Fix: added pseudo-likelihood normalization option in RBM #23179 #31099
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@antoinebaker this could use your review please 😊 |
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Reference Issues/PRs
Fixes #23179
What does this implement/fix? Explain your changes.
This PR introduces an optional normalize parameter to the score_samples method of the RBM class.
Currently, score_samples returns the pseudo-likelihood multiplied by the number of visible units (n_features), which causes the output to scale with input dimensionality. This can make it harder to compare models across datasets or inputs with different numbers of features.
When normalize=True, the method instead returns a per-feature pseudo-likelihood value.
The default behavior remains unchanged (normalize=False), ensuring backward compatibility.
Any other comments?
I believe this addition is appropriate given the scaling behavior of the current pseudo-likelihood implementation and I’m open to alternative suggestions or feedback on a better way to handle this!