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

[role="xpack"]
[testenv="platinum"]
[[get-dfanalytics-stats]]
=== Get {dfanalytics-jobs} statistics API
[subs="attributes"]
++++
<titleabbrev>Get {dfanalytics-jobs} stats</titleabbrev>
++++

Retrieves usage information for {dfanalytics-jobs}.

experimental[]

[[ml-get-dfanalytics-stats-request]]
==== {api-request-title}

`GET _ml/data_frame/analytics/<data_frame_analytics_id>/_stats` +

`GET _ml/data_frame/analytics/<data_frame_analytics_id>,<data_frame_analytics_id>/_stats` +

`GET _ml/data_frame/analytics/_stats` +

`GET _ml/data_frame/analytics/_all/_stats` +

`GET _ml/data_frame/analytics/*/_stats`


[[ml-get-dfanalytics-stats-prereq]]
==== {api-prereq-title}

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

* cluster: `monitor_ml`
  
For more information, see <<security-privileges>> and <<built-in-roles>>.

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

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


[[ml-get-dfanalytics-stats-query-params]]
==== {api-query-parms-title}

`allow_no_match`::
(Optional, boolean)
include::{docdir}/ml/ml-shared.asciidoc[tag=allow-no-match]

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

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

[role="child_attributes"]
[[ml-get-dfanalytics-stats-response-body]]
==== {api-response-body-title}

`data_frame_analytics`::
(array)
An array of objects that contain usage information for {dfanalytics-jobs}, which
are sorted by the `id` value in ascending order.
+
.Properties of {dfanalytics-job} usage resources
[%collapsible%open]
====
//Begin analysis_stats
`analysis_stats`:::
(object)
An object containing information about the analysis job.
+
.Properties of `analysis_stats`
[%collapsible%open]
=====
//Begin classification_stats
`classification_stats`::::
(object)
An object containing information about the {classanalysis} job.
+
.Properties of `classification_stats`
[%collapsible%open]
======
//Begin class_hyperparameters
`hyperparameters`::::
(object)
An object containing the parameters of the {classanalysis} job.
+
.Properties of `hyperparameters`
[%collapsible%open]
=======
`alpha`::::
(double)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-alpha]

`class_assignment_objective`::::
(string)
Defines whether class assignment maximizes the accuracy or the minimum recall 
metric. Possible values are `maximize_accuracy` and `maximize_minimum_recall`.

`downsample_factor`::::
(double)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-downsample-factor]

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

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

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

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

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

`max_attempts_to_add_tree`::::
(integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-max-attempts]

`max_optimization_rounds_per_hyperparameter`::::
(integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-max-optimization-rounds]

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

`num_folds`::::
(integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-num-folds]

`num_splits_per_feature`::::
(integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-num-splits]

`soft_tree_depth_limit`::::
(double)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-soft-limit]

`soft_tree_depth_tolerance`::::
(double)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-soft-tolerance]
=======
//End class_hyperparameters

`iteration`::::
(integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-iteration]

`timestamp`::::
(date)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timestamp]

//Begin class_timing_stats
`timing_stats`::::
(object)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats]
+
.Properties of `timing_stats`
[%collapsible%open]
=======
`elapsed_time`::::
(integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats-elapsed]

`iteration_time`::::
(integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats-iteration]
=======
//End class_timing_stats

//Begin class_validation_loss
`validation_loss`::::
(object)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-validation-loss]
+
.Properties of `validation_loss`
[%collapsible%open]
=======
`fold_values`::::
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-validation-loss-fold]

`loss_type`::::
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-validation-loss-type]
=======
//End class_validation_loss
======
//End classification_stats

//Begin outlier_detection_stats
`outlier_detection_stats`::::
(object)
An object containing information about the {oldetection} job.
+
.Properties of `outlier_detection_stats`
[%collapsible%open]
======
//Begin parameters
`parameters`::::
(object)
The list of job parameters specified by the user or determined by algorithmic 
heuristics.
+
.Properties of `parameters`
[%collapsible%open]
=======
`compute_feature_influence`::::
(boolean)
If true, feature influence calculation is enabled.

`feature_influence_threshold`::::
(double)
The minimum {olscore} that a document needs to have to calculate its feature 
influence score.

`method`::::
(string)
The method that {oldetection} uses. Possible values are `lof`, `ldof`, 
`distance_kth_nn`, `distance_knn`, and `ensemble`.

`n_neighbors`::::
(integer)
The value for how many nearest neighbors each method of {oldetection} uses to 
calculate its outlier score.

`outlier_fraction`::::
(double)
The proportion of the data set that is assumed to be outlying prior to 
{oldetection}.

`standardization_enabled`::::
(boolean)
If true, then the following operation is performed on the columns before 
computing {olscores}: (x_i - mean(x_i)) / sd(x_i).
=======
//End parameters

`timestamp`::::
(date)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timestamp]

//Begin od_timing_stats
`timing_stats`::::
(object)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats]
+
.Property of `timing_stats`
[%collapsible%open]
=======
`elapsed_time`::::
(integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats-elapsed]
=======
//End od_timing_stats
======
//End outlier_detection_stats

//Begin regression_stats
`regression_stats`::::
(object)
An object containing information about the {reganalysis} job.
+
.Properties of `regression_stats`
[%collapsible%open]
======
//Begin reg_hyperparameters
`hyperparameters`::::
(object)
An object containing the parameters of the {reganalysis} job.
+
.Properties of `hyperparameters`
[%collapsible%open]
=======
`alpha`::::
(double)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-alpha]

`downsample_factor`::::
(double)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-downsample-factor]

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

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

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

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

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

`max_attempts_to_add_tree`::::
(integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-max-attempts]

`max_optimization_rounds_per_hyperparameter`::::
(integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-max-optimization-rounds]

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

`num_folds`::::
(integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-num-folds]

`num_splits_per_feature`::::
(integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-num-splits]

`soft_tree_depth_limit`::::
(double)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-soft-limit]

`soft_tree_depth_tolerance`::::
(double)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-soft-tolerance]
=======
//End reg_hyperparameters

`iteration`::::
(integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-iteration]

`timestamp`::::
(date)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timestamp]

//Begin reg_timing_stats
`timing_stats`::::
(object)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats]
+
.Propertis of `timing_stats`
[%collapsible%open]
=======
`elapsed_time`::::
(integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats-elapsed]

`iteration_time`::::
(integer)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-timing-stats-iteration]
=======
//End reg_timing_stats

//Begin reg_validation_loss
`validation_loss`::::
(object)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-validation-loss]
+
.Properties of `validation_loss`
[%collapsible%open]
=======
`fold_values`::::
(array of strings)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-validation-loss-fold]

`loss_type`::::
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=dfas-validation-loss-type]
=======
//End reg_validation_loss
======
//End regression_stats
=====
//End analysis_stats

`assignment_explanation`:::
(string)
For running jobs only, contains messages relating to the selection of a node to 
run the job.

//Begin data_counts
`data_counts`:::
(object)
An object that provides counts for the quantity of documents skipped, used in
training, or available for testing.
+
.Properties of `data_counts`
[%collapsible%open]
=====
`skipped_docs_count`::::
(integer)
The number of documents that are skipped during the analysis because they 
contained values that are not supported by the analysis. For example, 
{oldetection} does not support missing fields so it skips documents with missing 
fields. Likewise, all types of analysis skip documents that contain arrays with 
more than one element.

`test_docs_count`::::
(integer)
The number of documents that are not used for training the model and can be used 
for testing.

`training_docs_count`::::
(integer)
The number of documents that are used for training the model.
=====
//End data_counts

`id`:::
(string)
The unique identifier of the {dfanalytics-job}.

`memory_usage`:::
(Optional, object)
An object describing memory usage of the analytics. It is present only after the 
job is started and memory usage is reported.
+
.Properties of `memory_usage`
[%collapsible%open]
=====
`peak_usage_bytes`::::
(long)
The number of bytes used at the highest peak of memory usage.

`timestamp`::::
(date)
The timestamp when memory usage was calculated.
=====

`node`:::
(object)
Contains properties for the node that runs the job. This information is 
available only for running jobs.
+
.Properties of `node`
[%collapsible%open]
=====
`attributes`::::
(object)
include::{docdir}/ml/ml-shared.asciidoc[tag=node-attributes]

`ephemeral_id`::::
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=node-ephemeral-id]

`id`::::
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=node-id]

`name`::::
(string)
The node name.

`transport_address`::::
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=node-transport-address]
=====

`progress`:::
(array) The progress report of the {dfanalytics-job} by phase.
+
.Properties of phase objects
[%collapsible%open]
=====
`phase`::::
(string) Defines the phase of the {dfanalytics-job}. Possible phases: 
`reindexing`, `loading_data`, `analyzing`, and `writing_results`.
    
`progress_percent`::::
(integer) The progress that the {dfanalytics-job} has made expressed in 
percentage.
=====

`state`:::
(string) The status of the {dfanalytics-job}, which can be one of the following
values: `analyzing`, `failed`, `reindexing`, `started`, `starting`,`stopping`, 
`stopped`.
====
//End of data_frame_analytics


[[ml-get-dfanalytics-stats-response-codes]]
==== {api-response-codes-title}

`404` (Missing resources)::
  If `allow_no_match` is `false`, this code indicates that there are no
  resources that match the request or only partial matches for the request.


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

The following API retrieves usage information for the
{ml-docs}/ecommerce-outliers.html[{oldetection} {dfanalytics-job} example]:

[source,console]
--------------------------------------------------
GET _ml/data_frame/analytics/ecommerce/_stats
--------------------------------------------------
// TEST[skip:Kibana sample data]

[source,console-result]
----
{
  "count" : 1,
  "data_frame_analytics" : [
    {
      "id" : "ecommerce",
      "state" : "stopped",
      "progress" : [
        {
          "phase" : "reindexing",
          "progress_percent" : 100
        },
        {
          "phase" : "loading_data",
          "progress_percent" : 100
        },
        {
          "phase" : "analyzing",
          "progress_percent" : 100
        },
        {
          "phase" : "writing_results",
          "progress_percent" : 100
        }
      ],
      "data_counts" : {
        "training_docs_count" : 3321,
        "test_docs_count" : 0,
        "skipped_docs_count" : 0
      },
      "memory_usage" : {
        "timestamp" : 1586905058000,
        "peak_usage_bytes" : 279484
      },
      "analysis_stats" : {
        "outlier_detection_stats" : {
          "timestamp" : 1586905058000,
          "parameters" : {
            "n_neighbors" : 0,
            "method" : "ensemble",
            "compute_feature_influence" : true,
            "feature_influence_threshold" : 0.1,
            "outlier_fraction" : 0.05,
            "standardization_enabled" : true
          },
          "timing_stats" : {
            "elapsed_time" : 245
          }
        }
      }
    }
  ]
}
----