REST Resource: projects.locations.models.evaluations.slices

Resource: ModelEvaluationSlice

A collection of metrics calculated by comparing Model's predictions on a slice of the test data against ground truth annotations.

Fields
name string

Output only. The resource name of the ModelEvaluationSlice.

slice object (Slice)

Output only. The slice of the test data that is used to evaluate the Model.

metricsSchemaUri string

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

metrics value (Value format)

Output only. Sliced evaluation metrics of the Model. The schema of the metrics is stored in metricsSchemaUri

createTime string (Timestamp format)

Output only. timestamp when this ModelEvaluationSlice was created.

A timestamp in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits. Examples: "2014-10-02T15:01:23Z" and "2014-10-02T15:01:23.045123456Z".

modelExplanation object (ModelExplanation)

Output only. 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 tabular Models.

JSON representation 
{
  "name": string,
  "slice": {
    object (Slice)
  },
  "metricsSchemaUri": string,
  "metrics": value,
  "createTime": string,
  "modelExplanation": {
    object (ModelExplanation)
  }
}

Slice

Definition of a slice.

Fields
dimension string

Output only. The dimension of the slice. Well-known dimensions are: * annotationSpec: This slice is on the test data that has either ground truth or prediction with AnnotationSpec.display_name equals to value. * slice: This slice is a user customized slice defined by its SliceSpec.

value string

Output only. The value of the dimension in this slice.

sliceSpec object (SliceSpec)

Output only. Specification for how the data was sliced.

JSON representation 
{
  "dimension": string,
  "value": string,
  "sliceSpec": {
    object (SliceSpec)
  }
}

SliceSpec

Specification for how the data should be sliced.

Fields
configs map (key: string, value: object (SliceConfig))

Mapping configuration for this SliceSpec. The key is the name of the feature. By default, the key will be prefixed by "instance" as a dictionary prefix for Vertex Batch Predictions output format.

JSON representation 
{
  "configs": {
    string: {
      object (SliceConfig)
    },
    ...
  }
}

SliceConfig

Specification message containing the config for this SliceSpec. When kind is selected as value and/or range, only a single slice will be computed. When allValues is present, a separate slice will be computed for each possible label/value for the corresponding key in config. Examples, with feature zip_code with values 12345, 23334, 88888 and feature country with values "US", "Canada", "Mexico" in the dataset:

Example 1:

{
  "zip_code": { "value": { "floatValue": 12345.0 } }
}

A single slice for any data with zip_code 12345 in the dataset.

Example 2:

{
  "zip_code": { "range": { "low": 12345, "high": 20000 } }
}

A single slice containing data where the zip_codes between 12345 and 20000 For this example, data with the zip_code of 12345 will be in this slice.

Example 3:

{
  "zip_code": { "range": { "low": 10000, "high": 20000 } },
  "country": { "value": { "stringValue": "US" } }
}

A single slice containing data where the zip_codes between 10000 and 20000 has the country "US". For this example, data with the zip_code of 12345 and country "US" will be in this slice.

Example 4:

{ "country": {"allValues": { "value": true } } }

Three slices are computed, one for each unique country in the dataset.

Example 5:

{
  "country": { "allValues": { "value": true } },
  "zip_code": { "value": { "floatValue": 12345.0 } }
}

Three slices are computed, one for each unique country in the dataset where the zip_code is also 12345. For this example, data with zip_code 12345 and country "US" will be in one slice, zip_code 12345 and country "Canada" in another slice, and zip_code 12345 and country "Mexico" in another slice, totaling 3 slices.

Fields

Union field kind.

kind can be only one of the following:

value object (Value)

A unique specific value for a given feature. Example: { "value": { "stringValue": "12345" } }

range object (Range)

A range of values for a numerical feature. Example: {"range":{"low":10000.0,"high":50000.0}} will capture 12345 and 23334 in the slice.

allValues boolean

If allValues is set to true, then all possible labels of the keyed feature will have another slice computed. Example: {"allValues":{"value":true}}

JSON representation 
{

  // Union field kind can be only one of the following:
  "value": {
    object (Value)
  },
  "range": {
    object (Range)
  },
  "allValues": boolean
  // End of list of possible types for union field kind.
}

Value

Single value that supports strings and floats.

Fields

Union field kind.

kind can be only one of the following:

stringValue string

String type.

floatValue number

Float type.

JSON representation 
{

  // Union field kind can be only one of the following:
  "stringValue": string,
  "floatValue": number
  // End of list of possible types for union field kind.
}

Range

A range of values for slice(s). low is inclusive, high is exclusive.

Fields
low number

Inclusive low value for the range.

high number

Exclusive high value for the range.

JSON representation 
{
  "low": number,
  "high": number
}

Methods

batchImport

 Imports a list of externally generated EvaluatedAnnotations.

get

 Gets a ModelEvaluationSlice.

list

 Lists ModelEvaluationSlices in a ModelEvaluation.