spark DecisionTreeRegressionExample 源码

spark DecisionTreeRegressionExample 代码

文件路径：/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeRegressionExample.scala

/*
 * 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.DecisionTreeRegressionModel
import org.apache.spark.ml.regression.DecisionTreeRegressor
// $example off$
import org.apache.spark.sql.SparkSession

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

    // $example on$
    // Load the data stored in LIBSVM format as a DataFrame.
    val data = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")

    // Automatically identify categorical features, and index them.
    // Here, we treat features with > 4 distinct values 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 DecisionTree model.
    val dt = new DecisionTreeRegressor()
      .setLabelCol("label")
      .setFeaturesCol("indexedFeatures")

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

    // 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 treeModel = model.stages(1).asInstanceOf[DecisionTreeRegressionModel]
    println(s"Learned regression tree model:\n ${treeModel.toDebugString}")
    // $example off$

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

相关信息

spark 源码目录

相关文章

spark AFTSurvivalRegressionExample 源码

spark ALSExample 源码

spark BinarizerExample 源码

spark BisectingKMeansExample 源码

spark BucketedRandomProjectionLSHExample 源码

spark BucketizerExample 源码

spark ChiSqSelectorExample 源码

spark ChiSquareTestExample 源码

spark CorrelationExample 源码

spark CountVectorizerExample 源码