Can someone please take me off this list? Thanks Sent from my iPhone
> On Aug 11, 2016, at 9:10 AM, Maciek Wójcikowski <[email protected]> wrote: > > First of all the pypi version is outdated, please install using >> >> pip install git+https://github.com/ajtulloch/sklearn-compiledtrees.git > > Secondly, which scikit-learn version are you using? > > ---- > Pozdrawiam, | Best regards, > Maciek Wójcikowski > [email protected] > > 2016-08-11 13:31 GMT+02:00 Ali Zude <[email protected]>: >> Thnx Maciek, >> >> I've tried to use it but I could not sort out the PyPi problem, see the >> error below. Thanks in advance. >> >> ---> 16 import compiledtrees >> >> /home/ali/anaconda2/lib/python2.7/site-packages/compiledtrees/__init__.py in >> <module>() >> ----> 1 from compiledtrees.compiled import CompiledRegressionPredictor >> 2 >> 3 __all__ = ["CompiledRegressionPredictor"] >> >> /home/ali/anaconda2/lib/python2.7/site-packages/compiledtrees/compiled.py in >> <module>() >> 1 from __future__ import print_function >> 2 >> ----> 3 from sklearn.utils import array2d >> 4 from sklearn.tree.tree import DecisionTreeRegressor, DTYPE >> 5 from sklearn.ensemble.gradient_boosting import >> GradientBoostingRegressor >> >> ImportError: cannot import name array2d >> >> >> Kind regards >> Ali >> >> Von: Maciek Wójcikowski <[email protected]> >> An: Ali Zude <[email protected]>; Scikit-learn user and developer mailing >> list <[email protected]> >> Gesendet: 12:26 Donnerstag, 11.August 2016 >> Betreff: Re: [scikit-learn] Speeding up RF regressors >> >> Hi Ali, >> >> I'm using sklearn-compiledtrees >> [https://github.com/ajtulloch/sklearn-compiledtrees] on quite large trees >> (pickle size ~1GB, compiled ~100MB) and the speedup is gigantic (never >> measured it properly) but I'd say it's over 10x. >> >> ---- >> Pozdrawiam, | Best regards, >> Maciek Wójcikowski >> [email protected] >> >> 2016-08-11 13:21 GMT+02:00 Ali Zude via scikit-learn >> <[email protected]>: >> Hi all, >> >> I've 6 RF models and I am using them online to predict 6 different variables >> (using the same features), models quality (error in test data is good). >> However, the online prediction is very very slow. >> How can I speed up the prediction? >> Can I import models into C++ code? >> Is it useful to upgrade to scikit-learn 0.18? and then use multi-output >> models? >> Is sklearn-compiledtreesuseful, they are claiming that it will speed the >> prediction (5x-8x)times? >> I could not use because of array2d error >>PyPi >> Thank you for your help >> >> Regards >> Ali >> >> ______________________________ _________________ >> scikit-learn mailing list >> [email protected] >> https://mail.python.org/ mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn
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