Class ExplanationMetadata.InputMetadata.Builder (3.4.1)

Version 3.4.1

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public static final class ExplanationMetadata.InputMetadata.Builder extends GeneratedMessageV3.Builder<ExplanationMetadata.InputMetadata.Builder> implements ExplanationMetadata.InputMetadataOrBuilder

Metadata of the input of a feature. Fields other than InputMetadata.input_baselines are applicable only for Models that are using Vertex AI-provided images for Tensorflow.

Protobuf type google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata

Inheritance

Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > ExplanationMetadata.InputMetadata.Builder

Implements

ExplanationMetadata.InputMetadataOrBuilder

Inherited Members

AbstractMessage.Builder.findInitializationErrors()

AbstractMessage.Builder.getInitializationErrorString()

AbstractMessage.Builder.internalMergeFrom(AbstractMessageLite)

AbstractMessage.Builder.mergeFrom(byte[])

AbstractMessage.Builder.mergeFrom(byte[],ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(byte[],int,int)

AbstractMessage.Builder.mergeFrom(byte[],int,int,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(ByteString)

AbstractMessage.Builder.mergeFrom(ByteString,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(CodedInputStream)

AbstractMessage.Builder.mergeFrom(CodedInputStream,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(Message)

AbstractMessage.Builder.mergeFrom(InputStream)

AbstractMessage.Builder.mergeFrom(InputStream,ExtensionRegistryLite)

AbstractMessage.Builder.newUninitializedMessageException(Message)

AbstractMessage.Builder.toString()

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,Collection<? super T>)

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,List<? super T>)

AbstractMessageLite.Builder.internalMergeFrom(MessageType)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream,ExtensionRegistryLite)

AbstractMessageLite.Builder.mergeFrom(MessageLite)

AbstractMessageLite.Builder.newUninitializedMessageException(MessageLite)

GeneratedMessageV3.Builder.addRepeatedField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.clear()

GeneratedMessageV3.Builder.clearField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.clearOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.clone()

GeneratedMessageV3.Builder.getAllFields()

GeneratedMessageV3.Builder.getDescriptorForType()

GeneratedMessageV3.Builder.getField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getFieldBuilder(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getOneofFieldDescriptor(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.getParentForChildren()

GeneratedMessageV3.Builder.getRepeatedField(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldBuilder(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldCount(Descriptors.FieldDescriptor)

com.google.protobuf.GeneratedMessageV3.Builder.getUnknownFieldSetBuilder()

GeneratedMessageV3.Builder.getUnknownFields()

GeneratedMessageV3.Builder.hasField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.hasOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.internalGetFieldAccessorTable()

GeneratedMessageV3.Builder.internalGetMapField(int)

GeneratedMessageV3.Builder.internalGetMutableMapField(int)

GeneratedMessageV3.Builder.isClean()

GeneratedMessageV3.Builder.isInitialized()

GeneratedMessageV3.Builder.markClean()

GeneratedMessageV3.Builder.mergeUnknownFields(UnknownFieldSet)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownLengthDelimitedField(int,com.google.protobuf.ByteString)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownVarintField(int,int)

GeneratedMessageV3.Builder.newBuilderForField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.onBuilt()

GeneratedMessageV3.Builder.onChanged()

com.google.protobuf.GeneratedMessageV3.Builder.parseUnknownField(com.google.protobuf.CodedInputStream,com.google.protobuf.ExtensionRegistryLite,int)

GeneratedMessageV3.Builder.setField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.setRepeatedField(Descriptors.FieldDescriptor,int,Object)

com.google.protobuf.GeneratedMessageV3.Builder.setUnknownFieldSetBuilder(com.google.protobuf.UnknownFieldSet.Builder)

GeneratedMessageV3.Builder.setUnknownFields(UnknownFieldSet)

GeneratedMessageV3.Builder.setUnknownFieldsProto3(UnknownFieldSet)

Object.equals(Object)

Object.finalize()

Object.getClass()

Object.hashCode()

Object.notify()

Object.notifyAll()

Object.wait()

Object.wait(long)

Object.wait(long,int)

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()

Returns

Type	Description
Descriptor

Methods

addAllEncodedBaselines(Iterable<? extends Value> values)

public ExplanationMetadata.InputMetadata.Builder addAllEncodedBaselines(Iterable<? extends Value> values)

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;

Parameter

Name	Description
values	Iterable<? extends com.google.protobuf.Value>

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

addAllIndexFeatureMapping(Iterable<String> values)

public ExplanationMetadata.InputMetadata.Builder addAllIndexFeatureMapping(Iterable<String> values)

A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Parameter

Name	Description
values	Iterable<String> The indexFeatureMapping to add.

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

addAllInputBaselines(Iterable<? extends Value> values)

public ExplanationMetadata.InputMetadata.Builder addAllInputBaselines(Iterable<? extends Value> values)

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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value input_baselines = 1;

Parameter

Name	Description
values	Iterable<? extends com.google.protobuf.Value>

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

addEncodedBaselines(Value value)

public ExplanationMetadata.InputMetadata.Builder addEncodedBaselines(Value value)

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;

Parameter

Name	Description
value	Value

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

addEncodedBaselines(Value.Builder builderForValue)

public ExplanationMetadata.InputMetadata.Builder addEncodedBaselines(Value.Builder builderForValue)

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;

Parameter

Name	Description
builderForValue	Builder

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

addEncodedBaselines(int index, Value value)

public ExplanationMetadata.InputMetadata.Builder addEncodedBaselines(int index, Value value)

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;

Parameters

Name	Description
index	int
value	Value

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

addEncodedBaselines(int index, Value.Builder builderForValue)

public ExplanationMetadata.InputMetadata.Builder addEncodedBaselines(int index, Value.Builder builderForValue)

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;

Parameters

Name	Description
index	int
builderForValue	Builder

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

addEncodedBaselinesBuilder()

public Value.Builder addEncodedBaselinesBuilder()

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;

Returns

Type	Description
Builder

addEncodedBaselinesBuilder(int index)

public Value.Builder addEncodedBaselinesBuilder(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;

Parameter

Name	Description
index	int

Returns

Type	Description
Builder

addIndexFeatureMapping(String value)

public ExplanationMetadata.InputMetadata.Builder addIndexFeatureMapping(String value)

A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Parameter

Name	Description
value	String The indexFeatureMapping to add.

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

addIndexFeatureMappingBytes(ByteString value)

public ExplanationMetadata.InputMetadata.Builder addIndexFeatureMappingBytes(ByteString value)

A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Parameter

Name	Description
value	ByteString The bytes of the indexFeatureMapping to add.

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

addInputBaselines(Value value)

public ExplanationMetadata.InputMetadata.Builder addInputBaselines(Value value)

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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value input_baselines = 1;

Parameter

Name	Description
value	Value

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

addInputBaselines(Value.Builder builderForValue)

public ExplanationMetadata.InputMetadata.Builder addInputBaselines(Value.Builder builderForValue)

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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value input_baselines = 1;

Parameter

Name	Description
builderForValue	Builder

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

addInputBaselines(int index, Value value)

public ExplanationMetadata.InputMetadata.Builder addInputBaselines(int index, Value value)

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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value input_baselines = 1;

Parameters

Name	Description
index	int
value	Value

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

addInputBaselines(int index, Value.Builder builderForValue)

public ExplanationMetadata.InputMetadata.Builder addInputBaselines(int index, Value.Builder builderForValue)

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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value input_baselines = 1;

Parameters

Name	Description
index	int
builderForValue	Builder

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

addInputBaselinesBuilder()

public Value.Builder addInputBaselinesBuilder()

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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value input_baselines = 1;

Returns

Type	Description
Builder

addInputBaselinesBuilder(int index)

public Value.Builder addInputBaselinesBuilder(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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value input_baselines = 1;

Parameter

Name	Description
index	int

Returns

Type	Description
Builder

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public ExplanationMetadata.InputMetadata.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)

Parameters

Name	Description
field	FieldDescriptor
value	Object

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.addRepeatedField(Descriptors.FieldDescriptor field, Object value)

build()

public ExplanationMetadata.InputMetadata build()

Returns

Type	Description
ExplanationMetadata.InputMetadata

buildPartial()

public ExplanationMetadata.InputMetadata buildPartial()

Returns

Type	Description
ExplanationMetadata.InputMetadata

clear()

public ExplanationMetadata.InputMetadata.Builder clear()

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clear()

clearDenseShapeTensorName()

public ExplanationMetadata.InputMetadata.Builder clearDenseShapeTensorName()

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

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

clearEncodedBaselines()

public ExplanationMetadata.InputMetadata.Builder clearEncodedBaselines()

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;

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

clearEncodedTensorName()

public ExplanationMetadata.InputMetadata.Builder clearEncodedTensorName()

Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or 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

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

clearEncoding()

public ExplanationMetadata.InputMetadata.Builder clearEncoding()

Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.

.google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

clearFeatureValueDomain()

public ExplanationMetadata.InputMetadata.Builder clearFeatureValueDomain()

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

Type	Description
ExplanationMetadata.InputMetadata.Builder

clearField(Descriptors.FieldDescriptor field)

public ExplanationMetadata.InputMetadata.Builder clearField(Descriptors.FieldDescriptor field)

Parameter

Name	Description
field	FieldDescriptor

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearField(Descriptors.FieldDescriptor field)

clearGroupName()

public ExplanationMetadata.InputMetadata.Builder clearGroupName()

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, keyed by the group name.

string group_name = 12;

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

clearIndexFeatureMapping()

public ExplanationMetadata.InputMetadata.Builder clearIndexFeatureMapping()

A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

clearIndicesTensorName()

public ExplanationMetadata.InputMetadata.Builder clearIndicesTensorName()

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

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

clearInputBaselines()

public ExplanationMetadata.InputMetadata.Builder clearInputBaselines()

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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value input_baselines = 1;

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

clearInputTensorName()

public ExplanationMetadata.InputMetadata.Builder clearInputTensorName()

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

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

clearModality()

public ExplanationMetadata.InputMetadata.Builder clearModality()

Modality of the feature. Valid values are: numeric, image. Defaults to numeric.

string modality = 4;

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

clearOneof(Descriptors.OneofDescriptor oneof)

public ExplanationMetadata.InputMetadata.Builder clearOneof(Descriptors.OneofDescriptor oneof)

Parameter

Name	Description
oneof	OneofDescriptor

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearOneof(Descriptors.OneofDescriptor oneof)

clearVisualization()

public ExplanationMetadata.InputMetadata.Builder clearVisualization()

Visualization configurations for image explanation.

.google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

clone()

public ExplanationMetadata.InputMetadata.Builder clone()

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clone()

getDefaultInstanceForType()

public ExplanationMetadata.InputMetadata getDefaultInstanceForType()

Returns

Type	Description
ExplanationMetadata.InputMetadata

getDenseShapeTensorName()

public 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

Type	Description
String	The denseShapeTensorName.

getDenseShapeTensorNameBytes()

public 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

Type	Description
ByteString	The bytes for denseShapeTensorName.

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()

Returns

Type	Description
Descriptor

Overrides

GeneratedMessageV3.Builder<BuilderType>.getDescriptorForType()

getEncodedBaselines(int index)

public 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;

Parameter

Name	Description
index	int

Returns

Type	Description
Value

getEncodedBaselinesBuilder(int index)

public Value.Builder getEncodedBaselinesBuilder(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;

Parameter

Name	Description
index	int

Returns

Type	Description
Builder

getEncodedBaselinesBuilderList()

public List<Value.Builder> getEncodedBaselinesBuilderList()

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;

Returns

Type	Description
List<Builder>

getEncodedBaselinesCount()

public 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;

Returns

Type	Description
int

getEncodedBaselinesList()

public List<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;

Returns

Type	Description
List<Value>

getEncodedBaselinesOrBuilder(int index)

public 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;

Parameter

Name	Description
index	int

Returns

Type	Description
ValueOrBuilder

getEncodedBaselinesOrBuilderList()

public List<? extends 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;

Returns

Type	Description
List<? extends com.google.protobuf.ValueOrBuilder>

getEncodedTensorName()

public String getEncodedTensorName()

Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or 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

Type	Description
String	The encodedTensorName.

getEncodedTensorNameBytes()

public ByteString getEncodedTensorNameBytes()

Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or 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

Type	Description
ByteString	The bytes for encodedTensorName.

getEncoding()

public 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

Type	Description
ExplanationMetadata.InputMetadata.Encoding	The encoding.

getEncodingValue()

public 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

Type	Description
int	The enum numeric value on the wire for encoding.

getFeatureValueDomain()

public 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

Type	Description
ExplanationMetadata.InputMetadata.FeatureValueDomain	The featureValueDomain.

getFeatureValueDomainBuilder()

public ExplanationMetadata.InputMetadata.FeatureValueDomain.Builder getFeatureValueDomainBuilder()

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

Type	Description
ExplanationMetadata.InputMetadata.FeatureValueDomain.Builder

getFeatureValueDomainOrBuilder()

public 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;

Returns

Type	Description
ExplanationMetadata.InputMetadata.FeatureValueDomainOrBuilder

getGroupName()

public 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, keyed by the group name.

string group_name = 12;

Returns

Type	Description
String	The groupName.

getGroupNameBytes()

public 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, keyed by the group name.

string group_name = 12;

Returns

Type	Description
ByteString	The bytes for groupName.

getIndexFeatureMapping(int index)

public String getIndexFeatureMapping(int index)

A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Parameter

Name	Description
index	int The index of the element to return.

Returns

Type	Description
String	The indexFeatureMapping at the given index.

getIndexFeatureMappingBytes(int index)

public ByteString getIndexFeatureMappingBytes(int index)

A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Parameter

Name	Description
index	int The index of the value to return.

Returns

Type	Description
ByteString	The bytes of the indexFeatureMapping at the given index.

getIndexFeatureMappingCount()

public int getIndexFeatureMappingCount()

A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Returns

Type	Description
int	The count of indexFeatureMapping.

getIndexFeatureMappingList()

public ProtocolStringList getIndexFeatureMappingList()

A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Returns

Type	Description
ProtocolStringList	A list containing the indexFeatureMapping.

getIndicesTensorName()

public 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

Type	Description
String	The indicesTensorName.

getIndicesTensorNameBytes()

public 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

Type	Description
ByteString	The bytes for indicesTensorName.

getInputBaselines(int index)

public 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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value input_baselines = 1;

Parameter

Name	Description
index	int

Returns

Type	Description
Value

getInputBaselinesBuilder(int index)

public Value.Builder getInputBaselinesBuilder(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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value input_baselines = 1;

Parameter

Name	Description
index	int

Returns

Type	Description
Builder

getInputBaselinesBuilderList()

public List<Value.Builder> getInputBaselinesBuilderList()

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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value input_baselines = 1;

Returns

Type	Description
List<Builder>

getInputBaselinesCount()

public 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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value input_baselines = 1;

Returns

Type	Description
int

getInputBaselinesList()

public List<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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value input_baselines = 1;

Returns

Type	Description
List<Value>

getInputBaselinesOrBuilder(int index)

public 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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value input_baselines = 1;

Parameter

Name	Description
index	int

Returns

Type	Description
ValueOrBuilder

getInputBaselinesOrBuilderList()

public List<? extends 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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value input_baselines = 1;

Returns

Type	Description
List<? extends com.google.protobuf.ValueOrBuilder>

getInputTensorName()

public 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

Type	Description
String	The inputTensorName.

getInputTensorNameBytes()

public 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

Type	Description
ByteString	The bytes for inputTensorName.

getModality()

public String getModality()

Modality of the feature. Valid values are: numeric, image. Defaults to numeric.

string modality = 4;

Returns

Type	Description
String	The modality.

getModalityBytes()

public ByteString getModalityBytes()

Modality of the feature. Valid values are: numeric, image. Defaults to numeric.

string modality = 4;

Returns

Type	Description
ByteString	The bytes for modality.

getVisualization()

public ExplanationMetadata.InputMetadata.Visualization getVisualization()

Visualization configurations for image explanation.

.google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

Returns

Type	Description
ExplanationMetadata.InputMetadata.Visualization	The visualization.

getVisualizationBuilder()

public ExplanationMetadata.InputMetadata.Visualization.Builder getVisualizationBuilder()

Visualization configurations for image explanation.

.google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

Returns

Type	Description
ExplanationMetadata.InputMetadata.Visualization.Builder

getVisualizationOrBuilder()

public ExplanationMetadata.InputMetadata.VisualizationOrBuilder getVisualizationOrBuilder()

Visualization configurations for image explanation.

.google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

Returns

Type	Description
ExplanationMetadata.InputMetadata.VisualizationOrBuilder

hasFeatureValueDomain()

public 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

Type	Description
boolean	Whether the featureValueDomain field is set.

hasVisualization()

public boolean hasVisualization()

Visualization configurations for image explanation.

.google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

Returns

Type	Description
boolean	Whether the visualization field is set.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Returns

Type	Description
FieldAccessorTable

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetFieldAccessorTable()

isInitialized()

public final boolean isInitialized()

Returns

Type	Description
boolean

Overrides

GeneratedMessageV3.Builder<BuilderType>.isInitialized()

mergeFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain value)

public ExplanationMetadata.InputMetadata.Builder mergeFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain value)

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;

Parameter

Name	Description
value	ExplanationMetadata.InputMetadata.FeatureValueDomain

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

mergeFrom(ExplanationMetadata.InputMetadata other)

public ExplanationMetadata.InputMetadata.Builder mergeFrom(ExplanationMetadata.InputMetadata other)

Parameter

Name	Description
other	ExplanationMetadata.InputMetadata

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public ExplanationMetadata.InputMetadata.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Parameters

Name	Description
input	CodedInputStream
extensionRegistry	ExtensionRegistryLite

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Exceptions

Type	Description
IOException

mergeFrom(Message other)

public ExplanationMetadata.InputMetadata.Builder mergeFrom(Message other)

Parameter

Name	Description
other	Message

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(Message other)

mergeUnknownFields(UnknownFieldSet unknownFields)

public final ExplanationMetadata.InputMetadata.Builder mergeUnknownFields(UnknownFieldSet unknownFields)

Parameter

Name	Description
unknownFields	UnknownFieldSet

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.mergeUnknownFields(UnknownFieldSet unknownFields)

mergeVisualization(ExplanationMetadata.InputMetadata.Visualization value)

public ExplanationMetadata.InputMetadata.Builder mergeVisualization(ExplanationMetadata.InputMetadata.Visualization value)

Visualization configurations for image explanation.

.google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

Parameter

Name	Description
value	ExplanationMetadata.InputMetadata.Visualization

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

removeEncodedBaselines(int index)

public ExplanationMetadata.InputMetadata.Builder removeEncodedBaselines(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;

Parameter

Name	Description
index	int

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

removeInputBaselines(int index)

public ExplanationMetadata.InputMetadata.Builder removeInputBaselines(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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value input_baselines = 1;

Parameter

Name	Description
index	int

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

setDenseShapeTensorName(String value)

public ExplanationMetadata.InputMetadata.Builder setDenseShapeTensorName(String value)

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;

Parameter

Name	Description
value	String The denseShapeTensorName to set.

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

setDenseShapeTensorNameBytes(ByteString value)

public ExplanationMetadata.InputMetadata.Builder setDenseShapeTensorNameBytes(ByteString value)

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;

Parameter

Name	Description
value	ByteString The bytes for denseShapeTensorName to set.

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

setEncodedBaselines(int index, Value value)

public ExplanationMetadata.InputMetadata.Builder setEncodedBaselines(int index, Value value)

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;

Parameters

Name	Description
index	int
value	Value

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

setEncodedBaselines(int index, Value.Builder builderForValue)

public ExplanationMetadata.InputMetadata.Builder setEncodedBaselines(int index, Value.Builder builderForValue)

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;

Parameters

Name	Description
index	int
builderForValue	Builder

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

setEncodedTensorName(String value)

public ExplanationMetadata.InputMetadata.Builder setEncodedTensorName(String value)

Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or 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;

Parameter

Name	Description
value	String The encodedTensorName to set.

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

setEncodedTensorNameBytes(ByteString value)

public ExplanationMetadata.InputMetadata.Builder setEncodedTensorNameBytes(ByteString value)

Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or 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;

Parameter

Name	Description
value	ByteString The bytes for encodedTensorName to set.

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

setEncoding(ExplanationMetadata.InputMetadata.Encoding value)

public ExplanationMetadata.InputMetadata.Builder setEncoding(ExplanationMetadata.InputMetadata.Encoding value)

Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.

.google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;

Parameter

Name	Description
value	ExplanationMetadata.InputMetadata.Encoding The encoding to set.

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

setEncodingValue(int value)

public ExplanationMetadata.InputMetadata.Builder setEncodingValue(int value)

Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.

.google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;

Parameter

Name	Description
value	int The enum numeric value on the wire for encoding to set.

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

setFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain value)

public ExplanationMetadata.InputMetadata.Builder setFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain value)

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;

Parameter

Name	Description
value	ExplanationMetadata.InputMetadata.FeatureValueDomain

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

setFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain.Builder builderForValue)

public ExplanationMetadata.InputMetadata.Builder setFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain.Builder builderForValue)

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;

Parameter

Name	Description
builderForValue	ExplanationMetadata.InputMetadata.FeatureValueDomain.Builder

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

setField(Descriptors.FieldDescriptor field, Object value)

public ExplanationMetadata.InputMetadata.Builder setField(Descriptors.FieldDescriptor field, Object value)

Parameters

Name	Description
field	FieldDescriptor
value	Object

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setField(Descriptors.FieldDescriptor field, Object value)

setGroupName(String value)

public ExplanationMetadata.InputMetadata.Builder setGroupName(String value)

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, keyed by the group name.

string group_name = 12;

Parameter

Name	Description
value	String The groupName to set.

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

setGroupNameBytes(ByteString value)

public ExplanationMetadata.InputMetadata.Builder setGroupNameBytes(ByteString value)

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, keyed by the group name.

string group_name = 12;

Parameter

Name	Description
value	ByteString The bytes for groupName to set.

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

setIndexFeatureMapping(int index, String value)

public ExplanationMetadata.InputMetadata.Builder setIndexFeatureMapping(int index, String value)

A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Parameters

Name	Description
index	int The index to set the value at.
value	String The indexFeatureMapping to set.

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

setIndicesTensorName(String value)

public ExplanationMetadata.InputMetadata.Builder setIndicesTensorName(String value)

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;

Parameter

Name	Description
value	String The indicesTensorName to set.

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

setIndicesTensorNameBytes(ByteString value)

public ExplanationMetadata.InputMetadata.Builder setIndicesTensorNameBytes(ByteString value)

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;

Parameter

Name	Description
value	ByteString The bytes for indicesTensorName to set.

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

setInputBaselines(int index, Value value)

public ExplanationMetadata.InputMetadata.Builder setInputBaselines(int index, Value value)

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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value input_baselines = 1;

Parameters

Name	Description
index	int
value	Value

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

setInputBaselines(int index, Value.Builder builderForValue)

public ExplanationMetadata.InputMetadata.Builder setInputBaselines(int index, Value.Builder builderForValue)

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. 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[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value input_baselines = 1;

Parameters

Name	Description
index	int
builderForValue	Builder

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

setInputTensorName(String value)

public ExplanationMetadata.InputMetadata.Builder setInputTensorName(String value)

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;

Parameter

Name	Description
value	String The inputTensorName to set.

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

setInputTensorNameBytes(ByteString value)

public ExplanationMetadata.InputMetadata.Builder setInputTensorNameBytes(ByteString value)

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;

Parameter

Name	Description
value	ByteString The bytes for inputTensorName to set.

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

setModality(String value)

public ExplanationMetadata.InputMetadata.Builder setModality(String value)

Modality of the feature. Valid values are: numeric, image. Defaults to numeric.

string modality = 4;

Parameter

Name	Description
value	String The modality to set.

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

setModalityBytes(ByteString value)

public ExplanationMetadata.InputMetadata.Builder setModalityBytes(ByteString value)

Modality of the feature. Valid values are: numeric, image. Defaults to numeric.

string modality = 4;

Parameter

Name	Description
value	ByteString The bytes for modality to set.

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder	This builder for chaining.

setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

public ExplanationMetadata.InputMetadata.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

Parameters

Name	Description
field	FieldDescriptor
index	int
value	Object

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

setUnknownFields(UnknownFieldSet unknownFields)

public final ExplanationMetadata.InputMetadata.Builder setUnknownFields(UnknownFieldSet unknownFields)

Parameter

Name	Description
unknownFields	UnknownFieldSet

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setUnknownFields(UnknownFieldSet unknownFields)

setVisualization(ExplanationMetadata.InputMetadata.Visualization value)

public ExplanationMetadata.InputMetadata.Builder setVisualization(ExplanationMetadata.InputMetadata.Visualization value)

Visualization configurations for image explanation.

.google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

Parameter

Name	Description
value	ExplanationMetadata.InputMetadata.Visualization

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder

setVisualization(ExplanationMetadata.InputMetadata.Visualization.Builder builderForValue)

public ExplanationMetadata.InputMetadata.Builder setVisualization(ExplanationMetadata.InputMetadata.Visualization.Builder builderForValue)

Visualization configurations for image explanation.

.google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

Parameter

Name	Description
builderForValue	ExplanationMetadata.InputMetadata.Visualization.Builder

Returns

Type	Description
ExplanationMetadata.InputMetadata.Builder