| /* |
| * 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(); |
| } |
| } |
| } |