Tuning for multiple columns part 2: Find candidate parameters for multiple aggregation #524
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dvadym
changed the title
miracvbasaran
approved these changes
Sep 10, 2024
analysis/dp_strategy_selector.py
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| # This is Select partitions case. | ||
| return self._get_strategy_for_select_partition(sensitivities.l0) | ||
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| n_metrics = len(self._metrics) | ||
| # Having n metrics is equivalent of multiplying of contributing for |
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nit: the equivalent of... or equivalent to
| num=max_candidates)).astype(int) | ||
| # In order to ensure that max_sum_per_partition > 0, let us skip 0-th | ||
| # bin if max = 0. | ||
| # TODO(dvadym): better algorithm for finding candidates. |
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Do you have some ideas here already?
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Yes, I have. There is work in this direction.
dvadym
commented
Sep 10, 2024
| num=max_candidates)).astype(int) | ||
| # In order to ensure that max_sum_per_partition > 0, let us skip 0-th | ||
| # bin if max = 0. | ||
| # TODO(dvadym): better algorithm for finding candidates. |
There was a problem hiding this comment.
Yes, I have. There is work in this direction.
analysis/dp_strategy_selector.py
Outdated
| # This is Select partitions case. | ||
| return self._get_strategy_for_select_partition(sensitivities.l0) | ||
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||
| n_metrics = len(self._metrics) | ||
| # Having n metrics is equivalent of multiplying of contributing for |
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This PR introduces finding candidates during tuning for computing utility analysis for case when utility analysis for multiple metrics is computed and SUM can be computed for multiple columns . This covers cases when DP aggregations can be presented in the pseudo-SQL terms as