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public interface ExplanationMetadataOrBuilder extends MessageOrBuilderImplements
MessageOrBuilderMethods
containsInputs(String key)
public abstract 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.v1.ExplanationMetadata.InputMetadata> inputs = 1 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
key | String |
| Type | Description |
boolean |
containsOutputs(String key)
public abstract 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.v1.ExplanationMetadata.OutputMetadata> outputs = 2 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
key | String |
| Type | Description |
boolean |
getFeatureAttributionsSchemaUri()
public abstract 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. |
getFeatureAttributionsSchemaUriBytes()
public abstract 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. |
getInputs()
public abstract Map<String,ExplanationMetadata.InputMetadata> getInputs()Use #getInputsMap() instead.
| Type | Description |
Map<String,InputMetadata> |
getInputsCount()
public abstract 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.v1.ExplanationMetadata.InputMetadata> inputs = 1 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
int |
getInputsMap()
public abstract 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.v1.ExplanationMetadata.InputMetadata> inputs = 1 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
Map<String,InputMetadata> |
getInputsOrDefault(String key, ExplanationMetadata.InputMetadata defaultValue)
public abstract 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.v1.ExplanationMetadata.InputMetadata> inputs = 1 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
key | String |
defaultValue | ExplanationMetadata.InputMetadata |
| Type | Description |
ExplanationMetadata.InputMetadata |
getInputsOrThrow(String key)
public abstract 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.v1.ExplanationMetadata.InputMetadata> inputs = 1 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
key | String |
| Type | Description |
ExplanationMetadata.InputMetadata |
getLatentSpaceSource()
public abstract String getLatentSpaceSource()Name of the source to generate embeddings for example based explanations.
string latent_space_source = 5;
| Type | Description |
String | The latentSpaceSource. |
getLatentSpaceSourceBytes()
public abstract 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. |
getOutputs()
public abstract Map<String,ExplanationMetadata.OutputMetadata> getOutputs()Use #getOutputsMap() instead.
| Type | Description |
Map<String,OutputMetadata> |
getOutputsCount()
public abstract 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.v1.ExplanationMetadata.OutputMetadata> outputs = 2 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
int |
getOutputsMap()
public abstract 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.v1.ExplanationMetadata.OutputMetadata> outputs = 2 [(.google.api.field_behavior) = REQUIRED];
| Type | Description |
Map<String,OutputMetadata> |
getOutputsOrDefault(String key, ExplanationMetadata.OutputMetadata defaultValue)
public abstract 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.v1.ExplanationMetadata.OutputMetadata> outputs = 2 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
key | String |
defaultValue | ExplanationMetadata.OutputMetadata |
| Type | Description |
ExplanationMetadata.OutputMetadata |
getOutputsOrThrow(String key)
public abstract 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.v1.ExplanationMetadata.OutputMetadata> outputs = 2 [(.google.api.field_behavior) = REQUIRED];
| Name | Description |
key | String |
| Type | Description |
ExplanationMetadata.OutputMetadata |