| /* |
| * 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 java.util.function.BiFunction; |
| import org.apache.ignite.Ignite; |
| import org.apache.ignite.IgniteCache; |
| import org.apache.ignite.Ignition; |
| import org.apache.ignite.ml.dataset.feature.extractor.Vectorizer; |
| import org.apache.ignite.ml.dataset.feature.extractor.impl.DummyVectorizer; |
| import org.apache.ignite.ml.environment.LearningEnvironmentBuilder; |
| 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.scoring.evaluator.Evaluator; |
| import org.apache.ignite.ml.selection.scoring.metric.MetricName; |
| import org.apache.ignite.ml.selection.split.TrainTestDatasetSplitter; |
| import org.apache.ignite.ml.selection.split.TrainTestSplit; |
| import org.apache.ignite.ml.trainers.DatasetTrainer; |
| import org.apache.ignite.ml.util.MLSandboxDatasets; |
| import org.apache.ignite.ml.util.SandboxMLCache; |
| |
| /** |
| * Example of using Linear Regression model in Apache Ignite for house prices prediction. |
| * <p> |
| * Description of model can be found in: https://en.wikipedia.org/wiki/Linear_regression . Original dataset can be |
| * downloaded from: https://archive.ics.uci.edu/ml/machine-learning-databases/housing/ . Copy of dataset are stored in: |
| * modules/ml/src/main/resources/datasets/boston_housing_dataset.txt . Score for regression estimation: R^2 (coefficient |
| * of determination). Description of score evaluation can be found in: https://stattrek.com/statistics/dictionary.aspx?definition=coefficient_of_determination |
| * . |
| */ |
| public class BostonHousePricesPredictionExample { |
| /** |
| * Runs example. |
| */ |
| public static void main(String[] args) throws IOException { |
| try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) { |
| System.out.println(">>> Ignite grid started."); |
| |
| IgniteCache<Integer, Vector> dataCache = null; |
| try { |
| System.out.println(">>> Fill dataset cache."); |
| dataCache = new SandboxMLCache(ignite).fillCacheWith(MLSandboxDatasets.BOSTON_HOUSE_PRICES); |
| DatasetTrainer<LinearRegressionModel, Double> trainer = new LinearRegressionLSQRTrainer() |
| .withEnvironmentBuilder(LearningEnvironmentBuilder.defaultBuilder().withRNGSeed(0)); |
| |
| // This vectorizer works with values in cache of Vector class. |
| Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>() |
| .labeled(Vectorizer.LabelCoordinate.FIRST); // FIRST means "label are stored at first coordinate of vector" |
| |
| // Splits dataset to train and test samples with 80/20 proportion. |
| TrainTestSplit<Integer, Vector> split = new TrainTestDatasetSplitter<Integer, Vector>().split(0.8); |
| |
| System.out.println(">>> Start training."); |
| LinearRegressionModel mdl = trainer.fit( |
| ignite, dataCache, |
| split.getTrainFilter(), |
| vectorizer |
| ); |
| |
| System.out.println(">>> Perform scoring."); |
| double score = Evaluator.evaluate( |
| dataCache, |
| split.getTestFilter(), |
| mdl, |
| vectorizer, |
| MetricName.R2 |
| ); |
| |
| System.out.println(">>> Model: " + toString(mdl)); |
| System.out.println(">>> R^2 score: " + score); |
| } |
| finally { |
| if (dataCache != null) |
| dataCache.destroy(); |
| } |
| } |
| finally { |
| System.out.flush(); |
| } |
| } |
| |
| /** |
| * Prepare pretty string for model. |
| * |
| * @param mdl Model. |
| * @return String representation of model. |
| */ |
| private static String toString(LinearRegressionModel mdl) { |
| BiFunction<Integer, Double, String> formatter = (idx, val) -> String.format("%.2f*f%d", val, idx); |
| |
| Vector weights = mdl.getWeights(); |
| StringBuilder sb = new StringBuilder(formatter.apply(0, weights.get(0))); |
| |
| for (int fid = 1; fid < weights.size(); fid++) { |
| double w = weights.get(fid); |
| sb.append(" ").append(w > 0 ? "+" : "-").append(" ") |
| .append(formatter.apply(fid, Math.abs(w))); |
| } |
| |
| double intercept = mdl.getIntercept(); |
| sb.append(" ").append(intercept > 0 ? "+" : "-").append(" ") |
| .append(String.format("%.2f", Math.abs(intercept))); |
| return sb.toString(); |
| } |
| } |