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Inference bucket aggregation

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Inference bucket aggregation

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This functionality is in beta and is subject to change. The design and code is less mature than official GA features and is being provided as-is with no warranties. Beta features are not subject to the support SLA of official GA features.

A parent pipeline aggregation which loads a pre-trained model and performs inference on the collated result fields from the parent bucket aggregation.

To use the inference bucket aggregation, you need to have the same security privileges that are required for using the Get trained models.

Syntax

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A inference aggregation looks like this in isolation:

{
  "inference": {
    "model_id": "a_model_for_inference", 
    "inference_config": { 
      "regression_config": {
        "num_top_feature_importance_values": 2
      }
    },
    "buckets_path": {
      "avg_cost": "avg_agg", 
          "max_cost": "max_agg"
    }
  }
}

	The ID of model to use.
	The optional inference config which overrides the model’s default settings
	Map the value of `avg_agg` to the model’s input field `avg_cost`

Table 53. inference Parameters

Parameter Name	Description	Required	Default Value
`model_id`	The ID of the model to load and infer against	Required	-
`inference_config`	Contains the inference type and its options. There are two types: `regression` and `classification`	Optional	-
`buckets_path`	Defines the paths to the input aggregations and maps the aggregation names to the field names expected by the model. See `buckets_path` Syntax for more details	Required	-

Configuration options for inference models

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The inference_config setting is optional and usually isn’t required as the pre-trained models come equipped with sensible defaults. In the context of aggregations some options can overridden for each of the 2 types of model.

Configuration options for regression models

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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.

Configuration options for classification models

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num_top_classes: (Optional, integer) Specifies the number of top class predictions to return. Defaults to 0.
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.
prediction_field_type: (Optional, string) Specifies the type of the predicted field to write. Acceptable values are: string, number, boolean. When boolean is provided 1.0 is transformed to true and 0.0 to false.

Example

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The following snippet aggregates a web log by client_ip and extracts a number of features via metric and bucket sub-aggregations as input to the inference aggregation configured with a model trained to identify suspicious client IPs:

GET kibana_sample_data_logs/_search
{
  "size": 0,
  "aggs": {
    "client_ip": { 
      "composite": {
        "sources": [
          {
            "client_ip": {
              "terms": {
                "field": "clientip"
              }
            }
          }
        ]
      },
      "aggs": { 
        "url_dc": {
          "cardinality": {
            "field": "url.keyword"
          }
        },
        "bytes_sum": {
          "sum": {
            "field": "bytes"
          }
        },
        "geo_src_dc": {
          "cardinality": {
            "field": "geo.src"
          }
        },
        "geo_dest_dc": {
          "cardinality": {
            "field": "geo.dest"
          }
        },
        "responses_total": {
          "value_count": {
            "field": "timestamp"
          }
        },
        "success": {
          "filter": {
            "term": {
              "response": "200"
            }
          }
        },
        "error404": {
          "filter": {
            "term": {
              "response": "404"
            }
          }
        },
        "error503": {
          "filter": {
            "term": {
              "response": "503"
            }
          }
        },
        "malicious_client_ip": { 
          "inference": {
            "model_id": "malicious_clients_model",
            "buckets_path": {
              "response_count": "responses_total",
              "url_dc": "url_dc",
              "bytes_sum": "bytes_sum",
              "geo_src_dc": "geo_src_dc",
              "geo_dest_dc": "geo_dest_dc",
              "success": "success._count",
              "error404": "error404._count",
              "error503": "error503._count"
            }
          }
        }
      }
    }
  }
}

	A composite bucket aggregation that aggregates the data by `client_ip`.
	A series of metrics and bucket sub-aggregations.
	Inference bucket aggregation that contains the model ID and maps the aggregation names to the model’s input fields.

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