Vertex AI V1 API - Class Google::Cloud::AIPlatform::V1::ExplainRequest (v0.46.0)

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Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::ExplainRequest.

Request message for PredictionService.Explain.

Inherits

• Object

Extended By

• Google::Protobuf::MessageExts::ClassMethods

Includes

• Google::Protobuf::MessageExts

Methods

#deployed_model_id

def deployed_model_id() -> ::String

Returns

• (::String) — If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding Endpoint.traffic_split.

#deployed_model_id=

def deployed_model_id=(value) -> ::String

Parameter

• value (::String) — If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding Endpoint.traffic_split.

Returns

• (::String) — If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding Endpoint.traffic_split.

#endpoint

def endpoint() -> ::String

Returns

• (::String) — Required. The name of the Endpoint requested to serve the explanation. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

#endpoint=

def endpoint=(value) -> ::String

Parameter

• value (::String) — Required. The name of the Endpoint requested to serve the explanation. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

Returns

• (::String) — Required. The name of the Endpoint requested to serve the explanation. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

#explanation_spec_override

def explanation_spec_override() -> ::Google::Cloud::AIPlatform::V1::ExplanationSpecOverride

Returns

• (::Google::Cloud::AIPlatform::V1::ExplanationSpecOverride) —

  If specified, overrides the explanation_spec of the DeployedModel. Can be used for explaining prediction results with different configurations, such as:

  • Explaining top-5 predictions results as opposed to top-1;
  • Increasing path count or step count of the attribution methods to reduce approximate errors;
  • Using different baselines for explaining the prediction results.

#explanation_spec_override=

def explanation_spec_override=(value) -> ::Google::Cloud::AIPlatform::V1::ExplanationSpecOverride

Parameter

• value (::Google::Cloud::AIPlatform::V1::ExplanationSpecOverride) —

  If specified, overrides the explanation_spec of the DeployedModel. Can be used for explaining prediction results with different configurations, such as:

  • Explaining top-5 predictions results as opposed to top-1;
  • Increasing path count or step count of the attribution methods to reduce approximate errors;
  • Using different baselines for explaining the prediction results.

Returns

• (::Google::Cloud::AIPlatform::V1::ExplanationSpecOverride) —

  If specified, overrides the explanation_spec of the DeployedModel. Can be used for explaining prediction results with different configurations, such as:

  • Explaining top-5 predictions results as opposed to top-1;
  • Increasing path count or step count of the attribution methods to reduce approximate errors;
  • Using different baselines for explaining the prediction results.

#instances

def instances() -> ::Array<::Google::Protobuf::Value>

Returns

• (::Array<::Google::Protobuf::Value>) — Required. The instances that are the input to the explanation call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the explanation call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] instance_schema_uri.

#instances=

def instances=(value) -> ::Array<::Google::Protobuf::Value>

Parameter

• value (::Array<::Google::Protobuf::Value>) — Required. The instances that are the input to the explanation call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the explanation call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] instance_schema_uri.

Returns

• (::Array<::Google::Protobuf::Value>) — Required. The instances that are the input to the explanation call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the explanation call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] instance_schema_uri.

#parameters

def parameters() -> ::Google::Protobuf::Value

Returns

• (::Google::Protobuf::Value) — The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' [Model's ][google.cloud.aiplatform.v1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] parameters_schema_uri.

#parameters=

def parameters=(value) -> ::Google::Protobuf::Value

Parameter

• value (::Google::Protobuf::Value) — The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' [Model's ][google.cloud.aiplatform.v1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] parameters_schema_uri.

Returns

• (::Google::Protobuf::Value) — The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' [Model's ][google.cloud.aiplatform.v1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] parameters_schema_uri.