## 准备数据
```
0,0,24,9.833333333333334,10,9.7,454,0
0,1,4,17.0,1,17.0,432,0
1,0,2,20.0,1,20.0,0,0
1,1,24,10.375,13,9.615384615384615,455,0
1,1,4,10.75,3,11.0,0,0
0,1,3,16.0,2,16.0,246,0
0,1,6,13.0,4,13.0,4767,0
```
## 转换
```
val sparkConf = new SparkConf()
.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
.set("spark.kryo.registrator", "org.nd4j.Nd4jRegistrator")
.setMaster("local[*]")
.setAppName("Dl4jTransform")
val useSparkLocal = true
val spark = SparkSession
.builder
.config(sparkConf)
.getOrCreate()
def main(args: Array[String]): Unit = {
val sc = spark.sparkContext
sc.setLogLevel("ERROR")
val inputDataSchema = new Schema.Builder()
.addColumnInteger("geneSid")
.addColumnInteger("platform")
.addColumnInteger("loginCount")
.addColumnDouble("loginHour")
.addColumnInteger("shareCount")
.addColumnDouble("shareHour")
.addColumnDouble("regHours")
.addColumnCategorical("shareIn", "YES", "NO")
.build()
val tp = new TransformProcess.Builder(inputDataSchema)
.removeColumns("shareHour", "loginHour")
.convertToInteger("regHours") //转成整数
// .transform(new BaseDoubleTransform("regHours") { //自定义转换
// override def map(writable: Writable): Writable = {
// new IntWritable(writable.toInt)
// }
//
// override def map(o: Any): AnyRef = {
// val d = o.asInstanceOf[Double]
// new IntWritable(d.toInt)
// }
// })
.categoricalToInteger("shareIn") // 转成数字 YES:0 NO:1
.build()
val lines = spark.sparkContext.textFile("hello.csv")
val readWritables = lines.map(new StringToWritablesFunction(new CSVRecordReader()).call(_))
val processed = SparkTransformExecutor.execute(readWritables, tp)
val toSave = processed.map(new WritablesToStringFunction("\t"))
import spark.implicits._
toSave.rdd.toDS().show(false)
}
```
输出结果
```
+------------------------+
|value |
+------------------------+
|0 0 24 10 454 0 |
|0 1 4 1 432 0 |
|1 0 2 1 0 0 |
|1 1 24 13 455 1 |
|1 1 4 3 0 0 |
|0 1 3 2 246 0 |
|0 1 6 4 4767 0 |
+------------------------+
```
---
