Package com.google.cloud.aiplatform.v1

Interface ExplanationMetadata.InputMetadataOrBuilder

    • Method Detail

      • getInputBaselinesList

        List<com.google.protobuf.Value> getInputBaselinesList()
         Baseline inputs for this feature.

 If no baseline is specified, Vertex AI chooses the baseline for this
 feature. If multiple baselines are specified, Vertex AI returns the
 average attributions across them in
 [Attribution.feature_attributions][google.cloud.aiplatform.v1.Attribution.feature_attributions].

 For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape
 of each baseline must match the shape of the input tensor. If a scalar is
 provided, we broadcast to the same shape as the input tensor.

 For custom images, the element of the baselines must be in the same
 format as the feature's input in the
 [instance][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].
        repeated .google.protobuf.Value input_baselines = 1;

      • getInputBaselines

        com.google.protobuf.Value getInputBaselines​(int index)
         Baseline inputs for this feature.

 If no baseline is specified, Vertex AI chooses the baseline for this
 feature. If multiple baselines are specified, Vertex AI returns the
 average attributions across them in
 [Attribution.feature_attributions][google.cloud.aiplatform.v1.Attribution.feature_attributions].

 For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape
 of each baseline must match the shape of the input tensor. If a scalar is
 provided, we broadcast to the same shape as the input tensor.

 For custom images, the element of the baselines must be in the same
 format as the feature's input in the
 [instance][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].
        repeated .google.protobuf.Value input_baselines = 1;

      • getInputBaselinesCount

        int getInputBaselinesCount()
         Baseline inputs for this feature.

 If no baseline is specified, Vertex AI chooses the baseline for this
 feature. If multiple baselines are specified, Vertex AI returns the
 average attributions across them in
 [Attribution.feature_attributions][google.cloud.aiplatform.v1.Attribution.feature_attributions].

 For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape
 of each baseline must match the shape of the input tensor. If a scalar is
 provided, we broadcast to the same shape as the input tensor.

 For custom images, the element of the baselines must be in the same
 format as the feature's input in the
 [instance][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].
        repeated .google.protobuf.Value input_baselines = 1;

      • getInputBaselinesOrBuilderList

        List<? extends com.google.protobuf.ValueOrBuilder> getInputBaselinesOrBuilderList()
         Baseline inputs for this feature.

 If no baseline is specified, Vertex AI chooses the baseline for this
 feature. If multiple baselines are specified, Vertex AI returns the
 average attributions across them in
 [Attribution.feature_attributions][google.cloud.aiplatform.v1.Attribution.feature_attributions].

 For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape
 of each baseline must match the shape of the input tensor. If a scalar is
 provided, we broadcast to the same shape as the input tensor.

 For custom images, the element of the baselines must be in the same
 format as the feature's input in the
 [instance][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].
        repeated .google.protobuf.Value input_baselines = 1;

      • getInputBaselinesOrBuilder

        com.google.protobuf.ValueOrBuilder getInputBaselinesOrBuilder​(int index)
         Baseline inputs for this feature.

 If no baseline is specified, Vertex AI chooses the baseline for this
 feature. If multiple baselines are specified, Vertex AI returns the
 average attributions across them in
 [Attribution.feature_attributions][google.cloud.aiplatform.v1.Attribution.feature_attributions].

 For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape
 of each baseline must match the shape of the input tensor. If a scalar is
 provided, we broadcast to the same shape as the input tensor.

 For custom images, the element of the baselines must be in the same
 format as the feature's input in the
 [instance][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].
        repeated .google.protobuf.Value input_baselines = 1;

      • getInputTensorName

        String getInputTensorName()
         Name of the input tensor for this feature. Required and is only
 applicable to Vertex AI-provided images for Tensorflow.
        string input_tensor_name = 2;
        Returns:
        The inputTensorName.

      • getInputTensorNameBytes

        com.google.protobuf.ByteString getInputTensorNameBytes()
         Name of the input tensor for this feature. Required and is only
 applicable to Vertex AI-provided images for Tensorflow.
        string input_tensor_name = 2;
        Returns:
        The bytes for inputTensorName.

      • getEncodingValue

        int getEncodingValue()
         Defines how the feature is encoded into the input tensor. Defaults to
 IDENTITY.
        .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;
        Returns:
        The enum numeric value on the wire for encoding.

      • getEncoding

        ExplanationMetadata.InputMetadata.Encoding getEncoding()
         Defines how the feature is encoded into the input tensor. Defaults to
 IDENTITY.
        .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;
        Returns:
        The encoding.

      • getModality

        String getModality()
         Modality of the feature. Valid values are: numeric, image. Defaults to
 numeric.
        string modality = 4;
        Returns:
        The modality.

      • getModalityBytes

        com.google.protobuf.ByteString getModalityBytes()
         Modality of the feature. Valid values are: numeric, image. Defaults to
 numeric.
        string modality = 4;
        Returns:
        The bytes for modality.

      • hasFeatureValueDomain

        boolean hasFeatureValueDomain()
         The domain details of the input feature value. Like min/max, original
 mean or standard deviation if normalized.
        .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;
        Returns:
        Whether the featureValueDomain field is set.

      • getFeatureValueDomain

        ExplanationMetadata.InputMetadata.FeatureValueDomain getFeatureValueDomain()
         The domain details of the input feature value. Like min/max, original
 mean or standard deviation if normalized.
        .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;
        Returns:
        The featureValueDomain.

      • getFeatureValueDomainOrBuilder

        ExplanationMetadata.InputMetadata.FeatureValueDomainOrBuilder getFeatureValueDomainOrBuilder()
         The domain details of the input feature value. Like min/max, original
 mean or standard deviation if normalized.
        .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;

      • getIndicesTensorName

        String getIndicesTensorName()
         Specifies the index of the values of the input tensor.
 Required when the input tensor is a sparse representation. Refer to
 Tensorflow documentation for more details:
 https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
        string indices_tensor_name = 6;
        Returns:
        The indicesTensorName.

      • getIndicesTensorNameBytes

        com.google.protobuf.ByteString getIndicesTensorNameBytes()
         Specifies the index of the values of the input tensor.
 Required when the input tensor is a sparse representation. Refer to
 Tensorflow documentation for more details:
 https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
        string indices_tensor_name = 6;
        Returns:
        The bytes for indicesTensorName.

      • getDenseShapeTensorName

        String getDenseShapeTensorName()
         Specifies the shape of the values of the input if the input is a sparse
 representation. Refer to Tensorflow documentation for more details:
 https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
        string dense_shape_tensor_name = 7;
        Returns:
        The denseShapeTensorName.

      • getDenseShapeTensorNameBytes

        com.google.protobuf.ByteString getDenseShapeTensorNameBytes()
         Specifies the shape of the values of the input if the input is a sparse
 representation. Refer to Tensorflow documentation for more details:
 https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
        string dense_shape_tensor_name = 7;
        Returns:
        The bytes for denseShapeTensorName.

      • getIndexFeatureMappingList

        List<String> getIndexFeatureMappingList()
         A list of feature names for each index in the input tensor.
 Required when the input
 [InputMetadata.encoding][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.encoding]
 is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
        repeated string index_feature_mapping = 8;
        Returns:
        A list containing the indexFeatureMapping.

      • getIndexFeatureMappingCount

        int getIndexFeatureMappingCount()
         A list of feature names for each index in the input tensor.
 Required when the input
 [InputMetadata.encoding][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.encoding]
 is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
        repeated string index_feature_mapping = 8;
        Returns:
        The count of indexFeatureMapping.

      • getIndexFeatureMapping

        String getIndexFeatureMapping​(int index)
         A list of feature names for each index in the input tensor.
 Required when the input
 [InputMetadata.encoding][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.encoding]
 is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
        repeated string index_feature_mapping = 8;
        Parameters:
        index - The index of the element to return.
        Returns:
        The indexFeatureMapping at the given index.

      • getIndexFeatureMappingBytes

        com.google.protobuf.ByteString getIndexFeatureMappingBytes​(int index)
         A list of feature names for each index in the input tensor.
 Required when the input
 [InputMetadata.encoding][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.encoding]
 is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
        repeated string index_feature_mapping = 8;
        Parameters:
        index - The index of the value to return.
        Returns:
        The bytes of the indexFeatureMapping at the given index.

      • getEncodedTensorName

        String getEncodedTensorName()
         Encoded tensor is a transformation of the input tensor. Must be provided
 if choosing
 [Integrated Gradients
 attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution]
 or [XRAI
 attribution][google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution]
 and the input tensor is not differentiable.

 An encoded tensor is generated if the input tensor is encoded by a lookup
 table.
        string encoded_tensor_name = 9;
        Returns:
        The encodedTensorName.

      • getEncodedTensorNameBytes

        com.google.protobuf.ByteString getEncodedTensorNameBytes()
         Encoded tensor is a transformation of the input tensor. Must be provided
 if choosing
 [Integrated Gradients
 attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution]
 or [XRAI
 attribution][google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution]
 and the input tensor is not differentiable.

 An encoded tensor is generated if the input tensor is encoded by a lookup
 table.
        string encoded_tensor_name = 9;
        Returns:
        The bytes for encodedTensorName.

      • getEncodedBaselinesList

        List<com.google.protobuf.Value> getEncodedBaselinesList()
         A list of baselines for the encoded tensor.

 The shape of each baseline should match the shape of the encoded tensor.
 If a scalar is provided, Vertex AI broadcasts to the same shape as the
 encoded tensor.
        repeated .google.protobuf.Value encoded_baselines = 10;

      • getEncodedBaselines

        com.google.protobuf.Value getEncodedBaselines​(int index)
         A list of baselines for the encoded tensor.

 The shape of each baseline should match the shape of the encoded tensor.
 If a scalar is provided, Vertex AI broadcasts to the same shape as the
 encoded tensor.
        repeated .google.protobuf.Value encoded_baselines = 10;

      • getEncodedBaselinesCount

        int getEncodedBaselinesCount()
         A list of baselines for the encoded tensor.

 The shape of each baseline should match the shape of the encoded tensor.
 If a scalar is provided, Vertex AI broadcasts to the same shape as the
 encoded tensor.
        repeated .google.protobuf.Value encoded_baselines = 10;

      • getEncodedBaselinesOrBuilderList

        List<? extends com.google.protobuf.ValueOrBuilder> getEncodedBaselinesOrBuilderList()
         A list of baselines for the encoded tensor.

 The shape of each baseline should match the shape of the encoded tensor.
 If a scalar is provided, Vertex AI broadcasts to the same shape as the
 encoded tensor.
        repeated .google.protobuf.Value encoded_baselines = 10;

      • getEncodedBaselinesOrBuilder

        com.google.protobuf.ValueOrBuilder getEncodedBaselinesOrBuilder​(int index)
         A list of baselines for the encoded tensor.

 The shape of each baseline should match the shape of the encoded tensor.
 If a scalar is provided, Vertex AI broadcasts to the same shape as the
 encoded tensor.
        repeated .google.protobuf.Value encoded_baselines = 10;

      • hasVisualization

        boolean hasVisualization()
         Visualization configurations for image explanation.
        .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;
        Returns:
        Whether the visualization field is set.

      • getVisualization

        ExplanationMetadata.InputMetadata.Visualization getVisualization()
         Visualization configurations for image explanation.
        .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;
        Returns:
        The visualization.

      • getGroupName

        String getGroupName()
         Name of the group that the input belongs to. Features with the same group
 name will be treated as one feature when computing attributions. Features
 grouped together can have different shapes in value. If provided, there
 will be one single attribution generated in
 [Attribution.feature_attributions][google.cloud.aiplatform.v1.Attribution.feature_attributions],
 keyed by the group name.
        string group_name = 12;
        Returns:
        The groupName.

      • getGroupNameBytes

        com.google.protobuf.ByteString getGroupNameBytes()
         Name of the group that the input belongs to. Features with the same group
 name will be treated as one feature when computing attributions. Features
 grouped together can have different shapes in value. If provided, there
 will be one single attribution generated in
 [Attribution.feature_attributions][google.cloud.aiplatform.v1.Attribution.feature_attributions],
 keyed by the group name.
        string group_name = 12;
        Returns:
        The bytes for groupName.