OpenSearch/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}

If the {es} {security-features} are enabled, you must have the following built-in roles and privileges:

* `machine_learning_admin`
* source indices: `read`, `view_index_metadata`
* destination index: `read`, `create_index`, `manage` and `index`

For more information, see <<built-in-roles>>, <<security-privileges>>, and
{ml-docs-setup-privileges}.


NOTE: The {dfanalytics-job} remembers which roles the user who created it had at
the time of creation. When you start the job, it performs the analysis using
those same roles. If you provide
<<http-clients-secondary-authorization,secondary authorization headers>>,
those credentials are used instead.

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

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

If the destination index does not exist, it is created automatically when you
start the job. See <<start-dfanalytics>>.

If you supply only a subset of the {regression} or {classification} parameters,
{ml-docs}/hyperparameters.html[hyperparameter optimization] occurs. It
determines a value for each of the undefined parameters.

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

`<data_frame_analytics_id>`::
(Required, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=job-id-data-frame-analytics-define]

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

`allow_lazy_start`::
(Optional, boolean)
Specifies whether this job can start when there is insufficient {ml} node
capacity for it to be immediately assigned to a node. The default is `false`; if
a {ml} node with capacity to run the job cannot immediately be found, the
<<start-dfanalytics>> API returns an error. 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`, the API does not return an error and the job
waits in the `starting` state until sufficient {ml} node capacity is available.

//Begin analysis
`analysis`::
(Required, object)
The analysis configuration, which contains the information necessary to perform
one of the following types of analysis: {classification}, {oldetection}, or
{regression}.
+
.Properties of `analysis`
[%collapsible%open]
====
//Begin classification
`classification`:::
(Required^*^, object)
The configuration information necessary to perform
{ml-docs}/dfa-classification.html[{classification}].
+
TIP: Advanced parameters are for fine-tuning {classanalysis}. They are set
automatically by hyperparameter optimization to give the minimum validation
error. It is highly recommended to use the default values unless you fully
understand the function of these parameters.
+
.Properties of `classification`
[%collapsible%open]
=====
`class_assignment_objective`::::
(Optional, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=class-assignment-objective]

`dependent_variable`::::
(Required, string)
+
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=dependent-variable]
+
The data type of the field must be numeric (`integer`, `short`, `long`, `byte`),
categorical (`ip` or `keyword`), or boolean. There must be no more than 30
different values in this field.

`eta`::::
(Optional, double)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=eta]

`feature_bag_fraction`::::
(Optional, double)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=feature-bag-fraction]

`feature_processors`::::
(Optional, list)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=dfas-feature-processors]

`gamma`::::
(Optional, double)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=gamma]

`lambda`::::
(Optional, double)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=lambda]

`max_trees`::::
(Optional, integer)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=max-trees]

`num_top_classes`::::
(Optional, integer)
Defines the number of categories for which the predicted probabilities are
reported. It must be non-negative. If it is greater than the total number of
categories, the API reports all category probabilities. Defaults to 2.

`num_top_feature_importance_values`::::
(Optional, integer)
Advanced configuration option. Specifies the maximum number of
{ml-docs}/ml-feature-importance.html[{feat-imp}] values per document to return.
By default, it is zero and no {feat-imp} calculation occurs.

`prediction_field_name`::::
(Optional, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=prediction-field-name]

`randomize_seed`::::
(Optional, long)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=randomize-seed]

`training_percent`::::
(Optional, integer)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=training-percent]
//End classification
=====
//Begin outlier_detection
`outlier_detection`:::
(Required^*^, object)
The configuration information necessary to perform
{ml-docs}/dfa-outlier-detection.html[{oldetection}]:
+
.Properties of `outlier_detection`
[%collapsible%open]
=====
`compute_feature_influence`::::
(Optional, boolean)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=compute-feature-influence]

`feature_influence_threshold`::::
(Optional, double)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=feature-influence-threshold]

`method`::::
(Optional, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=method]

`n_neighbors`::::
(Optional, integer)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=n-neighbors]

`outlier_fraction`::::
(Optional, double)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=outlier-fraction]

`standardization_enabled`::::
(Optional, boolean)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=standardization-enabled]
//End outlier_detection
=====
//Begin regression
`regression`:::
(Required^*^, object)
The configuration information necessary to perform
{ml-docs}/dfa-regression.html[{regression}].
+
TIP: Advanced parameters are for fine-tuning {reganalysis}. They are set
automatically by hyperparameter optimization to give minimum validation error.
It is highly recommended to use the default values unless you fully understand
the function of these parameters.
+
.Properties of `regression`
[%collapsible%open]
=====
`dependent_variable`::::
(Required, string)
+
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=dependent-variable]
+
The data type of the field must be numeric.

`eta`::::
(Optional, double)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=eta]

`feature_bag_fraction`::::
(Optional, double)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=feature-bag-fraction]

`feature_processors`::::
(Optional, list)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=dfas-feature-processors]

`gamma`::::
(Optional, double)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=gamma]

`lambda`::::
(Optional, double)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=lambda]

`loss_function`::::
(Optional, string)
The loss function used during {regression}. Available options are `mse` (mean
squared error), `msle` (mean squared logarithmic error),  `huber` (Pseudo-Huber
loss). Defaults to `mse`. Refer to
{ml-docs}/dfa-regression.html#dfa-regression-lossfunction[Loss functions for {regression} analyses]
to learn more.

`loss_function_parameter`::::
(Optional, double)
A positive number that is used as a parameter to the `loss_function`.

`max_trees`::::
(Optional, integer)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=max-trees]

`num_top_feature_importance_values`::::
(Optional, integer)
Advanced configuration option. Specifies the maximum number of
{ml-docs}/ml-feature-importance.html[{feat-imp}] values per document to return.
By default, it is zero and no {feat-imp} calculation occurs.

`prediction_field_name`::::
(Optional, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=prediction-field-name]

`randomize_seed`::::
(Optional, long)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=randomize-seed]

`training_percent`::::
(Optional, integer)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=training-percent]
=====
//End regression
====
//End analysis

//Begin analyzed_fields
`analyzed_fields`::
(Optional, object)
Specify `includes` and/or `excludes` patterns to select which fields will be
included in the analysis. The patterns specified in `excludes` are applied last,
therefore `excludes` takes precedence. In other words, if the same field is
specified in both `includes` and `excludes`, then the field will not be included
in the analysis.
+
--
[[dfa-supported-fields]]
The supported fields for each type of analysis are as follows:

* {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} 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} 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.

If `analyzed_fields` is not set, only the relevant fields will be included. For
example, all the numeric fields for {oldetection}. For more information about
field selection, see <<explain-dfanalytics>>.
--
+
.Properties of `analyzed_fields`
[%collapsible%open]
====
`excludes`:::
(Optional, array)
An array of strings that defines the fields that will be excluded from the
analysis. You do not need to add fields with unsupported data types to
`excludes`, these fields are excluded from the analysis automatically.

`includes`:::
(Optional, array)
An array of strings that defines the fields that will be included in the
analysis.
//End analyzed_fields
====

`description`::
(Optional, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=description-dfa]

`dest`::
(Required, object)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=dest]

`max_num_threads`::
(Optional, integer)
The maximum number of threads to be used by the analysis.
The default value is `1`. Using more threads may decrease the time
necessary to complete the analysis at the cost of using more CPU.
Note that the process may use additional threads for operational
functionality other than the analysis itself.

`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`::
(object)
The configuration of how to source the analysis data. It requires an `index`.
Optionally, `query` and `_source` may be specified.
+
.Properties of `source`
[%collapsible%open]
====
`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.
+
WARNING: If your source indices contain documents with the same IDs, only the
document that is indexed last appears in the destination index.

`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": {}}`.

`_source`:::
(Optional, object) Specify `includes` and/or `excludes` patterns to select which
fields will be present in the destination. Fields that are excluded cannot be
included in the analysis.
+
.Properties of `_source`
[%collapsible%open]
=====
`includes`::::
(array) An array of strings that defines the fields that will be included in the
destination.

`excludes`::::
(array) An array of strings that defines the fields that will be excluded from
the destination.
=====
====



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


[[ml-put-dfanalytics-example-preprocess]]
=== Preprocessing actions example

The following example shows how to limit the scope of the analysis to certain
fields, specify excluded fields in the destination index, and use a query to
filter your data before analysis.

[source,console]
--------------------------------------------------
PUT _ml/data_frame/analytics/model-flight-delays-pre
{
  "source": {
    "index": [
      "kibana_sample_data_flights" <1>
    ],
    "query": { <2>
      "range": {
        "DistanceKilometers": {
          "gt": 0
        }
      }
    },
    "_source": { <3>
      "includes": [],
      "excludes": [
        "FlightDelay",
        "FlightDelayType"
      ]
    }
  },
  "dest": { <4>
    "index": "df-flight-delays",
    "results_field": "ml-results"
  },
  "analysis": {
  "regression": {
    "dependent_variable": "FlightDelayMin",
    "training_percent": 90
    }
  },
  "analyzed_fields": { <5>
    "includes": [],
    "excludes": [
      "FlightNum"
    ]
  },
  "model_memory_limit": "100mb"
}
--------------------------------------------------
// TEST[skip:setup kibana sample data]

<1> Source index to analyze.
<2> This query filters out entire documents that will not be present in the
destination index.
<3> The `_source` object defines fields in the dataset that will be included or
excluded in the destination index.
<4> Defines the destination index that contains the results of the analysis and
the fields of the source index specified in the `_source` object. Also defines
the name of the `results_field`.
<5> Specifies fields to be included in or excluded from the analysis. This does
not affect whether the fields will be present in the destination index, only
affects whether they are used in the analysis.

In this example, we can see that all the fields of the source index are included
in the destination index except `FlightDelay` and `FlightDelayType` because
these are defined as excluded fields by the `excludes` parameter of the
`_source` object. The `FlightNum` field is included in the destination index,
however it is not included in the analysis because it is explicitly specified as
excluded field by the `excludes` parameter of the `analyzed_fields` object.


[[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" : "7.6.0",
  "allow_lazy_start" : false,
  "max_num_threads": 1
}
----
// TESTRESPONSE[s/1562265491319/$body.$_path/]
// TESTRESPONSE[s/"version" : "7.6.0"/"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>
       "randomize_seed": 19673948271  <2>
     }
   }
}
--------------------------------------------------
// TEST[skip:TBD]

<1> The percentage of the data set that is used for training the model.
<2> The seed that is used to randomly pick which data is used for training.


[[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]