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Implement scikit-learn compatibility

See original GitHub issue

It would be nice to be able to easily get posterior predictive distributions for arbitrary predictor values. The most obvious way to implement this would be to add a .predict() method to either the Model or ModelResults class. This would also bring the interface one little step closer to being scikit-learn-compatible, which is a separate goal that I think it would be nice to eventually achieve.

Issue Analytics

State:
Created 6 years ago
Comments:15 (5 by maintainers)

Top GitHub Comments

1reaction

aflaxmancommented, Nov 10, 2017

The recently revamped GaussianProcessRegression module in sklearn could be helpful inspiration. In addition to .predict, this class has a .sample_y method, which takes n_samples and random_state are arguments. https://github.com/scikit-learn/scikit-learn/blob/01e0639b15d515b446cb4589f01097b16fb4994a/sklearn/gaussian_process/gpr.py#L349

So fun to get to see your careful thinking about implementation!

1reaction

jake-westfallcommented, Nov 10, 2017

Just realized that having predict() wrap sample_ppc is exactly what you said you weren’t sure about :p

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