Open rhjohnstone opened 5 years ago
Is the maximum possible n_iter related to the size of the training data?
No, n_iter is really the number of individual sgd updates (not epoch). This is indeed the reason why even fairly large n_iter might result in very few passes over the training data if you have a lot of samples.
so I suppose fit also resets everything?
This is indeed true, some solver support fit(X,y, warm_start=Ture)
but the sgd solver doesn't.
And is there any way to continue training the bpr.FMRecommender after n_iter iterations have been performed?
No, see above.
A new BPR / SGD implementation is on it's way to solve this issues but it might take a while till we have it ready for release.
When I try to set a massive number of iterations, e.g.
I get as an error (which occurs when the
fit
method is called):OverflowError: value too large to convert to int
I also tried calling the
fit
method twice withn_iter=100
to see if I could train in batches, but found the output was the same after the first 100 as after the second 100, so I supposefit
also resets everything?Is the maximum possible
n_iter
related to the size of the training data?And is there any way to continue training the
bpr.FMRecommender
aftern_iter
iterations have been performed?