162 lines
4.8 KiB
Plaintext
162 lines
4.8 KiB
Plaintext
[role="xpack"]
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[testenv="platinum"]
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[[ml-get-influencer]]
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= Get influencers API
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++++
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<titleabbrev>Get influencers</titleabbrev>
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++++
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Retrieves {anomaly-job} results for one or more influencers.
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[[ml-get-influencer-request]]
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== {api-request-title}
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`GET _ml/anomaly_detectors/<job_id>/results/influencers`
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[[ml-get-influencer-prereqs]]
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== {api-prereq-title}
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* If the {es} {security-features} are enabled, you must have `monitor_ml`,
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`monitor`, `manage_ml`, or `manage` cluster privileges to use this API. You also
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need `read` index privilege on the index that stores the results. The
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`machine_learning_admin` and `machine_learning_user` roles provide these
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privileges. See <<security-privileges>> and
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<<built-in-roles>>.
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[[ml-get-influencer-desc]]
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== {api-description-title}
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Influencers are the entities that have contributed to, or are to blame for,
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the anomalies. Influencer results are available only if an
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`influencer_field_name` is specified in the job configuration.
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[[ml-get-influencer-path-parms]]
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== {api-path-parms-title}
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`<job_id>`::
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(Required, string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
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[[ml-get-influencer-request-body]]
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== {api-request-body-title}
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`desc`::
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(Optional, boolean)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=desc-results]
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`end`::
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(Optional, string) Returns influencers with timestamps earlier than this time.
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`exclude_interim`::
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(Optional, boolean)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=exclude-interim-results]
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`influencer_score`::
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(Optional, double) Returns influencers with anomaly scores greater than or equal
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to this value.
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`page`.`from`::
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(Optional, integer) Skips the specified number of influencers.
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`page`.`size`::
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(Optional, integer) Specifies the maximum number of influencers to obtain.
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`sort`::
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(Optional, string) Specifies the sort field for the requested influencers. By
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default, the influencers are sorted by the `influencer_score` value.
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`start`::
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(Optional, string) Returns influencers with timestamps after this time.
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[[ml-get-influencer-results]]
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== {api-response-body-title}
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The API returns an array of influencer objects, which have the following
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properties:
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`bucket_span`::
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(number)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=bucket-span-results]
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`influencer_score`::
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(number) A normalized score between 0-100, which is based on the probability of
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the influencer in this bucket aggregated across detectors. Unlike
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`initial_influencer_score`, this value will be updated by a re-normalization
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process as new data is analyzed.
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`influencer_field_name`::
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(string) The field name of the influencer.
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`influencer_field_value`::
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(string) The entity that influenced, contributed to, or was to blame for the
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anomaly.
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`initial_influencer_score`::
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(number) A normalized score between 0-100, which is based on the probability of
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the influencer aggregated across detectors. This is the initial value that was
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calculated at the time the bucket was processed.
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`is_interim`::
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(boolean)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=is-interim]
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`job_id`::
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(string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]
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`probability`::
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(number) The probability that the influencer has this behavior, in the range 0
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to 1. This value can be held to a high precision of over 300 decimal places, so
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the `influencer_score` is provided as a human-readable and friendly
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interpretation of this.
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`result_type`::
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(string) Internal. This value is always set to `influencer`.
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`timestamp`::
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(date)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=timestamp-results]
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NOTE: Additional influencer properties are added, depending on the fields being
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analyzed. For example, if it's analyzing `user_name` as an influencer, then a
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field `user_name` is added to the result document. This information enables you to
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filter the anomaly results more easily.
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[[ml-get-influencer-example]]
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== {api-examples-title}
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[source,console]
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--------------------------------------------------
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GET _ml/anomaly_detectors/high_sum_total_sales/results/influencers
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{
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"sort": "influencer_score",
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"desc": true
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}
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--------------------------------------------------
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// TEST[skip:Kibana sample data]
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In this example, the API returns the following information, sorted based on the
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influencer score in descending order:
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[source,js]
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----
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{
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"count": 189,
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"influencers": [
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{
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"job_id": "high_sum_total_sales",
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"result_type": "influencer",
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"influencer_field_name": "customer_full_name.keyword",
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"influencer_field_value": "Wagdi Shaw",
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"customer_full_name.keyword" : "Wagdi Shaw",
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"influencer_score": 99.02493,
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"initial_influencer_score" : 94.67233079580171,
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"probability" : 1.4784807245686567E-10,
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"bucket_span" : 3600,
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"is_interim" : false,
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"timestamp" : 1574661600000
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},
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...
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]
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}
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----
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