TimeAsFeature transform #2438
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TimeAsFeature transform #2438
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Summary: This implements a transform for adding `start_time` and `duration` as features for modeling. Currently, this adds them as `RangeParameter`s (to unblock time-sensitive applications), but in the future it would be good to revise this with a better treatment of non-tunable contextual information. `duration` appears to lead to better model fits on the synthetic example (notebook) than using `end_time`. This also works better than using the midpoint between start time and end time. Reviewed By: bernardbeckerman, Balandat Differential Revision: D57082939
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Summary: This implements a transform for adding `start_time` and `duration` as features for modeling. Currently, this adds them as `RangeParameter`s (to unblock time-sensitive applications), but in the future it would be good to revise this with a better treatment of non-tunable contextual information. `duration` appears to lead to better model fits on the synthetic example (notebook) than using `end_time`. This also works better than using the midpoint between start time and end time. Reviewed By: bernardbeckerman, Balandat Differential Revision: D57082939
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #2438 +/- ##
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Coverage 95.31% 95.32%
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Files 495 497 +2
Lines 48069 48193 +124
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+ Hits 45818 45938 +120
- Misses 2251 2255 +4 ☔ View full report in Codecov by Sentry. |
sdaulton
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May 8, 2024
Summary: This implements a transform for adding `start_time` and `duration` as features for modeling. Currently, this adds them as `RangeParameter`s (to unblock time-sensitive applications), but in the future it would be good to revise this with a better treatment of non-tunable contextual information. `duration` appears to lead to better model fits on the synthetic example (notebook) than using `end_time`. This also works better than using the midpoint between start time and end time. Reviewed By: bernardbeckerman, Balandat Differential Revision: D57082939
sdaulton
added a commit
to sdaulton/Ax-1
that referenced
this pull request
May 8, 2024
Summary: This implements a transform for adding `start_time` and `duration` as features for modeling. Currently, this adds them as `RangeParameter`s (to unblock time-sensitive applications), but in the future it would be good to revise this with a better treatment of non-tunable contextual information. `duration` appears to lead to better model fits on the synthetic example (notebook) than using `end_time`. This also works better than using the midpoint between start time and end time. Reviewed By: bernardbeckerman, Balandat Differential Revision: D57082939
sdaulton
added a commit
to sdaulton/Ax-1
that referenced
this pull request
May 8, 2024
Summary: This implements a transform for adding `start_time` and `duration` as features for modeling. Currently, this adds them as `RangeParameter`s (to unblock time-sensitive applications), but in the future it would be good to revise this with a better treatment of non-tunable contextual information. `duration` appears to lead to better model fits on the synthetic example (notebook) than using `end_time`. This also works better than using the midpoint between start time and end time. Reviewed By: bernardbeckerman, Balandat Differential Revision: D57082939
sdaulton
added a commit
to sdaulton/Ax-1
that referenced
this pull request
May 8, 2024
Summary: This implements a transform for adding `start_time` and `duration` as features for modeling. Currently, this adds them as `RangeParameter`s (to unblock time-sensitive applications), but in the future it would be good to revise this with a better treatment of non-tunable contextual information. `duration` appears to lead to better model fits on the synthetic example (notebook) than using `end_time`. This also works better than using the midpoint between start time and end time. Reviewed By: bernardbeckerman, Balandat Differential Revision: D57082939
sdaulton
added a commit
to sdaulton/Ax-1
that referenced
this pull request
May 8, 2024
Summary: This implements a transform for adding `start_time` and `duration` as features for modeling. Currently, this adds them as `RangeParameter`s (to unblock time-sensitive applications), but in the future it would be good to revise this with a better treatment of non-tunable contextual information. `duration` appears to lead to better model fits on the synthetic example (notebook) than using `end_time`. This also works better than using the midpoint between start time and end time. Reviewed By: bernardbeckerman, Balandat Differential Revision: D57082939
sdaulton
added a commit
to sdaulton/Ax-1
that referenced
this pull request
May 8, 2024
Summary: This implements a transform for adding `start_time` and `duration` as features for modeling. Currently, this adds them as `RangeParameter`s (to unblock time-sensitive applications), but in the future it would be good to revise this with a better treatment of non-tunable contextual information. `duration` appears to lead to better model fits on the synthetic example (notebook) than using `end_time`. This also works better than using the midpoint between start time and end time. Reviewed By: bernardbeckerman, Balandat Differential Revision: D57082939
sdaulton
added a commit
to sdaulton/Ax-1
that referenced
this pull request
May 9, 2024
Summary: This implements a transform for adding `start_time` and `duration` as features for modeling. Currently, this adds them as `RangeParameter`s (to unblock time-sensitive applications), but in the future it would be good to revise this with a better treatment of non-tunable contextual information. `duration` appears to lead to better model fits on the synthetic example (notebook) than using `end_time`. This also works better than using the midpoint between start time and end time. Reviewed By: bernardbeckerman, Balandat Differential Revision: D57082939
sdaulton
added a commit
to sdaulton/Ax-1
that referenced
this pull request
May 9, 2024
Summary: This implements a transform for adding `start_time` and `duration` as features for modeling. Currently, this adds them as `RangeParameter`s (to unblock time-sensitive applications), but in the future it would be good to revise this with a better treatment of non-tunable contextual information. `duration` appears to lead to better model fits on the synthetic example (notebook) than using `end_time`. This also works better than using the midpoint between start time and end time. Reviewed By: bernardbeckerman, Balandat Differential Revision: D57082939
sdaulton
added a commit
to sdaulton/Ax-1
that referenced
this pull request
May 9, 2024
Summary: This implements a transform for adding `start_time` and `duration` as features for modeling. Currently, this adds them as `RangeParameter`s (to unblock time-sensitive applications), but in the future it would be good to revise this with a better treatment of non-tunable contextual information. `duration` appears to lead to better model fits on the synthetic example (notebook) than using `end_time`. This also works better than using the midpoint between start time and end time. Reviewed By: bernardbeckerman, Balandat Differential Revision: D57082939
sdaulton
added a commit
to sdaulton/Ax-1
that referenced
this pull request
May 9, 2024
Summary: This implements a transform for adding `start_time` and `duration` as features for modeling. Currently, this adds them as `RangeParameter`s (to unblock time-sensitive applications), but in the future it would be good to revise this with a better treatment of non-tunable contextual information. `duration` appears to lead to better model fits on the synthetic example (notebook) than using `end_time`. This also works better than using the midpoint between start time and end time. Reviewed By: bernardbeckerman, Balandat Differential Revision: D57082939
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This pull request has been merged in 64f4bad. |
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Summary:
This implements a transform for adding
start_timeanddurationas features for modeling. Currently, this adds them asRangeParameters (to unblock time-sensitive applications), but in the future it would be good to revise this with a better treatment of non-tunable contextual information.durationappears to lead to better model fits on the synthetic example (notebook) than usingend_time. This also works better than using the midpoint between start time and end time.Reviewed By: bernardbeckerman, Balandat
Differential Revision: D57082939