public static final class ExplanationMetadata.OutputMetadata.Builder extends GeneratedMessageV3.Builder<ExplanationMetadata.OutputMetadata.Builder> implements ExplanationMetadata.OutputMetadataOrBuilder
Metadata of the prediction output to be explained.
Protobuf type google.cloud.aiplatform.v1beta1.ExplanationMetadata.OutputMetadata
Methods
public ExplanationMetadata.OutputMetadata.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)
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public ExplanationMetadata.OutputMetadata build()
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public ExplanationMetadata.OutputMetadata buildPartial()
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public ExplanationMetadata.OutputMetadata.Builder clear()
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public ExplanationMetadata.OutputMetadata.Builder clearDisplayNameMapping()
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public ExplanationMetadata.OutputMetadata.Builder clearDisplayNameMappingKey()
Specify a field name in the prediction to look for the display name.
Use this if the prediction contains the display names for the outputs.
The display names in the prediction must have the same shape of the
outputs, so that it can be located by Attribution.output_index for
a specific output.
string display_name_mapping_key = 2;
public ExplanationMetadata.OutputMetadata.Builder clearField(Descriptors.FieldDescriptor field)
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Overrides
public ExplanationMetadata.OutputMetadata.Builder clearIndexDisplayNameMapping()
Static mapping between the index and display name.
Use this if the outputs are a deterministic n-dimensional array, e.g. a
list of scores of all the classes in a pre-defined order for a
multi-classification Model. It's not feasible if the outputs are
non-deterministic, e.g. the Model produces top-k classes or sort the
outputs by their values.
The shape of the value must be an n-dimensional array of strings. The
number of dimensions must match that of the outputs to be explained.
The Attribution.output_display_name is populated by locating in the
mapping with Attribution.output_index.
.google.protobuf.Value index_display_name_mapping = 1;
public ExplanationMetadata.OutputMetadata.Builder clearOneof(Descriptors.OneofDescriptor oneof)
Parameter
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public ExplanationMetadata.OutputMetadata.Builder clearOutputTensorName()
Name of the output tensor. Required and is only applicable to Vertex
AI provided images for Tensorflow.
string output_tensor_name = 3;
public ExplanationMetadata.OutputMetadata.Builder clone()
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public ExplanationMetadata.OutputMetadata getDefaultInstanceForType()
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public static final Descriptors.Descriptor getDescriptor()
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public Descriptors.Descriptor getDescriptorForType()
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public ExplanationMetadata.OutputMetadata.DisplayNameMappingCase getDisplayNameMappingCase()
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public String getDisplayNameMappingKey()
Specify a field name in the prediction to look for the display name.
Use this if the prediction contains the display names for the outputs.
The display names in the prediction must have the same shape of the
outputs, so that it can be located by Attribution.output_index for
a specific output.
string display_name_mapping_key = 2;
| Type | Description |
| String | The displayNameMappingKey.
|
public ByteString getDisplayNameMappingKeyBytes()
Specify a field name in the prediction to look for the display name.
Use this if the prediction contains the display names for the outputs.
The display names in the prediction must have the same shape of the
outputs, so that it can be located by Attribution.output_index for
a specific output.
string display_name_mapping_key = 2;
| Type | Description |
| ByteString | The bytes for displayNameMappingKey.
|
public Value getIndexDisplayNameMapping()
Static mapping between the index and display name.
Use this if the outputs are a deterministic n-dimensional array, e.g. a
list of scores of all the classes in a pre-defined order for a
multi-classification Model. It's not feasible if the outputs are
non-deterministic, e.g. the Model produces top-k classes or sort the
outputs by their values.
The shape of the value must be an n-dimensional array of strings. The
number of dimensions must match that of the outputs to be explained.
The Attribution.output_display_name is populated by locating in the
mapping with Attribution.output_index.
.google.protobuf.Value index_display_name_mapping = 1;
| Type | Description |
| Value | The indexDisplayNameMapping.
|
public Value.Builder getIndexDisplayNameMappingBuilder()
Static mapping between the index and display name.
Use this if the outputs are a deterministic n-dimensional array, e.g. a
list of scores of all the classes in a pre-defined order for a
multi-classification Model. It's not feasible if the outputs are
non-deterministic, e.g. the Model produces top-k classes or sort the
outputs by their values.
The shape of the value must be an n-dimensional array of strings. The
number of dimensions must match that of the outputs to be explained.
The Attribution.output_display_name is populated by locating in the
mapping with Attribution.output_index.
.google.protobuf.Value index_display_name_mapping = 1;
public ValueOrBuilder getIndexDisplayNameMappingOrBuilder()
Static mapping between the index and display name.
Use this if the outputs are a deterministic n-dimensional array, e.g. a
list of scores of all the classes in a pre-defined order for a
multi-classification Model. It's not feasible if the outputs are
non-deterministic, e.g. the Model produces top-k classes or sort the
outputs by their values.
The shape of the value must be an n-dimensional array of strings. The
number of dimensions must match that of the outputs to be explained.
The Attribution.output_display_name is populated by locating in the
mapping with Attribution.output_index.
.google.protobuf.Value index_display_name_mapping = 1;
public String getOutputTensorName()
Name of the output tensor. Required and is only applicable to Vertex
AI provided images for Tensorflow.
string output_tensor_name = 3;
| Type | Description |
| String | The outputTensorName.
|
public ByteString getOutputTensorNameBytes()
Name of the output tensor. Required and is only applicable to Vertex
AI provided images for Tensorflow.
string output_tensor_name = 3;
| Type | Description |
| ByteString | The bytes for outputTensorName.
|
public boolean hasDisplayNameMappingKey()
Specify a field name in the prediction to look for the display name.
Use this if the prediction contains the display names for the outputs.
The display names in the prediction must have the same shape of the
outputs, so that it can be located by Attribution.output_index for
a specific output.
string display_name_mapping_key = 2;
| Type | Description |
| boolean | Whether the displayNameMappingKey field is set.
|
public boolean hasIndexDisplayNameMapping()
Static mapping between the index and display name.
Use this if the outputs are a deterministic n-dimensional array, e.g. a
list of scores of all the classes in a pre-defined order for a
multi-classification Model. It's not feasible if the outputs are
non-deterministic, e.g. the Model produces top-k classes or sort the
outputs by their values.
The shape of the value must be an n-dimensional array of strings. The
number of dimensions must match that of the outputs to be explained.
The Attribution.output_display_name is populated by locating in the
mapping with Attribution.output_index.
.google.protobuf.Value index_display_name_mapping = 1;
| Type | Description |
| boolean | Whether the indexDisplayNameMapping field is set.
|
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
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Overrides
public final boolean isInitialized()
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Overrides
public ExplanationMetadata.OutputMetadata.Builder mergeFrom(ExplanationMetadata.OutputMetadata other)
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public ExplanationMetadata.OutputMetadata.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
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Exceptions
public ExplanationMetadata.OutputMetadata.Builder mergeFrom(Message other)
Parameter
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Overrides
public ExplanationMetadata.OutputMetadata.Builder mergeIndexDisplayNameMapping(Value value)
Static mapping between the index and display name.
Use this if the outputs are a deterministic n-dimensional array, e.g. a
list of scores of all the classes in a pre-defined order for a
multi-classification Model. It's not feasible if the outputs are
non-deterministic, e.g. the Model produces top-k classes or sort the
outputs by their values.
The shape of the value must be an n-dimensional array of strings. The
number of dimensions must match that of the outputs to be explained.
The Attribution.output_display_name is populated by locating in the
mapping with Attribution.output_index.
.google.protobuf.Value index_display_name_mapping = 1;
| Name | Description |
| value | Value
|
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public final ExplanationMetadata.OutputMetadata.Builder mergeUnknownFields(UnknownFieldSet unknownFields)
Parameter
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Overrides
public ExplanationMetadata.OutputMetadata.Builder setDisplayNameMappingKey(String value)
Specify a field name in the prediction to look for the display name.
Use this if the prediction contains the display names for the outputs.
The display names in the prediction must have the same shape of the
outputs, so that it can be located by Attribution.output_index for
a specific output.
string display_name_mapping_key = 2;
| Name | Description |
| value | String
The displayNameMappingKey to set.
|
Returns
public ExplanationMetadata.OutputMetadata.Builder setDisplayNameMappingKeyBytes(ByteString value)
Specify a field name in the prediction to look for the display name.
Use this if the prediction contains the display names for the outputs.
The display names in the prediction must have the same shape of the
outputs, so that it can be located by Attribution.output_index for
a specific output.
string display_name_mapping_key = 2;
| Name | Description |
| value | ByteString
The bytes for displayNameMappingKey to set.
|
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public ExplanationMetadata.OutputMetadata.Builder setField(Descriptors.FieldDescriptor field, Object value)
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Returns
Overrides
public ExplanationMetadata.OutputMetadata.Builder setIndexDisplayNameMapping(Value value)
Static mapping between the index and display name.
Use this if the outputs are a deterministic n-dimensional array, e.g. a
list of scores of all the classes in a pre-defined order for a
multi-classification Model. It's not feasible if the outputs are
non-deterministic, e.g. the Model produces top-k classes or sort the
outputs by their values.
The shape of the value must be an n-dimensional array of strings. The
number of dimensions must match that of the outputs to be explained.
The Attribution.output_display_name is populated by locating in the
mapping with Attribution.output_index.
.google.protobuf.Value index_display_name_mapping = 1;
| Name | Description |
| value | Value
|
Returns
public ExplanationMetadata.OutputMetadata.Builder setIndexDisplayNameMapping(Value.Builder builderForValue)
Static mapping between the index and display name.
Use this if the outputs are a deterministic n-dimensional array, e.g. a
list of scores of all the classes in a pre-defined order for a
multi-classification Model. It's not feasible if the outputs are
non-deterministic, e.g. the Model produces top-k classes or sort the
outputs by their values.
The shape of the value must be an n-dimensional array of strings. The
number of dimensions must match that of the outputs to be explained.
The Attribution.output_display_name is populated by locating in the
mapping with Attribution.output_index.
.google.protobuf.Value index_display_name_mapping = 1;
| Name | Description |
| builderForValue | Builder
|
Returns
public ExplanationMetadata.OutputMetadata.Builder setOutputTensorName(String value)
Name of the output tensor. Required and is only applicable to Vertex
AI provided images for Tensorflow.
string output_tensor_name = 3;
| Name | Description |
| value | String
The outputTensorName to set.
|
Returns
public ExplanationMetadata.OutputMetadata.Builder setOutputTensorNameBytes(ByteString value)
Name of the output tensor. Required and is only applicable to Vertex
AI provided images for Tensorflow.
string output_tensor_name = 3;
| Name | Description |
| value | ByteString
The bytes for outputTensorName to set.
|
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public ExplanationMetadata.OutputMetadata.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
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public final ExplanationMetadata.OutputMetadata.Builder setUnknownFields(UnknownFieldSet unknownFields)
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