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" )
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.
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.