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Neo4j Graph Data Science :: Procedures :: Machine Learning

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/*
 * Copyright (c) "Neo4j"
 * Neo4j Sweden AB [http://neo4j.com]
 *
 * This file is part of Neo4j.
 *
 * Neo4j is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see .
 */
package org.neo4j.gds.ml.kge;

import org.neo4j.gds.procedures.GraphDataScienceProcedures;
import org.neo4j.gds.procedures.algorithms.machinelearning.KGEStreamResult;
import org.neo4j.procedure.Context;
import org.neo4j.procedure.Description;
import org.neo4j.procedure.Internal;
import org.neo4j.procedure.Mode;
import org.neo4j.procedure.Name;
import org.neo4j.procedure.Procedure;

import java.util.Map;
import java.util.stream.Stream;

public class KGEPredictStreamProc {
    @Context
    public GraphDataScienceProcedures facade;

    @Procedure(name = "gds.ml.kge.predict.stream", mode = Mode.READ)
    @Description("Predicts new relationships using an existing KGE model.")
    @Internal
    public Stream mutate(
        @Name(value = "graphName") String graphName,
        @Name(value = "configuration", defaultValue = "{}") Map configuration
    ) {
        return facade.algorithms().machineLearning().kgeStream(graphName, configuration);
    }
}