| /* |
| * Licensed to the Apache Software Foundation (ASF) under one or more |
| * contributor license agreements. See the NOTICE file distributed with |
| * this work for additional information regarding copyright ownership. |
| * The ASF licenses this file to You under the Apache License, Version 2.0 |
| * (the "License"); you may not use this file except in compliance with |
| * the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| package org.apache.ignite.examples.ml.inference; |
| |
| import java.io.IOException; |
| import java.util.concurrent.ExecutionException; |
| import java.util.concurrent.Future; |
| import javax.cache.Cache; |
| import org.apache.ignite.Ignite; |
| import org.apache.ignite.IgniteCache; |
| import org.apache.ignite.Ignition; |
| import org.apache.ignite.cache.query.QueryCursor; |
| import org.apache.ignite.cache.query.ScanQuery; |
| import org.apache.ignite.examples.ml.regression.linear.LinearRegressionLSQRTrainerExample; |
| import org.apache.ignite.ml.dataset.feature.extractor.Vectorizer; |
| import org.apache.ignite.ml.dataset.feature.extractor.impl.DummyVectorizer; |
| import org.apache.ignite.ml.inference.Model; |
| import org.apache.ignite.ml.inference.builder.IgniteDistributedModelBuilder; |
| import org.apache.ignite.ml.inference.parser.IgniteModelParser; |
| import org.apache.ignite.ml.inference.parser.ModelParser; |
| import org.apache.ignite.ml.inference.reader.InMemoryModelReader; |
| import org.apache.ignite.ml.inference.reader.ModelReader; |
| import org.apache.ignite.ml.math.primitives.vector.Vector; |
| import org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer; |
| import org.apache.ignite.ml.regressions.linear.LinearRegressionModel; |
| import org.apache.ignite.ml.util.MLSandboxDatasets; |
| import org.apache.ignite.ml.util.SandboxMLCache; |
| |
| /** |
| * This example is based on {@link LinearRegressionLSQRTrainerExample}, but to perform inference it uses an approach |
| * implemented in {@link org.apache.ignite.ml.inference} package. |
| */ |
| public class IgniteModelDistributedInferenceExample { |
| /** |
| * Run example. |
| */ |
| public static void main(String... args) throws IOException, ExecutionException, InterruptedException { |
| System.out.println(); |
| System.out.println(">>> Linear regression model over cache based dataset usage example started."); |
| // Start ignite grid. |
| try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) { |
| System.out.println(">>> Ignite grid started."); |
| |
| IgniteCache<Integer, Vector> dataCache = null; |
| try { |
| dataCache = new SandboxMLCache(ignite).fillCacheWith(MLSandboxDatasets.MORTALITY_DATA); |
| |
| System.out.println(">>> Create new linear regression trainer object."); |
| LinearRegressionLSQRTrainer trainer = new LinearRegressionLSQRTrainer(); |
| |
| System.out.println(">>> Perform the training to get the model."); |
| LinearRegressionModel mdl = trainer.fit( |
| ignite, |
| dataCache, |
| new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST) |
| ); |
| |
| System.out.println(">>> Linear regression model: " + mdl); |
| |
| System.out.println(">>> Preparing model reader and model parser."); |
| ModelReader reader = new InMemoryModelReader(mdl); |
| ModelParser<Vector, Double, ?> parser = new IgniteModelParser<>(); |
| try (Model<Vector, Future<Double>> infMdl = new IgniteDistributedModelBuilder(ignite, 4, 4) |
| .build(reader, parser)) { |
| System.out.println(">>> Inference model is ready."); |
| |
| System.out.println(">>> ---------------------------------"); |
| System.out.println(">>> | Prediction\t| Ground Truth\t|"); |
| System.out.println(">>> ---------------------------------"); |
| |
| try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) { |
| for (Cache.Entry<Integer, Vector> observation : observations) { |
| Vector val = observation.getValue(); |
| Vector inputs = val.copyOfRange(1, val.size()); |
| double groundTruth = val.get(0); |
| |
| double prediction = infMdl.predict(inputs).get(); |
| |
| System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", prediction, groundTruth); |
| } |
| } |
| } |
| |
| System.out.println(">>> ---------------------------------"); |
| |
| System.out.println(">>> Linear regression model over cache based dataset usage example completed."); |
| } |
| finally { |
| dataCache.destroy(); |
| } |
| } |
| finally { |
| System.out.flush(); |
| } |
| } |
| } |