Tpot: TPOTClassifier.set_params doesn't follow scikit-learn estimator API

[TomAugspurger]

Typically, calling old_estimator.set_params(new_param=foo) will copy all the other parameters from the old estimator, and only update new_param. Currently, TPOT will __init__ with the kwargs passed to new_param, and use the default or everything else.

Context of the issue

See http://scikit-learn.org/stable/developers/contributing.html#estimators and http://scikit-learn.org/stable/developers/contributing.html#get-params-and-set-params.

Process to reproduce the issue

clf = TPOTClassifier(random_state=0)
clf.set_params(n_jobs=-1).random_state
# output is None, the default random_state for TPOTClassifier

Expected result

>>> clf.set_params(n_jobs=-1).random_state
0

Possible fix

Hopefully just delete the custom set_params :) That may not be possible though, as it looks like there's some parameter handling logic in the __init__. That logic would need to be either moved to fit (and transform) or a helper method.

[rhiever]

@amueller, any thoughts on how this should be resolved? Is it generally recommended to avoid parameter handling logic in the __init__ of an estimator? If so, we could move said logic to fit/predict.

[amueller]

Traveling right now. But yeah, no validation or anything in init.

Sent from phone. Please excuse spelling and brevity.

On Thu, Aug 9, 2018, 16:14 Randy Olson [e-mail] wrote:

@amueller https://github.com/amueller, any thoughts on how this should
be resolved? Is it generally recommended to avoid parameter handling logic
in the __init__ of an estimator? If so, we could move said logic to
fit/predict.

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[weixuanfu]

OK, thanks! I think moving those logic to fit/predict is more clear way to fix this issue. I will submit a PR for it.

[weixuanfu]

This issue should be fixed in TPOT 0.9.4. Please feel free to reopen this issue if you have any questions.