SCML: Add warm_start parameter #345
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maxi-marufo
changed the title
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This change looks fine to me, though I'm not sure when this warm-start option is useful in practice. Sorry for the extreme delay in reviewing! @grudloff want to take a look? |
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LGTM! |
bellet
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Jun 22, 2022
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Thanks @maxi-marufo !
Maybe it is possible to design a better test, something in the spirit https://github.com/scikit-learn/scikit-learn/blob/80598905e517759b4696c74ecc35c6e2eb508cff/sklearn/linear_model/tests/test_sgd.py#L274
where the idea is to check that calling warm_start=True is equivalent to manually setting the parameters?
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Because SCML optimization procedure is based on stochastic subgradient descent, we can save the weights after fitting the model, and use them in a following fit call (with a different set of triplets). The decision to use the
warm_startparameter instead of a newpartial_fitmethod is because partial_fit in scikit-learn will only fit 1 epoch, whereas fit will fit for multiple epochs (until the loss converges or max_iter is reached), which is the case also for SCML.