Comments (7)
benjamin-work
commented on May 14, 2024
1
Okay, so that means NeuralNetRegressor and NeuralNetClassifier´ should not have y=None` as default.
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ottonemo
commented on May 14, 2024
The first part is done. Do we want to override fit in NeuralNetClassifier and enforce y @benjamin-work?
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benjamin-work
commented on May 14, 2024
Hmm, what do we lose if we don't?
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ottonemo
commented on May 14, 2024
Nothing. The user may (or may not) benefit from it because of additional error checking.
For example, sklearn SVMs do not allow for y=None.
OTOH making y optional allows for custom data loaders that extract y from X.
So I'm leaning towards leaving y optional in fit().
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benjamin-work
commented on May 14, 2024
Yes, this sounds reasonable.
The only case where I can think of where it's detrimental is when the user forgets to pass y to fit, at which point the error will only be noticed when get_loss is called. Not sure what the error message will be. Are we fine with this?
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ottonemo
commented on May 14, 2024
We could allow y=None but simply don't make it the default.
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ottonemo
commented on May 14, 2024
PR #27
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