| /* |
| * 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.tree.randomforest; |
| |
| import java.io.IOException; |
| import java.util.concurrent.atomic.AtomicInteger; |
| import java.util.stream.Collectors; |
| import java.util.stream.IntStream; |
| import javax.cache.Cache; |
| import org.apache.commons.math3.util.Precision; |
| 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.ml.composition.ModelsComposition; |
| import org.apache.ignite.ml.dataset.feature.FeatureMeta; |
| 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.tree.randomforest.RandomForestClassifierTrainer; |
| import org.apache.ignite.ml.tree.randomforest.data.FeaturesCountSelectionStrategies; |
| import org.apache.ignite.ml.util.MLSandboxDatasets; |
| import org.apache.ignite.ml.util.SandboxMLCache; |
| |
| /** |
| * Example represents a solution for the task of wine classification based on a |
| * <a href ="https://en.wikipedia.org/wiki/Random_forest">Random Forest</a> implementation for |
| * multi-classification. |
| * <p> |
| * Code in this example launches Ignite grid and fills the cache with test data points (based on the |
| * <a href="https://archive.ics.uci.edu/ml/machine-learning-databases/wine/">Wine recognition dataset</a>).</p> |
| * <p> |
| * After that it initializes the {@link RandomForestClassifierTrainer} with thread pool for multi-thread learning and |
| * trains the model based on the specified data using random forest regression algorithm.</p> |
| * <p> |
| * Finally, this example loops over the test set of data points, compares prediction of the trained model to the |
| * expected outcome (ground truth), and evaluates accuracy of the model.</p> |
| * <p> |
| * You can change the test data used in this example and re-run it to explore this algorithm further.</p> |
| */ |
| public class RandomForestClassificationExample { |
| /** |
| * Run example. |
| */ |
| public static void main(String[] args) throws IOException { |
| System.out.println(); |
| System.out.println(">>> Random Forest multi-class classification algorithm 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.WINE_RECOGNITION); |
| |
| AtomicInteger idx = new AtomicInteger(0); |
| RandomForestClassifierTrainer classifier = new RandomForestClassifierTrainer( |
| IntStream.range(0, dataCache.get(1).size() - 1).mapToObj( |
| x -> new FeatureMeta("", idx.getAndIncrement(), false)).collect(Collectors.toList()) |
| ).withAmountOfTrees(101) |
| .withFeaturesCountSelectionStrgy(FeaturesCountSelectionStrategies.ONE_THIRD) |
| .withMaxDepth(4) |
| .withMinImpurityDelta(0.) |
| .withSubSampleSize(0.3) |
| .withSeed(0); |
| |
| System.out.println(">>> Configured trainer: " + classifier.getClass().getSimpleName()); |
| |
| Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>() |
| .labeled(Vectorizer.LabelCoordinate.FIRST); |
| ModelsComposition randomForestMdl = classifier.fit(ignite, dataCache, vectorizer); |
| |
| System.out.println(">>> Trained model: " + randomForestMdl.toString(true)); |
| |
| int amountOfErrors = 0; |
| int totalAmount = 0; |
| |
| try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) { |
| 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 = randomForestMdl.predict(inputs); |
| |
| totalAmount++; |
| if (!Precision.equals(groundTruth, prediction, Precision.EPSILON)) |
| amountOfErrors++; |
| } |
| |
| System.out.println("\n>>> Evaluated model on " + totalAmount + " data points."); |
| |
| System.out.println("\n>>> Absolute amount of errors " + amountOfErrors); |
| System.out.println("\n>>> Accuracy " + (1 - amountOfErrors / (double)totalAmount)); |
| System.out.println(">>> Random Forest multi-class classification algorithm over cached dataset usage example completed."); |
| } |
| |
| } |
| finally { |
| if (dataCache != null) |
| dataCache.destroy(); |
| } |
| } |
| finally { |
| System.out.flush(); |
| } |
| } |
| } |