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Docs referencing unspecified SEM behavior updated to reflect SEM inference change
Summary: After making SEM optional, tutorials and docs need to be updated to reflect that unspecified SEM return values are treated as unknown instead of 0.0 Reviewed By: adamobeng Differential Revision: D16833117 fbshipit-source-id: 5e7caf1192a006f11a945e1822bfdfb26c4bdb44
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tutorials/gpei_hartmann_developer.ipynb

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"cell_type": "markdown",
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"If there is only one metric in the experiment – the objective – then evaluation function can return a single tuple of mean and SEM, in which case Ax will assume that evaluation corresponds to the objective. It can also return only the mean as a float, in which case Ax will assume that SEM is 0.0. For more details on evaluation function, refer to the \"Trial Evaluation\" section in the docs."
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"If there is only one metric in the experiment – the objective – then evaluation function can return a single tuple of mean and SEM, in which case Ax will assume that evaluation corresponds to the objective. It can also return only the mean as a float, in which case Ax will treat SEM as unknown and use a model that can infer it. For more details on evaluation function, refer to the \"Trial Evaluation\" section in the docs."
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tutorials/gpei_hartmann_loop.ipynb

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"cell_type": "markdown",
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"If there is only one metric in the experiment – the objective – then evaluation function can return a single tuple of mean and SEM, in which case Ax will assume that evaluation corresponds to the objective. It can also return only the mean as a float, in which case Ax will assume that SEM is 0.0. For more details on evaluation function, refer to the \"Trial Evaluation\" section in the docs."
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"If there is only one metric in the experiment – the objective – then evaluation function can return a single tuple of mean and SEM, in which case Ax will assume that evaluation corresponds to the objective. It can also return only the mean as a float, in which case Ax will treat SEM as unknown and use a model that can infer it. For more details on evaluation function, refer to the \"Trial Evaluation\" section in the docs."
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tutorials/gpei_hartmann_service.ipynb

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"cell_type": "markdown",
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"Result of the evaluation should generally be a mapping of the format: `{metric_name -> (mean, SEM)}`. If there is only one metric in the experiment – the objective – then evaluation function can return a single tuple of mean and SEM, in which case Ax will assume that evaluation corresponds to the objective. It can also return only the mean as a float, in which case Ax will assume that SEM is 0.0. For more details on evaluation function, refer to the \"Trial Evaluation\" section in the docs."
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"Result of the evaluation should generally be a mapping of the format: `{metric_name -> (mean, SEM)}`. If there is only one metric in the experiment – the objective – then evaluation function can return a single tuple of mean and SEM, in which case Ax will assume that evaluation corresponds to the objective. It can also return only the mean as a float, in which case Ax will treat SEM as unknown and use a model that can infer it. For more details on evaluation function, refer to the \"Trial Evaluation\" section in the docs."
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