| /* |
| * 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.regression.linear; |
| |
| import java.io.IOException; |
| import org.apache.ignite.Ignite; |
| import org.apache.ignite.IgniteCache; |
| import org.apache.ignite.Ignition; |
| 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.preprocessing.Preprocessor; |
| import org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerPreprocessor; |
| import org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerTrainer; |
| import org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer; |
| import org.apache.ignite.ml.regressions.linear.LinearRegressionModel; |
| import org.apache.ignite.ml.selection.scoring.evaluator.Evaluator; |
| import org.apache.ignite.ml.selection.scoring.metric.MetricName; |
| |
| /** |
| * Run linear regression model based on <a href="http://web.stanford.edu/group/SOL/software/lsqr/">LSQR algorithm</a> |
| * ({@link LinearRegressionLSQRTrainer}) over cached dataset that was created using a minmaxscaling preprocessor ({@link |
| * MinMaxScalerTrainer}, {@link MinMaxScalerPreprocessor}). |
| * <p> |
| * Code in this example launches Ignite grid, fills the cache with simple test data, and defines minmaxscaling trainer |
| * and preprocessor.</p> |
| * <p> |
| * After that it trains the linear regression model based on the specified data that has been processed using |
| * minmaxscaling.</p> |
| * <p> |
| * Finally, this example loops over the test set of data points, applies the trained model to predict predict the target |
| * value and compares prediction to expected outcome (ground truth).</p> |
| * <p> |
| * You can change the test data used in this example and re-run it to explore this algorithm further.</p> |
| */ |
| public class LinearRegressionLSQRTrainerWithMinMaxScalerExample { |
| /** |
| * Run example. |
| */ |
| public static void main(String[] args) throws IOException { |
| System.out.println(); |
| System.out.println(">>> Linear regression model with Min Max Scaling preprocessor over cached 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); |
| |
| Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>() |
| .labeled(Vectorizer.LabelCoordinate.FIRST); |
| |
| System.out.println(">>> Create new MinMaxScaler trainer object."); |
| MinMaxScalerTrainer<Integer, Vector> minMaxScalerTrainer = new MinMaxScalerTrainer<>(); |
| |
| System.out.println(">>> Perform the training to get the MinMaxScaler preprocessor."); |
| Preprocessor<Integer, Vector> preprocessor = minMaxScalerTrainer.fit( |
| ignite, |
| dataCache, |
| vectorizer |
| ); |
| |
| 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, preprocessor); //TODO: IGNITE-11581 |
| |
| System.out.println(">>> Linear regression model: " + mdl); |
| |
| double rmse = Evaluator.evaluate(dataCache, mdl, preprocessor, MetricName.RMSE); |
| |
| System.out.println("\n>>> Rmse = " + rmse); |
| |
| System.out.println(">>> ---------------------------------"); |
| System.out.println(">>> Linear regression model with MinMaxScaler preprocessor over cache based dataset usage example completed."); |
| } |
| finally { |
| if (dataCache != null) |
| dataCache.destroy(); |
| } |
| } |
| finally { |
| System.out.flush(); |
| } |
| } |
| } |