[MRG + 2] Allow f_regression to accept a sparse matrix with centering #8065
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Allows f_regression to accept a sparse matrix when centering=True.
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thanks this looks nice. We have a bit of a backlog on reviews though |
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LGTM +1 for merge after a what's new update. thx @acadiansith |
agramfort
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raghavrv
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Dec 19, 2016
| n_samples = X.shape[0] | ||
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| # compute centered values | ||
| # note that E[(x - mean(x))*(y - mean(y))] = E[x*(y - mean(y))], so we |
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This comment is applicable only when y is mean centered correct? In which case it would be E[x*y]? (Sorry if I'm misunderstanding)
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The comment is applicable even when y is not centered.
Yet you are right, here we compute E[x*(y - mean(y)) by first centering y and then computing E[x*y].
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Ah yes sorry my math is a bit rusty... Thanks heaps for the clarification (online and offline)!
raghavrv
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This needs a whatsnew entry as observed by @agramfort ... |
TomDLT
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Dec 19, 2016
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Thanks @acadiansith |
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Feb 28, 2017
…scikit-learn#8065) * Updated centering for f_regression Allows f_regression to accept a sparse matrix when centering=True. * Fixed E226 spacing issue. * Added f_regression sparse update to whats_new.rst
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…scikit-learn#8065) * Updated centering for f_regression Allows f_regression to accept a sparse matrix when centering=True. * Fixed E226 spacing issue. * Added f_regression sparse update to whats_new.rst
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…scikit-learn#8065) * Updated centering for f_regression Allows f_regression to accept a sparse matrix when centering=True. * Fixed E226 spacing issue. * Added f_regression sparse update to whats_new.rst
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…scikit-learn#8065) * Updated centering for f_regression Allows f_regression to accept a sparse matrix when centering=True. * Fixed E226 spacing issue. * Added f_regression sparse update to whats_new.rst
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…scikit-learn#8065) * Updated centering for f_regression Allows f_regression to accept a sparse matrix when centering=True. * Fixed E226 spacing issue. * Added f_regression sparse update to whats_new.rst
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Reference Issue
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What does this implement/fix? Explain your changes.
f_regressioncurrently doesn't accept sparse matrices whencenter=Trueto avoid allocating a dense matrix of the centeredXvalues, but the computation can be completed without this dense matrix. The numerators can take advantage of the observation thatE[(X - E[X])(Y - E[Y])] = E[X(Y - E[Y])], and the denominator can useE[(X - E[X])^2] = E[X^2] - E[X]^2.I've also included a unit test to verify that the output is the same for sparse and dense versions of a matrix.
Any other comments?
The output is the same as before (I've checked by hand), but I don't have any tests included to confirm this.