/* * 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.spark.examples.ml; // $example on$ import org.apache.spark.ml.classification.LogisticRegression; import org.apache.spark.ml.classification.OneVsRest; import org.apache.spark.ml.classification.OneVsRestModel; import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; // $example off$ import org.apache.spark.sql.SparkSession; /** * An example of Multiclass to Binary Reduction with One Vs Rest, * using Logistic Regression as the base classifier. * Run with * <pre> * bin/run-example ml.JavaOneVsRestExample * </pre> */ public class JavaOneVsRestExample { public static void main(String[] args) { SparkSession spark = SparkSession .builder() .appName("JavaOneVsRestExample") .getOrCreate(); // $example on$ // load data file. Dataset<Row> inputData = spark.read().format("libsvm") .load("data/mllib/sample_multiclass_classification_data.txt"); // generate the train/test split. Dataset<Row>[] tmp = inputData.randomSplit(new double[]{0.8, 0.2}); Dataset<Row> train = tmp[0]; Dataset<Row> test = tmp[1]; // configure the base classifier. LogisticRegression classifier = new LogisticRegression() .setMaxIter(10) .setTol(1E-6) .setFitIntercept(true); // instantiate the One Vs Rest Classifier. OneVsRest ovr = new OneVsRest().setClassifier(classifier); // train the multiclass model. OneVsRestModel ovrModel = ovr.fit(train); // score the model on test data. Dataset<Row> predictions = ovrModel.transform(test) .select("prediction", "label"); // obtain evaluator. MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() .setMetricName("accuracy"); // compute the classification error on test data. double accuracy = evaluator.evaluate(predictions); System.out.println("Test Error = " + (1 - accuracy)); // $example off$ spark.stop(); } }