Spark2.x AFTSurvivalRegression算法

Posted 2020-10-20 itboys

Spark2.0的机器学习算法比之前的改变最大的是2.0基本采用了dataframe来实现的，但之前的都是用的RDD，看官网说貌似在3.0的时候RDD就不用了！还有一个就是hiveContext和sqlContext进行了合并，统一是sessioncontext。

在spark.ml中，实现了加速失效时间（AFT）模型，这是一个用于检查数据的参数生存回归模型。 它描述了生存时间对数的模型，因此它通常被称为生存分析的对数线性模型。 不同于为相同目的设计的比例风险模型，AFT模型更容易并行化，因为每个实例独立地贡献于目标函数。

当在具有常量非零列的数据集上匹配AFTSurvivalRegressionModel而没有截距时，Spark MLlib为常量非零列输出零系数。 这种行为不同于R survival :: survreg。

val spark = SparkSession
  .builder
  .appName("AFTSurvivalRegressionExample").master("local")
  .getOrCreate()

AFTSurvivalRegression
#实现了加速失效时间（AFT）模型，这是一个用于检查数据的参数生存回归模型。 它描述了生存时间对数的模型，因此它通常被称为生存分析的对数线性模型
val training = spark.createDataFrame(Seq(
  (1.218, 1.0, Vectors.dense(1.560, -0.605)),
  (2.949, 0.0, Vectors.dense(0.346, 2.158)),
  (3.627, 0.0, Vectors.dense(1.380, 0.231)),
  (0.273, 1.0, Vectors.dense(0.520, 1.151)),
  (4.199, 0.0, Vectors.dense(0.795, -0.226))
)).toDF("label", "censor", "features")
第一个label表示的是存活的时间，你可以把这个模型看做是预测你能活多长时间的，当然是需要很多方面的参数的
不然就是在扯淡了，虽然这预测听起来很扯淡。。。。。。
第二个censor是结局，1表示死亡，0表示删失数据，病历失访或者尚存活
表现在病人身上就是，你这个人得了一个癌症，根据你的各项指标，用这个模型预测你能活的时间
听起来就很残酷，1表示这个人已经去世，0可能是还活着或者其他因素而没获取到数据
后面的几个参数就是各种病症或者身体情况的症状了，最终都要转化为数据的形式，俗称归一化

分位数概率数组参数。
分位数概率数组的值应在范围内（0，1）
数组应该是非空的。
val quantileProbabilities = Array(0.3, 0.6)
val aft = new AFTSurvivalRegression()
  .setQuantileProbabilities(quantileProbabilities)
#如果设置该列，则会输出相应的分位数概率的分位数
 .setQuantilesCol("quantiles")
val model = aft.fit(training)

#输出模型的系数

println(s"Coefficients: ${model.coefficients}")
#模型的截距 
println(s"Intercept: ${model.intercept}")

源码里面是这个 val scale = math.exp(parameters(0))
println(s"Scale: ${model.scale}")
Coefficients: [-0.4963111466650707,0.19844437699933098]
Intercept: 2.63809461510401
Scale: 1.5472345574364692
model.transform(training).show(false)

+-----+------+--------------+------------------+--------------------------------------+
|label|censor|features      |prediction        |quantiles                             |
+-----+------+--------------+------------------+--------------------------------------+
|1.218|1.0   |[1.56,-0.605] |5.718979487635007 |[1.1603238947151664,4.99545601027477] |
|2.949|0.0   |[0.346,2.158] |18.07652118149533 |[3.667545845471739,15.789611866277625]|
|3.627|0.0   |[1.38,0.231]  |7.381861804239096 |[1.4977061305190829,6.44796261233896] |
|0.273|1.0   |[0.52,1.151]  |13.577612501425284|[2.7547621481506854,11.8598722240697] |
|4.199|0.0   |[0.795,-0.226]|9.013097744073898 |[1.8286676321297826,7.87282650587843] |
+-----+------+--------------+------------------+--------------------------------------+
还可以通过类似sql的方式来选择展示结果
model.transform(training).
selectExpr(
  "label", "censor",
  "round(prediction,2) as prediction").orderBy("label")

导入包 ：

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.Dataset
import org.apache.spark.sql.Row
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.functions._
 
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.evaluation.RegressionEvaluator
import org.apache.spark.ml.regression.AFTSurvivalRegression
import org.apache.spark.ml.tuning.{ CrossValidator, ParamGridBuilder }

导入样本数据：val spark = SparkSession.builder().appName("Spark Survival regression").config("spark.some.config.option", "some-value").getOrCreate()

// For implicit conversions like converting RDDs to DataFrames
import spark.implicits._
 
  
val dataList: List[(Double, Double, Double, Double,Double, Double, Double, Double,Double, Double)] = List(
  (16, 1, 1, 0, 0, 1, 24, 82, 14, 0),
  (1, 1, 1, 0, 1, 0, 26, 85, 12, 0),
  (4, 0, 1, 0, 0, 0, 25, 85, 12, 0),
  (3, 1, 1, 0, 1, 1, 21, 85, 9, 0),
  (36, 1, 1, 0, 1, 0, 22, 82, 12, 0),
  (36, 1, 1, 0, 0, 0, 18, 82, 11, 0),
  (16, 1, 1, 1, 1, 0, 20, 81, 9, 0),
  (8, 0, 1, 0, 1, 0, 24, 85, 12, 0),
  (20, 1, 1, 1, 0, 0, 24, 85, 12, 0),
  (44, 1, 1, 0, 0, 0, 24, 82, 14, 0),
  (20, 1, 1, 0, 1, 0, 26, 84, 12, 0),
  (30, 1, 1, 0, 1, 0, 22, 84, 12, 1),
  (24, 1, 1, 0, 0, 0, 19, 83, 12, 0),
  (13, 1, 1, 0, 0, 0, 22, 80, 14, 0),
  (6, 1, 1, 0, 0, 0, 27, 84, 16, 0),
  (2, 1, 1, 0, 0, 0, 22, 81, 12, 1),
  (5, 1, 1, 0, 0, 0, 26, 85, 13, 0),
  (192, 1, 1, 0, 0, 0, 21, 78, 12, 0),
  (4, 1, 1, 0, 0, 0, 20, 83, 12, 0),
  (12, 1, 1, 0, 1, 0, 22, 81, 12, 0),
  (4, 1, 2, 0, 0, 0, 24, 86, 12, 0),
  (16, 0, 2, 1, 1, 1, 21, 84, 12, 1),
  (24, 1, 1, 0, 0, 0, 27, 84, 14, 0),
  (16, 1, 1, 0, 1, 0, 23, 81, 12, 0),
  (3, 1, 1, 0, 0, 0, 26, 84, 14, 0),
  (16, 1, 1, 0, 1, 0, 24, 82, 14, 0),
  (28, 1, 1, 0, 0, 0, 23, 81, 12, 0),
  (1, 1, 1, 0, 1, 0, 19, 81, 12, 0),
  (13, 1, 1, 0, 0, 1, 23, 85, 14, 0),
  (36, 1, 1, 0, 0, 0, 21, 83, 10, 0),
  (32, 1, 1, 0, 1, 0, 20, 82, 12, 1),
  (16, 1, 1, 0, 1, 1, 21, 80, 12, 0),
  (2, 1, 1, 0, 0, 1, 21, 83, 12, 0),
  (8, 1, 1, 0, 0, 1, 24, 83, 14, 1),
  (1, 1, 1, 0, 0, 0, 22, 83, 10, 0),
  (24, 1, 2, 0, 0, 0, 18, 79, 11, 0),
  (64, 1, 1, 0, 1, 0, 20, 84, 12, 0),
  (8, 1, 1, 0, 0, 0, 23, 84, 10, 1),
  (2, 1, 1, 0, 0, 0, 16, 80, 9, 1),
  (12, 1, 1, 0, 0, 0, 22, 80, 13, 0),
  (18, 1, 1, 0, 0, 0, 24, 83, 12, 0),
  (32, 1, 1, 0, 0, 0, 24, 85, 14, 0),
  (2, 1, 1, 0, 0, 0, 19, 78, 12, 0),
  (7, 1, 1, 0, 0, 0, 26, 85, 16, 0),
  (5, 1, 1, 0, 0, 0, 22, 81, 12, 0),
  (44, 1, 1, 0, 0, 0, 22, 82, 16, 0),
  (2, 1, 1, 1, 1, 0, 21, 81, 12, 1),
  (4, 1, 1, 0, 1, 0, 18, 81, 10, 1),
  (2, 0, 1, 0, 0, 0, 21, 86, 12, 1),
  (6, 1, 1, 0, 0, 0, 25, 85, 16, 0),
  (4, 1, 1, 0, 0, 0, 22, 81, 12, 0),
  (36, 1, 1, 0, 0, 0, 24, 83, 12, 0),
  (12, 1, 1, 0, 0, 0, 22, 83, 13, 1),
  (24, 1, 1, 0, 0, 0, 20, 85, 12, 0),
  (42, 1, 1, 0, 0, 0, 25, 82, 16, 0),
  (16, 1, 1, 0, 1, 0, 21, 79, 13, 0),
  (44, 1, 1, 0, 0, 0, 25, 84, 12, 0),
  (44, 1, 1, 0, 0, 0, 26, 84, 16, 0),
  (10, 1, 1, 0, 0, 0, 22, 83, 12, 0),
  (4, 1, 1, 1, 0, 0, 20, 81, 11, 0),
  (12, 1, 1, 0, 1, 0, 26, 84, 12, 0),
  (28, 1, 1, 0, 0, 0, 22, 82, 12, 0),
  (24, 1, 1, 0, 0, 0, 18, 82, 11, 0),
  (6, 1, 1, 0, 1, 0, 19, 78, 11, 0),
  (28, 1, 1, 0, 0, 0, 21, 83, 12, 0),
  (12, 1, 1, 1, 1, 0, 21, 81, 12, 0),
  (34, 1, 1, 0, 0, 0, 24, 82, 12, 0),
  (2, 1, 1, 0, 0, 0, 21, 84, 13, 0),
  (48, 1, 1, 0, 0, 0, 21, 84, 14, 0),
  (16, 1, 1, 0, 0, 0, 20, 80, 12, 0),
  (26, 1, 1, 0, 0, 0, 26, 84, 12, 1),
  (6, 1, 1, 0, 1, 0, 24, 84, 14, 0),
  (2, 1, 3, 0, 0, 0, 22, 85, 12, 1),
  (24, 1, 1, 0, 0, 0, 24, 85, 12, 0),
  (6, 1, 1, 0, 0, 0, 23, 83, 12, 0),
  (16, 1, 1, 0, 1, 0, 28, 85, 16, 0),
  (16, 1, 1, 1, 0, 0, 20, 80, 12, 0),
  (8, 1, 1, 0, 1, 0, 21, 84, 12, 0),
  (28, 1, 1, 0, 0, 0, 23, 85, 14, 0),
  (7, 1, 1, 0, 0, 0, 24, 83, 15, 0),
  (5, 1, 1, 1, 1, 0, 19, 82, 8, 0),
  (18, 1, 1, 0, 1, 0, 22, 80, 12, 0),
  (4, 1, 1, 0, 1, 0, 19, 82, 12, 1),
  (18, 1, 1, 0, 0, 0, 19, 83, 12, 0),
  (6, 1, 2, 0, 0, 0, 19, 83, 12, 0),
  (1, 1, 1, 1, 1, 0, 20, 84, 8, 1),
  (10, 1, 1, 1, 1, 1, 17, 78, 8, 0),
  (1, 1, 1, 0, 1, 0, 23, 81, 13, 0),
  (1, 1, 1, 0, 0, 0, 17, 81, 11, 0),
  (12, 1, 3, 0, 0, 0, 22, 81, 12, 0),
  (12, 1, 3, 0, 0, 0, 23, 85, 14, 0),
  (40, 1, 1, 0, 0, 0, 25, 83, 12, 0),
  (12, 1, 1, 0, 1, 0, 22, 83, 12, 0),
  (14, 1, 1, 0, 1, 0, 17, 79, 11, 0),
  (52, 1, 1, 0, 1, 0, 21, 81, 12, 0),
  (5, 1, 1, 0, 1, 0, 16, 78, 10, 0),
  (3, 1, 1, 0, 1, 0, 19, 83, 12, 0),
  (28, 1, 1, 0, 0, 0, 19, 80, 10, 0),
  (16, 1, 1, 0, 1, 0, 20, 82, 12, 1),
  (8, 1, 1, 0, 0, 0, 25, 86, 16, 0),
  (4, 1, 1, 1, 1, 0, 20, 85, 10, 0),
  (13, 1, 1, 0, 0, 0, 25, 84, 12, 0),
  (20, 1, 1, 0, 1, 0, 18, 78, 10, 0),
  (12, 1, 1, 0, 0, 0, 21, 79, 12, 0),
  (4, 1, 1, 0, 0, 0, 22, 84, 12, 0),
  (52, 1, 1, 0, 1, 0, 20, 80, 12, 1),
  (23, 0, 1, 0, 0, 0, 27, 84, 12, 0),
  (16, 1, 3, 1, 0, 0, 17, 79, 9, 1),
  (4, 0, 1, 0, 1, 0, 23, 85, 13, 0),
  (6, 1, 1, 0, 0, 1, 26, 85, 12, 0),
  (4, 1, 1, 0, 0, 0, 22, 85, 12, 0),
  (1, 1, 1, 0, 0, 0, 25, 84, 13, 0),
  (16, 1, 1, 0, 0, 0, 18, 81, 12, 0),
  (18, 1, 2, 0, 1, 0, 20, 81, 14, 0),
  (3, 1, 1, 0, 0, 0, 23, 85, 12, 0),
  (16, 1, 1, 0, 0, 0, 24, 85, 14, 1),
  (96, 1, 1, 0, 0, 0, 21, 80, 13, 0),
  (24, 1, 3, 0, 0, 0, 19, 83, 9, 0),
  (48, 1, 2, 0, 1, 0, 18, 79, 12, 0),
  (4, 1, 1, 0, 0, 0, 19, 83, 11, 0),
  (2, 1, 3, 0, 0, 0, 18, 82, 12, 1),
  (16, 1, 1, 0, 1, 1, 19, 79, 11, 0),
  (28, 1, 1, 0, 0, 0, 19, 82, 12, 0),
  (1, 1, 1, 0, 0, 0, 23, 85, 12, 0),
  (52, 1, 1, 0, 0, 0, 23, 82, 14, 0),
  (32, 1, 2, 1, 0, 0, 20, 82, 14, 0),
  (20, 1, 3, 0, 0, 0, 20, 82, 12, 0),
  (12, 1, 3, 0, 0, 0, 20, 85, 12, 0),
  (8, 1, 3, 0, 0, 0, 17, 78, 11, 0),
  (36, 1, 1, 0, 0, 0, 24, 81, 16, 0),
  (8, 1, 1, 0, 0, 0, 24, 85, 12, 1),
  (24, 1, 1, 0, 0, 0, 20, 83, 11, 1),
  (36, 1, 1, 0, 0, 0, 24, 83, 16, 0),
  (16, 1, 1, 0, 0, 0, 24, 84, 12, 0),
  (3, 1, 1, 0, 1, 0, 22, 80, 12, 0),
  (32, 1, 1, 0, 0, 0, 22, 80, 15, 0),
  (16, 1, 1, 0, 0, 0, 22, 84, 12, 0),
  (48, 1, 1, 0, 0, 0, 23, 85, 16, 0),
  (1, 1, 1, 0, 0, 0, 21, 80, 12, 0),
  (36, 1, 1, 0, 1, 0, 25, 84, 12, 1),
  (30, 1, 1, 0, 0, 0, 24, 81, 12, 0),
  (2, 1, 1, 0, 0, 0, 19, 78, 12, 0),
  (52, 1, 1, 0, 0, 0, 20, 79, 13, 0),
  (36, 1, 1, 1, 0, 1, 21, 83, 13, 0),
  (28, 1, 1, 0, 0, 0, 23, 82, 12, 1),
  (48, 1, 1, 0, 0, 0, 21, 80, 16, 0),
  (16, 1, 3, 0, 1, 0, 19, 78, 7, 0),
  (4, 1, 1, 0, 0, 0, 25, 83, 12, 0),
  (1, 1, 1, 0, 0, 0, 25, 85, 12, 0),
  (6, 1, 1, 0, 0, 0, 20, 82, 12, 1),
  (12, 1, 1, 0, 0, 0, 20, 78, 12, 0),
  (24, 1, 1, 0, 1, 0, 19, 80, 11, 0),
  (40, 1, 1, 1, 0, 0, 22, 80, 10, 0),
  (8, 1, 1, 1, 0, 0, 26, 85, 19, 0),
  (4, 1, 1, 0, 1, 0, 22, 82, 12, 0),
  (20, 1, 2, 0, 0, 0, 23, 84, 12, 0),
  (6, 1, 2, 0, 0, 0, 19, 83, 12, 0),
  (12, 1, 1, 0, 0, 0, 21, 82, 12, 0),
  (8, 1, 1, 1, 0, 0, 23, 84, 16, 0),
  (3, 0, 1, 0, 0, 0, 28, 85, 16, 0),
  (12, 1, 1, 0, 0, 1, 25, 85, 16, 0),
  (16, 1, 1, 0, 0, 0, 19, 80, 12, 0),
  (20, 1, 1, 0, 1, 0, 19, 78, 8, 0),
  (12, 1, 2, 0, 1, 0, 18, 79, 12, 0),
  (16, 1, 3, 0, 0, 0, 20, 83, 12, 0),
  (17, 1, 1, 0, 1, 0, 25, 83, 11, 0),
  (60, 1, 3, 0, 0, 0, 19, 80, 11, 1),
  (12, 1, 3, 1, 0, 0, 19, 81, 10, 1),
  (4, 1, 1, 1, 0, 0, 17, 79, 10, 1),
  (16, 1, 1, 0, 0, 0, 21, 81, 14, 0),
  (3, 1, 1, 0, 1, 0, 21, 83, 12, 0),
  (3, 1, 1, 0, 1, 0, 25, 85, 12, 0),
  (16, 1, 1, 0, 0, 0, 19, 82, 12, 0),
  (24, 1, 1, 1, 0, 1, 19, 78, 12, 0),
  (21, 1, 1, 0, 1, 0, 19, 78, 12, 0),
  (32, 1, 1, 0, 0, 0, 20, 81, 12, 0),
  (8, 1, 1, 1, 1, 0, 26, 85, 11, 0),
  (6, 1, 1, 0, 0, 0, 23, 84, 12, 0),
  (16, 1, 1, 0, 1, 0, 17, 82, 11, 0),
  (12, 1, 1, 0, 1, 0, 24, 81, 12, 0),
  (16, 1, 1, 1, 0, 0, 18, 82, 12, 0),
  (16, 1, 1, 1, 1, 0, 28, 85, 12, 0),
  (8, 1, 1, 0, 0, 0, 26, 85, 16, 1),
  (20, 1, 1, 0, 1, 0, 21, 85, 12, 0),
  (40, 1, 1, 0, 0, 0, 22, 83, 12, 0),
  (12, 1, 1, 0, 0, 0, 22, 83, 12, 0),
  (36, 1, 1, 0, 0, 0, 25, 84, 16, 0),
  (18, 1, 1, 0, 0, 0, 20, 82, 13, 0),
  (48, 1, 1, 0, 0, 0, 20, 81, 12, 0),
  (7, 1, 1, 1, 1, 0, 18, 81, 10, 1),
  (1, 1, 1, 0, 0, 0, 25, 85, 12, 0),
  (24, 1, 1, 0, 0, 0, 21, 85, 12, 0),
  (12, 1, 3, 1, 0, 0, 17, 80, 9, 0),
  (36, 1, 3, 1, 0, 0, 17, 82, 8, 0),
  (3, 1, 3, 1, 0, 0, 18, 79, 10, 0),
  (1, 1, 3, 0, 0, 0, 23, 81, 6, 0),
  (5, 1, 3, 0, 0, 0, 20, 83, 10, 0),
  (24, 1, 3, 1, 0, 0, 19, 83, 11, 1),
  (21, 1, 1, 1, 0, 1, 19, 81, 12, 1),
  (1, 1, 1, 0, 1, 0, 17, 78, 11, 0),
  (8, 1, 1, 0, 0, 0, 21, 79, 12, 0),
  (24, 1, 2, 0, 0, 0, 24, 84, 16, 0),
  (68, 1, 1, 1, 0, 0, 20, 80, 12, 0),
  (20, 1, 1, 0, 0, 0, 19, 80, 14, 0),
  (2, 1, 1, 0, 1, 0, 16, 80, 9, 0),
  (12, 1, 1, 0, 0, 0, 25, 84, 12, 0),
  (40, 1, 1, 0, 0, 1, 24, 82, 14, 0),
  (4, 1, 1, 0, 0, 0, 26, 84, 12, 0),
  (1, 1, 1, 0, 0, 1, 25, 85, 12, 0),
  (32, 1, 1, 0, 1, 0, 25, 84, 11, 1),
  (1, 1, 3, 0, 0, 0, 19, 80, 10, 0),
  (6, 1, 1, 0, 0, 0, 24, 81, 16, 0),
  (13, 1, 1, 0, 0, 1, 27, 85, 16, 0),
  (2, 1, 1, 1, 1, 0, 22, 85, 12, 0),
  (50, 1, 2, 0, 0, 0, 19, 83, 11, 1),
  (32, 1, 1, 0, 1, 0, 21, 78, 12, 0),
  (12, 1, 1, 0, 1, 0, 16, 79, 9, 0),
  (2, 1, 2, 0, 0, 0, 22, 79, 12, 0),
  (8, 1, 2, 0, 0, 0, 23, 82, 12, 1),
  (20, 1, 2, 0, 0, 0, 21, 83, 15, 0),
  (12, 1, 1, 0, 0, 0, 26, 85, 12, 0),
  (8, 1, 1, 0, 0, 0, 21, 85, 12, 0),
  (1, 1, 1, 0, 0, 0, 24, 83, 13, 0),
  (16, 1, 1, 0, 1, 0, 21, 83, 11, 0),
  (24, 1, 1, 0, 0, 0, 17, 80, 11, 0),
  (4, 1, 1, 0, 0, 0, 24, 85, 13, 0),
  (22, 1, 2, 0, 0, 0, 25, 83, 12, 0),
  (5, 1, 2, 0, 0, 0, 20, 82, 12, 0),
  (36, 1, 2, 1, 0, 0, 23, 84, 12, 0),
  (12, 1, 1, 0, 0, 0, 23, 80, 12, 0),
  (52, 1, 1, 0, 0, 0, 19, 79, 13, 0),
  (9, 1, 1, 0, 0, 0, 25, 85, 16, 0),
  (6, 1, 1, 0, 0, 0, 22, 85, 14, 0),
  (26, 1, 1, 0, 0, 0, 21, 81, 13, 0),
  (8, 1, 1, 0, 0, 0, 26, 84, 16, 0),
  (18, 0, 1, 1, 0, 0, 23, 84, 14, 0),
  (2, 1, 3, 0, 0, 0, 27, 85, 19, 0),
  (20, 1, 1, 0, 0, 0, 23, 82, 16, 0),
  (16, 1, 1, 0, 0, 0, 25, 85, 14, 1),
  (2, 0, 1, 0, 0, 0, 22, 86, 12, 0),
  (48, 1, 1, 0, 0, 1, 20, 80, 12, 1),
  (4, 1, 1, 0, 1, 0, 23, 83, 11, 0),
  (8, 1, 1, 0, 0, 0, 22, 80, 12, 0),
  (40, 1, 1, 0, 1, 0, 19, 81, 12, 1),
  (14, 0, 1, 0, 0, 0, 27, 85, 19, 0),
  (80, 1, 1, 0, 0, 0, 25, 83, 16, 0),
  (13, 0, 1, 0, 0, 0, 24, 85, 16, 0),
  (20, 1, 1, 0, 1, 0, 26, 84, 16, 1),
  (44, 1, 1, 0, 0, 0, 18, 78, 12, 0),
  (12, 1, 1, 0, 0, 0, 20, 84, 13, 0),
  (20, 1, 1, 0, 0, 0, 22, 79, 14, 0),
  (32, 1, 1, 0, 0, 0, 24, 84, 12, 1),
  (2, 1, 1, 0, 0, 0, 23, 83, 12, 0),
  (28, 1, 2, 0, 0, 0, 22, 85, 14, 1),
  (6, 1, 2, 0, 0, 0, 21, 84, 12, 0),
  (6, 1, 1, 0, 1, 0, 26, 83, 12, 0),
  (38, 1, 1, 0, 0, 0, 22, 81, 12, 0),
  (16, 1, 1, 0, 1, 0, 21, 80, 14, 0),
  (1, 1, 1, 0, 0, 0, 26, 85, 12, 0),
  (7, 1, 1, 0, 0, 0, 26, 84, 12, 0),
  (10, 1, 1, 0, 1, 0, 23, 83, 12, 0),
  (3, 1, 1, 0, 0, 0, 23, 82, 16, 0),
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  (2, 1, 1, 0, 1, 1, 25, 84, 13, 0),
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  (16, 0, 1, 0, 0, 1, 27, 84, 16, 1),
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  (40, 1, 1, 1, 1, 0, 22, 85, 12, 0),
  (3, 1, 3, 0, 0, 0, 20, 81, 9, 0),
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  (32, 1, 1, 0, 0, 0, 18, 80, 11, 0),
  (10, 0, 1, 0, 0, 0, 28, 85, 15, 1),
  (1, 1, 1, 0, 0, 0, 20, 84, 12, 1),
  (1, 1, 1, 0, 1, 0, 20, 80, 12, 0),
  (5, 1, 1, 1, 1, 1, 21, 78, 15, 0),
  (24, 1, 1, 0, 1, 0, 18, 81, 10, 0),
  (48, 1, 1, 0, 0, 0, 23, 83, 12, 0),
  (12, 1, 1, 0, 1, 0, 24, 83, 12, 0),
  (20, 1, 1, 0, 0, 0, 27, 85, 17, 0),
  (4, 1, 1, 0, 1, 0, 21, 84, 12, 0),
  (16, 1, 1, 1, 1, 0, 21, 82, 12, 0),
  (32, 1, 3, 1, 0, 0, 18, 82, 10, 1),
  (24, 1, 1, 0, 0, 0, 26, 85, 14, 1),
  (13, 1, 1, 0, 0, 0, 21, 82, 12, 0),
  (1, 1, 1, 1, 0, 0, 16, 81, 9, 1),
  (16, 1, 1, 0, 1, 0, 20, 81, 12, 0),
  (44, 1, 1, 0, 0, 0, 21, 80, 12, 0),
  (32, 1, 1, 0, 1, 1, 22, 83, 12, 0),
  (1, 1, 1, 0, 1, 1, 20, 83, 12, 0),
  (8, 1, 1, 0, 1, 0, 22, 79, 12, 0),
  (6, 1, 1, 0, 0, 0, 22, 82, 13, 1),
  (6, 1, 1, 1, 1, 0, 21, 81, 12, 0),
  (32, 1, 1, 0, 0, 1, 25, 84, 18, 0),
  (28, 1, 1, 0, 1, 1, 19, 80, 12, 0),
  (7, 1, 1, 0, 0, 1, 24, 83, 14, 0),
  (12, 1, 1, 1, 1, 1, 20, 80, 10, 1),
  (4, 1, 1, 1, 0, 0, 18, 81, 11, 0),
  (32, 1, 1, 0, 0, 0, 23, 81, 16, 0),
  (48, 1, 1, 1, 0, 0, 20, 84, 11, 1),
  (32, 1, 1, 1, 0, 0, 17, 78, 12, 1),
  (3, 1, 2, 0, 1, 0, 19, 79, 11, 1),
  (4, 1, 2, 0, 0, 0, 21, 78, 12, 0),
  (4, 1, 1, 0, 1, 0, 23, 82, 12, 0),
  (2, 1, 1, 0, 0, 0, 24, 82, 13, 0),
  (3, 1, 1, 0, 0, 1, 21, 80, 12, 0),
  (1, 1, 1, 0, 1, 1, 20, 81, 12, 0),
  (32, 1, 1, 0, 1, 0, 24, 81, 12, 0),
  (24, 1, 1, 0, 0, 0, 20, 80, 12, 0),
  (4, 1, 1, 0, 0, 0, 20, 79, 12, 0),
  (5, 1, 1, 0, 0, 1, 22, 81, 12, 0),
  (24, 1, 2, 0, 0, 0, 21, 80, 12, 0),
  (6, 1, 1, 0, 1, 0, 20, 80, 12, 0))
 
val data = dataList.toDF("label", "censor","race","poverty","smoke","alcohol","agemth","ybirth","yschool","pc3mth").orderBy("label")

建模并调优:

val colArray = Array("race", "poverty", "smoke", "alcohol", "agemth", "ybirth", "yschool", "pc3mth")
  
val assembler = new VectorAssembler().setInputCols(colArray).setOutputCol("features")
  
val vecDF: DataFrame = assembler.transform(data)
  
val Array(trainingDF, testDF) = vecDF.randomSplit(Array(0.7, 0.3))
  
//###########################
// 建立生存回归模型
val AFT = new AFTSurvivalRegression().setFeaturesCol("features").setLabelCol("label").setCensorCol("censor").fit(trainingDF)
  
// 设置管道
val pipeline = new Pipeline().setStages(Array(AFT))
  
// 设置参数网格
val paramGrid = new ParamGridBuilder().addGrid(AFT.maxIter, Array(100, 500, 1000)).addGrid(AFT.tol, Array(1E-2, 1E-6)).build()
  
// 选择(prediction, true label)，计算测试误差。
// 注意RegEvaluator.isLargerBetter，评估的度量值是大的好，还是小的好，系统会自动识别
val RegEvaluator = new RegressionEvaluator().setLabelCol(AFT.getLabelCol).setPredictionCol(AFT.getPredictionCol).setMetricName("rmse")
  
// 设置交叉验证
val cv = new CrossValidator().setEstimator(pipeline).setEvaluator(RegEvaluator).setEstimatorParamMaps(paramGrid).setNumFolds(3)
  
// 执行交叉验证，并选择出最好的参数集
val cvModel = cv.fit(trainingDF)
  
// 查看全部参数
cvModel.extractParamMap()
// cvModel.avgMetrics.length=cvModel.getEstimatorParamMaps.length
// cvModel.avgMetrics与cvModel.getEstimatorParamMaps中的元素一一对应
cvModel.avgMetrics.length
cvModel.avgMetrics // 参数对应的平均度量
  
cvModel.getEstimatorParamMaps.length
cvModel.getEstimatorParamMaps // 参数组合的集合
  
cvModel.getEvaluator.extractParamMap()  // 评估的参数
  
cvModel.getEvaluator.isLargerBetter // 评估的度量值是大的好，还是小的好
cvModel.getNumFolds // 交叉验证的折数
  
//################################
// 测试模型
val predictDF: DataFrame = cvModel.transform(testDF).selectExpr(
  //"race","poverty","smoke","alcohol","agemth","ybirth","yschool","pc3mth", "features",
  "label", "censor",
  "round(prediction,2) as prediction").orderBy("label")
predictDF.show
  
spark.stop()

代码执行结果 ：

// 查看全部参数
cvModel.extractParamMap()
res2: org.apache.spark.ml.param.ParamMap =
{
    cv_baf8c9af33b7-estimator: pipeline_20ba567066f7,
    cv_baf8c9af33b7-estimatorParamMaps: [Lorg.apache.spark.ml.param.ParamMap;@412a07c8,
    cv_baf8c9af33b7-evaluator: regEval_59075079f1c9,
    cv_baf8c9af33b7-numFolds: 3,
    cv_baf8c9af33b7-seed: -1191137437
}
  
// cvModel.avgMetrics.length=cvModel.getEstimatorParamMaps.length
// cvModel.avgMetrics与cvModel.getEstimatorParamMaps中的元素一一对应
cvModel.avgMetrics.length
res3: Int = 6
  
cvModel.avgMetrics // 参数对应的平均度量
res4: Array[Double] = Array(18.53, 17.53, 19.53, 17.63, 18.53, 18.93)
  
cvModel.getEstimatorParamMaps.length
res5: Int = 6
  
cvModel.getEstimatorParamMaps // 参数组合的集合
res6: Array[org.apache.spark.ml.param.ParamMap] =
Array({
    aftSurvReg_a7e5bc450599-maxIter: 100,
    aftSurvReg_a7e5bc450599-tol: 0.01
}, {
    aftSurvReg_a7e5bc450599-maxIter: 100,
    aftSurvReg_a7e5bc450599-tol: 1.0E-6
}, {
    aftSurvReg_a7e5bc450599-maxIter: 500,
    aftSurvReg_a7e5bc450599-tol: 0.01
}, {
    aftSurvReg_a7e5bc450599-maxIter: 500,
    aftSurvReg_a7e5bc450599-tol: 1.0E-6
}, {
    aftSurvReg_a7e5bc450599-maxIter: 1000,
    aftSurvReg_a7e5bc450599-tol: 0.01
}, {
    aftSurvReg_a7e5bc450599-maxIter: 1000,
    aftSurvReg_a7e5bc450599-tol: 1.0E-6
})
  
cvModel.getEvaluator.extractParamMap()  // 评估的参数
res7: org.apache.spark.ml.param.ParamMap =
{
    regEval_59075079f1c9-labelCol: label,
    regEval_59075079f1c9-metricName: rmse,
    regEval_59075079f1c9-predictionCol: prediction
}
  
cvModel.getEvaluator.isLargerBetter // 评估的度量值是大的好，还是小的好
res8: Boolean = false   // 这里显示“评估的度量值”是小的好
  
cvModel.getNumFolds // 交叉验证的折数
res9: Int = 3
  
//################################
// 测试模型
val predictDF: DataFrame = cvModel.transform(testDF).selectExpr(
     |   //"race","poverty","smoke","alcohol","agemth","ybirth","yschool","pc3mth", "features",
     |   "label", "censor",
     |   "round(prediction,2) as prediction").orderBy("label")
predictDF: org.apache.spark.sql.DataFrame = [label: double, censor: double ... 1 more field]
  
predictDF.show
+-----+------+----------+                                                     
|label|censor|prediction|
+-----+------+----------+
|  1.0|   1.0|      15.4|
|  1.0|   1.0|     20.02|
|  1.0|   1.0|     18.73|
|  1.0|   1.0|     21.58|
|  1.0|   1.0|      21.8|
|  1.0|   1.0|      21.8|
|  1.0|   1.0|     14.37|
|  1.0|   1.0|      13.5|
|  1.0|   1.0|     15.82|
|  1.0|   1.0|     19.51|
|  1.0|   1.0|     13.17|
|  1.0|   1.0|      11.9|
|  1.0|   1.0|     17.26|
|  1.0|   1.0|     13.57|
|  1.0|   1.0|     11.57|
|  1.0|   1.0|     13.55|
|  1.0|   1.0|     10.95|
|  1.0|   1.0|     14.92|
|  1.0|   1.0|     12.25|
|  1.0|   1.0|     19.62|
+-----+------+----------+
only showing top 20 rows