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This PR adds the reference documentation pages of the data frame analytics APIs (PUT, START, STOP, GET, GET stats, DELETE, Evaluate) to the ML APIs pool.
105 lines
2.5 KiB
Plaintext
105 lines
2.5 KiB
Plaintext
[role="xpack"]
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[testenv="platinum"]
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[[evaluate-dfanalytics]]
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=== Evaluate {dfanalytics} API
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[subs="attributes"]
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++++
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<titleabbrev>Evaluate {dfanalytics}</titleabbrev>
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++++
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experimental[]
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Evaluates the executed analysis on an index that is already annotated with a
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field that contains the results of the analytics (the `ground truth`) for each
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{dataframe} row. Evaluation is typically done via calculating a set of metrics
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that capture various aspects of the quality of the results over the data for
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which we have the `ground truth`. For different types of analyses different
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metrics are suitable. This API packages together commonly used metrics for
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various analyses.
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[[ml-evaluate-dfanalytics-request]]
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==== {api-request-title}
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`POST _ml/data_frame/_evaluate`
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[[ml-evaluate-dfanalytics-prereq]]
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==== {api-prereq-title}
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* You must have `monitor_ml` privilege to use this API. For more
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information, see {stack-ov}/security-privileges.html[Security privileges] and
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{stack-ov}/built-in-roles.html[Built-in roles].
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[[ml-evaluate-dfanalytics-request-body]]
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==== {api-request-body-title}
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`index` (Required)::
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(object) Defines the `index` in which the evaluation will be performed.
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`evaluation` (Required)::
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(object) Defines the type of evaluation you want to perform. For example:
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`binary_soft_classification`.
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See Evaluate API resources.
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[[ml-evaluate-dfanalytics-example]]
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==== {api-examples-title}
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[source,js]
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--------------------------------------------------
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POST _ml/data_frame/_evaluate
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{
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"index": "my_analytics_dest_index",
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"evaluation": {
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"binary_soft_classification": {
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"actual_field": "is_outlier",
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"predicted_probability_field": "ml.outlier_score"
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}
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}
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}
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--------------------------------------------------
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// CONSOLE
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// TEST[skip:TBD]
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The API returns the following results:
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[source,js]
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----
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{
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"binary_soft_classification": {
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"auc_roc": {
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"score": 0.92584757746414444
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},
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"confusion_matrix": {
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"0.25": {
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"tp": 5,
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"fp": 9,
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"tn": 204,
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"fn": 5
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},
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"0.5": {
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"tp": 1,
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"fp": 5,
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"tn": 208,
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"fn": 9
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},
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"0.75": {
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"tp": 0,
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"fp": 4,
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"tn": 209,
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"fn": 10
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}
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},
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"precision": {
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"0.25": 0.35714285714285715,
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"0.5": 0.16666666666666666,
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"0.75": 0
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},
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"recall": {
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"0.25": 0.5,
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"0.5": 0.1,
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"0.75": 0
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}
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}
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}
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----
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// TESTRESPONSE |