public static final class ExplanationMetadata.Builder extends GeneratedMessageV3.Builder<ExplanationMetadata.Builder> implements ExplanationMetadataOrBuilder
Metadata describing the Model's input and output for explanation.
Protobuf type google.cloud.aiplatform.v1beta1.ExplanationMetadata
Inherited Members
com.google.protobuf.GeneratedMessageV3.Builder.getUnknownFieldSetBuilder()
com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownLengthDelimitedField(int,com.google.protobuf.ByteString)
com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownVarintField(int,int)
com.google.protobuf.GeneratedMessageV3.Builder.parseUnknownField(com.google.protobuf.CodedInputStream,com.google.protobuf.ExtensionRegistryLite,int)
com.google.protobuf.GeneratedMessageV3.Builder.setUnknownFieldSetBuilder(com.google.protobuf.UnknownFieldSet.Builder)
Static Methods
public static final Descriptors.Descriptor getDescriptor()
Returns
Methods
public ExplanationMetadata.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)
Parameters
Returns
Overrides
public ExplanationMetadata build()
Returns
public ExplanationMetadata buildPartial()
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public ExplanationMetadata.Builder clear()
Returns
Overrides
public ExplanationMetadata.Builder clearFeatureAttributionsSchemaUri()
Points to a YAML file stored on Google Cloud Storage describing the format
of the feature attributions.
The schema is defined as an OpenAPI 3.0.2 Schema
Object.
AutoML tabular Models always have this field populated by Vertex AI.
Note: The URI given on output may be different, including the URI scheme,
than the one given on input. The output URI will point to a location where
the user only has a read access.
string feature_attributions_schema_uri = 3;
public ExplanationMetadata.Builder clearField(Descriptors.FieldDescriptor field)
Parameter
Returns
Overrides
public ExplanationMetadata.Builder clearInputs()
Returns
public ExplanationMetadata.Builder clearLatentSpaceSource()
Name of the source to generate embeddings for example based explanations.
string latent_space_source = 5;
public ExplanationMetadata.Builder clearOneof(Descriptors.OneofDescriptor oneof)
Parameter
Returns
Overrides
public ExplanationMetadata.Builder clearOutputs()
Returns
public ExplanationMetadata.Builder clone()
Returns
Overrides
public boolean containsInputs(String key)
Required. Map from feature names to feature input metadata. Keys are the name of the
features. Values are the specification of the feature.
An empty InputMetadata is valid. It describes a text feature which has the
name specified as the key in ExplanationMetadata.inputs. The baseline
of the empty feature is chosen by Vertex AI.
For Vertex AI-provided Tensorflow images, the key can be any friendly
name of the feature. Once specified,
featureAttributions are keyed by
this key (if not grouped with another feature).
For custom images, the key must match with the key in
instance.
map<string, .google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata> inputs = 1 [(.google.api.field_behavior) = REQUIRED];
public boolean containsOutputs(String key)
Required. Map from output names to output metadata.
For Vertex AI-provided Tensorflow images, keys can be any user defined
string that consists of any UTF-8 characters.
For custom images, keys are the name of the output field in the prediction
to be explained.
Currently only one key is allowed.
map<string, .google.cloud.aiplatform.v1beta1.ExplanationMetadata.OutputMetadata> outputs = 2 [(.google.api.field_behavior) = REQUIRED];
public ExplanationMetadata getDefaultInstanceForType()
Returns
public Descriptors.Descriptor getDescriptorForType()
Returns
Overrides
public String getFeatureAttributionsSchemaUri()
Points to a YAML file stored on Google Cloud Storage describing the format
of the feature attributions.
The schema is defined as an OpenAPI 3.0.2 Schema
Object.
AutoML tabular Models always have this field populated by Vertex AI.
Note: The URI given on output may be different, including the URI scheme,
than the one given on input. The output URI will point to a location where
the user only has a read access.
string feature_attributions_schema_uri = 3;
| Type | Description |
| String | The featureAttributionsSchemaUri.
|
public ByteString getFeatureAttributionsSchemaUriBytes()
Points to a YAML file stored on Google Cloud Storage describing the format
of the feature attributions.
The schema is defined as an OpenAPI 3.0.2 Schema
Object.
AutoML tabular Models always have this field populated by Vertex AI.
Note: The URI given on output may be different, including the URI scheme,
than the one given on input. The output URI will point to a location where
the user only has a read access.
string feature_attributions_schema_uri = 3;
| Type | Description |
| ByteString | The bytes for featureAttributionsSchemaUri.
|
public Map<String,ExplanationMetadata.InputMetadata> getInputs()
Returns
public int getInputsCount()
Required. Map from feature names to feature input metadata. Keys are the name of the
features. Values are the specification of the feature.
An empty InputMetadata is valid. It describes a text feature which has the
name specified as the key in ExplanationMetadata.inputs. The baseline
of the empty feature is chosen by Vertex AI.
For Vertex AI-provided Tensorflow images, the key can be any friendly
name of the feature. Once specified,
featureAttributions are keyed by
this key (if not grouped with another feature).
For custom images, the key must match with the key in
instance.
map<string, .google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata> inputs = 1 [(.google.api.field_behavior) = REQUIRED];
public Map<String,ExplanationMetadata.InputMetadata> getInputsMap()
Required. Map from feature names to feature input metadata. Keys are the name of the
features. Values are the specification of the feature.
An empty InputMetadata is valid. It describes a text feature which has the
name specified as the key in ExplanationMetadata.inputs. The baseline
of the empty feature is chosen by Vertex AI.
For Vertex AI-provided Tensorflow images, the key can be any friendly
name of the feature. Once specified,
featureAttributions are keyed by
this key (if not grouped with another feature).
For custom images, the key must match with the key in
instance.
map<string, .google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata> inputs = 1 [(.google.api.field_behavior) = REQUIRED];
public ExplanationMetadata.InputMetadata getInputsOrDefault(String key, ExplanationMetadata.InputMetadata defaultValue)
Required. Map from feature names to feature input metadata. Keys are the name of the
features. Values are the specification of the feature.
An empty InputMetadata is valid. It describes a text feature which has the
name specified as the key in ExplanationMetadata.inputs. The baseline
of the empty feature is chosen by Vertex AI.
For Vertex AI-provided Tensorflow images, the key can be any friendly
name of the feature. Once specified,
featureAttributions are keyed by
this key (if not grouped with another feature).
For custom images, the key must match with the key in
instance.
map<string, .google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata> inputs = 1 [(.google.api.field_behavior) = REQUIRED];
public ExplanationMetadata.InputMetadata getInputsOrThrow(String key)
Required. Map from feature names to feature input metadata. Keys are the name of the
features. Values are the specification of the feature.
An empty InputMetadata is valid. It describes a text feature which has the
name specified as the key in ExplanationMetadata.inputs. The baseline
of the empty feature is chosen by Vertex AI.
For Vertex AI-provided Tensorflow images, the key can be any friendly
name of the feature. Once specified,
featureAttributions are keyed by
this key (if not grouped with another feature).
For custom images, the key must match with the key in
instance.
map<string, .google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata> inputs = 1 [(.google.api.field_behavior) = REQUIRED];
public String getLatentSpaceSource()
Name of the source to generate embeddings for example based explanations.
string latent_space_source = 5;
| Type | Description |
| String | The latentSpaceSource.
|
public ByteString getLatentSpaceSourceBytes()
Name of the source to generate embeddings for example based explanations.
string latent_space_source = 5;
| Type | Description |
| ByteString | The bytes for latentSpaceSource.
|
public Map<String,ExplanationMetadata.InputMetadata> getMutableInputs()
Use alternate mutation accessors instead.
public Map<String,ExplanationMetadata.OutputMetadata> getMutableOutputs()
Use alternate mutation accessors instead.
public Map<String,ExplanationMetadata.OutputMetadata> getOutputs()
Returns
public int getOutputsCount()
Required. Map from output names to output metadata.
For Vertex AI-provided Tensorflow images, keys can be any user defined
string that consists of any UTF-8 characters.
For custom images, keys are the name of the output field in the prediction
to be explained.
Currently only one key is allowed.
map<string, .google.cloud.aiplatform.v1beta1.ExplanationMetadata.OutputMetadata> outputs = 2 [(.google.api.field_behavior) = REQUIRED];
public Map<String,ExplanationMetadata.OutputMetadata> getOutputsMap()
Required. Map from output names to output metadata.
For Vertex AI-provided Tensorflow images, keys can be any user defined
string that consists of any UTF-8 characters.
For custom images, keys are the name of the output field in the prediction
to be explained.
Currently only one key is allowed.
map<string, .google.cloud.aiplatform.v1beta1.ExplanationMetadata.OutputMetadata> outputs = 2 [(.google.api.field_behavior) = REQUIRED];
public ExplanationMetadata.OutputMetadata getOutputsOrDefault(String key, ExplanationMetadata.OutputMetadata defaultValue)
Required. Map from output names to output metadata.
For Vertex AI-provided Tensorflow images, keys can be any user defined
string that consists of any UTF-8 characters.
For custom images, keys are the name of the output field in the prediction
to be explained.
Currently only one key is allowed.
map<string, .google.cloud.aiplatform.v1beta1.ExplanationMetadata.OutputMetadata> outputs = 2 [(.google.api.field_behavior) = REQUIRED];
public ExplanationMetadata.OutputMetadata getOutputsOrThrow(String key)
Required. Map from output names to output metadata.
For Vertex AI-provided Tensorflow images, keys can be any user defined
string that consists of any UTF-8 characters.
For custom images, keys are the name of the output field in the prediction
to be explained.
Currently only one key is allowed.
map<string, .google.cloud.aiplatform.v1beta1.ExplanationMetadata.OutputMetadata> outputs = 2 [(.google.api.field_behavior) = REQUIRED];
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
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Overrides
protected MapField internalGetMapField(int number)
Parameter
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Overrides
protected MapField internalGetMutableMapField(int number)
Parameter
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Overrides
public final boolean isInitialized()
Returns
Overrides
public ExplanationMetadata.Builder mergeFrom(ExplanationMetadata other)
Parameter
Returns
public ExplanationMetadata.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Returns
Overrides
Exceptions
public ExplanationMetadata.Builder mergeFrom(Message other)
Parameter
Returns
Overrides
public final ExplanationMetadata.Builder mergeUnknownFields(UnknownFieldSet unknownFields)
Parameter
Returns
Overrides
public ExplanationMetadata.Builder putAllInputs(Map<String,ExplanationMetadata.InputMetadata> values)
Required. Map from feature names to feature input metadata. Keys are the name of the
features. Values are the specification of the feature.
An empty InputMetadata is valid. It describes a text feature which has the
name specified as the key in ExplanationMetadata.inputs. The baseline
of the empty feature is chosen by Vertex AI.
For Vertex AI-provided Tensorflow images, the key can be any friendly
name of the feature. Once specified,
featureAttributions are keyed by
this key (if not grouped with another feature).
For custom images, the key must match with the key in
instance.
map<string, .google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata> inputs = 1 [(.google.api.field_behavior) = REQUIRED];
public ExplanationMetadata.Builder putAllOutputs(Map<String,ExplanationMetadata.OutputMetadata> values)
Required. Map from output names to output metadata.
For Vertex AI-provided Tensorflow images, keys can be any user defined
string that consists of any UTF-8 characters.
For custom images, keys are the name of the output field in the prediction
to be explained.
Currently only one key is allowed.
map<string, .google.cloud.aiplatform.v1beta1.ExplanationMetadata.OutputMetadata> outputs = 2 [(.google.api.field_behavior) = REQUIRED];
public ExplanationMetadata.Builder putInputs(String key, ExplanationMetadata.InputMetadata value)
Required. Map from feature names to feature input metadata. Keys are the name of the
features. Values are the specification of the feature.
An empty InputMetadata is valid. It describes a text feature which has the
name specified as the key in ExplanationMetadata.inputs. The baseline
of the empty feature is chosen by Vertex AI.
For Vertex AI-provided Tensorflow images, the key can be any friendly
name of the feature. Once specified,
featureAttributions are keyed by
this key (if not grouped with another feature).
For custom images, the key must match with the key in
instance.
map<string, .google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata> inputs = 1 [(.google.api.field_behavior) = REQUIRED];
public ExplanationMetadata.Builder putOutputs(String key, ExplanationMetadata.OutputMetadata value)
Required. Map from output names to output metadata.
For Vertex AI-provided Tensorflow images, keys can be any user defined
string that consists of any UTF-8 characters.
For custom images, keys are the name of the output field in the prediction
to be explained.
Currently only one key is allowed.
map<string, .google.cloud.aiplatform.v1beta1.ExplanationMetadata.OutputMetadata> outputs = 2 [(.google.api.field_behavior) = REQUIRED];
public ExplanationMetadata.Builder removeInputs(String key)
Required. Map from feature names to feature input metadata. Keys are the name of the
features. Values are the specification of the feature.
An empty InputMetadata is valid. It describes a text feature which has the
name specified as the key in ExplanationMetadata.inputs. The baseline
of the empty feature is chosen by Vertex AI.
For Vertex AI-provided Tensorflow images, the key can be any friendly
name of the feature. Once specified,
featureAttributions are keyed by
this key (if not grouped with another feature).
For custom images, the key must match with the key in
instance.
map<string, .google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata> inputs = 1 [(.google.api.field_behavior) = REQUIRED];
public ExplanationMetadata.Builder removeOutputs(String key)
Required. Map from output names to output metadata.
For Vertex AI-provided Tensorflow images, keys can be any user defined
string that consists of any UTF-8 characters.
For custom images, keys are the name of the output field in the prediction
to be explained.
Currently only one key is allowed.
map<string, .google.cloud.aiplatform.v1beta1.ExplanationMetadata.OutputMetadata> outputs = 2 [(.google.api.field_behavior) = REQUIRED];
public ExplanationMetadata.Builder setFeatureAttributionsSchemaUri(String value)
Points to a YAML file stored on Google Cloud Storage describing the format
of the feature attributions.
The schema is defined as an OpenAPI 3.0.2 Schema
Object.
AutoML tabular Models always have this field populated by Vertex AI.
Note: The URI given on output may be different, including the URI scheme,
than the one given on input. The output URI will point to a location where
the user only has a read access.
string feature_attributions_schema_uri = 3;
| Name | Description |
| value | String
The featureAttributionsSchemaUri to set.
|
Returns
public ExplanationMetadata.Builder setFeatureAttributionsSchemaUriBytes(ByteString value)
Points to a YAML file stored on Google Cloud Storage describing the format
of the feature attributions.
The schema is defined as an OpenAPI 3.0.2 Schema
Object.
AutoML tabular Models always have this field populated by Vertex AI.
Note: The URI given on output may be different, including the URI scheme,
than the one given on input. The output URI will point to a location where
the user only has a read access.
string feature_attributions_schema_uri = 3;
| Name | Description |
| value | ByteString
The bytes for featureAttributionsSchemaUri to set.
|
Returns
public ExplanationMetadata.Builder setField(Descriptors.FieldDescriptor field, Object value)
Parameters
Returns
Overrides
public ExplanationMetadata.Builder setLatentSpaceSource(String value)
Name of the source to generate embeddings for example based explanations.
string latent_space_source = 5;
| Name | Description |
| value | String
The latentSpaceSource to set.
|
Returns
public ExplanationMetadata.Builder setLatentSpaceSourceBytes(ByteString value)
Name of the source to generate embeddings for example based explanations.
string latent_space_source = 5;
| Name | Description |
| value | ByteString
The bytes for latentSpaceSource to set.
|
Returns
public ExplanationMetadata.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
Parameters
Returns
Overrides
public final ExplanationMetadata.Builder setUnknownFields(UnknownFieldSet unknownFields)
Parameter
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Overrides
