REST Resource: projects.locations.models.evaluations

Resource: ModelEvaluation

A collection of metrics calculated by comparing Model's predictions on all of the test data against annotations from the test data.

Fields
name string

Output only. The resource name of the ModelEvaluation.

displayName string

The display name of the ModelEvaluation.

metricsSchemaUri string

Points to a YAML file stored on Google Cloud Storage describing the metrics of this ModelEvaluation. The schema is defined as an OpenAPI 3.0.2 Schema Object.

metrics value (Value format)

Evaluation metrics of the Model. The schema of the metrics is stored in metricsSchemaUri

createTime string (Timestamp format)

Output only. timestamp when this ModelEvaluation was created.

Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z", "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30".

sliceDimensions[] string

All possible dimensions of ModelEvaluationSlices. The dimensions can be used as the filter of the ModelService.ListModelEvaluationSlices request, in the form of slice.dimension = <dimension>.

dataItemSchemaUri string

Points to a YAML file stored on Google Cloud Storage describing [EvaluatedDataItemView.data_item_payload][] and EvaluatedAnnotation.data_item_payload. The schema is defined as an OpenAPI 3.0.2 Schema Object.

This field is not populated if there are neither EvaluatedDataItemViews nor EvaluatedAnnotations under this ModelEvaluation.

annotationSchemaUri string

Points to a YAML file stored on Google Cloud Storage describing [EvaluatedDataItemView.predictions][], [EvaluatedDataItemView.ground_truths][], EvaluatedAnnotation.predictions, and EvaluatedAnnotation.ground_truths. The schema is defined as an OpenAPI 3.0.2 Schema Object.

This field is not populated if there are neither EvaluatedDataItemViews nor EvaluatedAnnotations under this ModelEvaluation.

modelExplanation object (ModelExplanation)

Aggregated explanation metrics for the Model's prediction output over the data this ModelEvaluation uses. This field is populated only if the Model is evaluated with explanations, and only for AutoML tabular Models.

explanationSpecs[] object (ModelEvaluationExplanationSpec)

Describes the values of ExplanationSpec that are used for explaining the predicted values on the evaluated data.

metadata value (Value format)

The metadata of the ModelEvaluation. For the ModelEvaluation uploaded from Managed Pipeline, metadata contains a structured value with keys of "pipelineJobId", "evaluation_dataset_type", "evaluation_dataset_path", "row_based_metrics_path".

JSON representation 
{
  "name": string,
  "displayName": string,
  "metricsSchemaUri": string,
  "metrics": value,
  "createTime": string,
  "sliceDimensions": [
    string
  ],
  "dataItemSchemaUri": string,
  "annotationSchemaUri": string,
  "modelExplanation": {
    object (ModelExplanation)
  },
  "explanationSpecs": [
    {
      object (ModelEvaluationExplanationSpec)
    }
  ],
  "metadata": value
}

ModelEvaluationExplanationSpec

Fields
explanationType string

Explanation type.

For AutoML Image Classification models, possible values are:

  • image-integrated-gradients
  • image-xrai
explanationSpec object (ExplanationSpec)

Explanation spec details.

JSON representation 
{
  "explanationType": string,
  "explanationSpec": {
    object (ExplanationSpec)
  }
}

Methods

get

 Gets a ModelEvaluation.

import

 Imports an externally generated ModelEvaluation.

list

 Lists ModelEvaluations in a Model.