Spark - 从CSV文件创建（标签，功能）对的RDD

Question

我有一个CSV文件，想要对数据执行简单的LinearRegressionWithSGD。

示例数据如下（文件中的总行数为99，包括标签），目标是预测 y_3 变量：

y_3,x_6,x_7,x_73_1,x_73_2,x_73_3,x_8
2995.3846153846152,17.0,1800.0,0.0,1.0,0.0,12.0
2236.304347826087,17.0,1432.0,1.0,0.0,0.0,12.0
2001.9512195121952,35.0,1432.0,0.0,1.0,0.0,5.0
992.4324324324324,17.0,1430.0,1.0,0.0,0.0,12.0
4386.666666666667,26.0,1430.0,0.0,0.0,1.0,25.0
1335.9036144578313,17.0,1432.0,0.0,1.0,0.0,5.0
1097.560975609756,17.0,1100.0,0.0,1.0,0.0,5.0
3526.6666666666665,26.0,1432.0,0.0,1.0,0.0,12.0
506.8421052631579,17.0,1430.0,1.0,0.0,0.0,5.0
2095.890410958904,35.0,1430.0,1.0,0.0,0.0,12.0
720.0,35.0,1430.0,1.0,0.0,0.0,5.0
2416.5,17.0,1432.0,0.0,0.0,1.0,12.0
3306.6666666666665,35.0,1800.0,0.0,0.0,1.0,12.0
6105.974025974026,35.0,1800.0,1.0,0.0,0.0,25.0
1400.4624277456646,35.0,1800.0,1.0,0.0,0.0,5.0
1414.5454545454545,26.0,1430.0,1.0,0.0,0.0,12.0
5204.68085106383,26.0,1800.0,0.0,0.0,1.0,25.0
1812.2222222222222,17.0,1800.0,1.0,0.0,0.0,12.0
2763.5928143712576,35.0,1100.0,1.0,0.0,0.0,12.0

我已经使用以下命令读取数据：

val data = sc.textFile(datadir + "/data_2.csv");

当我想使用以下命令创建（标签，功能）对的RDD时：

val parsedData = data.map { line =>
    val parts = line.split(',')
    LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
    }.cache()

所以我不能继续训练模型，任何帮助？

P.S。我使用Windows 7 x64中的Scala IDE运行火花。

Answer 1

经过多方努力，我找到了解决方案。第一个问题与标题行有关，第二个问题与映射函数有关。这是完整的解决方案：

//To read the file
val csv = sc.textFile(datadir + "/data_2.csv");

//To find the headers
val header = csv.first;

//To remove the header
val data = csv.filter(_(0) != header(0));

//To create a RDD of (label, features) pairs
val parsedData = data.map { line =>
    val parts = line.split(',')
    LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
    }.cache()

我希望它可以节省你的时间。

Answer 2

当您在文件中读到第一行

时

y_3,x_6,x_7,x_73_1,x_73_2,x_73_3,x_8

还会在map功能中阅读和转换，以便您尝试在toDouble上致电y_3。您需要过滤掉第一行并使用剩余的行进行学习。