OpenSearch/docs/reference/ml/df-analytics/apis/evaluate-dfanalytics.asciidoc

[role="xpack"]
[testenv="platinum"]
[[evaluate-dfanalytics]]
=== Evaluate {dfanalytics} API

[subs="attributes"]
++++
<titleabbrev>Evaluate {dfanalytics}</titleabbrev>
++++

Evaluates the {dfanalytics} for an annotated index.

experimental[]


[[ml-evaluate-dfanalytics-request]]
==== {api-request-title}

`POST _ml/data_frame/_evaluate`


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

The API packages together commonly used evaluation metrics for various types of 
machine learning features. This has been designed for use on indexes created by 
{dfanalytics}. Evaluation requires both a ground truth field and an analytics 
result field to be present.


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

`evaluation`::
(Required, object) Defines the type of evaluation you want to perform. The 
value of this object can be different depending on the type of evaluation you 
want to perform. See <<ml-evaluate-dfanalytics-resources>>.
+
--
Available evaluation types:
* `binary_soft_classification`
* `regression`
* `classification`
--

`index`::
(Required, object) Defines the `index` in which the evaluation will be
performed.

`query`::
(Optional, object) A query clause that retrieves a subset of data from the 
source index. See <<query-dsl>>.

[[ml-evaluate-dfanalytics-resources]]
==== {dfanalytics-cap} evaluation resources

[[binary-sc-resources]]
===== Binary soft classification configuration objects

Binary soft classification evaluates the results of an analysis which outputs 
the probability that each document belongs to a certain class. For example, in 
the context of {oldetection}, the analysis outputs the probability whether each 
document is an outlier.

`actual_field`::
  (Required, string) The field of the `index` which contains the `ground truth`. 
  The data type of this field can be boolean or integer. If the data type is 
  integer, the value has to be either `0` (false) or `1` (true).

`predicted_probability_field`::
  (Required, string) The field of the `index` that defines the probability of 
  whether the item belongs to the class in question or not. It's the field that 
  contains the results of the analysis.

`metrics`::
  (Optional, object) Specifies the metrics that are used for the evaluation. 
  Available metrics:
  
  `auc_roc`::
    (Optional, object) The AUC ROC (area under the curve of the receiver 
    operating characteristic) score and optionally the curve. Default value is 
    {"includes_curve": false}.
    
  `precision`::
    (Optional, object) Set the different thresholds of the {olscore} at where 
    the metric is calculated. Default value is {"at": [0.25, 0.50, 0.75]}.
  
  `recall`::
    (Optional, object) Set the different thresholds of the {olscore} at where 
    the metric is calculated. Default value is {"at": [0.25, 0.50, 0.75]}.
  
  `confusion_matrix`::
    (Optional, object) Set the different thresholds of the {olscore} at where 
    the metrics (`tp` - true positive, `fp` - false positive, `tn` - true 
    negative, `fn` - false negative) are calculated. Default value is 
    {"at": [0.25, 0.50, 0.75]}.

    
[[regression-evaluation-resources]]
===== {regression-cap} evaluation objects

{regression-cap} evaluation evaluates the results of a {regression} analysis 
which outputs a prediction of values.

`actual_field`::
  (Required, string) The field of the `index` which contains the `ground truth`. 
  The data type of this field must be numerical.
  
`predicted_field`::
  (Required, string) The field in the `index` that contains the predicted value, 
  in other words the results of the {regression} analysis.
  
`metrics`::
  (Required, object) Specifies the metrics that are used for the evaluation. 
  Available metrics are `r_squared` and `mean_squared_error`.
  
  
[[classification-evaluation-resources]]
==== {classification-cap} evaluation objects

{classification-cap} evaluation evaluates the results of a {classanalysis} which 
outputs a prediction that identifies to which of the classes each document 
belongs.

`actual_field`::
  (Required, string) The field of the `index` which contains the ground truth. 
  The data type of this field must be keyword.
  
`metrics`::
  (Required, object) Specifies the metrics that are used for the evaluation. 
  Available metric is `multiclass_confusion_matrix`.
  
`predicted_field`::
  (Required, string) The field in the `index` that contains the predicted value, 
  in other words the results of the {classanalysis}. The data type of this field 
  is string. You need to add `.keyword` to the predicted field name (the name 
  you put in the {classanalysis} object as `prediction_field_name` or the 
  default value of the same field if you didn't specified explicitly). For 
  example, `predicted_field` : `ml.animal_class_prediction.keyword`.


////
[[ml-evaluate-dfanalytics-results]]
==== {api-response-body-title}

`binary_soft_classification`::
  (object) If you chose to do binary soft classification, the API returns the
  following evaluation metrics:
  
`auc_roc`::: TBD

`confusion_matrix`::: TBD
  
`precision`::: TBD

`recall`::: TBD
////


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


[[ml-evaluate-binary-soft-class-example]]
===== Binary soft classification

[source,console]
--------------------------------------------------
POST _ml/data_frame/_evaluate
{
  "index": "my_analytics_dest_index",
  "evaluation": {
    "binary_soft_classification": {
      "actual_field": "is_outlier",
      "predicted_probability_field": "ml.outlier_score"
    }
  }
}
--------------------------------------------------
// TEST[skip:TBD]

The API returns the following results:

[source,console-result]
----
{
  "binary_soft_classification": {
    "auc_roc": {
      "score": 0.92584757746414444
    },
    "confusion_matrix": {
      "0.25": {
          "tp": 5,
          "fp": 9,
          "tn": 204,
          "fn": 5
      },
      "0.5": {
          "tp": 1,
          "fp": 5,
          "tn": 208,
          "fn": 9
      },
      "0.75": {
          "tp": 0,
          "fp": 4,
          "tn": 209,
          "fn": 10
      }
    },
    "precision": {
        "0.25": 0.35714285714285715,
        "0.5": 0.16666666666666666,
        "0.75": 0
    },
    "recall": {
        "0.25": 0.5,
        "0.5": 0.1,
        "0.75": 0
    }
  }
}
----


[[ml-evaluate-regression-example]]
===== {regression-cap}

[source,console]
--------------------------------------------------
POST _ml/data_frame/_evaluate
{
  "index": "house_price_predictions", <1>
  "query": {
      "bool": {
        "filter": [
          { "term":  { "ml.is_training": false } } <2>
        ]
      }
  },
  "evaluation": {
    "regression": { 
      "actual_field": "price", <3>
      "predicted_field": "ml.price_prediction", <4>
      "metrics": {  
        "r_squared": {},
        "mean_squared_error": {}                             
      }
    }
  }
}
--------------------------------------------------
// TEST[skip:TBD]

<1> The output destination index from a {dfanalytics} {reganalysis}.
<2> In this example, a test/train split (`training_percent`) was defined for the 
{reganalysis}. This query limits evaluation to be performed on the test split 
only. 
<3> The ground truth value for the actual house price. This is required in order 
to evaluate results.
<4> The predicted value for house price calculated by the {reganalysis}.


The following example calculates the training error:

[source,console]
--------------------------------------------------
POST _ml/data_frame/_evaluate
{
  "index": "student_performance_mathematics_reg",
  "query": {
    "term": {
      "ml.is_training": {
        "value": true <1>
      }
    }
  },
  "evaluation": {
    "regression": { 
      "actual_field": "G3", <2>
      "predicted_field": "ml.G3_prediction", <3>
      "metrics": {  
        "r_squared": {},
        "mean_squared_error": {}                             
      }
    }
  }
}
--------------------------------------------------
// TEST[skip:TBD]

<1> In this example, a test/train split (`training_percent`) was defined for the 
{reganalysis}. This query limits evaluation to be performed on the train split 
only. It means that a training error will be calculated.
<2> The field that contains the ground truth value for the actual student 
performance. This is required in order to evaluate results.
<3> The field that contains the predicted value for student performance 
calculated by the {reganalysis}.


The next example calculates the testing error. The only difference compared with 
the previous example is that `ml.is_training` is set to `false` this time, so 
the query excludes the train split from the evaluation.

[source,console]
--------------------------------------------------
POST _ml/data_frame/_evaluate
{
  "index": "student_performance_mathematics_reg",
  "query": {
    "term": {
      "ml.is_training": {
        "value": false <1>
      }
    }
  },
  "evaluation": {
    "regression": { 
      "actual_field": "G3", <2>
      "predicted_field": "ml.G3_prediction", <3>
      "metrics": {  
        "r_squared": {},
        "mean_squared_error": {}                             
      }
    }
  }
}
--------------------------------------------------
// TEST[skip:TBD]

<1> In this example, a test/train split (`training_percent`) was defined for the 
{reganalysis}. This query limits evaluation to be performed on the test split 
only. It means that a testing error will be calculated.
<2> The field that contains the ground truth value for the actual student 
performance. This is required in order to evaluate results.
<3> The field that contains the predicted value for student performance 
calculated by the {reganalysis}.


[[ml-evaluate-classification-example]]
===== {classification-cap}


[source,console]
--------------------------------------------------
POST _ml/data_frame/_evaluate
{ 
   "index": "animal_classification",
   "evaluation": {
      "classification": { <1>
         "actual_field": "animal_class", <2>
         "predicted_field": "ml.animal_class_prediction.keyword", <3>
         "metrics": {  
           "multiclass_confusion_matrix" : {} <4>
         }
      }
   }
}
--------------------------------------------------
// TEST[skip:TBD]

<1> The evaluation type.
<2> The field that contains the ground truth value for the actual animal 
classification. This is required in order to evaluate results.
<3> The field that contains the predicted value for animal classification by 
the {classanalysis}. Since the field storing predicted class is dynamically 
mapped as text and keyword, you need to add the `.keyword` suffix to the name.
<4> Specifies the metric for the evaluation.


The API returns the following result:

[source,console-result]
--------------------------------------------------
{
   "classification" : {
      "multiclass_confusion_matrix" : {
         "confusion_matrix" : [
         {
            "actual_class" : "cat", <1>
            "actual_class_doc_count" : 12, <2>
            "predicted_classes" : [ <3>
              {
                "predicted_class" : "cat",
                "count" : 12 <4>
              },
              {
                "predicted_class" : "dog",
                "count" : 0 <5>
              }
            ],
            "other_predicted_class_doc_count" : 0 <6>
          },
          {
            "actual_class" : "dog",
            "actual_class_doc_count" : 11,
            "predicted_classes" : [
              {
                "predicted_class" : "dog",
                "count" : 7
              },
              {
                "predicted_class" : "cat",
                "count" : 4
              }
            ],
            "other_predicted_class_doc_count" : 0
          }
        ],
        "other_actual_class_count" : 0
      }
    }
  }
--------------------------------------------------
<1> The name of the actual class that the analysis tried to predict.
<2> The number of documents in the index that belong to the `actual_class`.
<3> This object contains the list of the predicted classes and the number of 
predictions associated with the class.
<4> The number of cats in the dataset that are correctly identified as cats.
<5> The number of cats in the dataset that are incorrectly classified as dogs.
<6> The number of documents that are classified as a class that is not listed as 
a `predicted_class`.