[scikit-learn] Speeding up RF regressors

[Vlad Deshkovich]

Please remove me as well.

On Thursday, August 11, 2016, o m <odaym2 at gmail.com> wrote:

> 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
> <javascript:_e(%7B%7D,'cvml','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 First of all the pypi version is outdated, please install using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
> <javascript:_e(%7B%7D,'cvml','maciek at wojcikowski.pl');>
>
> 2016-08-11 13:31 GMT+02:00 Ali Zude <zude07 at yahoo.com
> <javascript:_e(%7B%7D,'cvml','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      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 <maciek at wojcikowski.pl
>> <javascript:_e(%7B%7D,'cvml','maciek at wojcikowski.pl');>>
>> *An:* Ali Zude <zude07 at yahoo.com
>> <javascript:_e(%7B%7D,'cvml','zude07 at yahoo.com');>>; Scikit-learn user
>> and developer mailing list <scikit-learn at python.org
>> <javascript:_e(%7B%7D,'cvml','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
>> <javascript:_e(%7B%7D,'cvml','maciek at wojcikowski.pl');>
>>
>> 2016-08-11 13:21 GMT+02:00 Ali Zude via scikit-learn <
>> scikit-learn at python.org
>> <javascript:_e(%7B%7D,'cvml','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
>>
>> Regards
>> Ali
>>
>> ______________________________ _________________
>> scikit-learn mailing list
>> scikit-learn at python.org
>> <javascript:_e(%7B%7D,'cvml','scikit-learn at python.org');>
>> https://mail.python.org/ mailman/listinfo/scikit-learn
>> <https://mail.python.org/mailman/listinfo/scikit-learn>
>>
>>
>>
>>
>>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> <javascript:_e(%7B%7D,'cvml','scikit-learn at python.org');>
> https://mail.python.org/mailman/listinfo/scikit-learn
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20160811/dba36311/attachment.html>