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Elasticsearch Guide [7.7] » REST APIs » Machine learning anomaly detection APIs » Get influencers API
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Get influencers APIedit

Retrieves anomaly detection job results for one or more influencers.

Requestedit

GET _ml/anomaly_detectors/<job_id>/results/influencers

Prerequisitesedit

  • If the Elasticsearch security features are enabled, you must have monitor_ml, monitor, manage_ml, or manage cluster privileges to use this API. You also need read index privilege on the index that stores the results. The machine_learning_admin and machine_learning_user roles provide these privileges. See Security privileges and Built-in roles.

Descriptionedit

Influencers are the entities that have contributed to, or are to blame for, the anomalies. Influencer results are available only if an influencer_field_name is specified in the job configuration.

Path parametersedit

<job_id>
(Required, string) Identifier for the anomaly detection job.

Request bodyedit

desc
(Optional, boolean) If true, the results are sorted in descending order.
end
(Optional, string) Returns influencers with timestamps earlier than this time.
exclude_interim
(Optional, boolean) If true, the output excludes interim results. By default, interim results are included.
influencer_score
(Optional, double) Returns influencers with anomaly scores greater than or equal to this value.
page.from
(Optional, integer) Skips the specified number of influencers.
page.size
(Optional, integer) Specifies the maximum number of influencers to obtain.
sort
(Optional, string) Specifies the sort field for the requested influencers. By default, the influencers are sorted by the influencer_score value.
start
(Optional, string) Returns influencers with timestamps after this time.

Response bodyedit

The API returns an array of influencer objects, which have the following properties:

bucket_span
(number) The length of the bucket in seconds. This value matches the bucket_span that is specified in the job.
influencer_score
(number) A normalized score between 0-100, which is based on the probability of the influencer in this bucket aggregated across detectors. Unlike initial_influencer_score, this value will be updated by a re-normalization process as new data is analyzed.
influencer_field_name
(string) The field name of the influencer.
influencer_field_value
(string) The entity that influenced, contributed to, or was to blame for the anomaly.
initial_influencer_score
(number) A normalized score between 0-100, which is based on the probability of the influencer aggregated across detectors. This is the initial value that was calculated at the time the bucket was processed.
is_interim
(boolean) If true, this is an interim result. In other words, the results are calculated based on partial input data.
job_id
(string) Identifier for the anomaly detection job.
probability
(number) The probability that the influencer has this behavior, in the range 0 to 1. This value can be held to a high precision of over 300 decimal places, so the influencer_score is provided as a human-readable and friendly interpretation of this.
result_type
(string) Internal. This value is always set to influencer.
timestamp
(date) The start time of the bucket for which these results were calculated.

Additional influencer properties are added, depending on the fields being analyzed. For example, if it’s analyzing user_name as an influencer, then a field user_name is added to the result document. This information enables you to filter the anomaly results more easily.

Examplesedit 

GET _ml/anomaly_detectors/high_sum_total_sales/results/influencers
{
  "sort": "influencer_score",
  "desc": true
}

In this example, the API returns the following information, sorted based on the influencer score in descending order:

{
  "count": 189,
  "influencers": [
    {
      "job_id": "high_sum_total_sales",
      "result_type": "influencer",
      "influencer_field_name": "customer_full_name.keyword",
      "influencer_field_value": "Wagdi Shaw",
      "customer_full_name.keyword" : "Wagdi Shaw",
      "influencer_score": 99.02493,
      "initial_influencer_score" : 94.67233079580171,
      "probability" : 1.4784807245686567E-10,
      "bucket_span" : 3600,
      "is_interim" : false,
      "timestamp" : 1574661600000
    },
  ...
  ]
}
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