elasticsearch/docs/reference/ml/df-analytics/apis/put-dfanalytics.asciidoc

[role="xpack"]
[testenv="platinum"]
[[put-dfanalytics]]
=== Create {dfanalytics-jobs} API
[subs="attributes"]
++++
<titleabbrev>Create {dfanalytics-jobs}</titleabbrev>
++++

Instantiates a {dfanalytics-job}.

experimental[]

[[ml-put-dfanalytics-request]]
==== {api-request-title}

`PUT _ml/data_frame/analytics/<data_frame_analytics_id>`


[[ml-put-dfanalytics-prereq]]
==== {api-prereq-title}

* You must have `machine_learning_admin` built-in role to use this API. You must 
also have `read` and `view_index_metadata` privileges on the source index and 
`read`, `create_index`, and `index` privileges on the destination index. For 
more information, see <<security-privileges>> and <<built-in-roles>>.


[[ml-put-dfanalytics-desc]]
==== {api-description-title}

This API creates a {dfanalytics-job} that performs an analysis on the source 
index and stores the outcome in a destination index.

The destination index will be automatically created if it does not exist. The 
`index.number_of_shards` and `index.number_of_replicas` settings of the source 
index will be copied over the destination index. When the source index matches 
multiple indices, these settings will be set to the maximum values found in the 
source indices.

The mappings of the source indices are also attempted to be copied over
to the destination index, however, if the mappings of any of the fields don't 
match among the source indices, the attempt will fail with an error message.

If the destination index already exists, then it will be use as is. This makes 
it possible to set up the destination index in advance with custom settings 
and mappings.

[[ml-put-dfanalytics-supported-fields]]
===== Supported fields

====== {oldetection-cap}

{oldetection-cap} requires numeric or boolean data to analyze. The algorithms 
don't support missing values therefore fields that have data types other than 
numeric or boolean are ignored. Documents where included fields contain missing 
values, null values, or an array are also ignored. Therefore the `dest` index 
may contain documents that don't have an {olscore}.


====== {regression-cap}

{regression-cap} supports fields that are numeric, boolean, text, keyword and ip. It 
is also tolerant of missing values. Fields that are supported are included in 
the analysis, other fields are ignored. Documents where included fields contain 
an array with two or more values are also ignored. Documents in the `dest` index 
that don’t contain a results field are not included in the {reganalysis}.


====== {classification-cap}

{classification-cap} supports fields that are numeric, boolean, text, keyword 
and ip. It is also tolerant of missing values. Fields that are supported are 
included in the analysis, other fields are ignored. Documents where included 
fields contain an array with two or more values are also ignored. Documents in 
the `dest` index that don’t contain a results field are not included in the 
{classanalysis}.

{classanalysis-cap} can be improved by mapping ordinal variable values to a 
single number. For example, in case of age ranges, you can model the values as 
"0-14" = 0, "15-24" = 1, "25-34" = 2, and so on.

Fields that are highly correlated to the `dependent_variable` should be excluded 
from the analysis. For example, if you have a multi-value field as 
`dependent_variable`, {es} will be mapping it both as text and keyword which 
results in two fields (`field` and `field.keyword`). It is required to exclude 
the field with the text mapping to get exact results from the analysis.


[[ml-put-dfanalytics-path-params]]
==== {api-path-parms-title}

`<data_frame_analytics_id>`::
  (Required, string) A numerical character string that uniquely identifies the 
  {dfanalytics-job}. This identifier can contain lowercase alphanumeric
  characters (a-z and 0-9), hyphens, and underscores. It must start and end with
  alphanumeric characters.


[[ml-put-dfanalytics-request-body]]
==== {api-request-body-title}

`analysis`::
  (Required, object) Defines the type of {dfanalytics} you want to perform on 
  your source index. For example: `outlier_detection`. See 
  <<dfanalytics-types>>.
  
`analyzed_fields`::
  (Optional, object) You can specify both `includes` and/or `excludes` patterns. 
  If `analyzed_fields` is not set, only the relevant fields will be included. 
  For example, all the numeric fields for {oldetection}. For the supported field 
  types, see <<ml-put-dfanalytics-supported-fields>>.
  
  `includes`:::
    (Optional, array) An array of strings that defines the fields that will be 
    included in the analysis.
    
  `excludes`:::
    (Optional, array) An array of strings that defines the fields that will be 
    excluded from the analysis.

`description`::
  (Optional, string) A description of the job.

`dest`::
  (Required, object) The destination configuration, consisting of `index` and 
  optionally `results_field` (`ml` by default).
  
    `index`:::
      (Required, string) Defines the _destination index_ to store the results of 
      the {dfanalytics-job}.
    
    `results_field`:::
      (Optional, string) Defines the name of the field in which to store the 
      results of the analysis. Default to `ml`.
  
`model_memory_limit`::
  (Optional, string) The approximate maximum amount of memory resources that are 
  permitted for analytical processing. The default value for {dfanalytics-jobs} 
  is `1gb`. If your `elasticsearch.yml` file contains an 
  `xpack.ml.max_model_memory_limit` setting, an error occurs when you try to 
  create {dfanalytics-jobs} that have `model_memory_limit` values greater than 
  that setting. For more information, see <<ml-settings>>.
  
`source`::
  (Required, object) The source configuration, consisting of `index` and 
  optionally a `query`.
  
    `index`:::
      (Required, string or array) Index or indices on which to perform the 
      analysis. It can be a single index or index pattern as well as an array of 
      indices or patterns.
  
    `query`:::
      (Optional, object) The {es} query domain-specific language 
      (<<query-dsl,DSL>>). This value corresponds to the query object in an {es} 
      search POST body. All the options that are supported by {es} can be used, 
      as this object is passed verbatim to {es}. By default, this property has 
      the following value: `{"match_all": {}}`.

`allow_lazy_start`::
  (Optional, boolean) Whether this job should be allowed to start when there
  is insufficient {ml} node capacity for it to be immediately assigned to a node.
  The default is `false`, which means that the <<start-dfanalytics>>
  will return an error if a {ml} node with capacity to run the
  job cannot immediately be found. (However, this is also subject to
  the cluster-wide `xpack.ml.max_lazy_ml_nodes` setting - see
  <<advanced-ml-settings>>.) If this option is set to `true` then
  the <<start-dfanalytics>> will not return an error, and the job will
  wait in the `starting` state until sufficient {ml} node capacity
  is available.


[[ml-put-dfanalytics-example]]
==== {api-examples-title}


[[ml-put-dfanalytics-example-od]]
===== {oldetection-cap} example

The following example creates the `loganalytics` {dfanalytics-job}, the analysis 
type is `outlier_detection`:

[source,console]
--------------------------------------------------
PUT _ml/data_frame/analytics/loganalytics
{
  "description": "Outlier detection on log data",
  "source": {
    "index": "logdata"
  },
  "dest": {
    "index": "logdata_out"
  },
  "analysis": {
    "outlier_detection": {
      "compute_feature_influence": true,
      "outlier_fraction": 0.05,
      "standardization_enabled": true
    }
  }
}
--------------------------------------------------
// TEST[setup:setup_logdata]


The API returns the following result:

[source,console-result]
----
{
    "id": "loganalytics",
    "description": "Outlier detection on log data",
    "source": {
        "index": ["logdata"],
        "query": {
            "match_all": {}
        }
    },
    "dest": {
        "index": "logdata_out",
        "results_field": "ml"
    },
    "analysis": {
        "outlier_detection": {
            "compute_feature_influence": true,
            "outlier_fraction": 0.05,
            "standardization_enabled": true
        }
    },
    "model_memory_limit": "1gb",
    "create_time" : 1562265491319,
    "version" : "8.0.0",
    "allow_lazy_start" : false
}
----
// TESTRESPONSE[s/1562265491319/$body.$_path/]
// TESTRESPONSE[s/"version": "8.0.0"/"version": $body.version/]


[[ml-put-dfanalytics-example-r]]
===== {regression-cap} examples

The following example creates the `house_price_regression_analysis` 
{dfanalytics-job}, the analysis type is `regression`:

[source,console]
--------------------------------------------------
PUT _ml/data_frame/analytics/house_price_regression_analysis
{
  "source": {
    "index": "houses_sold_last_10_yrs"
  },
  "dest": {
    "index": "house_price_predictions"
  },
  "analysis": 
    {
      "regression": {
        "dependent_variable": "price"
      }
    }
}
--------------------------------------------------
// TEST[skip:TBD]


The API returns the following result:

[source,console-result]
----
{
  "id" : "house_price_regression_analysis",
  "source" : {
    "index" : [
      "houses_sold_last_10_yrs"
    ],
    "query" : {
      "match_all" : { }
    }
  },
  "dest" : {
    "index" : "house_price_predictions",
    "results_field" : "ml"
  },
  "analysis" : {
    "regression" : {
      "dependent_variable" : "price",
      "training_percent" : 100
    }
  },
  "model_memory_limit" : "1gb",
  "create_time" : 1567168659127,
  "version" : "8.0.0",
  "allow_lazy_start" : false
}
----
// TESTRESPONSE[s/1567168659127/$body.$_path/]
// TESTRESPONSE[s/"version": "8.0.0"/"version": $body.version/]


The following example creates a job and specifies a training percent:

[source,console]
--------------------------------------------------
PUT _ml/data_frame/analytics/student_performance_mathematics_0.3
{
 "source": {
   "index": "student_performance_mathematics"
 },
 "dest": {
   "index":"student_performance_mathematics_reg"
 },
 "analysis":
   {
     "regression": {
       "dependent_variable": "G3",
       "training_percent": 70  <1>
     }
   }
}
--------------------------------------------------
// TEST[skip:TBD]

<1> The `training_percent` defines the percentage of the data set that will be used 
for training the model.


[[ml-put-dfanalytics-example-c]]
===== {classification-cap} example

The following example creates the `loan_classification` {dfanalytics-job}, the 
analysis type is `classification`:

[source,console]
--------------------------------------------------
PUT _ml/data_frame/analytics/loan_classification
{
  "source" : {
    "index": "loan-applicants"
  },
  "dest" : {
    "index": "loan-applicants-classified"
  },
  "analysis" : {
    "classification": {
      "dependent_variable": "label",
      "training_percent": 75,
      "num_top_classes": 2
    }
  }
}
--------------------------------------------------
// TEST[skip:TBD]