在Apache Spark中为具有大量列的数据集创建ml管道的最佳方法

Question

我正在使用Spark 2.1.1处理具有~2000个特征的数据集,并尝试创建一个基本的ML管道,包括一些变形金刚和分类器.

让我们假设为了简单起见,我正在使用的Pipeline包含一个VectorAssembler,StringIndexer和一个Classifier,这将是一个相当常见的用例.

// Pipeline elements
val assmbleFeatures: VectorAssembler = new VectorAssembler()
  .setInputCols(featureColumns)
  .setOutputCol("featuresRaw")

val labelIndexer: StringIndexer = new StringIndexer()
  .setInputCol("TARGET")
  .setOutputCol("indexedLabel")

// Train a RandomForest model.
val rf: RandomForestClassifier = new RandomForestClassifier()
  .setLabelCol("indexedLabel")
  .setFeaturesCol("featuresRaw")
  .setMaxBins(30)

// add the params, unique to this classifier
val paramGrid = new ParamGridBuilder()
  .addGrid(rf.numTrees, Array(5))
  .addGrid(rf.maxDepth, Array(5))
  .build()

// Treat the Pipeline as an Estimator, to jointly choose parameters for all Pipeline stages.
val evaluator = new BinaryClassificationEvaluator()
  .setMetricName("areaUnderROC")
  .setLabelCol("indexedLabel")

如果管道步骤被分成变换器管道(VectorAssembler + StringIndexer)和第二个分类器管道,并且如果在两个管道之间删除不必要的列,则训练成功.这意味着重新使用模型,必须在训练后保存两个PipelineModel,并且必须引入中间预处理步骤.

// Split indexers and forest in two Pipelines.
val prePipeline = new Pipeline().setStages(Array(labelIndexer, assmbleFeatures)).fit(dfTrain)
// Transform data and drop all columns, except those needed for training 
val dfTrainT = prePipeline.transform(dfTrain)
val columnsToDrop = dfTrainT.columns.filter(col => !Array("featuresRaw", "indexedLabel").contains(col))
val dfTrainRdy = dfTrainT.drop(columnsToDrop:_*)

val mainPipeline = new Pipeline().setStages(Array(rf))

val cv = new CrossValidator()
  .setEstimator(mainPipeline)
  .setEvaluator(evaluator)
  .setEstimatorParamMaps(paramGrid)
  .setNumFolds(2)

val bestModel = cv.fit(dfTrainRdy).bestModel.asInstanceOf[PipelineModel]

(imho)更清洁的解决方案是将所有管道阶段合并为一个管道.

val pipeline = new Pipeline()
  .setStages(Array(labelIndexer, assmbleFeatures, rf))

val cv = new CrossValidator()
  .setEstimator(pipeline)
  .setEvaluator(evaluator)
  .setEstimatorParamMaps(paramGrid)
  .setNumFolds(2)

// This will fail! 
val bestModel = cv.fit(dfTrain).bestModel.asInstanceOf[PipelineModel]

但是,将所有PipelineStages放入一个Pipeline会导致以下异常,可能是由于此 PR最终将解决的问题:

错误CodeGenerator:无法编译:org.codehaus.janino.JaninoRuntimeException:类org.apache.spark.sql.catalyst.expressions.GeneratedClass的常量池$ SpecificUnsafeProjection已超过JVM限制0xFFFF

这样做的原因是VectorAssembler有效地(在本例中)加倍了DataFrame中的数据量,因为没有可以丢弃不必要列的变换器.(参见spark pipe矢量汇编程序删除其他列)

该示例适用于golub数据集,并且必须执行以下预处理步骤:

import org.apache.spark.sql.types.DoubleType
import org.apache.spark.ml.classification.RandomForestClassifier
import org.apache.spark.ml.{Pipeline, PipelineModel, PipelineStage}
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
import org.apache.spark.ml.feature._
import org.apache.spark.sql._
import org.apache.spark.ml.tuning.{CrossValidator, ParamGridBuilder}

val df = spark.read.option("header", true).option("inferSchema", true).csv("/path/to/dataset/golub_merged.csv").drop("_c0").repartition(100)

// Those steps are necessary, otherwise training would fail either way
val colsToDrop = df.columns.take(5000)
val dfValid = df.withColumn("TARGET", df("TARGET_REAL").cast(DoubleType)).drop("TARGET_REAL").drop(colsToDrop:_*)

// Split df in train and test sets
val Array(dfTrain, dfTest) = dfValid.randomSplit(Array(0.7, 0.3))

// Feature columns are columns except "TARGET"
val featureColumns = dfTrain.columns.filter(col => col != "TARGET")

由于我是Spark的新手,我不确定什么是解决这个问题的最佳方法.你会建议......

1. 创建一个新的变压器,它可以丢弃列并可以合并到管道中？
2. 拆分两条管道并引入中间步骤
3. 还要别的吗？:)

或者我错过了解决这个问题的重要事项(管道步骤,公关等)？

---

编辑:

我实现了一个新的Transformer DroppingVectorAssembler,它会删除不必要的列,但是会抛出相同的异常.

除此之外,设置spark.sql.codegen.wholeStage以false不解决问题.

Answer 1

该janino错误是由于优化器过程中创建的常量变量的数量所致。JVM 中允许的常量变量的最大限制是 ((2^16) -1)。如果超过此限制，那么您将得到Constant pool for class ... has grown past JVM limit of 0xFFFF

解决此问题的 JIRA 是SPARK-18016，但目前仍在进行中。

VectorAssembler当您的代码必须在单个优化任务期间对数千列执行时， 您的代码很可能会在该阶段失败。

我针对此问题开发的解决方法是通过对列的子集进行操作来创建“向量的向量”，然后最后将结果汇总在一起以创建奇异特征向量。这可以防止任何单个优化任务超出 JVM 常量限制。它并不优雅，但我已经在达到 10k 列范围的数据集上使用了它。

此方法还允许您仍然保留单个管道，尽管它需要一些额外的步骤才能使其工作（创建子向量）。从子向量创建特征向量后，如果需要，可以删除原始源列。

示例代码：

// IMPORT DEPENDENCIES
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._
import org.apache.spark.sql.{SQLContext, Row, DataFrame, Column}
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.{Pipeline, PipelineModel}

// Create first example dataframe
val exampleDF = spark.createDataFrame(Seq(
  (1, 1, 2, 3, 8, 4, 5, 1, 3, 2, 0, 4, 2, 8, 1, 1, 2, 3, 8, 4, 5),
  (2, 4, 3, 8, 7, 9, 8, 2, 3, 3, 2, 6, 5, 4, 2, 4, 3, 8, 7, 9, 8),
  (3, 6, 1, 9, 2, 3, 6, 3, 8, 5, 1, 2, 3, 5, 3, 6, 1, 9, 2, 3, 6),
  (4, 7, 8, 6, 9, 4, 5, 4, 9, 8, 2, 4, 9, 2, 4, 7, 8, 6, 9, 4, 5),
  (5, 9, 2, 7, 8, 7, 3, 5, 3, 4, 8, 0, 6, 2, 5, 9, 2, 7, 8, 7, 3),
  (6, 1, 1, 4, 2, 8, 4, 6, 3, 9, 8, 8, 9, 3, 6, 1, 1, 4, 2, 8, 4)
)).toDF("uid", "col1", "col2", "col3", "col4", "col5", 
        "col6", "col7", "col8", "col9", "colA", "colB", 
        "colC", "colD", "colE", "colF", "colG", "colH", 
        "colI", "colJ", "colK")

// Create multiple column lists using the sliding method
val Array(colList1, colList2, colList3, colList4) = exampleDF.columns.filter(_ != "uid").sliding(5,5).toArray

// Create a vector assembler for each column list
val colList1_assembler = new VectorAssembler().setInputCols(colList1).setOutputCol("colList1_vec")
val colList2_assembler = new VectorAssembler().setInputCols(colList2).setOutputCol("colList2_vec")
val colList3_assembler = new VectorAssembler().setInputCols(colList3).setOutputCol("colList3_vec")
val colList4_assembler = new VectorAssembler().setInputCols(colList4).setOutputCol("colList4_vec")

// Create a vector assembler using column list vectors as input
val features_assembler = new VectorAssembler().setInputCols(Array("colList1_vec","colList2_vec","colList3_vec","colList4_vec")).setOutputCol("features")

// Create the pipeline with column list vector assemblers first, then the final vector of vectors assembler last
val pipeline = new Pipeline().setStages(Array(colList1_assembler,colList2_assembler,colList3_assembler,colList4_assembler,features_assembler))

// Fit and transform the data
val featuresDF = pipeline.fit(exampleDF).transform(exampleDF)

// Get the number of features in "features" vector
val featureLength = (featuresDF.schema(featuresDF.schema.fieldIndex("features")).metadata.getMetadata("ml_attr").getLong("num_attrs"))

// Print number of features in "features vector"
print(featureLength)

（注意：创建列列表的方法实际上应该以编程方式完成，但为了理解这个概念，我使这个示例保持简单。）