推荐系统-01-简单逻辑回归
Posted 心飞翔<
tags:
篇首语：本文由小常识网(cha138.com)小编为大家整理，主要介绍了推荐系统-01-简单逻辑回归相关的知识，希望对你有一定的参考价值。
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")}
}}
以上是关于推荐系统-01-简单逻辑回归的主要内容，如果未能解决你的问题，请参考以下文章