308 lines
9.2 KiB
Plaintext
308 lines
9.2 KiB
Plaintext
[role="xpack"]
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[testenv="platinum"]
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[[put-dfanalytics]]
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=== Create {dfanalytics-jobs} API
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[subs="attributes"]
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++++
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<titleabbrev>Create {dfanalytics-jobs}</titleabbrev>
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++++
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Instantiates a {dfanalytics-job}.
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experimental[]
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[[ml-put-dfanalytics-request]]
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==== {api-request-title}
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`PUT _ml/data_frame/analytics/<data_frame_analytics_id>`
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[[ml-put-dfanalytics-prereq]]
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==== {api-prereq-title}
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* You must have `machine_learning_admin` built-in role to use this API. You must
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also have `read` and `view_index_metadata` privileges on the source index and
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`read`, `create_index`, and `index` privileges on the destination index. For
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more information, see <<security-privileges>> and <<built-in-roles>>.
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[[ml-put-dfanalytics-desc]]
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==== {api-description-title}
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This API creates a {dfanalytics-job} that performs an analysis on the source
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index and stores the outcome in a destination index.
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The destination index will be automatically created if it does not exist. The
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`index.number_of_shards` and `index.number_of_replicas` settings of the source
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index will be copied over the destination index. When the source index matches
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multiple indices, these settings will be set to the maximum values found in the
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source indices.
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The mappings of the source indices are also attempted to be copied over
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to the destination index, however, if the mappings of any of the fields don't
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match among the source indices, the attempt will fail with an error message.
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If the destination index already exists, then it will be use as is. This makes
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it possible to set up the destination index in advance with custom settings
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and mappings.
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[[ml-put-dfanalytics-supported-fields]]
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===== Supported fields
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====== {oldetection-cap}
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{oldetection-cap} requires numeric or boolean data to analyze. The algorithms
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don't support missing values therefore fields that have data types other than
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numeric or boolean are ignored. Documents where included fields contain missing
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values, null values, or an array are also ignored. Therefore the `dest` index
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may contain documents that don't have an {olscore}.
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====== {regression-cap}
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{regression-cap} supports fields that are numeric, boolean, text, keyword and ip. It
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is also tolerant of missing values. Fields that are supported are included in
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the analysis, other fields are ignored. Documents where included fields contain
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an array with two or more values are also ignored. Documents in the `dest` index
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that don’t contain a results field are not included in the {reganalysis}.
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[[ml-put-dfanalytics-path-params]]
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==== {api-path-parms-title}
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`<data_frame_analytics_id>`::
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(Required, string) A numerical character string that uniquely identifies the
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{dfanalytics-job}. This identifier can contain lowercase alphanumeric
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characters (a-z and 0-9), hyphens, and underscores. It must start and end with
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alphanumeric characters.
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[[ml-put-dfanalytics-request-body]]
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==== {api-request-body-title}
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`analysis`::
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(Required, object) Defines the type of {dfanalytics} you want to perform on
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your source index. For example: `outlier_detection`. See
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<<dfanalytics-types>>.
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`analyzed_fields`::
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(Optional, object) You can specify both `includes` and/or `excludes` patterns.
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If `analyzed_fields` is not set, only the relevant fields will be included.
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For example, all the numeric fields for {oldetection}. For the supported field
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types, see <<ml-put-dfanalytics-supported-fields>>.
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`includes`:::
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(Optional, array) An array of strings that defines the fields that will be
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included in the analysis.
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`excludes`:::
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(Optional, array) An array of strings that defines the fields that will be
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excluded from the analysis.
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`description`::
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(Optional, string) A description of the job.
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`dest`::
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(Required, object) The destination configuration, consisting of `index` and
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optionally `results_field` (`ml` by default).
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`index`:::
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(Required, string) Defines the _destination index_ to store the results of
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the {dfanalytics-job}.
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`results_field`:::
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(Optional, string) Defines the name of the field in which to store the
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results of the analysis. Default to `ml`.
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`model_memory_limit`::
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(Optional, string) The approximate maximum amount of memory resources that are
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permitted for analytical processing. The default value for {dfanalytics-jobs}
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is `1gb`. If your `elasticsearch.yml` file contains an
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`xpack.ml.max_model_memory_limit` setting, an error occurs when you try to
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create {dfanalytics-jobs} that have `model_memory_limit` values greater than
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that setting. For more information, see <<ml-settings>>.
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`source`::
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(Required, object) The source configuration, consisting of `index` and
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optionally a `query`.
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`index`:::
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(Required, string or array) Index or indices on which to perform the
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analysis. It can be a single index or index pattern as well as an array of
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indices or patterns.
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`query`:::
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(Optional, object) The {es} query domain-specific language
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(<<query-dsl,DSL>>). This value corresponds to the query object in an {es}
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search POST body. All the options that are supported by {es} can be used,
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as this object is passed verbatim to {es}. By default, this property has
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the following value: `{"match_all": {}}`.
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`allow_lazy_start`::
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(Optional, boolean) Whether this job should be allowed to start when there
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is insufficient {ml} node capacity for it to be immediately assigned to a node.
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The default is `false`, which means that the <<start-dfanalytics>>
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will return an error if a {ml} node with capacity to run the
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job cannot immediately be found. (However, this is also subject to
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the cluster-wide `xpack.ml.max_lazy_ml_nodes` setting - see
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<<advanced-ml-settings>>.) If this option is set to `true` then
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the <<start-dfanalytics>> will not return an error, and the job will
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wait in the `starting` state until sufficient {ml} node capacity
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is available.
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[[ml-put-dfanalytics-example]]
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==== {api-examples-title}
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[[ml-put-dfanalytics-example-od]]
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===== {oldetection-cap} example
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The following example creates the `loganalytics` {dfanalytics-job}, the analysis
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type is `outlier_detection`:
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[source,console]
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--------------------------------------------------
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PUT _ml/data_frame/analytics/loganalytics
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{
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"description": "Outlier detection on log data",
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"source": {
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"index": "logdata"
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},
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"dest": {
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"index": "logdata_out"
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},
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"analysis": {
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"outlier_detection": {
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"compute_feature_influence": true,
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"outlier_fraction": 0.05,
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"standardization_enabled": true
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}
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}
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}
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--------------------------------------------------
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// TEST[setup:setup_logdata]
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The API returns the following result:
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[source,console-result]
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----
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{
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"id" : "loganalytics",
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"description": "Outlier detection on log data",
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"source" : {
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"index" : [
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"logdata"
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],
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"query" : {
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"match_all" : { }
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}
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},
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"dest" : {
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"index" : "logdata_out",
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"results_field" : "ml"
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},
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"analysis": {
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"outlier_detection": {
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"compute_feature_influence": true,
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"outlier_fraction": 0.05,
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"standardization_enabled": true
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}
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},
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"model_memory_limit" : "1gb",
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"create_time" : 1562351429434,
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"version" : "7.3.0",
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"allow_lazy_start" : false
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}
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----
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// TESTRESPONSE[s/1562351429434/$body.$_path/]
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// TESTRESPONSE[s/"version" : "7.3.0"/"version" : $body.version/]
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[[ml-put-dfanalytics-example-r]]
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===== {regression-cap} examples
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The following example creates the `house_price_regression_analysis`
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{dfanalytics-job}, the analysis type is `regression`:
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[source,console]
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--------------------------------------------------
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PUT _ml/data_frame/analytics/house_price_regression_analysis
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{
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"source": {
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"index": "houses_sold_last_10_yrs"
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},
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"dest": {
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"index": "house_price_predictions"
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},
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"analysis":
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{
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"regression": {
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"dependent_variable": "price"
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}
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}
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}
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--------------------------------------------------
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// TEST[skip:TBD]
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The API returns the following result:
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[source,console-result]
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----
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{
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"id" : "house_price_regression_analysis",
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"source" : {
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"index" : [
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"houses_sold_last_10_yrs"
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],
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"query" : {
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"match_all" : { }
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}
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},
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"dest" : {
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"index" : "house_price_predictions",
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"results_field" : "ml"
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},
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"analysis" : {
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"regression" : {
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"dependent_variable" : "price",
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"training_percent" : 100
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}
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},
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"model_memory_limit" : "1gb",
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"create_time" : 1567168659127,
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"version" : "8.0.0",
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"allow_lazy_start" : false
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}
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----
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// TESTRESPONSE[s/1567168659127/$body.$_path/]
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// TESTRESPONSE[s/"version": "8.0.0"/"version": $body.version/]
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The following example creates a job and specifies a training percent:
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[source,console]
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--------------------------------------------------
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PUT _ml/data_frame/analytics/student_performance_mathematics_0.3
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{
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"source": {
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"index": "student_performance_mathematics"
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},
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"dest": {
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"index":"student_performance_mathematics_reg"
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},
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"analysis":
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{
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"regression": {
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"dependent_variable": "G3",
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"training_percent": 70 <1>
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}
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}
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}
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--------------------------------------------------
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// TEST[skip:TBD]
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<1> The `training_percent` defines the percentage of the data set that will be used
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for training the model.
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