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Elastic Docs ›Elasticsearch Guide [7.7] ›Ingest node ›Processors

Inference Processor

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Inference Processor

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This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.

Uses a pre-trained data frame analytics model to infer against the data that is being ingested in the pipeline.

Table 52. Inference Options

Name Required Default Description

model_id

yes

-

(String) The ID of the model to load and infer against.

target_field

no

ml.inference.<processor_tag>

(String) Field added to incoming documents to contain results objects.

field_map

yes

-

(Object) Maps the document field names to the known field names of the model. This mapping takes precedence over any default mappings provided in the model configuration.

inference_config

yes

-

(Object) Contains the inference type and its options. There are two types: regression and classification.

if

no

-

Conditionally execute this processor.

on_failure

no

-

Handle failures for this processor. See Handling Failures in Pipelines.

ignore_failure

no

false

Ignore failures for this processor. See Handling Failures in Pipelines.

tag

no

-

An identifier for this processor. Useful for debugging and metrics.

 
{
  "inference": {
    "model_id": "flight_delay_regression-1571767128603",
    "target_field": "FlightDelayMin_prediction_infer",
    "field_map": {},
    "inference_config": { "regression": {} }
  }
}

Regression configuration options

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results_field

(Optional, string) Specifies the field to which the inference prediction is written. Defaults to predicted_value.

num_top_feature_importance_values
(Optional, integer) Specifies the maximum number of feature importance values per document. By default, it is zero and no feature importance calculation occurs.

Classification configuration options

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results_field
(Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to predicted_value.
num_top_classes
(Optional, integer) Specifies the number of top class predictions to return. Defaults to 0.
top_classes_results_field

(Optional, string) Specifies the field to which the top classes are written. Defaults to top_classes.

num_top_feature_importance_values
(Optional, integer) Specifies the maximum number of feature importance values per document. By default, it is zero and no feature importance calculation occurs.

inference_config examples

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{
  "inference_config": {
    "regression": {
      "results_field": "my_regression"
    }
  }
}

This configuration specifies a regression inference and the results are written to the my_regression field contained in the target_field results object.

{
  "inference_config": {
    "classification": {
      "num_top_classes": 2,
      "results_field": "prediction",
      "top_classes_results_field": "probabilities"
    }
  }
}

This configuration specifies a classification inference. The number of categories for which the predicted probabilities are reported is 2 (num_top_classes). The result is written to the prediction field and the top classes to the probabilities field. Both fields are contained in the target_field results object.

Feature importance object mapping

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Update your index mapping of the feature importance result field as you can see below to get the full benefit of aggregating and searching for feature importance.

"ml.inference.feature_importance": {
  "type": "nested",
  "dynamic": true,
  "properties": {
    "feature_name": {
      "type": "keyword"
    },
    "importance": {
      "type": "double"
    }
  }
}

The mapping field name for feature importance is compounded as follows:

<ml.inference.target_field>.<inference.tag>.feature_importance

If inference.tag is not provided in the processor definition, it is not part of the field path. The <ml.inference.target_field> defaults to ml.inference.

For example, you provide a tag foo in the definition as you can see below:

{
  "tag": "foo",
  ...
}

The feature importance value is written to the ml.inference.foo.feature_importance field.

You can also specify a target field as follows:

{
  "tag": "foo",
  "target_field": "my_field"
}

In this case, feature importance is exposed in the my_field.foo.feature_importance field.

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