Spark MlLib 线性回归(线性最小二乘法)给出随机结果 [英] Spark MlLib linear regression (Linear least squares) giving random results

问题描述

我是 Spark 和机器学习方面的新手.我已经成功地遵循了一些 Mllib 教程，但我无法让这个教程起作用:

Im new in spark and Machine learning in general. I have followed with success some of the Mllib tutorials, i can't get this one working:

我在这里找到了示例代码:https:///spark.apache.org/docs/latest/mllib-linear-methods.html#linear-least-squares-lasso-and-ridge-regression

i found the sample code here : https://spark.apache.org/docs/latest/mllib-linear-methods.html#linear-least-squares-lasso-and-ridge-regression

(LinearRegressionWithSGD 部分)

(section LinearRegressionWithSGD)

代码如下:

import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.regression.LinearRegressionModel
import org.apache.spark.mllib.regression.LinearRegressionWithSGD
import org.apache.spark.mllib.linalg.Vectors

// Load and parse the data
val data = sc.textFile("data/mllib/ridge-data/lpsa.data")
val parsedData = data.map { line =>
  val parts = line.split(',')
  LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
}.cache()

// Building the model
val numIterations = 100
val model = LinearRegressionWithSGD.train(parsedData, numIterations)

// Evaluate model on training examples and compute training error
val valuesAndPreds = parsedData.map { point =>
  val prediction = model.predict(point.features)
  (point.label, prediction)
}
val MSE = valuesAndPreds.map{case(v, p) => math.pow((v - p), 2)}.mean()
println("training Mean Squared Error = " + MSE)

// Save and load model
model.save(sc, "myModelPath")
val sameModel = LinearRegressionModel.load(sc, "myModelPath")

(这正是网站上的内容)

(that's exactly what's is on the website)

结果是

训练均方误差 = 6.2087803138063045

和

valuesAndPreds.collect

给予

    Array[(Double, Double)] = Array((-0.4307829,-1.8383286021929077),
 (-0.1625189,-1.4955700806407322), (-0.1625189,-1.118820892849544), 
(-0.1625189,-1.6134108278724875), (0.3715636,-0.45171266551058276), 
(0.7654678,-1.861316066986158), (0.8544153,-0.3588282725617985), 
(1.2669476,-0.5036812148225209), (1.2669476,-1.1534698170911792), 
(1.2669476,-0.3561392231695041), (1.3480731,-0.7347031705813306), 
(1.446919,-0.08564658011814863), (1.4701758,-0.656725375080344), 
(1.4929041,-0.14020483324910105), (1.5581446,-1.9438858658143454), 
(1.5993876,-0.02181165554398845), (1.6389967,-0.3778677315868635), 
(1.6956156,-1.1710092824030043), (1.7137979,0.27583044213064634), 
(1.8000583,0.7812664902440078), (1.8484548,0.94605507153074), 
(1.8946169,-0.7217282082851512), (1.9242487,-0.24422843221437684),...

我的问题是预测看起来完全随机(而且是错误的)，而且由于它是网站示例的完美副本，具有相同的输入数据(训练集)，我不知道该往哪里看，我是不是遗漏了什么?

My problem here is predictions looks totally random (and wrong), and since its the perfect copy of the website example, with the same input data (training set), i don't know where to look, am i missing something ?

请给我一些关于在哪里搜索的建议或线索，我可以阅读和实验.

Please give me some advices or clue about where to search, i can read and experiment.

谢谢

推荐答案

线性回归基于 SGD，需要调整步长，请参阅 http://spark.apache.org/docs/latest/mllib-optimization.html 了解更多详情.

Linear Regression is SGD based and requires tweaking the step size, see http://spark.apache.org/docs/latest/mllib-optimization.html for more details.

在您的示例中，如果您将步长设置为 0.1，则会获得更好的结果 (MSE = 0.5).

In your example, if you set the step size to 0.1 you get better results (MSE = 0.5).

import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.regression.LinearRegressionModel
import org.apache.spark.mllib.regression.LinearRegressionWithSGD
import org.apache.spark.mllib.linalg.Vectors

// Load and parse the data
val data = sc.textFile("data/mllib/ridge-data/lpsa.data")
val parsedData = data.map { line =>
  val parts = line.split(',')
  LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
}.cache()

// Build the model
var regression = new LinearRegressionWithSGD().setIntercept(true)
regression.optimizer.setStepSize(0.1)
val model = regression.run(parsedData)

// Evaluate model on training examples and compute training error
val valuesAndPreds = parsedData.map { point =>
  val prediction = model.predict(point.features)
  (point.label, prediction)
}
val MSE = valuesAndPreds.map{case(v, p) => math.pow((v - p), 2)}.mean()
println("training Mean Squared Error = " + MSE)

有关更真实数据集的另一个示例，请参阅

For another example on a more realistic dataset, see

https://github.com/selvinsource/spark-pmml-exporter-validator/blob/master/src/main/resources/datasets/winequalityred_linearregression.md

https://github.com/selvinsource/spark-pmml-exporter-validator/blob/master/src/main/resources/spark_shell_exporter/linearregression_winequalityred.scala

这篇关于Spark MlLib 线性回归(线性最小二乘法)给出随机结果的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！