| /* |
| * 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.knn; |
| |
| 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.environment.LearningEnvironmentBuilder; |
| import org.apache.ignite.ml.knn.classification.KNNClassificationModel; |
| import org.apache.ignite.ml.knn.classification.KNNClassificationTrainer; |
| import org.apache.ignite.ml.math.distances.EuclideanDistance; |
| 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.classification.Accuracy; |
| import org.apache.ignite.ml.selection.split.TrainTestDatasetSplitter; |
| import org.apache.ignite.ml.selection.split.TrainTestSplit; |
| |
| /** |
| * Example of using Knn model in Apache Ignite for iris class predicion. |
| * <p> |
| * Description of model can be found in: https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm . Original dataset |
| * can be downloaded from: https://archive.ics.uci.edu/ml/datasets/Wholesale+customers . Copy of dataset are stored in: |
| * https://archive.ics.uci.edu/ml/datasets/iris . Score for classifier estimation: accuracy . Description of score can |
| * be found in: https://stattrek.com/statistics/dictionary.aspx?definition=accuracy . |
| */ |
| public class IrisClassificationExample { |
| /** |
| * 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.IRIS); |
| KNNClassificationTrainer trainer = ((KNNClassificationTrainer)new KNNClassificationTrainer() |
| .withEnvironmentBuilder(LearningEnvironmentBuilder.defaultBuilder().withRNGSeed(0))) |
| .withK(3) |
| .withDistanceMeasure(new EuclideanDistance()) |
| .withWeighted(true); |
| |
| // 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 60/40 proportion. |
| TrainTestSplit<Integer, Vector> split = new TrainTestDatasetSplitter<Integer, Vector>().split(0.6); |
| |
| System.out.println(">>> Start traininig."); |
| KNNClassificationModel mdl = trainer.fit( |
| ignite, dataCache, |
| split.getTrainFilter(), |
| vectorizer |
| ); |
| |
| System.out.println(">>> Perform scoring."); |
| double accuracy = Evaluator.evaluate( |
| dataCache, |
| split.getTestFilter(), |
| mdl, |
| vectorizer, |
| new Accuracy<>() |
| ); |
| |
| System.out.println(">> Model accuracy: " + accuracy); |
| } |
| finally { |
| if (dataCache != null) |
| dataCache.destroy(); |
| } |
| } |
| finally { |
| System.out.flush(); |
| } |
| } |
| } |