Method: slices.batchImport

Full name: projects.locations.models.evaluations.slices.batchImport

Imports a list of externally generated EvaluatedAnnotations.

Endpoint

post https://{service-endpoint}/v1/{parent}:batchImport

Where {service-endpoint} is one of the supported service endpoints.

Path parameters

parent string

Required. The name of the parent ModelEvaluationSlice resource. Format: projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}/slices/{slice}

Request body

The request body contains data with the following structure:

Fields
evaluatedAnnotations[] object (EvaluatedAnnotation)

Required. Evaluated annotations resource to be imported.

Response body

Response message for ModelService.BatchImportEvaluatedAnnotations

If successful, the response body contains data with the following structure:

Fields
importedEvaluatedAnnotationsCount integer

Output only. Number of EvaluatedAnnotations imported.

JSON representation 
{
  "importedEvaluatedAnnotationsCount": integer
}

EvaluatedAnnotation

True positive, false positive, or false negative.

EvaluatedAnnotation is only available under ModelEvaluationSlice with slice of annotationSpec dimension.

Fields
type enum (EvaluatedAnnotationType)

Output only. type of the EvaluatedAnnotation.

predictions[] value (Value format)

Output only. The model predicted annotations.

For true positive, there is one and only one prediction, which matches the only one ground truth annotation in groundTruths.

For false positive, there is one and only one prediction, which doesn't match any ground truth annotation of the corresponding data_item_view_id.

For false negative, there are zero or more predictions which are similar to the only ground truth annotation in groundTruths but not enough for a match.

The schema of the prediction is stored in ModelEvaluation.annotation_schema_uri

groundTruths[] value (Value format)

Output only. The ground truth Annotations, i.e. the Annotations that exist in the test data the Model is evaluated on.

For true positive, there is one and only one ground truth annotation, which matches the only prediction in predictions.

For false positive, there are zero or more ground truth annotations that are similar to the only prediction in predictions, but not enough for a match.

For false negative, there is one and only one ground truth annotation, which doesn't match any predictions created by the model.

The schema of the ground truth is stored in ModelEvaluation.annotation_schema_uri

dataItemPayload value (Value format)

Output only. The data item payload that the Model predicted this EvaluatedAnnotation on.

evaluatedDataItemViewId string

Output only. id of the EvaluatedDataItemView under the same ancestor ModelEvaluation. The EvaluatedDataItemView consists of all ground truths and predictions on dataItemPayload.

explanations[] object (EvaluatedAnnotationExplanation)

Explanations of predictions. Each element of the explanations indicates the explanation for one explanation method.

The attributions list in the EvaluatedAnnotationExplanation.explanation object corresponds to the predictions list. For example, the second element in the attributions list explains the second element in the predictions list.

errorAnalysisAnnotations[] object (ErrorAnalysisAnnotation)

Annotations of model error analysis results.

JSON representation 
{
  "type": enum (EvaluatedAnnotationType),
  "predictions": [
    value
  ],
  "groundTruths": [
    value
  ],
  "dataItemPayload": value,
  "evaluatedDataItemViewId": string,
  "explanations": [
    {
      object (EvaluatedAnnotationExplanation)
    }
  ],
  "errorAnalysisAnnotations": [
    {
      object (ErrorAnalysisAnnotation)
    }
  ]
}

EvaluatedAnnotationType

Describes the type of the EvaluatedAnnotation. The type is determined

Enums
EVALUATED_ANNOTATION_TYPE_UNSPECIFIED Invalid value.
TRUE_POSITIVE The EvaluatedAnnotation is a true positive. It has a prediction created by the Model and a ground truth Annotation which the prediction matches.
FALSE_POSITIVE The EvaluatedAnnotation is false positive. It has a prediction created by the Model which does not match any ground truth annotation.
FALSE_NEGATIVE The EvaluatedAnnotation is false negative. It has a ground truth annotation which is not matched by any of the model created predictions.

EvaluatedAnnotationExplanation

Explanation result of the prediction produced by the Model.

Fields
explanationType string

Explanation type.

For AutoML Image Classification models, possible values are:

  • image-integrated-gradients
  • image-xrai
explanation object (Explanation)

Explanation attribution response details.

JSON representation 
{
  "explanationType": string,
  "explanation": {
    object (Explanation)
  }
}

ErrorAnalysisAnnotation

Model error analysis for each annotation.

Fields
attributedItems[] object (AttributedItem)

Attributed items for a given annotation, typically representing neighbors from the training sets constrained by the query type.

queryType enum (QueryType)

The query type used for finding the attributed items.

outlierScore number

The outlier score of this annotated item. Usually defined as the min of all distances from attributed items.

outlierThreshold number

The threshold used to determine if this annotation is an outlier or not.

JSON representation 
{
  "attributedItems": [
    {
      object (AttributedItem)
    }
  ],
  "queryType": enum (QueryType),
  "outlierScore": number,
  "outlierThreshold": number
}

AttributedItem

Attributed items for a given annotation, typically representing neighbors from the training sets constrained by the query type.

Fields
annotationResourceName string

The unique id for each annotation. Used by FE to allocate the annotation in DB.

distance number

The distance of this item to the annotation.

JSON representation 
{
  "annotationResourceName": string,
  "distance": number
}

QueryType

The query type used for finding the attributed items.

Enums
QUERY_TYPE_UNSPECIFIED Unspecified query type for model error analysis.
ALL_SIMILAR Query similar samples across all classes in the dataset.
SAME_CLASS_SIMILAR Query similar samples from the same class of the input sample.
SAME_CLASS_DISSIMILAR Query dissimilar samples from the same class of the input sample.