Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/5530#discussion_r28533086
--- Diff:
examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala
---
@@ -0,0 +1,322 @@
+/*
+ * 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.ml
+
+import scala.collection.mutable
+import scala.language.reflectiveCalls
+
+import scopt.OptionParser
+
+import org.apache.spark.ml.regression.{DecisionTreeRegressionModel,
DecisionTreeRegressor}
+import org.apache.spark.ml.util.MetadataUtils
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.examples.mllib.AbstractParams
+import org.apache.spark.ml.{Pipeline, PipelineStage}
+import org.apache.spark.ml.attribute.{Attribute, BinaryAttribute,
NominalAttribute}
+import
org.apache.spark.ml.classification.{DecisionTreeClassificationModel,
DecisionTreeClassifier}
+import org.apache.spark.ml.feature.{VectorIndexer, StringIndexer}
+import org.apache.spark.ml.impl.tree.DecisionTreeModel
+import org.apache.spark.mllib.evaluation.{RegressionMetrics,
MulticlassMetrics}
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.types.StringType
+import org.apache.spark.sql.{SQLContext, DataFrame}
+import org.apache.spark.sql.functions.callUDF
+
+
+/**
+ * An example runner for decision trees and random forests. Run with
+ * {{{
+ * ./bin/run-example ml.DecisionTreeExample [options]
+ * }}}
+ * If you use it as a template to create your own app, please use
`spark-submit` to submit your app.
+ */
+object DecisionTreeExample {
+
+ case class Params(
+ input: String = null,
+ testInput: String = "",
+ dataFormat: String = "libsvm",
+ algo: String = "Classification",
+ maxDepth: Int = 5,
+ maxBins: Int = 32,
+ minInstancesPerNode: Int = 1,
+ minInfoGain: Double = 0.0,
+ numTrees: Int = 1,
+ featureSubsetStrategy: String = "auto",
+ fracTest: Double = 0.2,
+ cacheNodeIds: Boolean = false,
+ checkpointDir: Option[String] = None,
+ checkpointInterval: Int = 10) extends AbstractParams[Params]
+
+ def main(args: Array[String]) {
+ val defaultParams = Params()
+
+ val parser = new OptionParser[Params]("DecisionTreeExample") {
+ head("DecisionTreeExample: an example decision tree app.")
+ opt[String]("algo")
+ .text(s"algorithm (Classification, Regression), 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]("maxBins")
+ .text(s"max number of bins, default: ${defaultParams.maxBins}")
+ .action((x, c) => c.copy(maxBins = x))
+ opt[Int]("minInstancesPerNode")
+ .text(s"min number of instances required at child nodes to create
the parent split," +
+ s" default: ${defaultParams.minInstancesPerNode}")
+ .action((x, c) => c.copy(minInstancesPerNode = x))
+ opt[Double]("minInfoGain")
+ .text(s"min info gain required to create a split, default:
${defaultParams.minInfoGain}")
+ .action((x, c) => c.copy(minInfoGain = 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[Boolean]("cacheNodeIds")
+ .text(s"whether to use node Id cache during training, " +
+ s"default: ${defaultParams.cacheNodeIds}")
+ .action((x, c) => c.copy(cacheNodeIds = x))
+ opt[String]("checkpointDir")
+ .text(s"checkpoint directory where intermediate node Id caches
will be stored, " +
+ s"default: ${defaultParams.checkpointDir match {
+ case Some(strVal) => strVal
+ case None => "None"
+ }}")
+ .action((x, c) => c.copy(checkpointDir = Some(x)))
+ opt[Int]("checkpointInterval")
+ .text(s"how often to checkpoint the node Id cache, " +
+ s"default: ${defaultParams.checkpointInterval}")
+ .action((x, c) => c.copy(checkpointInterval = 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)
+ }
+ }
+
+ /** Load a dataset from the given path, using the given format */
+ private[ml] def loadData(
+ sc: SparkContext,
+ path: String,
+ format: String,
+ expectedNumFeatures: Option[Int] = None): RDD[LabeledPoint] = {
+ format match {
+ case "dense" => MLUtils.loadLabeledPoints(sc, path)
+ case "libsvm" => expectedNumFeatures match {
+ case Some(numFeatures) => MLUtils.loadLibSVMFile(sc, path,
numFeatures)
+ case None => MLUtils.loadLibSVMFile(sc, path)
+ }
+ case _ => throw new IllegalArgumentException(s"Bad data format:
$format")
+ }
+ }
+
+ /**
+ * Load training and test data from files.
+ * @param input Path to input dataset.
+ * @param dataFormat "libsvm" or "dense"
+ * @param testInput Path to test dataset.
+ * @param algo Classification or Regression
+ * @param fracTest Fraction of input data to hold out for testing.
Ignored if testInput given.
+ * @return (training dataset, test dataset)
+ */
+ private[ml] def loadDatasets(
+ sc: SparkContext,
+ input: String,
+ dataFormat: String,
+ testInput: String,
+ algo: String,
+ fracTest: Double): (DataFrame, DataFrame) = {
+ val sqlContext = new SQLContext(sc)
+ import sqlContext.implicits._
+
+ // Load training data
+ val origExamples: RDD[LabeledPoint] = loadData(sc, input, dataFormat)
+
+ // Load or create test set
+ val splits: Array[RDD[LabeledPoint]] = if (testInput != "") {
+ // Load testInput.
+ val numFeatures = origExamples.take(1)(0).features.size
+ val origTestExamples: RDD[LabeledPoint] = loadData(sc, input,
dataFormat, Some(numFeatures))
+ Array(origExamples, origTestExamples)
+ } else {
+ // Split input into training, test.
+ origExamples.randomSplit(Array(1.0 - fracTest, fracTest))
+ }
+
+ // For classification, convert labels to Strings since we will index
them later with
+ // StringIndexer.
+ def labelsToStrings(data: DataFrame): DataFrame = {
+ algo.toLowerCase match {
+ case "classification" =>
+ val convertToString: Double => String = (label: Double) =>
label.toString
+ data.select(
+ callUDF(convertToString, StringType,
data("label")).as("labelString"), data("features"))
+ case "regression" =>
+ data
+ case _ =>
+ throw new IllegalArgumentException("Algo ${params.algo} not
supported.")
+ }
+ }
+ val dataframes =
splits.map(_.toDF()).map(labelsToStrings).map(_.cache())
+
+ (dataframes(0), dataframes(1))
+ }
+
+ def run(params: Params) {
+ val conf = new SparkConf().setAppName(s"DecisionTreeExample with
$params")
+ val sc = new SparkContext(conf)
+ params.checkpointDir.foreach(sc.setCheckpointDir)
+ val algo = params.algo.toLowerCase
+
+ println(s"DecisionTreeExample with parameters:\n$params")
+
+ // Load training and test data and cache it.
+ val (training: DataFrame, test: DataFrame) =
+ loadDatasets(sc, params.input, params.dataFormat, params.testInput,
algo, params.fracTest)
+
+ val numTraining = training.count()
+ val numTest = test.count()
+ val numFeatures =
training.select("features").first().getAs[Vector](0).size
+ println("Loaded data:")
+ println(s" numTraining = $numTraining, numTest = $numTest")
+ println(s" numFeatures = $numFeatures")
+
+ // Set up Pipeline
+ val stages = new mutable.ArrayBuffer[PipelineStage]()
+ // (1) For classification, re-index classes.
+ if (algo == "classification") {
+ val labelIndexer = new
StringIndexer().setInputCol("labelString").setOutputCol("label")
+ stages += labelIndexer
+ }
+ // (2) Identify categorical features using VectorIndexer.
+ // Features with more than maxCategories values will be treated as
continuous.
+ val featuresIndexer = new VectorIndexer().setInputCol("features")
+ .setOutputCol("indexedFeatures").setMaxCategories(10)
+ stages += featuresIndexer
+ // (3) Learn DecisionTree
+ val dt = algo match {
+ case "classification" =>
+ new DecisionTreeClassifier().setFeaturesCol("indexedFeatures")
+ .setMaxDepth(params.maxDepth)
+ .setMaxBins(params.maxBins)
+ .setMinInstancesPerNode(params.minInstancesPerNode)
+ .setMinInfoGain(params.minInfoGain)
+ .setCacheNodeIds(params.cacheNodeIds)
+ .setCheckpointInterval(params.checkpointInterval)
+ case "regression" =>
+ new DecisionTreeRegressor().setFeaturesCol("indexedFeatures")
+ .setMaxDepth(params.maxDepth)
+ .setMaxBins(params.maxBins)
+ .setMinInstancesPerNode(params.minInstancesPerNode)
+ .setMinInfoGain(params.minInfoGain)
+ .setCacheNodeIds(params.cacheNodeIds)
+ .setCheckpointInterval(params.checkpointInterval)
+ case _ => throw new IllegalArgumentException("Algo ${params.algo}
not supported.")
+ }
+ stages += dt
+ val pipeline = new Pipeline().setStages(stages.toArray)
+
+ // Fit the Pipeline
+ val startTime = System.nanoTime()
+ val pipelineModel = pipeline.fit(training)
+ val elapsedTime = (System.nanoTime() - startTime) / 1e9
+ println(s"Training time: $elapsedTime seconds")
+
+ // Get the trained Decision Tree from the fitted PipelineModel
+ val treeModel: DecisionTreeModel = algo match {
+ case "classification" =>
+ pipelineModel.getModel[DecisionTreeClassificationModel](
--- End diff --
Yep. I think it's because "dt" can't be resolved to a concrete type since
I use it for both Classifier and Regressor.
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