/* * 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.selection.split; import java.io.IOException; 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.util.MLSandboxDatasets; import org.apache.ignite.examples.ml.util.SandboxMLCache; import org.apache.ignite.ml.dataset.feature.extractor.Vectorizer; import org.apache.ignite.ml.dataset.feature.extractor.impl.DummyVectorizer; 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.selection.split.TrainTestDatasetSplitter; import org.apache.ignite.ml.selection.split.TrainTestSplit; /** * Run linear regression model over dataset split on train and test subsets ({@link TrainTestDatasetSplitter}). * <p> * Code in this example launches Ignite grid and fills the cache with simple test data.</p> * <p> * After that it creates dataset splitter and trains the linear regression model based on the specified data using this * splitter.</p> * <p> * Finally, this example loops over the test set of data points, applies the trained model to predict the target value * and compares prediction to expected outcome (ground truth).</p> * <p> * You can change the test data and split parameters used in this example and re-run it to explore this functionality * further.</p> */ public class TrainTestDatasetSplitterExample { /** * Run example. */ public static void main(String[] args) throws IOException { 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(">>> Create new training dataset splitter object."); TrainTestSplit<Integer, Vector> split = new TrainTestDatasetSplitter<Integer, Vector>() .split(0.75); System.out.println(">>> Perform the training to get the model."); Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>() .labeled(Vectorizer.LabelCoordinate.FIRST); LinearRegressionModel mdl = trainer.fit(ignite, dataCache, split.getTrainFilter(), vectorizer); System.out.println(">>> Linear regression model: " + mdl); System.out.println(">>> ---------------------------------"); System.out.println(">>> | Prediction\t| Ground Truth\t|"); System.out.println(">>> ---------------------------------"); ScanQuery<Integer, Vector> qry = new ScanQuery<>(); qry.setFilter(split.getTestFilter()); try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(qry)) { 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 = mdl.predict(inputs); 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 { if (dataCache != null) dataCache.destroy(); } } finally { System.out.flush(); } } }