| /* |
| * 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.binary; |
| |
| 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.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.LogisticRegressionModel; |
| 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; |
| |
| /** |
| * Run logistic regression model based on <a href="https://en.wikipedia.org/wiki/Stochastic_gradient_descent"> |
| * stochastic gradient descent</a> algorithm ({@link LogisticRegressionSGDTrainer}) over distributed cache. |
| * <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 the logistic regression 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 LogisticRegressionSGDTrainerExample { |
| /** |
| * 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(100000) |
| .withLocIterations(100) |
| .withBatchSize(10) |
| .withSeed(123L); |
| |
| System.out.println(">>> Perform the training to get the model."); |
| Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>() |
| .labeled(Vectorizer.LabelCoordinate.FIRST); |
| |
| LogisticRegressionModel mdl = trainer.fit(ignite, dataCache, vectorizer); |
| |
| System.out.println(">>> Logistic regression model: " + mdl); |
| |
| double accuracy = Evaluator.evaluate(dataCache, |
| mdl, vectorizer, MetricName.ACCURACY |
| ); |
| |
| System.out.println("\n>>> Accuracy " + accuracy); |
| |
| System.out.println(">>> Logistic regression model over partitioned dataset usage example completed."); |
| } |
| finally { |
| if (dataCache != null) |
| dataCache.destroy(); |
| } |
| } |
| finally { |
| System.out.flush(); |
| } |
| } |
| } |