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[MRG+1] GaussianProcessRegressor: faster prediction of std #8591

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merged 2 commits into from Mar 16, 2017

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@hbertrand
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@hbertrand hbertrand commented Mar 15, 2017

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What does this implement/fix? Explain your changes.

Predicting the std in GaussianProcessRegressor is very slow because of a particular np.einsum call. Simplifying the call gives the same result much faster.

Any other comments?

See this SO thread for a very similar situation solved the same way:
http://stackoverflow.com/questions/14758283/is-there-a-numpy-scipy-dot-product-calculating-only-the-diagonal-entries-of-the

See this gist for benchmarking:
https://gist.github.com/hbertrand/191f94fc2a7b2c14a6a6739e9a5afe45

Even for medium size matrix, the new call is over 10x faster.

@agramfort
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@agramfort agramfort commented Mar 16, 2017

LGTM

@agramfort agramfort changed the title [MRG] GaussianProcessRegressor: faster prediction of std [MRG+1] GaussianProcessRegressor: faster prediction of std Mar 16, 2017
@lesteve
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@lesteve lesteve commented Mar 16, 2017

LGTM, can you add an entry in doc/whats_new.rst?

@hbertrand
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@hbertrand hbertrand commented Mar 16, 2017

Done :-)

@lesteve
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@lesteve lesteve commented Mar 16, 2017

Merging, thanks a lot!

@lesteve lesteve merged commit d01a02b into scikit-learn:master Mar 16, 2017
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