[scikit-learn] Speeding up RF regressors
zude07 at yahoo.com
Thu Aug 11 07:31:24 EDT 2016
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
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
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 at wojcikowski.pl
2016-08-11 13:21 GMT+02:00 Ali Zude via scikit-learn <scikit-learn at python.org>:
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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