, *sorry*, I should probably have mentioned this earlier:
joblib.dump takes a "compress" kwarg, which I used, probably 3 as recommended
by the docstring, so that I wouldn't have a bajillion files representing my RF.
So the zipping error makes perfect sense, except that I wouldn't expect g
On Wed, Jan 21, 2015 at 6:58 PM, Mark Regan wrote:
> Hi Diego. Not sure if it'll be useful but I coded up a DTW classifier with
> the same sklearn style fit/predict methods here.
We have an ancient pull request lying around for scikit-image:
https://github.com/scikit-image/scikit-image/pull/518
Could you provide the traceback when using pickle? The joblib error is
about zipping, which should not be applicable there...
On 23 January 2015 at 13:30, Juan Nunez-Iglesias wrote:
> Nope, the Py2 RF was saved with joblib!
>
> The SO response might work for standard pickling though, I'll give t
Nope, the Py2 RF was saved with joblib!
The SO response might work for standard pickling though, I'll give that a try,
thanks!
On Fri, Jan 23, 2015 at 11:18 AM, Sebastian Raschka
wrote:
> Sorry, I think my previous message was a little bit ambiguous.
> What I would try is:
> 1) Unpickle the
That's not the learnt estimator. You're looking at the initial input (i.e.
the parameters that are or are not changed during the search). The learnt
estimators are cloned from that one, and the best is stored at
clf.best_estimator_ (if refit=True).
Cheers, Joel
On 23 January 2015 at 12:20, Aardva
I just started with s-l, and was playing around with it in iPython using
the Iris set.
I created an SVM classifier thusly:
clf = grid_search.GridSearchCV(svm.SVC(), param_grid={'kernel':('linear',
'rbf'), 'C':arange(10,20)})
(Basically, I want to grid-search for different parameters of "C", and
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 ver
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 T
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 impo
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
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