use of ai.grakn.graql.internal.analytics.CorenessVertexProgram in project grakn by graknlabs.
the class TinkerComputeQueryRunner method run.
public ComputeJob<Map<Long, Set<String>>> run(CorenessQuery query) {
return runCompute(query, tinkerComputeQuery -> {
long k = query.minK();
if (k < 2L)
throw GraqlQueryException.kValueSmallerThanTwo();
Set<Label> ofLabels;
// Check if ofType is valid before returning emptyMap
if (query.targetLabels().isEmpty()) {
ofLabels = tinkerComputeQuery.subLabels();
} else {
ofLabels = query.targetLabels().stream().flatMap(typeLabel -> {
Type type = tx.getSchemaConcept(typeLabel);
if (type == null)
throw GraqlQueryException.labelNotFound(typeLabel);
if (type.isRelationshipType())
throw GraqlQueryException.kCoreOnRelationshipType(typeLabel);
return type.subs();
}).map(SchemaConcept::getLabel).collect(Collectors.toSet());
}
Set<Label> subLabels = Sets.union(tinkerComputeQuery.subLabels(), ofLabels);
if (!tinkerComputeQuery.selectedTypesHaveInstance()) {
return Collections.emptyMap();
}
ComputerResult result;
Set<LabelId> subLabelIds = convertLabelsToIds(subLabels);
Set<LabelId> ofLabelIds = convertLabelsToIds(ofLabels);
try {
result = tinkerComputeQuery.compute(new CorenessVertexProgram(k), new DegreeDistributionMapReduce(ofLabelIds, CorenessVertexProgram.CORENESS), subLabelIds);
} catch (NoResultException e) {
return Collections.emptyMap();
}
return result.memory().get(DegreeDistributionMapReduce.class.getName());
});
}
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