[role="xpack"]
[testenv="platinum"]
[[ml-dfanalytics-resources]]
=== {dfanalytics-cap} job resources

{dfanalytics-cap} resources relate to APIs such as <<put-dfanalytics>> and
<<get-dfanalytics>>.	

[discrete]	
[[ml-dfanalytics-properties]]	
==== {api-definitions-title}

`analysis`::
  (object) The type of analysis that is performed on the `source`. For example: 
  `outlier_detection`. For more information, see <<dfanalytics-types>>.
  
`analyzed_fields`::
  (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}.
  
  `analyzed_fields.includes`:::
    (array) An array of strings that defines the fields that will be included in 
    the analysis.
    
  `analyzed_fields.excludes`:::
    (array) An array of strings that defines the fields that will be excluded 
    from the analysis.
  

[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) A description of the job.

`dest`::
  (object) The destination configuration of the analysis.
  
  `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`.

`id`::
  (string) The unique identifier for 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. This property 
  is informational; you cannot change the identifier for existing jobs.
  
`model_memory_limit`::
  (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 source configuration consisting an `index` and optionally a 
  `query` object.
  
  `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": {}}`.

[[dfanalytics-types]]
==== Analysis objects

{dfanalytics-cap} resources contain `analysis` objects. For example, when you
create a {dfanalytics-job}, you must define the type of analysis it performs. 
Currently, `outlier_detection` is the only available type of analysis, however, 
other types will be added, for example `regression`.
  
[discrete]
[[oldetection-resources]]
==== {oldetection-cap} configuration objects 

An {oldetection} configuration object has the following properties:

`compute_feature_influence`::
  (boolean) If `true`, the feature influence calculation is enabled. Defaults to 
  `true`.
  
`feature_influence_threshold`:: 
  (double) The minimum {olscore} that a document needs to have in order to 
  calculate its {fiscore}. Value range: 0-1 (`0.1` by default).

`method`::
  (string) Sets the method that {oldetection} uses. If the method is not set 
  {oldetection} uses an ensemble of different methods and normalises and 
  combines their individual {olscores} to obtain the overall {olscore}. We 
  recommend to use the ensemble method. Available methods are `lof`, `ldof`, 
  `distance_kth_nn`, `distance_knn`.
  
`n_neighbors`::
  (integer) Defines the value for how many nearest neighbors each method of 
  {oldetection} will use to calculate its {olscore}. When the value is not set, 
  different values will be used for different ensemble members. This helps 
  improve diversity in the ensemble. Therefore, only override this if you are 
  confident that the value you choose is appropriate for the data set.
  
`outlier_fraction`::
  (double) Sets the proportion of the data set that is assumed to be outlying prior to 
  {oldetection}. For example, 0.05 means it is assumed that 5% of values are real outliers 
  and 95% are inliers.
  
`standardize_columns`::
  (boolean) If `true`, then the following operation is performed on the columns 
  before computing outlier scores: (x_i - mean(x_i)) / sd(x_i). Defaults to 
  `true`. For more information, see 
  https://en.wikipedia.org/wiki/Feature_scaling#Standardization_(Z-score_Normalization)[this wiki page about standardization].