AFAIK, you might not want all the missing values to be imputed at once,
especially if the dimensions of X are large. Maybe something like:
X_transformed = estimator.fit_transform(X) # X contains missing values
X_subset = estimator.inverse_transform(X_transformed,row_subset) # impute
only a subset of the rows of X
Lee.
On Tue, Mar 26, 2013 at 4:31 AM, Mathieu Blondel <math...@mblondel.org>wrote:
> On Tue, Mar 26, 2013 at 3:28 PM, Gael Varoquaux
> <gael.varoqu...@normalesup.org> wrote:
>
> > * For matrix factorization to be useful in the context of recomender
> > systems, there needs to be an API for recomender systems. While I'd
> > love to see this, I am afraid that it might be premature and should
> > probably happen after the release of 1.0.
>
> It seems to me that inverse_transform would do the job:
> X_transformed = estimator.fit_transform(X) # X contains missing values
> X = estimator.inverse_transform(X_transformed) # missing values were
> imputed
>
> Mathieu
>
>
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