| /* |
| * 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.regression.logistic.bagged; |
| |
| 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.composition.bagging.BaggedTrainer; |
| import org.apache.ignite.ml.composition.predictionsaggregator.OnMajorityPredictionsAggregator; |
| 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.math.primitives.vector.Vector; |
| import org.apache.ignite.ml.nn.UpdatesStrategy; |
| import org.apache.ignite.ml.optimization.updatecalculators.SimpleGDParameterUpdate; |
| import org.apache.ignite.ml.optimization.updatecalculators.SimpleGDUpdateCalculator; |
| import org.apache.ignite.ml.regressions.logistic.LogisticRegressionSGDTrainer; |
| import org.apache.ignite.ml.selection.scoring.evaluator.Evaluator; |
| import org.apache.ignite.ml.selection.scoring.metric.MetricName; |
| import org.apache.ignite.ml.trainers.TrainerTransformers; |
| |
| /** |
| * This example shows how bagging technique may be applied to arbitrary trainer. As an example (a bit synthetic) |
| * logistic regression is considered. |
| * <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 bootstrapped (or bagged) version of logistic regression trainer. Bootstrapping is done on both |
| * samples and features (<a href="https://en.wikipedia.org/wiki/Bootstrap_aggregating"></a>Samples bagging</a>, |
| * <a href="https://en.wikipedia.org/wiki/Random_subspace_method"></a>Features bagging</a>).</p> |
| * <p> |
| * Finally, this example applies cross-validation to resulted model and prints accuracy if each fold.</p> |
| */ |
| public class BaggedLogisticRegressionSGDTrainerExample { |
| /** |
| * Run example. |
| */ |
| public static void main(String[] args) throws IOException { |
| System.out.println(); |
| System.out.println(">>> Logistic regression 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 = null; |
| try { |
| dataCache = new SandboxMLCache(ignite).fillCacheWith(MLSandboxDatasets.TWO_CLASSED_IRIS); |
| |
| System.out.println(">>> Create new logistic regression trainer object."); |
| LogisticRegressionSGDTrainer trainer = new LogisticRegressionSGDTrainer() |
| .withUpdatesStgy(new UpdatesStrategy<>( |
| new SimpleGDUpdateCalculator(0.2), |
| SimpleGDParameterUpdate.SUM_LOCAL, |
| SimpleGDParameterUpdate.AVG |
| )) |
| .withMaxIterations(100) |
| .withLocIterations(10) |
| .withBatchSize(10) |
| .withSeed(123L); |
| |
| System.out.println(">>> Perform the training to get the model."); |
| |
| BaggedTrainer<Double> baggedTrainer = TrainerTransformers.makeBagged( |
| trainer, |
| 10, |
| 0.6, |
| 4, |
| 3, |
| new OnMajorityPredictionsAggregator()) |
| .withEnvironmentBuilder(LearningEnvironmentBuilder.defaultBuilder().withRNGSeed(1)); |
| |
| System.out.println(">>> Perform evaluation of the model."); |
| |
| Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>() |
| .labeled(Vectorizer.LabelCoordinate.FIRST); |
| |
| double accuracy = Evaluator.evaluate(dataCache, |
| baggedTrainer.fit(ignite, dataCache, vectorizer), |
| vectorizer, |
| MetricName.ACCURACY |
| ); |
| |
| System.out.println(">>> ---------------------------------"); |
| |
| System.out.println("\n>>> Accuracy " + accuracy); |
| |
| System.out.println(">>> Bagged logistic regression model over partitioned dataset usage example completed."); |
| } |
| finally { |
| if (dataCache != null) |
| dataCache.destroy(); |
| } |
| } |
| finally { |
| System.out.flush(); |
| } |
| } |
| } |