add NaiveBayesTraining Stop

piflow-bundle/src/main/scala/cn/piflow/bundle/ml/NaiveBayesTraining.scala

@@ -0,0 +1,55 @@
+package cn.piflow.bundle.ml
+
+import cn.piflow.{JobContext, JobInputStream, JobOutputStream, ProcessContext}
+import cn.piflow.bundle.util.{JedisClusterImplSer, RedisUtil}
+import cn.piflow.conf.{ConfigurableStop, StopGroupEnum}
+import cn.piflow.conf.bean.PropertyDescriptor
+import cn.piflow.conf.util.MapUtil
+import org.apache.spark.ml.classification.NaiveBayes
+import org.apache.spark.sql.SparkSession
+
+class NaiveBayesTraining extends ConfigurableStop{
+  val inportCount: Int = 1
+  val outportCount: Int = 0
+  var training_data_path:String =_
+
+  def perform(in: JobInputStream, out: JobOutputStream, pec: JobContext): Unit = {
+    val spark = pec.get[SparkSession]()
+
+    //load data stored in libsvm format as a dataframe
+    val data=spark.read.format("libsvm").load("/root/watermellonDataset.txt")
+
+    //training a NaiveBayes model
+    val model=new NaiveBayes().fit(data)
+
+    val predictions=model.transform(data)
+    predictions.show()
+
+  }
+
+  def initialize(ctx: ProcessContext): Unit = {
+
+  }
+
+
+  def setProperties(map: Map[String, Any]): Unit = {
+    training_data_path=MapUtil.get(map,key="training_data_path").asInstanceOf[String]
+
+  }
+
+  override def getPropertyDescriptor(): List[PropertyDescriptor] = {
+    var descriptor : List[PropertyDescriptor] = List()
+    val training_data_path = new PropertyDescriptor().name("training_data_path").displayName("TRAINING_DATA_PATH").defaultValue("").required(true)
+    descriptor = training_data_path :: descriptor
+    descriptor
+  }
+
+  override def getIcon(): Array[Byte] = ???
+
+  override def getGroup(): List[String] = {
+    List(StopGroupEnum.MLGroup.toString)
+  }
+
+  override val authorEmail: String = "xiaoxiao@cnic.cn"
+
+}