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

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/*
 * 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.Pipeline
import org.apache.spark.ml.evaluation.RegressionEvaluator
import org.apache.spark.ml.feature.VectorIndexer
import org.apache.spark.ml.regression.{GBTRegressionModel, GBTRegressor}
// $example off$
import org.apache.spark.sql.SparkSession

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

    // $example on$
    // Load and parse the data file, converting it to a DataFrame.
    val data = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")

    // Automatically identify categorical features, and index them.
    // Set maxCategories so features with > 4 distinct values are treated as continuous.
    val featureIndexer = new VectorIndexer()
      .setInputCol("features")
      .setOutputCol("indexedFeatures")
      .setMaxCategories(4)
      .fit(data)

    // Split the data into training and test sets (30% held out for testing).
    val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))

    // Train a GBT model.
    val gbt = new GBTRegressor()
      .setLabelCol("label")
      .setFeaturesCol("indexedFeatures")
      .setMaxIter(10)

    // Chain indexer and GBT in a Pipeline.
    val pipeline = new Pipeline()
      .setStages(Array(featureIndexer, gbt))

    // Train model. This also runs the indexer.
    val model = pipeline.fit(trainingData)

    // Make predictions.
    val predictions = model.transform(testData)

    // Select example rows to display.
    predictions.select("prediction", "label", "features").show(5)

    // Select (prediction, true label) and compute test error.
    val evaluator = new RegressionEvaluator()
      .setLabelCol("label")
      .setPredictionCol("prediction")
      .setMetricName("rmse")
    val rmse = evaluator.evaluate(predictions)
    println(s"Root Mean Squared Error (RMSE) on test data = $rmse")

    val gbtModel = model.stages(1).asInstanceOf[GBTRegressionModel]
    println(s"Learned regression GBT model:\n ${gbtModel.toDebugString}")
    // $example off$

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