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：