| /* |
| * 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.selection.scoring; |
| |
| 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.knn.regression.KNNRegressionModel; |
| import org.apache.ignite.ml.knn.regression.KNNRegressionTrainer; |
| import org.apache.ignite.ml.knn.utils.indices.SpatialIndexType; |
| import org.apache.ignite.ml.math.distances.ManhattanDistance; |
| import org.apache.ignite.ml.math.primitives.vector.Vector; |
| import org.apache.ignite.ml.selection.scoring.evaluator.Evaluator; |
| import org.apache.ignite.ml.selection.scoring.metric.MetricName; |
| |
| /** |
| * Run kNN regression trainer ({@link KNNRegressionTrainer}) over distributed dataset. |
| * <p> |
| * After that it trains the model based on the specified data using |
| * <a href="https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm">kNN</a> regression 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 or trainer object settings and re-run it to explore this algorithm |
| * further.</p> |
| */ |
| public class RegressionMetricExample { |
| /** |
| * Run example. |
| */ |
| public static void main(String[] args) throws IOException { |
| System.out.println(); |
| System.out.println(">>> kNN regression 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.CLEARED_MACHINES); |
| |
| KNNRegressionTrainer trainer = new KNNRegressionTrainer() |
| .withK(5) |
| .withDistanceMeasure(new ManhattanDistance()) |
| .withIdxType(SpatialIndexType.BALL_TREE) |
| .withWeighted(true); |
| |
| Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>() |
| .labeled(Vectorizer.LabelCoordinate.FIRST); |
| |
| KNNRegressionModel knnMdl = trainer.fit(ignite, dataCache, vectorizer); |
| |
| double mae = Evaluator.evaluate(dataCache, |
| knnMdl, vectorizer, MetricName.MAE |
| ); |
| |
| System.out.println("\n>>> Mae " + mae); |
| } |
| finally { |
| if (dataCache != null) |
| dataCache.destroy(); |
| } |
| } |
| finally { |
| System.out.flush(); |
| } |
| } |
| } |