| /* |
| * 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.naivebayes; |
| |
| import java.io.IOException; |
| import org.apache.ignite.Ignite; |
| import org.apache.ignite.IgniteCache; |
| import org.apache.ignite.Ignition; |
| 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.naivebayes.compound.CompoundNaiveBayesModel; |
| import org.apache.ignite.ml.naivebayes.compound.CompoundNaiveBayesTrainer; |
| import org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesTrainer; |
| import org.apache.ignite.ml.naivebayes.gaussian.GaussianNaiveBayesTrainer; |
| import org.apache.ignite.ml.selection.scoring.evaluator.Evaluator; |
| import org.apache.ignite.ml.selection.scoring.metric.MetricName; |
| import org.apache.ignite.ml.util.MLSandboxDatasets; |
| import org.apache.ignite.ml.util.SandboxMLCache; |
| |
| import static java.util.Arrays.asList; |
| |
| /** |
| * Run naive Compound Bayes classification model based on <a href="https://en.wikipedia.org/wiki/Naive_Bayes_classifier"> |
| * Nnaive Bayes classifier</a> algorithm ({@link GaussianNaiveBayesTrainer})and <a |
| * href=https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes"> Discrete naive Bayes |
| * classifier</a> algorithm ({@link DiscreteNaiveBayesTrainer}) over distributed cache. |
| * <p> |
| * Code in this example launches Ignite grid and fills the cache with test data points. |
| * <p> |
| * After that it trains the naive Bayes classification model based on the specified data.</p> |
| * <p> |
| * Finally, this example loops over the test set of data points, applies the trained model to predict the target value, |
| * 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> |
| */ |
| public class CompoundNaiveBayesExample { |
| /** Run example. */ |
| public static void main(String[] args) throws IOException { |
| System.out.println(); |
| System.out.println(">>> Compound Naive Bayes classification model over partitioned 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 = new SandboxMLCache(ignite) |
| .fillCacheWith(MLSandboxDatasets.MIXED_DATASET); |
| |
| double[] priorProbabilities = new double[] {.5, .5}; |
| double[][] thresholds = new double[][] {{.5}, {.5}, {.5}, {.5}, {.5}}; |
| |
| System.out.println(">>> Create new naive Bayes classification trainer object."); |
| CompoundNaiveBayesTrainer trainer = new CompoundNaiveBayesTrainer() |
| .withPriorProbabilities(priorProbabilities) |
| .withGaussianNaiveBayesTrainer(new GaussianNaiveBayesTrainer()) |
| .withGaussianFeatureIdsToSkip(asList(3, 4, 5, 6, 7)) |
| .withDiscreteNaiveBayesTrainer(new DiscreteNaiveBayesTrainer() |
| .setBucketThresholds(thresholds)) |
| .withDiscreteFeatureIdsToSkip(asList(0, 1, 2)); |
| System.out.println(">>> Perform the training to get the model."); |
| |
| Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>() |
| .labeled(Vectorizer.LabelCoordinate.FIRST); |
| |
| CompoundNaiveBayesModel mdl = trainer.fit(ignite, dataCache, vectorizer); |
| |
| System.out.println(">>> Compound Naive Bayes model: " + mdl); |
| |
| double accuracy = Evaluator.evaluate( |
| dataCache, |
| mdl, |
| vectorizer, |
| MetricName.ACCURACY |
| ); |
| |
| System.out.println("\n>>> Accuracy " + accuracy); |
| |
| System.out.println(">>> Compound Naive bayes model over partitioned dataset usage example completed."); |
| } |
| } |
| } |