Extend CRP to Pitman Yor and make hyper-parameters Loom compatible #256
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This is taking code from Joao's branch - and modifies it so all tests pass.
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Joaoloula
reviewed
Jul 15, 2024
| @@ -1332,7 +1259,7 @@ def hypothetical(self, rowid): | |||
| def _check_partitions(self): | |||
| if not cu.check_env_debug(): | |||
| return | |||
| assert self.alpha() > 0. | |||
| assert self.crp.hypers['alpha'] > 0. | |||
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You might want to also check that the discount is between 0 and 1 (optionally, you could change the alpha check to the more permissive > -discount, even though given cgpm and loom's grid, we'll never enter the negative regime.
| @@ -315,11 +315,11 @@ def gen_simple_engine(multiprocess=1): | |||
| rng=gu.gen_rng(1), | |||
| multiprocess=multiprocess, | |||
| outputs=outputs, | |||
| alpha=1., | |||
| structure_hypers=1., | |||
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Maybe this should be a dict with alpha and discount as keys?
| @@ -331,11 +331,11 @@ def gen_simple_state(): | |||
| state = State( | |||
| X=data, | |||
| outputs=outputs, | |||
| alpha=1., | |||
| structure_hypers=1., | |||
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What does this do?
Extends the CRP to a Pitman-Yor Process and uses Loom-style hyper-parameter grids for inference; using and modifying code from this branch but ensuring all tests pass again.
Why do we want this?
We're using CGPM in combination with Loom for structure learning. Here we use for initial inference CGPM for clean-up with fully Bayesian inference. Currently, that process is wasteful because we discard all hyper-parameters.
How was this tested?
The test suite was modified so that all CRP tests pass for Pitman-Yor with discount parameter = 0.
This PR is best-reviewed commit-by-commit.