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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.
*/
package org.apache.spark.examples.mllib
import scopt.OptionParser
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.evaluation.MulticlassMetrics
import org.apache.spark.mllib.tree.GradientBoostedTrees
import org.apache.spark.mllib.tree.configuration.{BoostingStrategy, Algo}
import org.apache.spark.util.Utils
/**
* An example runner for Gradient Boosting using decision trees as weak learners. Run with
* {{{
* ./bin/run-example mllib.GradientBoostedTreesRunner [options]
* }}}
* If you use it as a template to create your own app, please use `spark-submit` to submit your app.
*
* Note: This script treats all features as real-valued (not categorical).
* To include categorical features, modify categoricalFeaturesInfo.
*/
object GradientBoostedTreesRunner {
case class Params(
input: String = null,
testInput: String = "",
dataFormat: String = "libsvm",
algo: String = "Classification",
maxDepth: Int = 5,
numIterations: Int = 10,
fracTest: Double = 0.2) extends AbstractParams[Params]
def main(args: Array[String]) {
val defaultParams = Params()
val parser = new OptionParser[Params]("GradientBoostedTrees") {
head("GradientBoostedTrees: an example decision tree app.")
opt[String]("algo")
.text(s"algorithm (${Algo.values.mkString(",")}), default: ${defaultParams.algo}")
.action((x, c) => c.copy(algo = x))
opt[Int]("maxDepth")
.text(s"max depth of the tree, default: ${defaultParams.maxDepth}")
.action((x, c) => c.copy(maxDepth = x))
opt[Int]("numIterations")
.text(s"number of iterations of boosting," + s" default: ${defaultParams.numIterations}")
.action((x, c) => c.copy(numIterations = x))
opt[Double]("fracTest")
.text(s"fraction of data to hold out for testing. If given option testInput, " +
s"this option is ignored. default: ${defaultParams.fracTest}")
.action((x, c) => c.copy(fracTest = x))
opt[String]("testInput")
.text(s"input path to test dataset. If given, option fracTest is ignored." +
s" default: ${defaultParams.testInput}")
.action((x, c) => c.copy(testInput = x))
opt[String]("<dataFormat>")
.text("data format: libsvm (default), dense (deprecated in Spark v1.1)")
.action((x, c) => c.copy(dataFormat = x))
arg[String]("<input>")
.text("input path to labeled examples")
.required()
.action((x, c) => c.copy(input = x))
checkConfig { params =>
if (params.fracTest < 0 || params.fracTest > 1) {
failure(s"fracTest ${params.fracTest} value incorrect; should be in [0,1].")
} else {
success
}
}
}
parser.parse(args, defaultParams).map { params =>
run(params)
}.getOrElse {
sys.exit(1)
}
}
def run(params: Params) {
val conf = new SparkConf().setAppName(s"GradientBoostedTreesRunner with $params")
val sc = new SparkContext(conf)
println(s"GradientBoostedTreesRunner with parameters:\n$params")
// Load training and test data and cache it.
val (training, test, numClasses) = DecisionTreeRunner.loadDatasets(sc, params.input,
params.dataFormat, params.testInput, Algo.withName(params.algo), params.fracTest)
val boostingStrategy = BoostingStrategy.defaultParams(params.algo)
boostingStrategy.treeStrategy.numClasses = numClasses
boostingStrategy.numIterations = params.numIterations
boostingStrategy.treeStrategy.maxDepth = params.maxDepth
val randomSeed = Utils.random.nextInt()
if (params.algo == "Classification") {
val startTime = System.nanoTime()
val model = GradientBoostedTrees.train(training, boostingStrategy)
val elapsedTime = (System.nanoTime() - startTime) / 1e9
println(s"Training time: $elapsedTime seconds")
if (model.totalNumNodes < 30) {
println(model.toDebugString) // Print full model.
} else {
println(model) // Print model summary.
}
val trainAccuracy =
new MulticlassMetrics(training.map(lp => (model.predict(lp.features), lp.label)))
.precision
println(s"Train accuracy = $trainAccuracy")
val testAccuracy =
new MulticlassMetrics(test.map(lp => (model.predict(lp.features), lp.label))).precision
println(s"Test accuracy = $testAccuracy")
} else if (params.algo == "Regression") {
val startTime = System.nanoTime()
val model = GradientBoostedTrees.train(training, boostingStrategy)
val elapsedTime = (System.nanoTime() - startTime) / 1e9
println(s"Training time: $elapsedTime seconds")
if (model.totalNumNodes < 30) {
println(model.toDebugString) // Print full model.
} else {
println(model) // Print model summary.
}
val trainMSE = DecisionTreeRunner.meanSquaredError(model, training)
println(s"Train mean squared error = $trainMSE")
val testMSE = DecisionTreeRunner.meanSquaredError(model, test)
println(s"Test mean squared error = $testMSE")
}
sc.stop()
}
}
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