Fix fantasization with FixedNoiseGP and outcome transforms and use FantasizeMixin #2011
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@@ Coverage Diff @@
## main #2011 +/- ##
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- Coverage 100.00% 99.99% -0.01%
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Files 179 179
Lines 15798 15806 +8
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+ Hits 15798 15805 +7
- Misses 0 1 +1
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…ntasizeMixin (pytorch#2011) Summary: This fixes fantasization with FixedNoiseGP when using outcome transforms----previously, already-transformed noise was transformed again during fantasization. This also improves the fantasization for batched and batched multi-output models to use the average noise for each batch and output. This also removes repeated code and uses the logic in `FantasizeMixin.fantasize` for handling `X` with size 0 on the -2 dimension. This also deprecates the use of `observation_noise` as a boolean argument to fantasize. Differential Revision: D49200325
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…ntasizeMixin (pytorch#2011) Summary: This fixes fantasization with FixedNoiseGP when using outcome transforms----previously, already-transformed noise was transformed again during fantasization. This also improves the fantasization for batched and batched multi-output models to use the average noise for each batch and output. This also removes repeated code and uses the logic in `FantasizeMixin.fantasize` for handling `X` with size 0 on the -2 dimension. This also deprecates the use of `observation_noise` as a boolean argument to fantasize. Differential Revision: D49200325
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…ntasizeMixin (pytorch#2011) Summary: This fixes fantasization with FixedNoiseGP when using outcome transforms----previously, already-transformed noise was transformed again during fantasization. This also improves the fantasization for batched and batched multi-output models to use the average noise for each batch and output. This also removes repeated code and uses the logic in `FantasizeMixin.fantasize` for handling `X` with size 0 on the -2 dimension. This also deprecates the use of `observation_noise` as a boolean argument to fantasize. Differential Revision: D49200325
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…ntasizeMixin (pytorch#2011) Summary: This fixes fantasization with FixedNoiseGP when using outcome transforms----previously, already-transformed noise was transformed again during fantasization. This also improves the fantasization for batched and batched multi-output models to use the average noise for each batch and output. This also removes repeated code and uses the logic in `FantasizeMixin.fantasize` for handling `X` with size 0 on the -2 dimension. This also deprecates the use of `observation_noise` as a boolean argument to fantasize. Differential Revision: D49200325
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…ntasizeMixin (pytorch#2011) Summary: This fixes fantasization with FixedNoiseGP when using outcome transforms----previously, already-transformed noise was transformed again during fantasization. This also improves the fantasization for batched and batched multi-output models to use the average noise for each batch and output. This also removes repeated code and uses the logic in `FantasizeMixin.fantasize` for handling `X` with size 0 on the -2 dimension. This also deprecates the use of `observation_noise` as a boolean argument to fantasize. Reviewed By: Balandat Differential Revision: D49200325
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…ntasizeMixin (pytorch#2011) Summary: This fixes fantasization with FixedNoiseGP when using outcome transforms----previously, already-transformed noise was transformed again during fantasization. This also improves the fantasization for batched and batched multi-output models to use the average noise for each batch and output. This also removes repeated code and uses the logic in `FantasizeMixin.fantasize` for handling `X` with size 0 on the -2 dimension. This also deprecates the use of `observation_noise` as a boolean argument to fantasize. Reviewed By: Balandat Differential Revision: D49200325
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…ntasizeMixin (pytorch#2011) Summary: This fixes fantasization with FixedNoiseGP when using outcome transforms----previously, already-transformed noise was transformed again during fantasization. This also improves the fantasization for batched and batched multi-output models to use the average noise for each batch and output. This also removes repeated code and uses the logic in `FantasizeMixin.fantasize` for handling `X` with size 0 on the -2 dimension. This also deprecates the use of `observation_noise` as a boolean argument to fantasize. Reviewed By: Balandat Differential Revision: D49200325
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…ntasizeMixin (pytorch#2011) Summary: Pull Request resolved: pytorch#2011 This fixes fantasization with FixedNoiseGP when using outcome transforms----previously, already-transformed noise was transformed again during fantasization. This also improves the fantasization for batched and batched multi-output models to use the average noise for each batch and output. This also removes repeated code and uses the logic in `FantasizeMixin.fantasize` for handling `X` with size 0 on the -2 dimension. This also deprecates the use of `observation_noise` as a boolean argument to fantasize. Differential Revision: https://internalfb.com/D49200325 fbshipit-source-id: f2150e08009bfdf0f86b0b9e5908610dbb6709ee
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Summary: see title. This is causing a failure in the tutorials on pytorch#2011 Differential Revision: D49382057 fbshipit-source-id: f1c62d17d7485ce7e2a381646fad84885ce0d94b
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…ntasizeMixin (pytorch#2011) Summary: Pull Request resolved: pytorch#2011 This fixes fantasization with FixedNoiseGP when using outcome transforms----previously, already-transformed noise was transformed again during fantasization. This also improves the fantasization for batched and batched multi-output models to use the average noise for each batch and output. This also removes repeated code and uses the logic in `FantasizeMixin.fantasize` for handling `X` with size 0 on the -2 dimension. This also deprecates the use of `observation_noise` as a boolean argument to fantasize. Differential Revision: https://internalfb.com/D49200325 fbshipit-source-id: 998c649302579960e6ee2618dfadb87c52d6309b
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Summary: Pull Request resolved: pytorch#2013 see title. This is causing a failure in the tutorials on pytorch#2011 Reviewed By: Balandat Differential Revision: D49382057 fbshipit-source-id: 2b88bfeea442e6c1b3d521ee97b4631ff2c77d60
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…ntasizeMixin (pytorch#2011) Summary: Pull Request resolved: pytorch#2011 This fixes fantasization with FixedNoiseGP when using outcome transforms----previously, already-transformed noise was transformed again during fantasization. This also improves the fantasization for batched and batched multi-output models to use the average noise for each batch and output. This also removes repeated code and uses the logic in `FantasizeMixin.fantasize` for handling `X` with size 0 on the -2 dimension. This also deprecates the use of `observation_noise` as a boolean argument to fantasize. Differential Revision: https://internalfb.com/D49200325 fbshipit-source-id: 686663284452f02114695d1bb2da973a16c3267e
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Summary: Pull Request resolved: pytorch#2013 see title. This is causing a failure in the tutorials on pytorch#2011 Reviewed By: Balandat Differential Revision: D49382057 fbshipit-source-id: 04ba98192b26117c762b4188fae454dca3f8899f
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Summary:
This fixes fantasization with FixedNoiseGP and outcome transforms where transformed
noisewas outcome-transformed again.This also improves the fantasization for batched and batched multi-output models to use the average noise for each batch and output.
This also removes repeated code and uses the logic in
FantasizeMixin.fantasizefor handlingXwith size 0 on the -2 dimension.Differential Revision: D49200325