| /* |
| * 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.clustering; |
| |
| 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.clustering.kmeans.KMeansModel; |
| import org.apache.ignite.ml.clustering.kmeans.KMeansTrainer; |
| 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.Tracer; |
| import org.apache.ignite.ml.math.primitives.vector.Vector; |
| |
| /** |
| * Run KMeans clustering algorithm ({@link KMeansTrainer}) over distributed dataset. |
| * <p> |
| * Code in this example launches Ignite grid and fills the cache with test data points (based on the |
| * <a href="https://en.wikipedia.org/wiki/Iris_flower_data_set"></a>Iris dataset</a>).</p> |
| * <p> |
| * After that it trains the model based on the specified data using |
| * <a href="https://en.wikipedia.org/wiki/K-means_clustering">KMeans</a> algorithm.</p> |
| * <p> |
| * Finally, this example loops over the test set of data points, applies the trained model to predict what cluster does |
| * this point belong to, 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 KMeansClusterizationExample { |
| /** |
| * Run example. |
| */ |
| public static void main(String[] args) throws IOException { |
| System.out.println(); |
| System.out.println(">>> KMeans clustering 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.TWO_CLASSED_IRIS); |
| |
| Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>().labeled(Vectorizer.LabelCoordinate.FIRST); |
| |
| KMeansTrainer trainer = new KMeansTrainer(); |
| |
| KMeansModel mdl = trainer.fit( |
| ignite, |
| dataCache, |
| vectorizer |
| ); |
| |
| System.out.println(">>> KMeans centroids"); |
| Tracer.showAscii(mdl.getCenters()[0]); |
| Tracer.showAscii(mdl.getCenters()[1]); |
| System.out.println(">>>"); |
| |
| System.out.println(">>> --------------------------------------------"); |
| System.out.println(">>> | Predicted cluster\t| Erased class label\t|"); |
| System.out.println(">>> --------------------------------------------"); |
| |
| 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 = mdl.predict(inputs); |
| |
| System.out.printf(">>> | %.4f\t\t\t| %.4f\t\t|\n", prediction, groundTruth); |
| } |
| |
| System.out.println(">>> ---------------------------------"); |
| System.out.println(">>> KMeans clustering algorithm over cached dataset usage example completed."); |
| } |
| } |
| finally { |
| if (dataCache != null) |
| dataCache.destroy(); |
| } |
| } |
| finally { |
| System.out.flush(); |
| } |
| } |
| } |