| /* |
| * 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.inference.spark; |
| |
| import java.io.File; |
| import java.io.FileInputStream; |
| import java.io.IOException; |
| import java.io.InputStream; |
| import javax.xml.bind.JAXBException; |
| 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.internal.util.IgniteUtils; |
| 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.math.primitives.vector.impl.DenseVector; |
| import org.apache.ignite.ml.regressions.logistic.LogisticRegressionModel; |
| import org.apache.ignite.ml.selection.scoring.evaluator.Evaluator; |
| import org.apache.ignite.ml.selection.scoring.metric.classification.Accuracy; |
| import org.dmg.pmml.PMML; |
| import org.dmg.pmml.regression.RegressionModel; |
| import org.dmg.pmml.regression.RegressionTable; |
| import org.jpmml.model.PMMLUtil; |
| import org.xml.sax.SAXException; |
| |
| /** |
| * Run logistic regression model loaded from PMML file. The PMML file was generated by Spark MLLib toPMML operator. |
| * <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> |
| * You can change the test data used in this example and re-run it to explore this algorithm further.</p> |
| */ |
| public class LogRegFromSparkThroughPMMLExample { |
| /** Run example. */ |
| public static void main(String[] args) throws IOException { |
| System.out.println(); |
| System.out.println(">>> Logistic regression model loaded from PMML 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 { |
| |
| Vectorizer<Integer, Vector, Integer, Double> vectorizer = new DummyVectorizer<Integer>() |
| .labeled(Vectorizer.LabelCoordinate.FIRST); |
| |
| dataCache = new SandboxMLCache(ignite).fillCacheWith(MLSandboxDatasets.TWO_CLASSED_IRIS); |
| |
| String path = IgniteUtils.resolveIgnitePath("examples/src/main/resources/models/spark/iris.pmml") |
| .toPath().toAbsolutePath().toString(); |
| LogisticRegressionModel mdl = PMMLParser.load(path); |
| |
| System.out.println(">>> Logistic regression model: " + mdl); |
| |
| double accuracy = Evaluator.evaluate( |
| dataCache, |
| mdl, |
| vectorizer, |
| new Accuracy<>() |
| ); |
| |
| System.out.println("\n>>> Accuracy " + accuracy); |
| System.out.println("\n>>> Test Error " + (1 - accuracy)); |
| } finally { |
| if (dataCache != null) |
| dataCache.destroy(); |
| } |
| } |
| } |
| |
| /** Util class to build the LogReg model. */ |
| private static class PMMLParser { |
| /** |
| * @param path Path. |
| */ |
| public static LogisticRegressionModel load(String path) { |
| try (InputStream is = new FileInputStream(new File(path))) { |
| PMML pmml = PMMLUtil.unmarshal(is); |
| |
| RegressionModel logRegMdl = (RegressionModel)pmml.getModels().get(0); |
| |
| RegressionTable regTbl = logRegMdl.getRegressionTables().get(0); |
| |
| Vector coefficients = new DenseVector(regTbl.getNumericPredictors().size()); |
| |
| for (int i = 0; i < regTbl.getNumericPredictors().size(); i++) |
| coefficients.set(i, regTbl.getNumericPredictors().get(i).getCoefficient()); |
| |
| double interceptor = regTbl.getIntercept(); |
| |
| return new LogisticRegressionModel(coefficients, interceptor); |
| } |
| catch (IOException | JAXBException | SAXException e) { |
| e.printStackTrace(); |
| } |
| |
| return null; |
| } |
| } |
| } |