Hi.
Also check out this:
https://github.com/scikit-learn/scikit-learn/pull/5491
auto-sklearn (which uses meta-learning) might also be of interest to you.
From your description TuPAQ seems to assume that there is some notion
of iterations.
That is true only for some models. It might be easier to run models on
subsets of the data.
That's actually something data robot does to screen models faster.
I don't think Tupaq is ready for inclusion in scikit-learn (way too
fresh, 2 citations?).
But if you want to create a scikit-learn compatible implementation,
please go ahead, that would be great to have for reference.
cheers,
Andy
On 01/27/2016 12:01 PM, Pedro Rodriguez wrote:
Hi,
I am considering working on a project which would result in a PR to
scikit-learn, but would like to check that something like it doesn't
already exist or is in progress (in our out of SKLearn).
Goal: Implement the algorithm (TuPAQ) described here:
http://web.cs.ucla.edu/~ameet/tupaq_socc.pdf
<http://web.cs.ucla.edu/%7Eameet/tupaq_socc.pdf> to make something
similar to GridSearchCV
Result: Potentially much faster training time over the parameter/model
space than GridSearchCV
Description of Algorithm:
1. Train all models by some number of iterations to kick start
2. Drop out all models that are not within some margin of the best model
3. Repeat steps 1 and 2 based on some heuristic
4. Return best model
Existing Code:
Didn't find anything in SKLearn like this, closest thing I found was
this: https://github.com/hyperopt/hyperopt-sklearn but it doesn't
include some of the other methods used in the paper (like early model
termination)
Thanks!
--
Pedro Rodriguez
PhD Student in Distributed Machine Learning | CU Boulder
UC Berkeley AMPLab Alumni
ski.rodrig...@gmail.com <mailto:ski.rodrig...@gmail.com> |
pedrorodriguez.io <http://pedrorodriguez.io/> | 909-353-4423
<tel:909-353-4423>
Github: github.com/EntilZha <http://github.com/EntilZha> | LinkedIn:
https://www.linkedin.com/in/pedrorodriguezscience
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