examples/ml/JavaLogisticRegressionWithElasticNetExample.java

0001 /*
0002  * Licensed to the Apache Software Foundation (ASF) under one or more
0003  * contributor license agreements.  See the NOTICE file distributed with
0004  * this work for additional information regarding copyright ownership.
0005  * The ASF licenses this file to You under the Apache License, Version 2.0
0006  * (the "License"); you may not use this file except in compliance with
0007  * the License.  You may obtain a copy of the License at
0008  *
0009  *    http://www.apache.org/licenses/LICENSE-2.0
0010  *
0011  * Unless required by applicable law or agreed to in writing, software
0012  * distributed under the License is distributed on an "AS IS" BASIS,
0013  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
0014  * See the License for the specific language governing permissions and
0015  * limitations under the License.
0016  */
0017
0018 package org.apache.spark.examples.ml;
0019
0020 // $example on$
0021 import org.apache.spark.ml.classification.LogisticRegression;
0022 import org.apache.spark.ml.classification.LogisticRegressionModel;
0023 import org.apache.spark.sql.Dataset;
0024 import org.apache.spark.sql.Row;
0025 import org.apache.spark.sql.SparkSession;
0026 // $example off$
0027
0028 public class JavaLogisticRegressionWithElasticNetExample {
0029   public static void main(String[] args) {
0030     SparkSession spark = SparkSession
0031       .builder()
0032       .appName("JavaLogisticRegressionWithElasticNetExample")
0033       .getOrCreate();
0034
0035     // $example on$
0036     // Load training data
0037     Dataset<Row> training = spark.read().format("libsvm")
0038       .load("data/mllib/sample_libsvm_data.txt");
0039
0040     LogisticRegression lr = new LogisticRegression()
0041       .setMaxIter(10)
0042       .setRegParam(0.3)
0043       .setElasticNetParam(0.8);
0044
0045     // Fit the model
0046     LogisticRegressionModel lrModel = lr.fit(training);
0047
0048     // Print the coefficients and intercept for logistic regression
0049     System.out.println("Coefficients: "
0050       + lrModel.coefficients() + " Intercept: " + lrModel.intercept());
0051
0052     // We can also use the multinomial family for binary classification
0053     LogisticRegression mlr = new LogisticRegression()
0054             .setMaxIter(10)
0055             .setRegParam(0.3)
0056             .setElasticNetParam(0.8)
0057             .setFamily("multinomial");
0058
0059     // Fit the model
0060     LogisticRegressionModel mlrModel = mlr.fit(training);
0061
0062     // Print the coefficients and intercepts for logistic regression with multinomial family
0063     System.out.println("Multinomial coefficients: " + lrModel.coefficientMatrix()
0064       + "\nMultinomial intercepts: " + mlrModel.interceptVector());
0065     // $example off$
0066
0067     spark.stop();
0068   }
0069 }