使用漸變增強多類分類Spark中的樹：僅支持二進制分類

Question

嘗試使用Spark mllib中的漸變增強樹來運行多分類分類。但它給出了一個錯誤「僅支持二進制分類」。因變量有8個等級。數據有276列和7000個實例。運行模型後

import org.apache.spark.SparkContext._ 
import org.apache.spark.mllib.linalg.Vectors 
import org.apache.spark.mllib.regression.LabeledPoint 
import org.apache.spark.mllib.util.MLUtils 
import org.apache.spark.mllib.feature.ChiSqSelector 

import org.apache.spark.mllib.tree.GradientBoostedTrees 
import org.apache.spark.mllib.tree.configuration.BoostingStrategy 
import org.apache.spark.mllib.tree.model.GradientBoostedTreesModel 
import org.apache.spark.mllib.util.MLUtils 

val data = sc.textFile("data/mllib/train.csv") 

val parsedData = data.map { line => 
    val parts = line.split(',').map(_.toDouble) 
    LabeledPoint(parts(0), Vectors.dense(parts.tail)) 
} 


// Discretize data in 10 equal bins since ChiSqSelector requires categorical features 
// Even though features are doubles, the ChiSqSelector treats each unique value as a category 
val discretizedData = parsedData.map { lp => 
    LabeledPoint(lp.label, Vectors.dense(lp.features.toArray.map { x => (x/20).floor })) 
} 

// Create ChiSqSelector that will select top 5 features 
val selector = new ChiSqSelector(200) 

// Create ChiSqSelector model (selecting features) 
val transformer = selector.fit(discretizedData) 

// Filter the top 5 features from each feature vector 
val filteredData = discretizedData.map { lp => 
    LabeledPoint(lp.label, transformer.transform(lp.features)) 
} 

//Splitting the data 
val splits = filteredData.randomSplit(Array(0.7, 0.3), seed = 11L) 
val training = splits(0).cache() 
val test = splits(1) 


// Train a GradientBoostedTrees model. 
// The defaultParams for Classification use LogLoss by default. 
val boostingStrategy = BoostingStrategy.defaultParams("Classification") 
boostingStrategy.numIterations = 20 // Note: Use more iterations in practice. 
boostingStrategy.treeStrategy.numClasses = 8 
boostingStrategy.treeStrategy.maxDepth = 10 
// Empty categoricalFeaturesInfo indicates all features are continuous. 
boostingStrategy.treeStrategy.categoricalFeaturesInfo = Map[Int, Int]() 

val model = GradientBoostedTrees.train(training, boostingStrategy) 

錯誤：

java.lang.IllegalArgumentException: requirement failed: Only binary classificati 
on is supported for boosting. 
     at scala.Predef$.require(Predef.scala:233) 
     at org.apache.spark.mllib.tree.configuration.BoostingStrategy.assertVali 
d(BoostingStrategy.scala:60) 
     at org.apache.spark.mllib.tree.GradientBoostedTrees$.org$apache$spark$ml 
lib$tree$GradientBoostedTrees$$boost(GradientBoostedTrees.scala:173) 
     at org.apache.spark.mllib.tree.GradientBoostedTrees.run(GradientBoostedT 
rees.scala:71) 
     at org.apache.spark.mllib.tree.GradientBoostedTrees$.train(GradientBoost 
edTrees.scala:143) 

是否有可以做到這一點任何其他方式？

Answer 1

不幸的是，在這個時候，只有logistic迴歸，決策樹，隨機森林和樸素貝葉斯支持火花mllib/ml中的多類分類。

所以，我建議改變分類方法。