根据LinearRegressionSummary (Spark 2.1.0 JavaDoc),p值仅适用于"正常"解算器。
此值仅在使用" normal"解算器。
到底是什么"正常"解算器?
我这样做:
import org.apache.spark.ml.{Pipeline, PipelineModel}
import org.apache.spark.ml.evaluation.RegressionEvaluator
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.regression.LinearRegressionModel
import org.apache.spark.ml.tuning.{CrossValidator, CrossValidatorModel, ParamGridBuilder}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.{DataFrame, SparkSession}
.
.
.
val (trainingData, testData): (DataFrame, DataFrame) =
com.acme.pta.accuracy.Util.splitData(output, testProportion)
.
.
.
val lr =
new org.apache.spark.ml.regression.LinearRegression()
.setSolver("normal").setMaxIter(maxIter)
val pipeline = new Pipeline()
.setStages(Array(lr))
val paramGrid = new ParamGridBuilder()
.addGrid(lr.elasticNetParam, Array(0.2, 0.4, 0.8, 0.9))
.addGrid(lr.regParam, Array(0,6, 0.3, 0.1, 0.01))
.build()
val cv = new CrossValidator()
.setEstimator(pipeline)
.setEvaluator(evaluator)
.setEstimatorParamMaps(paramGrid)
.setNumFolds(numFolds) // Use 3+ in practice
val cvModel: CrossValidatorModel = cv.fit(trainingData)
val pipelineModel: PipelineModel = cvModel.bestModel.asInstanceOf[PipelineModel]
val lrModel: LinearRegressionModel =
pipelineModel.stages(0).asInstanceOf[LinearRegressionModel]
val modelSummary = lrModel.summary
Holder.log.info("lrModel.summary: " + modelSummary)
try {
Holder.log.info("feature p values: ")
// Exception occurs on line below.
val featuresAndPValues = features.zip(lrModel.summary.pValues)
featuresAndPValues.foreach(
(featureAndPValue: (String, Double)) =>
Holder.log.info(
"feature: " + featureAndPValue._1 + ": " + featureAndPValue._2))
} catch {
case _: java.lang.UnsupportedOperationException
=> Holder.log.error("Cannot compute p-values")
}
我仍然得到UnsupportedOperationException
。
异常消息是:
此LinearRegressionModel
没有可用的p值
我还需要做些什么吗?我正在使用
"org.apache.spark" %% "spark-mllib" % "2.1.1"
该版本是否支持pValues?
答案 0 :(得分:7)
<强>更新强>
在正常LinearRegression
p值和其他“正常”统计信息仅在其中一个参数elasticNetParam
或regParam
为零时才会出现。所以你可以改变
.addGrid( lr.elasticNetParam, Array( 0.0 ) )
或
.addGrid( lr.regParam, Array( 0.0 ) )
制作明确使用
的LinearRegression
自定义版本
Cholesky
求解WeightedLeastSquares
。我将此类作为ml.regression
包的扩展名。
package org.apache.spark.ml.regression
import scala.collection.mutable
import org.apache.spark.SparkException
import org.apache.spark.internal.Logging
import org.apache.spark.ml.feature.Instance
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.optim.WeightedLeastSquares
import org.apache.spark.ml.param.{Param, ParamMap, ParamValidators}
import org.apache.spark.ml.util._
import org.apache.spark.mllib.linalg.VectorImplicits._
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Dataset, Row}
import org.apache.spark.sql.functions._
class CholeskyLinearRegression ( override val uid: String )
extends Regressor[ Vector, CholeskyLinearRegression, LinearRegressionModel ]
with LinearRegressionParams with DefaultParamsWritable with Logging {
import CholeskyLinearRegression._
def this() = this(Identifiable.randomUID("linReg"))
def setRegParam(value: Double): this.type = set(regParam, value)
setDefault(regParam -> 0.0)
def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
setDefault(fitIntercept -> true)
def setStandardization(value: Boolean): this.type = set(standardization, value)
setDefault(standardization -> true)
def setElasticNetParam(value: Double): this.type = set(elasticNetParam, value)
setDefault(elasticNetParam -> 0.0)
def setMaxIter(value: Int): this.type = set(maxIter, value)
setDefault(maxIter -> 100)
def setTol(value: Double): this.type = set(tol, value)
setDefault(tol -> 1E-6)
def setWeightCol(value: String): this.type = set(weightCol, value)
def setSolver(value: String): this.type = set(solver, value)
setDefault(solver -> Auto)
def setAggregationDepth(value: Int): this.type = set(aggregationDepth, value)
setDefault(aggregationDepth -> 2)
override protected def train(dataset: Dataset[_]): LinearRegressionModel = {
// Extract the number of features before deciding optimization solver.
val numFeatures = dataset.select(col($(featuresCol))).first().getAs[Vector](0).size
val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))
val instances: RDD[Instance] =
dataset
.select( col( $(labelCol) ), w, col( $(featuresCol) ) )
.rdd.map {
case Row(label: Double, weight: Double, features: Vector) =>
Instance(label, weight, features)
}
// if (($(solver) == Auto &&
// numFeatures <= WeightedLeastSquares.MAX_NUM_FEATURES) || $(solver) == Normal) {
// For low dimensional data, WeightedLeastSquares is more efficient since the
// training algorithm only requires one pass through the data. (SPARK-10668)
val optimizer = new WeightedLeastSquares(
$(fitIntercept),
$(regParam),
elasticNetParam = $(elasticNetParam),
$(standardization),
true,
solverType = WeightedLeastSquares.Cholesky,
maxIter = $(maxIter),
tol = $(tol)
)
val model = optimizer.fit(instances)
val lrModel = copyValues(new LinearRegressionModel(uid, model.coefficients, model.intercept))
val (summaryModel, predictionColName) = lrModel.findSummaryModelAndPredictionCol()
val trainingSummary = new LinearRegressionTrainingSummary(
summaryModel.transform(dataset),
predictionColName,
$(labelCol),
$(featuresCol),
summaryModel,
model.diagInvAtWA.toArray,
model.objectiveHistory
)
lrModel
.setSummary( Some( trainingSummary ) )
lrModel
}
override def copy(extra: ParamMap): CholeskyLinearRegression = defaultCopy(extra)
}
object CholeskyLinearRegression
extends DefaultParamsReadable[CholeskyLinearRegression] {
override def load(path: String): CholeskyLinearRegression = super.load(path)
val MAX_FEATURES_FOR_NORMAL_SOLVER: Int = WeightedLeastSquares.MAX_NUM_FEATURES
/** String name for "auto". */
private[regression] val Auto = "auto"
/** String name for "normal". */
private[regression] val Normal = "normal"
/** String name for "l-bfgs". */
private[regression] val LBFGS = "l-bfgs"
/** Set of solvers that LinearRegression supports. */
private[regression] val supportedSolvers = Array(Auto, Normal, LBFGS)
}
您只需将其粘贴到项目中的单独文件中,然后在代码中将LinearRegression
更改为CholeskyLinearRegression
。
val lr = new CholeskyLinearRegression() // new LinearRegression()
.setSolver( "normal" )
.setMaxIter( maxIter )
它适用于非零参数并提供pValues 。测试了以下参数网格。
val paramGrid = new ParamGridBuilder()
.addGrid( lr.elasticNetParam, Array( 0.2, 0.4, 0.8, 0.9 ) )
.addGrid( lr.regParam, Array( 0.6, 0.3, 0.1, 0.01 ) )
.build()
我最初认为主要问题是模型没有完全保留。在CrossValidator
中拟合后,训练模型不会被保留。由于内存消耗,这是可以理解的。关于如何解决问题,正在进行debate。 JIRA Issue。
您可以在评论部分看到我尝试从最佳模型中提取参数,以便再次运行它。然后我发现模型摘要没问题,只是某些参数diagInvAtWa
的长度为1,基本上为零。
对于岭回归或Tikhonov正则化(elasticNet = 0
)和任何regParam
pValues和其他“正常”统计可以计算,但对于Lasso方法和介于两者之间的东西(弹性网)不是。同样适用于regParam = 0
:计算任何elasticNet
个p值。
为什么
LinearRegression
uses加权最小二乘优化器,用于solverType = WeightedLeastSquares.Auto
的“普通”求解器。此优化程序的求解程序为two options:QuasiNewton
或Cholesky
。仅当regParam
和elasticNetParam
都是非零时才选择前者。
val solver = if (
( solverType == WeightedLeastSquares.Auto &&
elasticNetParam != 0.0 &&
regParam != 0.0 ) ||
( solverType == WeightedLeastSquares.QuasiNewton ) ) {
...
new QuasiNewtonSolver(fitIntercept, maxIter, tol, effectiveL1RegFun)
} else {
new CholeskySolver
}
因此,在您的参数网格中,QuasiNewtonSolver
将始终使用,因为regParam
和elasticNetParam
没有组合,其中一个为零。
我们知道为了获得pValues和其他“正常”统计数据,例如t-statistic或std。系数误差矩阵的对角线(A ^ T * W * A)^ - 1(diagInvAtWA
)不能是只有一个零的向量。此条件在pValues的定义中设置。
diagInvAtWA
是压缩上三角矩阵(solution.aaInv
)的对角元素的向量。
val diagInvAtWA = solution.aaInv.map { inv => ...
对于Cholesky solver
,它是calculated但QuasiNewton
not。 NormalEquationSolution
的第二个parameter就是这个矩阵。
从技术上讲,您可以使用
创建自己的LinearRegression版本在此示例中,我使用了来自here的数据sample_linear_regression_data.txt
。
完整的复制代码
import org.apache.spark._
import org.apache.spark.ml.{Pipeline, PipelineModel}
import org.apache.spark.ml.evaluation.{RegressionEvaluator, BinaryClassificationEvaluator}
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.regression.{LinearRegressionModel, LinearRegression}
import org.apache.spark.ml.tuning.{CrossValidator, CrossValidatorModel, ParamGridBuilder}
import org.apache.spark.sql.functions._
import org.apache.spark.sql.{DataFrame, SparkSession}
import org.apache.spark.ml.param.ParamMap
object Main {
def main( args: Array[ String ] ): Unit = {
val spark =
SparkSession
.builder()
.appName( "SO" )
.master( "local[*]" )
.config( "spark.driver.host", "localhost" )
.getOrCreate()
import spark.implicits._
val data =
spark
.read
.format( "libsvm" )
.load( "./sample_linear_regression_data.txt" )
val Array( training, test ) =
data
.randomSplit( Array( 0.9, 0.1 ), seed = 12345 )
val maxIter = 10;
val lr = new LinearRegression()
.setSolver( "normal" )
.setMaxIter( maxIter )
val paramGrid = new ParamGridBuilder()
// .addGrid( lr.elasticNetParam, Array( 0.2, 0.4, 0.8, 0.9 ) )
.addGrid( lr.elasticNetParam, Array( 0.0 ) )
.addGrid( lr.regParam, Array( 0.6, 0.3, 0.1, 0.01 ) )
.build()
val pipeline = new Pipeline()
.setStages( Array( lr ) )
val cv = new CrossValidator()
.setEstimator( pipeline )
.setEvaluator( new RegressionEvaluator )
.setEstimatorParamMaps( paramGrid )
.setNumFolds( 2 ) // Use 3+ in practice
val cvModel =
cv
.fit( training )
val pipelineModel: PipelineModel =
cvModel
.bestModel
.asInstanceOf[ PipelineModel ]
val lrModel: LinearRegressionModel =
pipelineModel
.stages( 0 )
.asInstanceOf[ LinearRegressionModel ]
// Technically there is a way to use exact ParamMap
// to build a new LR but for the simplicity I'll
// get and set them explicitly
// lrModel.params.foreach( ( param ) => {
// println( param )
// } )
// val bestLr = new LinearRegression()
// .setSolver( "normal" )
// .setMaxIter( maxIter )
// .setRegParam( lrModel.getRegParam )
// .setElasticNetParam( lrModel.getElasticNetParam )
// val bestLrModel = bestLr.fit( training )
val modelSummary =
lrModel
.summary
println( "lrModel pValues: " + modelSummary.pValues.mkString( ", " ) )
spark.stop()
}
}
<强>原始强>
有三种解算器算法available:
l-bfgs
- 限制记忆Broyden-Fletcher-Goldfarb-Shanno算法,这是一种有限记忆的准牛顿优化method。normal
- 使用Normal Equation作为线性回归问题的解析解。它基本上是weighted least squares approach或reweighted least squares approach。auto
- 自动选择求解器算法。在可能的情况下将使用正规方程求解器,但这将在需要时自动回退到迭代优化方法 coefficientStandardErrors
,tValues
和pValues
仅在使用“普通”求解器时可用,因为它们都基于diagInvAtWA
- 矩阵的对角线(A ^ T * W * A)^ - 1。