370 lines
9.4 KiB
Plaintext
370 lines
9.4 KiB
Plaintext
[role="xpack"]
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[testenv="basic"]
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[[get-trained-models]]
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= Get trained models API
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[subs="attributes"]
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++++
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<titleabbrev>Get trained models</titleabbrev>
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++++
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Retrieves configuration information for a trained model.
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beta::[]
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[[ml-get-trained-models-request]]
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== {api-request-title}
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`GET _ml/trained_models/` +
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`GET _ml/trained_models/<model_id>` +
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`GET _ml/trained_models/_all` +
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`GET _ml/trained_models/<model_id1>,<model_id2>` +
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`GET _ml/trained_models/<model_id_pattern*>`
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[[ml-get-trained-models-prereq]]
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== {api-prereq-title}
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If the {es} {security-features} are enabled, you must have the following
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privileges:
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* cluster: `monitor_ml`
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For more information, see <<security-privileges>> and
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{ml-docs-setup-privileges}.
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[[ml-get-trained-models-desc]]
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== {api-description-title}
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You can get information for multiple trained models in a single API request by
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using a comma-separated list of model IDs or a wildcard expression.
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[[ml-get-trained-models-path-params]]
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== {api-path-parms-title}
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`<model_id>`::
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(Optional, string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-id]
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[[ml-get-trained-models-query-params]]
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== {api-query-parms-title}
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`allow_no_match`::
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(Optional, Boolean)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=allow-no-match-models]
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`decompress_definition`::
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(Optional, Boolean)
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Specifies whether the included model definition should be returned as a JSON map
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(`true`) or in a custom compressed format (`false`). Defaults to `true`.
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`exclude_generated`::
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(Optional, Boolean)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=exclude-generated]
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`from`::
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(Optional, integer)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=from-models]
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`include`::
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(Optional, string)
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A comma delimited string of optional fields to include in the response body. The
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default value is empty, indicating no optional fields are included. Valid
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options are:
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- `definition`: Includes the model definition
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- `feature_importance_baseline`: Includes the baseline for {feat-imp} values.
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- `total_feature_importance`: Includes the total {feat-imp} for the training
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data set.
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The baseline and total {feat-imp} values are returned in the `metadata` field
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in the response body.
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`size`::
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(Optional, integer)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=size-models]
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`tags`::
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(Optional, string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=tags]
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[role="child_attributes"]
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[[ml-get-trained-models-results]]
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== {api-response-body-title}
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`trained_model_configs`::
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(array)
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An array of trained model resources, which are sorted by the `model_id` value in
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ascending order.
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+
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.Properties of trained model resources
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[%collapsible%open]
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====
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`created_by`:::
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(string)
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Information on the creator of the trained model.
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`create_time`:::
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(<<time-units,time units>>)
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The time when the trained model was created.
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`default_field_map` :::
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(object)
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A string to string object that contains the default field map to use
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when inferring against the model. For example, data frame analytics
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may train the model on a specific multi-field `foo.keyword`.
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The analytics job would then supply a default field map entry for
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`"foo" : "foo.keyword"`.
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+
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Any field map described in the inference configuration takes precedence.
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`description`:::
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(string)
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The free-text description of the trained model.
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`estimated_heap_memory_usage_bytes`:::
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(integer)
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The estimated heap usage in bytes to keep the trained model in memory.
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`estimated_operations`:::
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(integer)
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The estimated number of operations to use the trained model.
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`inference_config`:::
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(object)
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The default configuration for inference. This can be either a `regression`
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or `classification` configuration. It must match the underlying
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`definition.trained_model`'s `target_type`.
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+
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.Properties of `inference_config`
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[%collapsible%open]
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=====
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`classification`::::
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(object)
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Classification configuration for inference.
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+
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.Properties of classification inference
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[%collapsible%open]
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======
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`num_top_classes`:::
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(integer)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-config-classification-num-top-classes]
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`num_top_feature_importance_values`:::
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(integer)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-config-classification-num-top-feature-importance-values]
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`prediction_field_type`:::
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(string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-config-classification-prediction-field-type]
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`results_field`:::
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(string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-config-results-field]
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`top_classes_results_field`:::
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(string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-config-classification-top-classes-results-field]
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======
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`regression`::::
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(object)
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Regression configuration for inference.
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+
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.Properties of regression inference
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[%collapsible%open]
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======
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`num_top_feature_importance_values`:::
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(integer)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-config-regression-num-top-feature-importance-values]
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`results_field`:::
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(string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-config-results-field]
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======
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=====
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`input`:::
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(object)
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The input field names for the model definition.+
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+
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.Properties of `input`
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[%collapsible%open]
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=====
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`field_names`::::
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(string)
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An array of input field names for the model.
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=====
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`license_level`:::
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(string)
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The license level of the trained model.
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`metadata`:::
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(object)
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An object containing metadata about the trained model. For example, models
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created by {dfanalytics} contain `analysis_config` and `input` objects.
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+
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.Properties of metadata
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[%collapsible%open]
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=====
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`feature_importance_baseline`:::
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(object)
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An object that contains the baseline for {feat-imp} values. For {reganalysis},
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it is a single value. For {classanalysis}, there is a value for each class.
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`hyperparameters`:::
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(array)
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List of the available hyperparameters optimized during the
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`fine_parameter_tuning` phase as well as specified by the user.
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+
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.Properties of hyperparameters
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[%collapsible%open]
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======
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`absolute_importance`::::
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(Optional, double)
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A positive number showing how much the parameter influences the variation of the
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{ml-docs}/dfa-regression.html#dfa-regression-lossfunction[loss function]. For
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hyperparameters with values that are not specified by the user but tuned during
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hyperparameter optimization.
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`name`::::
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(string)
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Name of the hyperparameter.
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`relative_importance`::::
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(Optional, double)
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A number between 0 and 1 showing the proportion of influence on the variation of
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the loss function among all tuned hyperparameters. For hyperparameters with
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values that are not specified by the user but tuned during hyperparameter
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optimization.
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`supplied`::::
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(Boolean)
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Indicates if the hyperparameter is specified by the user (`true`) or optimized
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(`false`).
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`value`::::
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(double)
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The value of the hyperparameter, either optimized or specified by the user.
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======
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`total_feature_importance`:::
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(array)
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An array of the total {feat-imp} for each feature used from
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the training data set. This array of objects is returned if {dfanalytics} trained
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the model and the request includes `total_feature_importance` in the `include`
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request parameter.
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+
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.Properties of total {feat-imp}
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[%collapsible%open]
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======
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`feature_name`:::
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(string)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-metadata-feature-importance-feature-name]
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`importance`:::
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(object)
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A collection of {feat-imp} statistics related to the training data set for this particular feature.
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+
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.Properties of {feat-imp}
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[%collapsible%open]
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=======
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`mean_magnitude`:::
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(double)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-metadata-feature-importance-magnitude]
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`max`:::
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(int)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-metadata-feature-importance-max]
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`min`:::
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(int)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-metadata-feature-importance-min]
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=======
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`classes`:::
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(array)
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If the trained model is a classification model, {feat-imp} statistics are gathered
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per target class value.
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+
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.Properties of class {feat-imp}
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[%collapsible%open]
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=======
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`class_name`:::
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(string)
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The target class value. Could be a string, boolean, or number.
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`importance`:::
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(object)
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A collection of {feat-imp} statistics related to the training data set for this particular feature.
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+
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.Properties of {feat-imp}
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[%collapsible%open]
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========
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`mean_magnitude`:::
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(double)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-metadata-feature-importance-magnitude]
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`max`:::
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(int)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-metadata-feature-importance-max]
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`min`:::
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(int)
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include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=inference-metadata-feature-importance-min]
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========
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=======
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======
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=====
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`model_id`:::
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(string)
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Identifier for the trained model.
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`tags`:::
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(string)
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A comma delimited string of tags. A trained model can have many tags, or none.
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`version`:::
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(string)
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The {es} version number in which the trained model was created.
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====
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[[ml-get-trained-models-response-codes]]
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== {api-response-codes-title}
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`400`::
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If `include_model_definition` is `true`, this code indicates that more than
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one models match the ID pattern.
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`404` (Missing resources)::
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If `allow_no_match` is `false`, this code indicates that there are no
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resources that match the request or only partial matches for the request.
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[[ml-get-trained-models-example]]
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== {api-examples-title}
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The following example gets configuration information for all the trained models:
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[source,console]
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--------------------------------------------------
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GET _ml/trained_models/
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--------------------------------------------------
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// TEST[skip:TBD]
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