Inference Processor
editInference Processor
editThis 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 |
---|---|---|---|
|
yes |
- |
(String) The ID of the model to load and infer against. |
|
no |
|
(String) Field added to incoming documents to contain results objects. |
|
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. |
|
yes |
- |
(Object) Contains the inference type and its options. There are two types: |
|
no |
- |
Conditionally execute this processor. |
|
no |
- |
Handle failures for this processor. See Handling Failures in Pipelines. |
|
no |
|
Ignore failures for this processor. See Handling Failures in Pipelines. |
|
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
edit-
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
edit-
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
edit{ "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
editUpdate 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.