[scikit-learn] Speeding up RF regressors
odaym2 at gmail.com
Thu Aug 11 09:41:52 EDT 2016
Can someone please take me off this list? Thanks
Sent from my iPhone
> On Aug 11, 2016, at 9:10 AM, Maciek Wójcikowski <maciek at wojcikowski.pl> 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 First of all the pypi version is outdated, please install using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
> maciek at wojcikowski.pl
> 2016-08-11 13:31 GMT+02:00 Ali Zude <zude07 at yahoo.com>:
>> 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
>> 3 __all__ = ["CompiledRegressionPredictor"]
>> /home/ali/anaconda2/lib/python2.7/site-packages/compiledtrees/compiled.py in <module>()
>> 1 from __future__ import print_function
>> ----> 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
>> Von: Maciek Wójcikowski <maciek at wojcikowski.pl>
>> An: Ali Zude <zude07 at yahoo.com>; Scikit-learn user and developer mailing list <scikit-learn at python.org>
>> 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
>> maciek at wojcikowski.pl
>> 2016-08-11 13:21 GMT+02:00 Ali Zude via scikit-learn <scikit-learn at python.org>:
>> 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
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