LinearRegression

ML API

• 功能描述

  传入Dataset格式的样本数据，调用fit接口，输出LinearRegression模型。

• 输入输出
  1. 包名：package org.apache.spark.ml.regression
  2. 类名：LinearRegression
  3. 方法名：fit
  4. 输入：Dataset[_]，训练样本数据，必须字段如下。

     Param name	Type(s)	Default	Description
     labelCol	Double	"label"	Label
     featuresCol	Vector	"features"	特征标签

  5. 算法参数

     算法参数

     def setRegParam(value: Double): LinearRegression.this.type def setFitIntercept(value: Boolean): LinearRegression.this.type def setStandardization(value: Boolean): LinearRegression.this.type def setElasticNetParam(value: Double): LinearRegression.this.type def setMaxIter(value: Int): LinearRegression.this.type def setTol(value: Double): LinearRegression.this.type def setWeightCol(value: String): LinearRegression.this.type def setSolver(value: String): LinearRegression.this.type def setAggregationDepth(value: Int): LinearRegression.this.type def setLoss(value: String): LinearRegression.this.type def setEpsilon(value: Double): LinearRegression.this.type

     

     参数及fit代码接口示例：

     import org.apache.spark.ml.param.{ParamMap, ParamPair}
     
     val linR = new LinearRegression()
     //定义def fit(dataset: Dataset[_], paramMap: ParamMap) 接口参数
     val paramMap = ParamMap(linR.maxIter -> maxIter)
     .put(linR.regParam, regParam)
     
     // 定义def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): 接口参数
     val paramMaps: Array[ParamMap] = new Array[ParamMap](2)
     for (i <- 0 to  2) {
     paramMaps(i) = ParamMap(linR.maxIter -> maxIter)
     .put(linR.regParam, regParam)
     }//对paramMaps进行赋值
     
     // 定义def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*) 接口参数
     val regParamPair = ParamPair(linR.regParam, regParam)
     val maxIterParamPair = ParamPair(linR.maxIter, maxIter)
     val tolParamPair = ParamPair(linR.tol, tol)
     
     // 调用各个fit接口
     model = linR.fit(trainingData)
     model = linR.fit(trainingData, paramMap)
     models = linR.fit(trainingData, paramMaps)
     model = linR.fit(trainingData, regParamPair, maxIterParamPair, tolParamPair)

  6. 输出：LinearRegressionModel，模型预测时的输出字段如下。

     Param name	Type(s)	Default	Description
     predictionCol	Int	"prediction"	predictionCol

• 使用样例

  import org.apache.spark.ml.regression.LinearRegression
  
  // Load training data
  val training = spark.read.format("libsvm")
  .load("data/mllib/sample_linear_regression_data.txt")
  
  val lr = new LinearRegression()
  .setMaxIter(10)
  .setRegParam(0.3)
  .setElasticNetParam(0.8)
  
  // Fit the model
  val lrModel = lr.fit(training)
  
  // Summarize the model over the training set and print out some metrics
  val trainingSummary = lrModel.summary