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`
* `kibana_user` (UI only)


* source index: `read`, `view_index_metadata`
* destination index: `read`, `create_index`, `manage` and `index`
* cluster: `monitor` (UI only)
  
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.


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

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

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

`analysis`::
(Required, object)
include::{docdir}/ml/ml-shared.asciidoc[tag=analysis]
  
`analyzed_fields`::
(Optional, object)
include::{docdir}/ml/ml-shared.asciidoc[tag=analyzed-fields]

[source,console]
--------------------------------------------------
PUT _ml/data_frame/analytics/loganalytics
{
  "source": {
    "index": "logdata"
  },
  "dest": {
    "index": "logdata_out"
  },
  "analysis": {
    "outlier_detection": {
    }
  },
  "analyzed_fields": {
        "includes": [ "request.bytes", "response.counts.error" ],
        "excludes": [ "source.geo" ]
  }
}
--------------------------------------------------
// TEST[setup:setup_logdata]


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

`dest`::
(Required, object)
include::{docdir}/ml/ml-shared.asciidoc[tag=dest]
  
`model_memory_limit`::
(Optional, string)
include::{docdir}/ml/ml-shared.asciidoc[tag=model-memory-limit-dfa]
  
`source`::
(object)
include::{docdir}/ml/ml-shared.asciidoc[tag=source-put-dfa]

`allow_lazy_start`::
(Optional, boolean) 
include::{docdir}/ml/ml-shared.asciidoc[tag=allow-lazy-start]


[[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> The 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. In this case, `includes` does not specify any 
fields, so the default behavior takes place: all the fields of the source index 
will included except the ones that are explicitly specified in `excludes`.
<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
}
----
// 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 `training_percent` defines the percentage of the data set that will be 
used for training the model.
<2> The `randomize_seed` is the seed 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]