使用jpmml-sparkml-executable生成PMML模型文件-优快云博客

本文介绍如何使用 Apache Spark MLlib 和 JPMML-SparkML 库将机器学习模型转换为 PMML 格式。通过加载 CSV 数据集，应用 RFormula 和 Logistic Regression 构建 Pipeline，并最终导出 PMML 文件。

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加载依赖的jar包
通过./spark-shell  --jars ./jpmml-sparkml-executable-1.2.13.jar 启动spark

import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.PipelineStage
import org.apache.spark.ml.feature.RFormula
import org.jpmml.sparkml.PMMLBuilder
import java.io.File

val df = (spark.read.format("csv")
  .option("sep", ",")
  .option("inferSchema", "true")
  .option("header", "true")
  .load("/user/spark/security/Wholesale_customers_data.csv"))
  
  
val formula = new RFormula().setFormula("target ~ .")
val lr = new LogisticRegression()

val pipeline = new Pipeline().setStages(Array(formula,lr))
  
val schema = df.schema

val pipelineModel = pipeline.fit(df)
val pmml = new PMMLBuilder(schema, pipelineModel)

val file = pmml.buildFile(new File("/data/data2/tmp/logit_pipeline.pmml"))

转载于:https://my.oschina.net/kyo4321/blog/2878932