Feature/POLCA Algorithm: epsilon-net and summarizer#60
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chinganc merged 14 commits intoexperimentalfrom Mar 23, 2026
Merged
Feature/POLCA Algorithm: epsilon-net and summarizer#60chinganc merged 14 commits intoexperimentalfrom
chinganc merged 14 commits intoexperimentalfrom
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Fix missed updating of the number of samples processed.
…es" before proposing.
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| # identically to objective_config=None, making the "scalar" mode a no-op. | ||
| cfg = getattr(self, 'objective_config', None) | ||
| if cfg is not None and cfg.mode != "scalar": | ||
| if cfg is not None: |
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why do you remove the condition cfg.mode != "scalar"
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In the compute_exploration_priority function, when cfg.mode == "scalar" (not None), we still need this score_dict data? I want to make them consistent.
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LGTM. Thanks for the contribution @xuanfeiren |
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In this PR, I added the POLCA algorithm (epsilon net and summarizer features built on PS).
summarizer.pyunderpriority_search. (Updated: currently summarizer supports the multi-objective feature)module_regressor.py. This could be used to create different types of regressors. In this PR, it is used to get the embeddings of candidates.polca.pyunder priority_search, containingPOLCA, which is a subclass of PrioritySearch.