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Reference documentation and code samples for the Google Cloud Ai Platform V1 Client class ExplainRequest.
Request message for PredictionService.Explain .
Generated from protobuf message google.cloud.aiplatform.v1.ExplainRequest
Methods
build
endpoint
string
Required. The name of the Endpoint requested to serve the explanation.
Format: projects/{project}/locations/{location}/endpoints/{endpoint}
Please see {@see \Google\Cloud\AIPlatform\V1\PredictionServiceClient::endpointName()} for help formatting this field.
instances
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 PredictSchemata's instance_schema_uri .
parameters
Google\Protobuf\Value
The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' Model's PredictSchemata's parameters_schema_uri .
deployedModelId
string
If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding Endpoint.traffic_split .
__construct
Constructor.
data
array
Optional. Data for populating the Message object.
↳ endpoint
string
Required. The name of the Endpoint requested to serve the explanation. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
↳ instances
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 PredictSchemata's instance_schema_uri .
↳ parameters
Google\Protobuf\Value
The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' Model's PredictSchemata's parameters_schema_uri .
↳ explanation_spec_override
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.
↳ deployed_model_id
string
If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding Endpoint.traffic_split .
getEndpoint
Required. The name of the Endpoint requested to serve the explanation.
Format: projects/{project}/locations/{location}/endpoints/{endpoint}
string
setEndpoint
Required. The name of the Endpoint requested to serve the explanation.
Format: projects/{project}/locations/{location}/endpoints/{endpoint}
var
string
$this
getInstances
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 PredictSchemata's instance_schema_uri .
setInstances
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 PredictSchemata's instance_schema_uri .
$this
getParameters
The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' Model's PredictSchemata's parameters_schema_uri .
hasParameters
clearParameters
setParameters
The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' Model's PredictSchemata's parameters_schema_uri .
$this
getExplanationSpecOverride
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.
hasExplanationSpecOverride
clearExplanationSpecOverride
setExplanationSpecOverride
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.
$this
getDeployedModelId
If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding Endpoint.traffic_split .
string
setDeployedModelId
If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding Endpoint.traffic_split .
var
string
$this
