SPARK官方实例：两种方法实现随机森林模型（ML/MLlib）

在spark2.0以上版本中，存在两种对机器学习算法的实现库MLlib与ML，比如随机森林：

org.apache.spark.mllib.tree.RandomForest

和

org.apache.spark.ml.classification.RandomForestClassificationModel

两种库对应的使用方法也不同，Mllib是RDD-based API，

ML是基于ML pipeline的API与dataframe的数据结构。

详见http://spark.apache.org/docs/latest/ml-guide.html

所以官方实例也是有很大区别的，下面分别给出了源码和注释：

MLlib的模型实现

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

import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.tree.RandomForest
import org.apache.spark.mllib.tree.model.RandomForestModel
import org.apache.spark.mllib.util.MLUtils
// $example off$

object RandomForestClassificationExample {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("RandomForestClassificationExample")
val sc = new SparkContext(conf)
// $example on$
// Load and parse the data file.
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
// Split the data into training and test sets (30% held out for testing)
val splits = data.randomSplit(Array(0.7, 0.3))
val (trainingData, testData) = (splits(0), splits(1))

// Train a RandomForest model.
// Empty categoricalFeaturesInfo indicates all features are continuous.
val numClasses = 2
val categoricalFeaturesInfo = Map[Int, Int]()
val numTrees = 3 // Use more in practice.
val featureSubsetStrategy = "auto" // Let the algorithm choose.
val impurity = "gini"
val maxDepth = 4
val maxBins = 32

val model = RandomForest.trainClassifier(trainingData, numClasses, categoricalFeaturesInfo,
numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins)

// Evaluate model on test instances and compute test error
val labelAndPreds = testData.map { point =>
val prediction = model.predict(point.features)
(point.label, prediction)
}
val testErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / testData.count()
println("Test Error = " + testErr)
println("Learned classification forest model:\n" + model.toDebugString)

// Save and load model
model.save(sc, "target/tmp/myRandomForestClassificationModel")
val sameModel = RandomForestModel.load(sc, "target/tmp/myRandomForestClassificationModel")
// $example off$
}
}
// scalastyle:on println

ML的模型实现

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

// $example on$
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.{RandomForestClassificationModel, RandomForestClassifier}
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import org.apache.spark.ml.feature.{IndexToString, StringIndexer, VectorIndexer}
// $example off$
import org.apache.spark.sql.SparkSession

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

// Index labels, adding metadata to the label column.
// Fit on whole dataset to include all labels in index.
val labelIndexer = new StringIndexer()
.setInputCol("label")
.setOutputCol("indexedLabel")
.fit(data)
// 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 RandomForest model.
val rf = new RandomForestClassifier()
.setLabelCol("indexedLabel")
.setFeaturesCol("indexedFeatures")
.setNumTrees(10)

// Convert indexed labels back to original labels.
val labelConverter = new IndexToString()
.setInputCol("prediction")
.setOutputCol("predictedLabel")
.setLabels(labelIndexer.labels)

// Chain indexers and forest in a Pipeline.
val pipeline = new Pipeline()
.setStages(Array(labelIndexer, featureIndexer, rf, labelConverter))

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

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

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

// Select (prediction, true label) and compute test error.
val evaluator = new MulticlassClassificationEvaluator()
.setLabelCol("indexedLabel")
.setPredictionCol("prediction")
.setMetricName("accuracy")
val accuracy = evaluator.evaluate(predictions)
println("Test Error = " + (1.0 - accuracy))

val rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel]
println("Learned classification forest model:\n" + rfModel.toDebugString)
// $example off$

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

TIPS：

想看http://spark.apache.org/docs里面示例代码的全部吗？一种方法是去github上找，另一种方法是进spark的安装目录，所有的源码都在

spark/examples/src/main/scala/

里面，

如ML的算法scala实现：

spark/examples/src/main/scala/org/apache/spark/examples/ml

MLlib的算法scala实现：

spark/examples/src/main/scala/org/apache/spark/examples/mllib