[ML][Inference] Adding a warning_field for warning msgs. #49838
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…a prediction if all the fields are missing
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Pinging @elastic/ml-core (:ml) |
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run elasticsearch-ci/1 |
przemekwitek
reviewed
Dec 9, 2019
.../ml/src/main/java/org/elasticsearch/xpack/ml/dataframe/process/AnalyticsResultProcessor.java
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.../ml/src/main/java/org/elasticsearch/xpack/ml/dataframe/process/AnalyticsResultProcessor.java
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.../plugin/ml/src/main/java/org/elasticsearch/xpack/ml/inference/ingest/InferenceProcessor.java
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.../plugin/ml/src/main/java/org/elasticsearch/xpack/ml/inference/ingest/InferenceProcessor.java
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.../plugin/ml/src/main/java/org/elasticsearch/xpack/ml/inference/ingest/InferenceProcessor.java
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przemekwitek
approved these changes
Dec 13, 2019
| @@ -252,7 +266,9 @@ void setMaxIngestProcessors(int maxIngestProcessors) { | |||
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| InferenceConfig inferenceConfigFromMap(Map<String, Object> inferenceConfig) { | |||
| ExceptionsHelper.requireNonNull(inferenceConfig, INFERENCE_CONFIG); | |||
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| //throw ExceptionsHelper.badRequestException("Cannot create processor as configured." + | |||
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I think these commented lines should be removed.
benwtrent
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feature/ml-inference-add-warnings-when-all-fields-missing
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davidkyle
approved these changes
Dec 13, 2019
.../ml/src/main/java/org/elasticsearch/xpack/ml/dataframe/process/AnalyticsResultProcessor.java
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| response.getInferenceResults().get(0).writeResult(ingestDocument, this.targetField); | ||
| InferenceResults inferenceResults = response.getInferenceResults().get(0); | ||
| if (inferenceResults instanceof WarningInferenceResults) { | ||
| inferenceResults.writeResult(ingestDocument, this.targetField); |
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Both branches of this if else look equivalent to me, am I missing something?
benwtrent
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Dec 13, 2019
This adds a new field for the inference processor. `warning_field` is a place for us to write warnings provided from the inference call. When there are warnings we are not going to write an inference result. The goal of this is to indicate that the data provided was too poor or too different for the model to make an accurate prediction. The user could optionally include the `warning_field`. When it is not provided, it is assumed no warnings were desired to be written. The first of these warnings is when ALL of the input fields are missing. If none of the trained fields are present, we don't bother inferencing against the model and instead provide a warning stating that the fields were missing. Also, this adds checks to not allow duplicated fields during processor creation.
benwtrent
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Dec 13, 2019
…0183) This adds a new field for the inference processor. `warning_field` is a place for us to write warnings provided from the inference call. When there are warnings we are not going to write an inference result. The goal of this is to indicate that the data provided was too poor or too different for the model to make an accurate prediction. The user could optionally include the `warning_field`. When it is not provided, it is assumed no warnings were desired to be written. The first of these warnings is when ALL of the input fields are missing. If none of the trained fields are present, we don't bother inferencing against the model and instead provide a warning stating that the fields were missing. Also, this adds checks to not allow duplicated fields during processor creation.
SivagurunathanV
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Jan 23, 2020
This adds a new field for the inference processor. `warning_field` is a place for us to write warnings provided from the inference call. When there are warnings we are not going to write an inference result. The goal of this is to indicate that the data provided was too poor or too different for the model to make an accurate prediction. The user could optionally include the `warning_field`. When it is not provided, it is assumed no warnings were desired to be written. The first of these warnings is when ALL of the input fields are missing. If none of the trained fields are present, we don't bother inferencing against the model and instead provide a warning stating that the fields were missing. Also, this adds checks to not allow duplicated fields during processor creation.
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This adds a new field for the inference processor.
warning_fieldis a place for us to write warnings provided from the inference call. When there are warnings we are not going to write an inference result. The goal of this is to indicate that the data provided was too poor or too different for the model to make an accurate prediction.The user could optionally include the
warning_field. When it is not provided, it is assumed no warnings were desired to be written.The first of these warnings is when ALL of the input fields are missing. If none of the trained fields are present, we don't bother inferencing against the model and instead provide a warning stating that the fields were missing.
Also, this adds checks to not allow duplicated fields during processor creation.