[ML] Validate at least one feature is available for DF analytics #55876
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We were previously checking at least one supported field existed when the _explain API was called. However, in the case of analyses with required fields (e.g. regression) we were not accounting that the dependent variable is not a feature and thus if the source index only contains the dependent variable field there are no features to train a model on. This commit adds a validation that at least one feature is available for analysis. Note that we also move that validation away from `ExtractedFieldsDetector` and the _explain API and straight into the _start API. The reason for doing this is to allow the user to use the _explain API in order to understand why they would be seeing an error like this one. For example, the user might be using an index that has fields but they are of unsupported types. If they start the job and get an error that there are no features, they will wonder why that is. Calling the _explain API will show them that all their fields are unsupported. If the _explain API was failing instead, there would be no way for the user to understand why all those fields are ignored. Closes elastic#55593
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Pinging @elastic/ml-core (:ml) |
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@elasticmachine update branch |
dimitris-athanasiou
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Apr 29, 2020
…#55876) (#55914) We were previously checking at least one supported field existed when the _explain API was called. However, in the case of analyses with required fields (e.g. regression) we were not accounting that the dependent variable is not a feature and thus if the source index only contains the dependent variable field there are no features to train a model on. This commit adds a validation that at least one feature is available for analysis. Note that we also move that validation away from `ExtractedFieldsDetector` and the _explain API and straight into the _start API. The reason for doing this is to allow the user to use the _explain API in order to understand why they would be seeing an error like this one. For example, the user might be using an index that has fields but they are of unsupported types. If they start the job and get an error that there are no features, they will wonder why that is. Calling the _explain API will show them that all their fields are unsupported. If the _explain API was failing instead, there would be no way for the user to understand why all those fields are ignored. Closes #55593 Backport of #55876
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We were previously checking at least one supported field existed
when the _explain API was called. However, in the case of analyses
with required fields (e.g. regression) we were not accounting that
the dependent variable is not a feature and thus if the source index
only contains the dependent variable field there are no features to
train a model on.
This commit adds a validation that at least one feature is available
for analysis. Note that we also move that validation away from
ExtractedFieldsDetectorand the _explain API and straight intothe _start API. The reason for doing this is to allow the user to use
the _explain API in order to understand why they would be seeing an
error like this one.
For example, the user might be using an index that has fields but
they are of unsupported types. If they start the job and get
an error that there are no features, they will wonder why that is.
Calling the _explain API will show them that all their fields are
unsupported. If the _explain API was failing instead, there would
be no way for the user to understand why all those fields are
ignored.
Closes #55593