| /* |
| * 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.nn.UpdatesStrategy; |
| import org.apache.ignite.ml.optimization.updatecalculators.RPropParameterUpdate; |
| import org.apache.ignite.ml.optimization.updatecalculators.RPropUpdateCalculator; |
| import org.apache.ignite.ml.regressions.linear.LinearRegressionModel; |
| import org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer; |
| import org.apache.ignite.ml.selection.scoring.evaluator.Evaluator; |
| import org.apache.ignite.ml.selection.scoring.metric.MetricName; |
| |
| /** |
| * Run linear regression model based on based on |
| * <a href="https://en.wikipedia.org/wiki/Stochastic_gradient_descent">stochastic gradient descent</a> algorithm |
| * ({@link LinearRegressionSGDTrainer}) over cached dataset. |
| * <p> |
| * Code in this example launches Ignite grid and fills the cache with simple test data.</p> |
| * <p> |
| * After that it trains the linear regression model based on stochastic gradient descent algorithm using the specified |
| * data.</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 used in this example and re-run it to explore this algorithm further.</p> |
| */ |
| public class LinearRegressionSGDTrainerExample { |
| /** |
| * Run example. |
| */ |
| public static void main(String[] args) throws IOException { |
| System.out.println(); |
| System.out.println(">>> Linear regression model over sparse distributed matrix API 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."); |
| LinearRegressionSGDTrainer<?> trainer = new LinearRegressionSGDTrainer<>(new UpdatesStrategy<>( |
| new RPropUpdateCalculator(), |
| RPropParameterUpdate.SUM_LOCAL, |
| RPropParameterUpdate.AVG |
| ), 100000, 10, 100, 123L); |
| |
| 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, vectorizer); |
| |
| System.out.println(">>> Linear regression model: " + mdl); |
| |
| double rmse = Evaluator.evaluate(dataCache, mdl, vectorizer, MetricName.RMSE); |
| |
| System.out.println("\n>>> Rmse = " + rmse); |
| |
| 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(); |
| } |
| } |
| } |