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fit_on_batch
for SklearnModel
s where BaseEstimator
supports partial_fit
#3973
Comments
fit_on_batch
for SklearnModel
s where BaseEstimator
supports partial_fit
I'd be open to this as a new feature. Sounds potentially useful for the community. Would you be able to come by OH (9am PST, MWF ) some day to discuss with us? |
Sure! I can drop by the next OH (May 15th). |
Great! Please join the discord (https://discord.gg/ArRuv9Eu) if you haven't already. We announce timing adjustments for OH there. |
馃殌 Feature
Some scikit-learn models (
BaseEstimator
s) support the methodpartial_fit
(see here). For cases where these models are being wrapped bySklearnModel
, it may make sense to allow the user to callfit_on_batch
for theSklearnModel
.Motivation
Assuming the given
BaseEstimator
supportspartial_fit
, allowing calls tofit_on_batch
would allow forSklearnModel
to train on data that does not fit completely into memory (e.g., data from aDiskDataset
).Additional context
If the
BaseEstimator
does not supportpartial_fit
, we could raise anAttributeError
or other appropriate error iffit_on_batch
is called.Another option is to inform users in the docs that they can subclass
SklearnModel
to implementfit_on_batch
, e.g. :The text was updated successfully, but these errors were encountered: