Spark ML逻辑回归

 import org.apache.log4j.{Level, Logger}

 import org.apache.spark.ml.classification.LogisticRegression

 import org.apache.spark.ml.linalg.Vectors

 import org.apache.spark.sql.SparkSession

 /**

   * 逻辑回归

   * Created by zhen on 2018/11/20.

   */

 object LogisticRegression {

   Logger.getLogger("org").setLevel(Level.WARN) // 设置日志级别

   def main(args: Array[String]) {

     val spark = SparkSession.builder()

       .appName("LogisticRegression")

       .master("local[2]")

       .getOrCreate()

     val sqlContext = spark.sqlContext

     // 加载训练数据和测试数据

     val data = sqlContext.createDataFrame(Seq(

       (1.0, Vectors.dense(0.0, 1.1, 0.1)),

       (0.0, Vectors.dense(2.0, 1.0, -1.1)),

       (1.0, Vectors.dense(1.0, 2.1, 0.1)),

       (0.0, Vectors.dense(2.0, -1.3, 1.1)),

       (0.0, Vectors.dense(2.0, 1.0, -1.1)),

       (1.0, Vectors.dense(1.0, 2.1, 0.1)),

       (1.0, Vectors.dense(2.0, 1.3, 1.1)),

       (0.0, Vectors.dense(-2.0, 1.0, -1.1)),

       (1.0, Vectors.dense(1.0, 2.1, 0.1)),

       (0.0, Vectors.dense(2.0, -1.3, 1.1)),

       (1.0, Vectors.dense(2.0, 1.0, -1.1)),

       (1.0, Vectors.dense(1.0, 2.1, 0.1)),

       (0.0, Vectors.dense(-2.0, 1.3, 1.1)),

       (1.0, Vectors.dense(0.0, 1.2, -0.4))

     ))

     .toDF("label", "features")

     val weights = Array(0.8,0.2) //设置训练集和测试集的比例

     val split_data = data.randomSplit(weights) // 拆分训练集和测试集

     // 创建逻辑回归对象

     val lr = new LogisticRegression()

     // 设置参数

     lr.setMaxIter(10).setRegParam(0.01)

     // 训练模型

     val model = lr.fit(split_data(0))

     model.transform(split_data(1))

     .select("label", "features", "probability", "prediction")

     .collect()

     .foreach(println(_))

     //关闭spark

     spark.stop()

   }

 }

结果：