Package com.google.cloud.aiplatform.v1

Interface ExplanationMetadataOrBuilder

  • All Superinterfaces:
    com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
    All Known Implementing Classes:
    ExplanationMetadata, ExplanationMetadata.Builder
    public interface ExplanationMetadataOrBuilder
extends com.google.protobuf.MessageOrBuilder

    • Method Detail

      • getInputsCount

        int getInputsCount()
         Required. Map from feature names to feature input metadata. Keys are the
 name of the features. Values are the specification of the feature.

 An empty InputMetadata is valid. It describes a text feature which has the
 name specified as the key in
 [ExplanationMetadata.inputs][google.cloud.aiplatform.v1.ExplanationMetadata.inputs].
 The baseline of the empty feature is chosen by Vertex AI.

 For Vertex AI-provided Tensorflow images, the key can be any friendly
 name of the feature. Once specified,
 [featureAttributions][google.cloud.aiplatform.v1.Attribution.feature_attributions]
 are keyed by this key (if not grouped with another feature).

 For custom images, the key must match with the key in
 [instance][google.cloud.aiplatform.v1.ExplainRequest.instances].
        map<string, .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata> inputs = 1 [(.google.api.field_behavior) = REQUIRED];

      • containsInputs

        boolean containsInputs​(String key)
         Required. Map from feature names to feature input metadata. Keys are the
 name of the features. Values are the specification of the feature.

 An empty InputMetadata is valid. It describes a text feature which has the
 name specified as the key in
 [ExplanationMetadata.inputs][google.cloud.aiplatform.v1.ExplanationMetadata.inputs].
 The baseline of the empty feature is chosen by Vertex AI.

 For Vertex AI-provided Tensorflow images, the key can be any friendly
 name of the feature. Once specified,
 [featureAttributions][google.cloud.aiplatform.v1.Attribution.feature_attributions]
 are keyed by this key (if not grouped with another feature).

 For custom images, the key must match with the key in
 [instance][google.cloud.aiplatform.v1.ExplainRequest.instances].
        map<string, .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata> inputs = 1 [(.google.api.field_behavior) = REQUIRED];

      • getInputsMap

        Map<String,​ExplanationMetadata.InputMetadata> getInputsMap()
         Required. Map from feature names to feature input metadata. Keys are the
 name of the features. Values are the specification of the feature.

 An empty InputMetadata is valid. It describes a text feature which has the
 name specified as the key in
 [ExplanationMetadata.inputs][google.cloud.aiplatform.v1.ExplanationMetadata.inputs].
 The baseline of the empty feature is chosen by Vertex AI.

 For Vertex AI-provided Tensorflow images, the key can be any friendly
 name of the feature. Once specified,
 [featureAttributions][google.cloud.aiplatform.v1.Attribution.feature_attributions]
 are keyed by this key (if not grouped with another feature).

 For custom images, the key must match with the key in
 [instance][google.cloud.aiplatform.v1.ExplainRequest.instances].
        map<string, .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata> inputs = 1 [(.google.api.field_behavior) = REQUIRED];

      • getInputsOrDefault

        ExplanationMetadata.InputMetadata getInputsOrDefault​(String key,
                                                     ExplanationMetadata.InputMetadata defaultValue)
         Required. Map from feature names to feature input metadata. Keys are the
 name of the features. Values are the specification of the feature.

 An empty InputMetadata is valid. It describes a text feature which has the
 name specified as the key in
 [ExplanationMetadata.inputs][google.cloud.aiplatform.v1.ExplanationMetadata.inputs].
 The baseline of the empty feature is chosen by Vertex AI.

 For Vertex AI-provided Tensorflow images, the key can be any friendly
 name of the feature. Once specified,
 [featureAttributions][google.cloud.aiplatform.v1.Attribution.feature_attributions]
 are keyed by this key (if not grouped with another feature).

 For custom images, the key must match with the key in
 [instance][google.cloud.aiplatform.v1.ExplainRequest.instances].
        map<string, .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata> inputs = 1 [(.google.api.field_behavior) = REQUIRED];

      • getInputsOrThrow

        ExplanationMetadata.InputMetadata getInputsOrThrow​(String key)
         Required. Map from feature names to feature input metadata. Keys are the
 name of the features. Values are the specification of the feature.

 An empty InputMetadata is valid. It describes a text feature which has the
 name specified as the key in
 [ExplanationMetadata.inputs][google.cloud.aiplatform.v1.ExplanationMetadata.inputs].
 The baseline of the empty feature is chosen by Vertex AI.

 For Vertex AI-provided Tensorflow images, the key can be any friendly
 name of the feature. Once specified,
 [featureAttributions][google.cloud.aiplatform.v1.Attribution.feature_attributions]
 are keyed by this key (if not grouped with another feature).

 For custom images, the key must match with the key in
 [instance][google.cloud.aiplatform.v1.ExplainRequest.instances].
        map<string, .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata> inputs = 1 [(.google.api.field_behavior) = REQUIRED];

      • getOutputsCount

        int getOutputsCount()
         Required. Map from output names to output metadata.

 For Vertex AI-provided Tensorflow images, keys can be any user defined
 string that consists of any UTF-8 characters.

 For custom images, keys are the name of the output field in the prediction
 to be explained.

 Currently only one key is allowed.
        map<string, .google.cloud.aiplatform.v1.ExplanationMetadata.OutputMetadata> outputs = 2 [(.google.api.field_behavior) = REQUIRED];

      • containsOutputs

        boolean containsOutputs​(String key)
         Required. Map from output names to output metadata.

 For Vertex AI-provided Tensorflow images, keys can be any user defined
 string that consists of any UTF-8 characters.

 For custom images, keys are the name of the output field in the prediction
 to be explained.

 Currently only one key is allowed.
        map<string, .google.cloud.aiplatform.v1.ExplanationMetadata.OutputMetadata> outputs = 2 [(.google.api.field_behavior) = REQUIRED];

      • getOutputsMap

        Map<String,​ExplanationMetadata.OutputMetadata> getOutputsMap()
         Required. Map from output names to output metadata.

 For Vertex AI-provided Tensorflow images, keys can be any user defined
 string that consists of any UTF-8 characters.

 For custom images, keys are the name of the output field in the prediction
 to be explained.

 Currently only one key is allowed.
        map<string, .google.cloud.aiplatform.v1.ExplanationMetadata.OutputMetadata> outputs = 2 [(.google.api.field_behavior) = REQUIRED];

      • getOutputsOrDefault

        ExplanationMetadata.OutputMetadata getOutputsOrDefault​(String key,
                                                       ExplanationMetadata.OutputMetadata defaultValue)
         Required. Map from output names to output metadata.

 For Vertex AI-provided Tensorflow images, keys can be any user defined
 string that consists of any UTF-8 characters.

 For custom images, keys are the name of the output field in the prediction
 to be explained.

 Currently only one key is allowed.
        map<string, .google.cloud.aiplatform.v1.ExplanationMetadata.OutputMetadata> outputs = 2 [(.google.api.field_behavior) = REQUIRED];

      • getOutputsOrThrow

        ExplanationMetadata.OutputMetadata getOutputsOrThrow​(String key)
         Required. Map from output names to output metadata.

 For Vertex AI-provided Tensorflow images, keys can be any user defined
 string that consists of any UTF-8 characters.

 For custom images, keys are the name of the output field in the prediction
 to be explained.

 Currently only one key is allowed.
        map<string, .google.cloud.aiplatform.v1.ExplanationMetadata.OutputMetadata> outputs = 2 [(.google.api.field_behavior) = REQUIRED];

      • getFeatureAttributionsSchemaUri

        String getFeatureAttributionsSchemaUri()
         Points to a YAML file stored on Google Cloud Storage describing the format
 of the [feature
 attributions][google.cloud.aiplatform.v1.Attribution.feature_attributions].
 The schema is defined as an OpenAPI 3.0.2 [Schema
 Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
 AutoML tabular Models always have this field populated by Vertex AI.
 Note: The URI given on output may be different, including the URI scheme,
 than the one given on input. The output URI will point to a location where
 the user only has a read access.
        string feature_attributions_schema_uri = 3;
        Returns:
        The featureAttributionsSchemaUri.

      • getFeatureAttributionsSchemaUriBytes

        com.google.protobuf.ByteString getFeatureAttributionsSchemaUriBytes()
         Points to a YAML file stored on Google Cloud Storage describing the format
 of the [feature
 attributions][google.cloud.aiplatform.v1.Attribution.feature_attributions].
 The schema is defined as an OpenAPI 3.0.2 [Schema
 Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
 AutoML tabular Models always have this field populated by Vertex AI.
 Note: The URI given on output may be different, including the URI scheme,
 than the one given on input. The output URI will point to a location where
 the user only has a read access.
        string feature_attributions_schema_uri = 3;
        Returns:
        The bytes for featureAttributionsSchemaUri.

      • getLatentSpaceSource

        String getLatentSpaceSource()
         Name of the source to generate embeddings for example based explanations.
        string latent_space_source = 5;
        Returns:
        The latentSpaceSource.

      • getLatentSpaceSourceBytes

        com.google.protobuf.ByteString getLatentSpaceSourceBytes()
         Name of the source to generate embeddings for example based explanations.
        string latent_space_source = 5;
        Returns:
        The bytes for latentSpaceSource.