在spark2.0之前，逻辑回归只能做二分类的。之后加上了多分类。
根据我的理解，逻辑回归既可以用来分类，也可以用来回归，但是官网并没有给出logistic regression回归的例子，只有线性回归（linear regression）的例子
此外，官网给出了逻辑回归二分类和多分类的例子，但是多分类中的setFamlily方法无法在spark1.6和spark2.0的版本上运行，无论是用IDEA还是spark-shell。
这里给出完整的逻辑回归二分类代码，数据创建方式有两种：手动代码输入和从文件读取。
代码如下：
/**
* Created by wangtuntun on 17-3-7.
* 将数据代入模型进行预测
* 本来想用logistic regression做回归的，结果点开的是官网的classification下的代码
* 而且还是只支持二分类的代码
*/
import org.apache.spark.{SparkContext, SparkConf}
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.sql.Row
import org.apache.spark.sql.SQLContext
object predict_with_logistic_regression_classification {
def main(args: Array[String]): Unit = {
// Prepare training data from a list of (label, features) tuples.
val conf=new SparkConf().setAppName("tianchi").setMaster("local")
val sc=new SparkContext(conf)
val sqc=new SQLContext(sc)
//Load TrainData
//Seq类型是有顺序的数据结构
//training为dataFrame
val training = sqc.createDataFrame(Seq(
(1.0, Vectors.dense(0.0, 1.1, 0.1)),
(0.0, Vectors.dense(2.0, 1.0, -1.0)),
(0.0, Vectors.dense(2.0, 1.3, 1.0)),
(1.0, Vectors.dense(0.0, 1.2, -0.5))
)).toDF("label", "features")//转变为数据框
// val raw_data=sc.textFile("/home/wangtuntun/IJCAI/Data/lr_format_data.txt")
// val map_data=raw_data.map{line=>
// val list_split=line.split(",")
// val mylable=list_split.last.toDouble //最后一个元素
// val features=list_split.dropRight(1)//除去右边第一个的所有剩余元素
// var myarr:Array[Double]=Array()
// features.foreach{x=>
// myarr = myarr :+ x.toDouble
// }
// val myvector=Vectors.dense(myarr)
// (mylable,myvector)
// }
// val training=sqc.createDataFrame(map_data).toDF("label","features")
// 创建logistics regression实例
val lr = new LogisticRegression()
// Print out the parameters, documentation, and any default values.
println("LogisticRegression parameters:\n" + lr.explainParams() + "\n")
//重新设置模型参数方法
lr.setMaxIter(10)//最大迭代步数
.setRegParam(0.01)
// 根据设定的模型参数与training data拟合训练得到模型
val model1 = lr.fit(training)
// Since model1 is a Model (i.e., a Transformer produced by an Estimator),
// we can view the parameters it used during fit().
// This prints the parameter (name: value) pairs, where names are unique IDs for this
println("Model 1 was fit using parameters: " + model1.parent.extractParamMap)
// val testData = sqlContext.createDataFrame(Seq(
// (1.0, 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)),
// (1.0, Vectors.dense(2.0, 2.2, -1.0))
// )).toDF("label", "features")
//
model1.transform(training)
.select("features", "label", "probability", "prediction")//选择数据框的某些列
.collect()//一般在filter或者足够小的结果的时候，再用collect封装返回一个数组
.foreach(println(_))
sc.stop()
}
}
部分数据如下：
942,0,193,7,3,1,0,1,0,2016,10,30,1
942,0,193,7,3,1,0,1,0,2016,10,29,1
942,0,193,7,3,1,0,1,0,2016,10,28,1
942,0,193,7,3,1,0,1,0,2016,10,27,0
942,0,193,7,3,1,0,1,0,2016,10,26,1
942,0,193,7,3,1,0,1,0,2016,10,25,0
942,0,193,7,3,1,0,1,0,2016,10,24,0
942,0,193,7,3,1,0,1,0,2016,10,23,1
942,0,193,7,3,1,0,1,0,2016,10,22,1
942,0,193,7,3,1,0,1,0,2016,10,21,0
942,0,193,7,3,1,0,1,0,2016,10,20,1
942,0,193,7,3,1,0,1,0,2016,10,19,0
942,0,193,7,3,1,0,1,0,2016,10,18,1
942,0,193,7,3,1,0,1,0,2016,10,17,1
942,0,193,7,3,1,0,1,0,2016,10,16,0
942,0,193,7,3,1,0,1,0,2016,10,15,0
942,0,193,7,3,1,0,1,0,2016,10,14,0
942,0,193,7,3,1,0,1,0,2016,10,13,1