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Would you consider a pull request that allowed access to the ds_p field of fit_transform() as a member variable of _LSIWrapper? I have a use case where I want to do further processing of the transformed data. I realize I could access this using _load_features, but this means loading it again from disk, which seems like a waste.
This will, of course, increase the memory consumption of _LSIWrapper for other cases where the transformed data is not needed. Another option is to have fit_transform() return ds_p. This would fit more into the pattern of scikit-learn for fit_transform().
The text was updated successfully, but these errors were encountered:
This will, of course, increase the memory consumption of _LSIWrapper for other cases where the transformed data is not needed. Another option is to have fit_transform() return ds_p. This would fit more into the pattern of scikit-learn for fit_transform().
Yes, because datasets can be quite large, including the LSI transformed data, I think it's better to avoid storing it as attribute unless we absolutely need to.
Returning ds_p in fit_transform() sounds like a good idea indeed, that way you could store it as needed for your applications. PR would be very welcome!
Hi,
Would you consider a pull request that allowed access to the
ds_p
field offit_transform()
as a member variable of_LSIWrapper
? I have a use case where I want to do further processing of the transformed data. I realize I could access this using_load_features
, but this means loading it again from disk, which seems like a waste.This will, of course, increase the memory consumption of _LSIWrapper for other cases where the transformed data is not needed. Another option is to have
fit_transform()
return ds_p. This would fit more into the pattern of scikit-learn forfit_transform()
.The text was updated successfully, but these errors were encountered: