If I'm not mistaken (I just read the source code on github), the copy
that Peter is experiencing is due to ravel() in this method:
https://github.com/scipy/scipy/blob/master/scipy/sparse/compressed.py#L264

This method in turn invokes csr_matvecs which is implemented here:
https://github.com/scipy/scipy/blob/master/scipy/sparse/sparsetools/csr.h#L1010

This method takes a sparse matrix and a flat array (C-style ordered)
as inputs. The advantage of using ravel() here is that another
implementation is not needed to handle Fortran-style arrays. However,
it does result in a copy.

In predict, SGDClassifier does a safe_sparse_dot(X, self.coef_.T).
Therefore, if coef_ is Fortran-style, coef_.T becomes C-style, which
is the format expected by ravel() to avoid a copy.

Olivier's solution sounds good. Another would be to implement a
routine that can handle the dot product with a Fortran-style array
directly in utils/sparsefuncs.pyx.

Mathieu

On Mon, Jan 9, 2012 at 5:21 AM, Olivier Grisel <[email protected]> wrote:
> If the only change would be to do a:
>
> self.coef_ = np.asfortranarray(coef_)
>
> at the end of the fit method of the SGDClassifier and SGDRegressor
> then I am all for it.
>
> We should just check that this indeed solves the memory copy issue you 
> suspect.
>
> --
> Olivier
>
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