Neptune ML predicates used in Gremlin inference queries - Amazon Neptune
Neptune#ml.endpointNeptune#ml.iamRoleArnNeptune#ml.limitNeptune#ml.thresholdNeptune#ml.classificationNeptune#ml.regressionNeptune#ml.predictionNeptune#ml.score
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Neptune ML predicates used in Gremlin inference queries

Neptune#ml.endpoint

The Neptune#ml.endpoint predicate is used in a with() step to specify the inference endpoint, if necessary:

 .with("Neptune#ml.endpoint", "the model's SageMaker inference endpoint")

You can identify the endpoint either by its id or its URL. For example:

 .with( "Neptune#ml.endpoint", "node-classification-movie-lens-endpoint" )

Or:

 .with( "Neptune#ml.endpoint", "https://runtime.sagemaker.us-east-1.amazonaws.com/endpoints/node-classification-movie-lens-endpoint/invocations" )
Note

If you set the neptune_ml_endpoint parameter in your Neptune DB cluster parameter group to the endpoint id or URL, you don't need to include the Neptune#ml.endpoint predicate in each query.

Neptune#ml.iamRoleArn

Neptune#ml.iamRoleArn is used in a with() step to specify the ARN of the SageMaker execution IAM role, if necessary:

 .with("Neptune#ml.iamRoleArn", "the ARN for the SageMaker execution IAM role")

For information about how to create the SageMaker execution IAM role, see Create a custom NeptuneSageMakerIAMRole role.

Note

If you set the neptune_ml_iam_role parameter in your Neptune DB cluster parameter group to the ARN of your SageMaker execution IAM role, you don't need to include the Neptune#ml.iamRoleArn predicate in each query.

Neptune#ml.limit

The Neptune#ml.limit predicate optionally limits the number of results returned per entity:

 .with( "Neptune#ml.limit", 2 )

By default, the limit is 1, and the maximum number that can be set is 100.

Neptune#ml.threshold

The Neptune#ml.threshold predicate optionally establishes a cutoff threshold for result scores:

 .with( "Neptune#ml.threshold", 0.5D )

This lets you discard all results with scores below the specified threshold.

Neptune#ml.classification

The Neptune#ml.classification predicate is attached to the properties() step to establish that the properties need to be fetched from the SageMaker endpoint of the node classification model:

 .properties( "property key of the node classification model" ).with( "Neptune#ml.classification" )

Neptune#ml.regression

The Neptune#ml.regression predicate is attached to the properties() step to establish that the properties need to be fetched from the SageMaker endpoint of the node regression model:

 .properties( "property key of the node regression model" ).with( "Neptune#ml.regression" )

Neptune#ml.prediction

The Neptune#ml.prediction predicate is attached to in() and out() steps to establish that this a link-prediction query:

 .in("edge label of the link prediction model").with("Neptune#ml.prediction").hasLabel("target node label")

Neptune#ml.score

The Neptune#ml.score predicate is used in Gremlin node or edge classification queries to fetch a machine-learning confidence Score. The Neptune#ml.score predicate should be passed together with the query predicate in the properties() step to obtain an ML confidence score for node or edge classification queries.

You can find a node classification example with other node classification examples, and an edge classification example in the edge classification section.