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推荐系统-01-简单逻辑回归

时间:2018-05-11 23:19:02      阅读:216      评论:0      收藏:0      [点我收藏+]

标签:dataframe   label   basic   UI   abi   transform   sele   features   官方文档   

import org.apache.spark.ml.feature._
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.{Pipeline,PipelineModel}
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.sql.Row
import org.apache.spark.sql.SparkSession

object BasicStastic{
def main(args: Array[String]) {

val spark = SparkSession.builder().
master("local").
appName("my App Name").
getOrCreate()

// 创建数据帧(id, 内容,标签)
val training = spark.createDataFrame(Seq(
(0.0, Vectors.dense(2.0, 1.1, 0.1)),
(1.0, Vectors.dense(0.0, 1.0, -1.0)),
(2.0, Vectors.dense(0.0, 1.3, 1.0)),
(3.0, Vectors.dense(2.0, 1.2, -0.5))
)).toDF("label", "features")

// 相关参数,可以在官方文档有介绍
// http://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.ml.classification.LogisticRegression
val lr = new LogisticRegression().
setMaxIter(10).
setRegParam(0.01)

val model1 = lr.fit(training)
model1.parent.extractParamMap

val paramMap = ParamMap(lr.maxIter -> 20).put(lr.regParam -> 0.1, lr.threshold -> 0.55)
val paramMap2 = ParamMap(lr.probabilityCol -> "myProbability")
val paramMapCombined = paramMap ++ paramMap2

val model2 = lr.fit(training, paramMapCombined)
model2.parent.extractParamMap

// 测试数据
val test = spark.createDataFrame(Seq(
(3.0, Vectors.dense(-1.0, 1.5, 1.3)),
(0.0, Vectors.dense(3.0, 2.0, -0.1)),
(1.0, Vectors.dense(0.0, 2.2, -1.5))
)).toDF("label", "features")

val result = model1.transform(test)
// 显示结果
result.show(false)
result.select("label", "features", "probability", "prediction").show(false)
result.select("label", "features", "probability", "prediction").collect().foreach{case Row(label:Double, features:Vector, probability:Vector, prediction:Double) => println(s"($features, \(label) -> probability=\)probability, prediction=$prediction")}

}}

推荐系统-01-简单逻辑回归

标签:dataframe   label   basic   UI   abi   transform   sele   features   官方文档   

原文地址:https://www.cnblogs.com/freebird92/p/9026311.html

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