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}

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

* `machine_learning_admin`
* `kibana_admin` (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>>.

+
--
NOTE: It is possible that secondary authorization headers are supplied in the
request. If this is the case, the secondary authorization headers are used
instead of the primary headers.
--

[[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.

[discrete]
[[ml-hyperparam-optimization]]
===== Hyperparameter optimization

If you don't supply {regression} or {classification} parameters, _hyperparameter 
optimization_ occurs, which sets a value for the undefined parameters. The
starting point is calculated for data dependent parameters by examining the loss
on the training data. Subject to the size constraint, this operation provides an
upper bound on the improvement in validation loss.

A fixed number of rounds is used for optimization which depends on the number of 
parameters being optimized. The optimization starts with random search, then 
Bayesian optimization is performed that is targeting maximum expected 
improvement. If you override any parameters by explicitely setting it, the 
optimization calculates the value of the remaining parameters accordingly and 
uses the value you provided for the overridden parameter. The number of rounds 
are reduced respectively. The validation error is estimated in each round by 
using 4-fold cross validation.

[[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]

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

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

//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 <<ml-hyperparam-optimization,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 `classification`
[%collapsible%open]
=====
`class_assignment_objective`::::
(Optional, string)
include::{docdir}/ml/ml-shared.asciidoc[tag=class-assignment-objective]

`dependent_variable`::::
(Required, string)
+
include::{docdir}/ml/ml-shared.asciidoc[tag=dependent-variable]
+
The data type of the field must be numeric (`integer`, `short`, `long`, `byte`), 
categorical (`ip`, `keyword`, `text`), or boolean.

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

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

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

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

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

`num_top_classes`::::
(Optional, integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=num-top-classes]

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

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

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

`training_percent`::::
(Optional, integer)
include::{docdir}/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::{docdir}/ml/ml-shared.asciidoc[tag=compute-feature-influence]
  
`feature_influence_threshold`:::: 
(Optional, double) 
include::{docdir}/ml/ml-shared.asciidoc[tag=feature-influence-threshold]

`method`::::
(Optional, string)
include::{docdir}/ml/ml-shared.asciidoc[tag=method]
  
`n_neighbors`::::
(Optional, integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=n-neighbors]
  
`outlier_fraction`::::
(Optional, double) 
include::{docdir}/ml/ml-shared.asciidoc[tag=outlier-fraction]
  
`standardization_enabled`::::
(Optional, boolean) 
include::{docdir}/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 <<ml-hyperparam-optimization,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::{docdir}/ml/ml-shared.asciidoc[tag=dependent-variable]
+
The data type of the field must be numeric.

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

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

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

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

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

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

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

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

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

//Begin analyzed_fields
`analyzed_fields`::
(Optional, object)
include::{docdir}/ml/ml-shared.asciidoc[tag=analyzed-fields]
+
.Properties of `analyzed_fields`
[%collapsible%open]
====
`excludes`:::
(Optional, array)
include::{docdir}/ml/ml-shared.asciidoc[tag=analyzed-fields-excludes]

`includes`:::
(Optional, array)
include::{docdir}/ml/ml-shared.asciidoc[tag=analyzed-fields-includes]
//End analyzed_fields
====

`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]

[[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" : "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>
       "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]