org.apache.spark.examples.ml.LogisticRegressionWithElasticNetExample.scala Maven / Gradle / Ivy

Go to download
Show more of this group Show more artifacts with this name
Show all versions of spark-examples_2.12 Show documentation
The newest version!
/*
 * 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.
 */

// scalastyle:off println
package org.apache.spark.examples.ml

// $example on$
import org.apache.spark.ml.classification.LogisticRegression
// $example off$
import org.apache.spark.sql.SparkSession

object LogisticRegressionWithElasticNetExample {

  def main(args: Array[String]): Unit = {
    val spark = SparkSession
      .builder
      .appName("LogisticRegressionWithElasticNetExample")
      .getOrCreate()

    // $example on$
    // Load training data
    val training = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")

    val lr = new LogisticRegression()
      .setMaxIter(10)
      .setRegParam(0.3)
      .setElasticNetParam(0.8)

    // Fit the model
    val lrModel = lr.fit(training)

    // Print the coefficients and intercept for logistic regression
    println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}")

    // We can also use the multinomial family for binary classification
    val mlr = new LogisticRegression()
      .setMaxIter(10)
      .setRegParam(0.3)
      .setElasticNetParam(0.8)
      .setFamily("multinomial")

    val mlrModel = mlr.fit(training)

    // Print the coefficients and intercepts for logistic regression with multinomial family
    println(s"Multinomial coefficients: ${mlrModel.coefficientMatrix}")
    println(s"Multinomial intercepts: ${mlrModel.interceptVector}")
    // $example off$

    spark.stop()
  }
}
// scalastyle:on println