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
RegardsAli
______________________________ _________________
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
