org.neo4j.gds.procedures.pipelines.PipelineRepository Maven / Gradle / Ivy
/*
* 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.procedures.pipelines;
import org.neo4j.gds.api.User;
import org.neo4j.gds.ml.pipeline.PipelineCatalog;
import org.neo4j.gds.ml.pipeline.TrainingPipeline;
import org.neo4j.gds.ml.pipeline.linkPipeline.LinkPredictionTrainingPipeline;
import org.neo4j.gds.ml.pipeline.nodePipeline.classification.NodeClassificationTrainingPipeline;
import org.neo4j.gds.ml.pipeline.nodePipeline.regression.NodeRegressionTrainingPipeline;
import java.util.Optional;
import java.util.stream.Stream;
/**
* One day we can replace the big singleton with local state, and manage things better.
* For now this is the groundwork, rolling out this half way house.
*/
public class PipelineRepository {
LinkPredictionTrainingPipeline createLinkPredictionTrainingPipeline(User user, PipelineName pipelineName) {
var pipeline = new LinkPredictionTrainingPipeline();
PipelineCatalog.set(user.getUsername(), pipelineName.value, pipeline);
return pipeline;
}
NodeClassificationTrainingPipeline createNodeClassificationTrainingPipeline(User user, PipelineName pipelineName) {
var pipeline = new NodeClassificationTrainingPipeline();
registerPipeline(user, pipelineName, pipeline);
return pipeline;
}
NodeRegressionTrainingPipeline createNodeRegressionTrainingPipeline(User user, PipelineName pipelineName) {
var pipeline = new NodeRegressionTrainingPipeline();
registerPipeline(user, pipelineName, pipeline);
return pipeline;
}
/**
* Underlying catalog throws exception if pipeline does not exist
*/
TrainingPipeline> drop(User user, PipelineName pipelineName) {
return PipelineCatalog.drop(user.getUsername(), pipelineName.value);
}
boolean exists(User user, PipelineName pipelineName) {
return PipelineCatalog.exists(user.getUsername(), pipelineName.value);
}
Stream getAll(User user) {
return PipelineCatalog.getAllPipelines(user.getUsername());
}
LinkPredictionTrainingPipeline getLinkPredictionTrainingPipeline(User user, PipelineName pipelineName) {
return PipelineCatalog.getTyped(user.getUsername(), pipelineName.value, LinkPredictionTrainingPipeline.class);
}
NodeClassificationTrainingPipeline getNodeClassificationTrainingPipeline(User user, PipelineName pipelineName) {
return PipelineCatalog.getTyped(
user.getUsername(),
pipelineName.value,
NodeClassificationTrainingPipeline.class
);
}
NodeRegressionTrainingPipeline getNodeRegressionTrainingPipeline(User user, PipelineName pipelineName) {
return PipelineCatalog.getTyped(
user.getUsername(),
pipelineName.value,
NodeRegressionTrainingPipeline.class
);
}
Optional> getSingle(User user, PipelineName pipelineName) {
if (!exists(user, pipelineName)) return Optional.empty();
var pipeline = get(user, pipelineName);
return Optional.of(pipeline);
}
String getType(User user, PipelineName pipelineName) {
var pipeline = get(user, pipelineName);
return pipeline.type();
}
private TrainingPipeline> get(User user, PipelineName pipelineName) {
return PipelineCatalog.get(user.getUsername(), pipelineName.value);
}
private void registerPipeline(
User user,
PipelineName pipelineName,
TrainingPipeline> pipeline
) {
PipelineCatalog.set(user.getUsername(), pipelineName.value, pipeline);
}
}
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