[scikit-learn] Speeding up RF regressors

[Maciek Wójcikowski]

First of all the pypi version is outdated, please install using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      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>
> *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
>
> Regards
> Ali
>
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