| /* |
| * 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.multiclass; |
| |
| import java.io.IOException; |
| import java.util.Arrays; |
| 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.examples.ml.util.MLSandboxDatasets; |
| import org.apache.ignite.examples.ml.util.SandboxMLCache; |
| import org.apache.ignite.ml.dataset.feature.extractor.impl.DummyVectorizer; |
| import org.apache.ignite.ml.math.primitives.vector.Vector; |
| import org.apache.ignite.ml.multiclass.MultiClassModel; |
| import org.apache.ignite.ml.multiclass.OneVsRestTrainer; |
| import org.apache.ignite.ml.preprocessing.Preprocessor; |
| import org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerTrainer; |
| import org.apache.ignite.ml.svm.SVMLinearClassificationModel; |
| import org.apache.ignite.ml.svm.SVMLinearClassificationTrainer; |
| |
| /** |
| * Run One-vs-Rest multi-class classification trainer ({@link OneVsRestTrainer}) parametrized by binary SVM classifier |
| * ({@link SVMLinearClassificationTrainer}) over distributed dataset to build two models: one with min-max scaling and |
| * one without min-max scaling. |
| * <p> |
| * Code in this example launches Ignite grid and fills the cache with test data points (preprocessed |
| * <a href="https://archive.ics.uci.edu/ml/datasets/Glass+Identification">Glass dataset</a>).</p> |
| * <p> |
| * After that it trains two One-vs-Rest multi-class models based on the specified data - one model is with min-max |
| * scaling and one without min-max scaling.</p> |
| * <p> |
| * Finally, this example loops over the test set of data points, applies the trained models to predict what cluster does |
| * this point belong to, compares prediction to expected outcome (ground truth), and builds |
| * <a href="https://en.wikipedia.org/wiki/Confusion_matrix">confusion matrix</a>.</p> |
| * <p> |
| * You can change the test data used in this example and re-run it to explore this algorithm further.</p> NOTE: the |
| * smallest 3rd class could not be classified via linear SVM here. |
| */ |
| public class OneVsRestClassificationExample { |
| /** |
| * Run example. |
| */ |
| public static void main(String[] args) throws IOException { |
| System.out.println(); |
| System.out.println(">>> One-vs-Rest SVM Multi-class classification model 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.GLASS_IDENTIFICATION); |
| |
| OneVsRestTrainer<SVMLinearClassificationModel> trainer |
| = new OneVsRestTrainer<>(new SVMLinearClassificationTrainer() |
| .withAmountOfIterations(20) |
| .withAmountOfLocIterations(50) |
| .withLambda(0.2) |
| .withSeed(1234L) |
| ); |
| |
| MultiClassModel<SVMLinearClassificationModel> mdl = trainer.fit( |
| ignite, |
| dataCache, |
| new DummyVectorizer<Integer>().labeled(0) |
| ); |
| |
| System.out.println(">>> One-vs-Rest SVM Multi-class model"); |
| System.out.println(mdl.toString()); |
| |
| MinMaxScalerTrainer<Integer, Vector> minMaxScalerTrainer = new MinMaxScalerTrainer<>(); |
| |
| Preprocessor<Integer, Vector> preprocessor = minMaxScalerTrainer.fit( |
| ignite, |
| dataCache, |
| new DummyVectorizer<Integer>().labeled(0) |
| ); |
| |
| MultiClassModel<SVMLinearClassificationModel> mdlWithScaling = trainer.fit( |
| ignite, |
| dataCache, |
| preprocessor |
| ); |
| |
| System.out.println(">>> One-vs-Rest SVM Multi-class model with MinMaxScaling"); |
| System.out.println(mdlWithScaling.toString()); |
| |
| System.out.println(">>> ----------------------------------------------------------------"); |
| System.out.println(">>> | Prediction\t| Prediction with MinMaxScaling\t| Ground Truth\t|"); |
| System.out.println(">>> ----------------------------------------------------------------"); |
| |
| int amountOfErrors = 0; |
| int amountOfErrorsWithMinMaxScaling = 0; |
| int totalAmount = 0; |
| |
| // Build confusion matrix. See https://en.wikipedia.org/wiki/Confusion_matrix |
| int[][] confusionMtx = {{0, 0, 0}, {0, 0, 0}, {0, 0, 0}}; |
| int[][] confusionMtxWithMinMaxScaling = {{0, 0, 0}, {0, 0, 0}, {0, 0, 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 = mdl.predict(inputs); |
| double predictionWithMinMaxScaling = mdlWithScaling.predict(inputs); |
| |
| totalAmount++; |
| |
| // Collect data for model |
| if (!Precision.equals(groundTruth, prediction, Precision.EPSILON)) |
| amountOfErrors++; |
| |
| int idx1 = (int)prediction == 1 ? 0 : ((int)prediction == 3 ? 1 : 2); |
| int idx2 = (int)groundTruth == 1 ? 0 : ((int)groundTruth == 3 ? 1 : 2); |
| |
| confusionMtx[idx1][idx2]++; |
| |
| // Collect data for model with min-max scaling |
| if (!Precision.equals(groundTruth, predictionWithMinMaxScaling, Precision.EPSILON)) |
| amountOfErrorsWithMinMaxScaling++; |
| |
| idx1 = (int)predictionWithMinMaxScaling == 1 ? 0 : ((int)predictionWithMinMaxScaling == 3 ? 1 : 2); |
| idx2 = (int)groundTruth == 1 ? 0 : ((int)groundTruth == 3 ? 1 : 2); |
| |
| confusionMtxWithMinMaxScaling[idx1][idx2]++; |
| |
| System.out.printf(">>> | %.4f\t\t| %.4f\t\t\t\t\t\t| %.4f\t\t|\n", prediction, predictionWithMinMaxScaling, groundTruth); |
| } |
| System.out.println(">>> ----------------------------------------------------------------"); |
| System.out.println("\n>>> -----------------One-vs-Rest SVM model-------------"); |
| System.out.println("\n>>> Absolute amount of errors " + amountOfErrors); |
| System.out.println("\n>>> Accuracy " + (1 - amountOfErrors / (double)totalAmount)); |
| System.out.println("\n>>> Confusion matrix is " + Arrays.deepToString(confusionMtx)); |
| |
| System.out.println("\n>>> -----------------One-vs-Rest SVM model with MinMaxScaling-------------"); |
| System.out.println("\n>>> Absolute amount of errors " + amountOfErrorsWithMinMaxScaling); |
| System.out.println("\n>>> Accuracy " + (1 - amountOfErrorsWithMinMaxScaling / (double)totalAmount)); |
| System.out.println("\n>>> Confusion matrix is " + Arrays.deepToString(confusionMtxWithMinMaxScaling)); |
| |
| System.out.println(">>> One-vs-Rest SVM model over cache based dataset usage example completed."); |
| } |
| } |
| finally { |
| if (dataCache != null) |
| dataCache.destroy(); |
| } |
| } |
| finally { |
| System.out.flush(); |
| } |
| } |
| } |