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Elastic Docs ›Elasticsearch Guide [8.15] ›REST APIs ›Machine learning anomaly detection APIs

Get influencers API

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Get influencers API

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Retrieves anomaly detection job results for one or more influencers.

Request

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

Prerequisites

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Requires the monitor_ml cluster privilege. This privilege is included in the machine_learning_user built-in role.`

Description

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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 parameters

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

Query parameters

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desc
(Optional, Boolean) If true, the results are sorted in descending order.
end
(Optional, string) Returns influencers with timestamps earlier than this time. Defaults to -1, which means it is unset and results are not limited to specific timestamps.
exclude_interim
(Optional, Boolean) If true, the output excludes interim results. Defaults to false, which means interim results are included.
from
(Optional, integer) Skips the specified number of influencers. Defaults to 0.
influencer_score
(Optional, double) Returns influencers with anomaly scores greater than or equal to this value. Defaults to 0.0.
size
(Optional, integer) Specifies the maximum number of influencers to obtain. Defaults to 100.
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. Defaults to -1, which means it is unset and results are not limited to specific timestamps.

Request body

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You can also specify the query parameters in the request body; the exception are from and size, use page instead:

page
Properties of page
from
(Optional, integer) Skips the specified number of influencers. Defaults to 0.
size
(Optional, integer) Specifies the maximum number of influencers to obtain. Defaults to 100.

Response body

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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. For example, 0.0000109783. 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.

Examples

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resp = client.ml.get_influencers(
    job_id="high_sum_total_sales",
    sort="influencer_score",
    desc=True,
)
print(resp)
response = client.ml.get_influencers(
  job_id: 'high_sum_total_sales',
  body: {
    sort: 'influencer_score',
    desc: true
  }
)
puts response
const response = await client.ml.getInfluencers({
  job_id: "high_sum_total_sales",
  sort: "influencer_score",
  desc: true,
});
console.log(response);
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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