Class ExplanationMetadata.InputMetadata (3.2.0)

public static final class ExplanationMetadata.InputMetadata extends GeneratedMessageV3 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<MessageType,BuilderType> > AbstractMessage > GeneratedMessageV3 > ExplanationMetadata.InputMetadata

Static Fields

DENSE_SHAPE_TENSOR_NAME_FIELD_NUMBER

public static final int DENSE_SHAPE_TENSOR_NAME_FIELD_NUMBER
Field Value
TypeDescription
int

ENCODED_BASELINES_FIELD_NUMBER

public static final int ENCODED_BASELINES_FIELD_NUMBER
Field Value
TypeDescription
int

ENCODED_TENSOR_NAME_FIELD_NUMBER

public static final int ENCODED_TENSOR_NAME_FIELD_NUMBER
Field Value
TypeDescription
int

ENCODING_FIELD_NUMBER

public static final int ENCODING_FIELD_NUMBER
Field Value
TypeDescription
int

FEATURE_VALUE_DOMAIN_FIELD_NUMBER

public static final int FEATURE_VALUE_DOMAIN_FIELD_NUMBER
Field Value
TypeDescription
int

GROUP_NAME_FIELD_NUMBER

public static final int GROUP_NAME_FIELD_NUMBER
Field Value
TypeDescription
int

INDEX_FEATURE_MAPPING_FIELD_NUMBER

public static final int INDEX_FEATURE_MAPPING_FIELD_NUMBER
Field Value
TypeDescription
int

INDICES_TENSOR_NAME_FIELD_NUMBER

public static final int INDICES_TENSOR_NAME_FIELD_NUMBER
Field Value
TypeDescription
int

INPUT_BASELINES_FIELD_NUMBER

public static final int INPUT_BASELINES_FIELD_NUMBER
Field Value
TypeDescription
int

INPUT_TENSOR_NAME_FIELD_NUMBER

public static final int INPUT_TENSOR_NAME_FIELD_NUMBER
Field Value
TypeDescription
int

MODALITY_FIELD_NUMBER

public static final int MODALITY_FIELD_NUMBER
Field Value
TypeDescription
int

VISUALIZATION_FIELD_NUMBER

public static final int VISUALIZATION_FIELD_NUMBER
Field Value
TypeDescription
int

Static Methods

getDefaultInstance()

public static ExplanationMetadata.InputMetadata getDefaultInstance()
Returns
TypeDescription
ExplanationMetadata.InputMetadata

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()
Returns
TypeDescription
Descriptor

newBuilder()

public static ExplanationMetadata.InputMetadata.Builder newBuilder()
Returns
TypeDescription
ExplanationMetadata.InputMetadata.Builder

newBuilder(ExplanationMetadata.InputMetadata prototype)

public static ExplanationMetadata.InputMetadata.Builder newBuilder(ExplanationMetadata.InputMetadata prototype)
Parameter
NameDescription
prototypeExplanationMetadata.InputMetadata
Returns
TypeDescription
ExplanationMetadata.InputMetadata.Builder

parseDelimitedFrom(InputStream input)

public static ExplanationMetadata.InputMetadata parseDelimitedFrom(InputStream input)
Parameter
NameDescription
inputInputStream
Returns
TypeDescription
ExplanationMetadata.InputMetadata
Exceptions
TypeDescription
IOException

parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static ExplanationMetadata.InputMetadata parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ExplanationMetadata.InputMetadata
Exceptions
TypeDescription
IOException

parseFrom(byte[] data)

public static ExplanationMetadata.InputMetadata parseFrom(byte[] data)
Parameter
NameDescription
databyte[]
Returns
TypeDescription
ExplanationMetadata.InputMetadata
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)

public static ExplanationMetadata.InputMetadata parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
databyte[]
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ExplanationMetadata.InputMetadata
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(ByteString data)

public static ExplanationMetadata.InputMetadata parseFrom(ByteString data)
Parameter
NameDescription
dataByteString
Returns
TypeDescription
ExplanationMetadata.InputMetadata
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)

public static ExplanationMetadata.InputMetadata parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
dataByteString
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ExplanationMetadata.InputMetadata
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(CodedInputStream input)

public static ExplanationMetadata.InputMetadata parseFrom(CodedInputStream input)
Parameter
NameDescription
inputCodedInputStream
Returns
TypeDescription
ExplanationMetadata.InputMetadata
Exceptions
TypeDescription
IOException

parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public static ExplanationMetadata.InputMetadata parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputCodedInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ExplanationMetadata.InputMetadata
Exceptions
TypeDescription
IOException

parseFrom(InputStream input)

public static ExplanationMetadata.InputMetadata parseFrom(InputStream input)
Parameter
NameDescription
inputInputStream
Returns
TypeDescription
ExplanationMetadata.InputMetadata
Exceptions
TypeDescription
IOException

parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static ExplanationMetadata.InputMetadata parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ExplanationMetadata.InputMetadata
Exceptions
TypeDescription
IOException

parseFrom(ByteBuffer data)

public static ExplanationMetadata.InputMetadata parseFrom(ByteBuffer data)
Parameter
NameDescription
dataByteBuffer
Returns
TypeDescription
ExplanationMetadata.InputMetadata
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)

public static ExplanationMetadata.InputMetadata parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
dataByteBuffer
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ExplanationMetadata.InputMetadata
Exceptions
TypeDescription
InvalidProtocolBufferException

parser()

public static Parser<ExplanationMetadata.InputMetadata> parser()
Returns
TypeDescription
Parser<InputMetadata>

Methods

equals(Object obj)

public boolean equals(Object obj)
Parameter
NameDescription
objObject
Returns
TypeDescription
boolean
Overrides

getDefaultInstanceForType()

public ExplanationMetadata.InputMetadata getDefaultInstanceForType()
Returns
TypeDescription
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
TypeDescription
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
TypeDescription
ByteString

The bytes for denseShapeTensorName.

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
NameDescription
indexint
Returns
TypeDescription
Value

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
TypeDescription
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
TypeDescription
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
NameDescription
indexint
Returns
TypeDescription
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
TypeDescription
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
TypeDescription
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
TypeDescription
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
TypeDescription
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
TypeDescription
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
TypeDescription
ExplanationMetadata.InputMetadata.FeatureValueDomain

The featureValueDomain.

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
TypeDescription
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
TypeDescription
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
TypeDescription
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
NameDescription
indexint

The index of the element to return.

Returns
TypeDescription
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
NameDescription
indexint

The index of the value to return.

Returns
TypeDescription
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
TypeDescription
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
TypeDescription
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
TypeDescription
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
TypeDescription
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
NameDescription
indexint
Returns
TypeDescription
Value

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
TypeDescription
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
TypeDescription
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
NameDescription
indexint
Returns
TypeDescription
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
TypeDescription
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
TypeDescription
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
TypeDescription
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
TypeDescription
String

The modality.

getModalityBytes()

public ByteString getModalityBytes()

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

string modality = 4;

Returns
TypeDescription
ByteString

The bytes for modality.

getParserForType()

public Parser<ExplanationMetadata.InputMetadata> getParserForType()
Returns
TypeDescription
Parser<InputMetadata>
Overrides

getSerializedSize()

public int getSerializedSize()
Returns
TypeDescription
int
Overrides

getUnknownFields()

public final UnknownFieldSet getUnknownFields()
Returns
TypeDescription
UnknownFieldSet
Overrides

getVisualization()

public ExplanationMetadata.InputMetadata.Visualization getVisualization()

Visualization configurations for image explanation.

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

Returns
TypeDescription
ExplanationMetadata.InputMetadata.Visualization

The visualization.

getVisualizationOrBuilder()

public ExplanationMetadata.InputMetadata.VisualizationOrBuilder getVisualizationOrBuilder()

Visualization configurations for image explanation.

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

Returns
TypeDescription
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
TypeDescription
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
TypeDescription
boolean

Whether the visualization field is set.

hashCode()

public int hashCode()
Returns
TypeDescription
int
Overrides

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
TypeDescription
FieldAccessorTable
Overrides

isInitialized()

public final boolean isInitialized()
Returns
TypeDescription
boolean
Overrides

newBuilderForType()

public ExplanationMetadata.InputMetadata.Builder newBuilderForType()
Returns
TypeDescription
ExplanationMetadata.InputMetadata.Builder

newBuilderForType(GeneratedMessageV3.BuilderParent parent)

protected ExplanationMetadata.InputMetadata.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Parameter
NameDescription
parentBuilderParent
Returns
TypeDescription
ExplanationMetadata.InputMetadata.Builder
Overrides

newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)

protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
Parameter
NameDescription
unusedUnusedPrivateParameter
Returns
TypeDescription
Object
Overrides

toBuilder()

public ExplanationMetadata.InputMetadata.Builder toBuilder()
Returns
TypeDescription
ExplanationMetadata.InputMetadata.Builder

writeTo(CodedOutputStream output)

public void writeTo(CodedOutputStream output)
Parameter
NameDescription
outputCodedOutputStream
Overrides Exceptions
TypeDescription
IOException