• Spark2.x AFTSurvivalRegression算法


    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(01)
    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),
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      (12, 1, 3, 0, 0, 0, 19, 78, 12, 0),
      (12, 1, 3, 0, 0, 0, 22, 83, 14, 0),
      (9, 1, 3, 1, 0, 0, 27, 84, 18, 1),
      (6, 1, 2, 0, 0, 0, 22, 82, 13, 0),
      (16, 1, 1, 0, 1, 0, 28, 85, 12, 0),
      (1, 1, 3, 0, 0, 0, 25, 83, 11, 0),
      (6, 1, 3, 1, 0, 0, 21, 84, 12, 0),
      (4, 1, 3, 0, 0, 0, 18, 81, 10, 1),
      (2, 1, 3, 0, 0, 0, 19, 79, 11, 0),
      (4, 1, 3, 0, 0, 0, 20, 80, 11, 0),
      (8, 1, 1, 0, 1, 0, 27, 85, 11, 1),
      (6, 1, 1, 0, 0, 0, 25, 82, 16, 0),
      (40, 1, 1, 0, 0, 0, 23, 81, 11, 0),
      (24, 1, 3, 0, 0, 0, 22, 81, 16, 0),
      (1, 1, 3, 0, 0, 0, 24, 82, 13, 0),
      (5, 1, 3, 0, 0, 0, 20, 79, 12, 0),
      (2, 1, 3, 0, 0, 0, 22, 79, 13, 0),
      (8, 1, 3, 0, 1, 0, 24, 81, 12, 0),
      (4, 1, 3, 0, 0, 0, 21, 85, 13, 0),
      (12, 1, 3, 0, 0, 0, 22, 79, 3, 0),
      (12, 1, 3, 1, 0, 0, 18, 79, 6, 0),
      (3, 1, 3, 1, 1, 0, 16, 78, 7, 0),
      (48, 1, 1, 0, 0, 0, 23, 82, 14, 0),
      (8, 1, 3, 1, 0, 0, 16, 79, 8, 0),
      (14, 1, 1, 0, 1, 0, 23, 83, 11, 1),
      (9, 1, 3, 1, 0, 0, 21, 85, 12, 0),
      (4, 1, 1, 0, 1, 0, 24, 83, 12, 0),
      (14, 1, 3, 0, 0, 0, 26, 85, 14, 0),
      (3, 1, 3, 0, 0, 0, 19, 83, 10, 1),
      (3, 1, 3, 0, 0, 0, 20, 78, 11, 0),
      (6, 1, 3, 0, 0, 0, 23, 85, 12, 0),
      (12, 1, 1, 0, 0, 1, 22, 83, 12, 0),
      (8, 0, 1, 0, 1, 0, 25, 85, 12, 0),
      (3, 1, 3, 0, 0, 0, 24, 81, 12, 1),
      (3, 1, 3, 0, 0, 0, 19, 83, 12, 0),
      (10, 1, 3, 0, 0, 0, 22, 80, 12, 0),
      (3, 1, 3, 1, 1, 0, 19, 81, 11, 1),
      (44, 1, 3, 0, 0, 0, 25, 83, 16, 0),
      (2, 1, 1, 0, 0, 0, 22, 84, 13, 0),
      (1, 1, 1, 1, 0, 0, 16, 80, 8, 0),
      (1, 1, 2, 1, 0, 0, 23, 83, 12, 0),
      (12, 1, 2, 0, 0, 0, 23, 82, 14, 0),
      (4, 1, 2, 1, 0, 0, 19, 82, 12, 0),
      (16, 1, 2, 0, 1, 0, 18, 79, 11, 0),
      (5, 0, 1, 0, 0, 0, 22, 85, 12, 0),
      (24, 1, 1, 0, 0, 0, 20, 81, 9, 0),
      (6, 0, 1, 0, 0, 0, 28, 85, 14, 0),
      (52, 1, 1, 1, 0, 0, 20, 78, 12, 0),
      (2, 1, 3, 1, 0, 0, 15, 80, 8, 1),
      (2, 1, 1, 0, 1, 1, 25, 84, 13, 0),
      (10, 1, 1, 0, 0, 0, 24, 84, 12, 0),
      (48, 1, 1, 0, 0, 0, 23, 81, 12, 1),
      (52, 1, 1, 0, 0, 0, 20, 81, 12, 0),
      (2, 1, 1, 0, 0, 0, 25, 85, 14, 1),
      (24, 1, 1, 0, 1, 0, 19, 80, 9, 1),
      (12, 1, 1, 1, 1, 0, 21, 82, 10, 0),
      (8, 1, 2, 0, 0, 0, 16, 78, 11, 0),
      (4, 1, 1, 1, 0, 1, 24, 84, 12, 0),
      (4, 1, 1, 0, 0, 0, 19, 78, 12, 1),
      (4, 1, 1, 1, 0, 0, 16, 78, 9, 0),
      (8, 1, 1, 0, 0, 0, 20, 80, 12, 0),
      (48, 1, 2, 0, 0, 1, 24, 81, 14, 1),
      (24, 1, 1, 0, 0, 1, 27, 84, 18, 1),
      (3, 1, 1, 0, 1, 0, 26, 84, 16, 1),
      (20, 1, 1, 0, 1, 0, 25, 84, 15, 0),
      (52, 1, 1, 0, 1, 0, 25, 82, 16, 0),
      (60, 1, 1, 0, 1, 0, 24, 84, 14, 0),
      (2, 1, 1, 0, 0, 0, 21, 79, 13, 0),
      (2, 1, 3, 0, 0, 0, 22, 84, 14, 0),
      (5, 1, 3, 1, 0, 0, 20, 81, 12, 0),
      (12, 1, 3, 1, 0, 0, 17, 80, 8, 0),
      (24, 1, 3, 0, 0, 0, 27, 84, 10, 0),
      (12, 1, 3, 0, 0, 0, 19, 81, 12, 0),
      (4, 1, 3, 0, 0, 0, 23, 85, 13, 0),
      (2, 1, 3, 0, 0, 0, 16, 79, 10, 0),
      (6, 1, 3, 0, 0, 0, 24, 85, 13, 0),
      (1, 1, 1, 0, 0, 0, 22, 81, 15, 0),
      (4, 1, 3, 0, 0, 0, 17, 80, 10, 0),
      (2, 1, 1, 0, 1, 1, 19, 82, 8, 0),
      (48, 1, 2, 0, 0, 0, 21, 83, 12, 1),
      (8, 1, 2, 1, 0, 0, 20, 79, 9, 1),
      (24, 1, 1, 0, 0, 0, 20, 81, 12, 0),
      (12, 1, 2, 0, 0, 0, 26, 85, 14, 0),
      (2, 1, 1, 1, 1, 0, 22, 82, 12, 1),
      (3, 1, 1, 0, 1, 0, 21, 84, 9, 0),
      (96, 1, 1, 1, 0, 0, 19, 80, 8, 0),
      (3, 1, 1, 1, 0, 0, 19, 83, 10, 0),
      (16, 1, 2, 0, 1, 0, 23, 82, 16, 0),
      (6, 1, 1, 0, 1, 1, 21, 84, 12, 0),
      (4, 1, 1, 1, 0, 0, 19, 79, 10, 0),
      (56, 1, 2, 1, 0, 0, 17, 80, 11, 0),
      (4, 1, 2, 0, 0, 0, 20, 84, 12, 0),
      (6, 1, 1, 1, 0, 0, 19, 81, 10, 1),
      (2, 1, 2, 1, 0, 0, 21, 81, 14, 0),
      (8, 1, 1, 0, 0, 0, 21, 84, 14, 0),
      (14, 1, 2, 0, 0, 0, 22, 82, 13, 0),
      (8, 1, 3, 0, 0, 0, 19, 83, 10, 0),
      (1, 1, 3, 0, 0, 0, 24, 85, 9, 0),
      (2, 1, 3, 0, 0, 0, 19, 83, 11, 1),
      (16, 0, 1, 0, 0, 1, 27, 84, 16, 1),
      (8, 1, 1, 0, 0, 0, 26, 84, 16, 0),
      (48, 1, 1, 1, 1, 1, 18, 80, 8, 0),
      (52, 1, 1, 1, 1, 0, 17, 81, 7, 0),
      (25, 1, 1, 1, 1, 0, 21, 82, 11, 0),
      (20, 1, 1, 1, 1, 0, 22, 83, 12, 0),
      (12, 1, 1, 1, 1, 0, 19, 81, 11, 1),
      (8, 1, 3, 0, 0, 0, 23, 83, 14, 0),
      (5, 1, 3, 1, 1, 1, 20, 81, 10, 1),
      (4, 1, 1, 0, 1, 1, 25, 84, 16, 0),
      (16, 1, 1, 0, 0, 0, 25, 84, 16, 0),
      (1, 1, 1, 0, 0, 0, 22, 81, 15, 0),
      (5, 1, 1, 0, 1, 0, 26, 85, 16, 0),
      (3, 1, 1, 1, 1, 0, 19, 83, 11, 0),
      (46, 1, 1, 1, 1, 0, 20, 78, 12, 0),
      (8, 1, 3, 1, 0, 0, 18, 78, 12, 0),
      (40, 1, 1, 0, 0, 0, 18, 80, 10, 0),
      (1, 1, 3, 1, 1, 1, 22, 82, 12, 0),
      (4, 1, 2, 0, 0, 1, 24, 86, 13, 0),
      (3, 1, 1, 0, 1, 0, 22, 81, 12, 0),
      (2, 1, 3, 0, 0, 1, 25, 84, 12, 0),
      (2, 1, 1, 0, 0, 0, 27, 85, 15, 0),
      (3, 1, 1, 0, 0, 0, 22, 80, 16, 0),
      (46, 1, 1, 0, 0, 0, 25, 83, 14, 0),
      (2, 1, 3, 0, 0, 0, 19, 81, 12, 1),
      (36, 1, 1, 0, 0, 0, 21, 82, 12, 0),
      (6, 0, 3, 0, 0, 0, 22, 85, 12, 0),
      (8, 1, 1, 0, 0, 0, 18, 81, 11, 0),
      (16, 1, 1, 0, 0, 0, 19, 79, 12, 0),
      (16, 1, 1, 1, 1, 0, 19, 78, 9, 0),
      (12, 1, 3, 1, 0, 0, 23, 80, 9, 1),
      (6, 1, 2, 1, 0, 0, 20, 83, 13, 0),
      (5, 0, 3, 0, 0, 0, 21, 85, 14, 0),
      (16, 1, 3, 1, 1, 0, 19, 84, 11, 0),
      (12, 1, 3, 0, 0, 0, 19, 81, 12, 0),
      (1, 1, 3, 0, 0, 0, 26, 85, 11, 0),
      (4, 1, 1, 1, 1, 0, 21, 78, 12, 0),
      (24, 1, 1, 1, 0, 0, 19, 78, 11, 0),
      (1, 1, 1, 0, 0, 0, 22, 80, 12, 0),
      (10, 1, 3, 0, 1, 1, 22, 81, 13, 0),
      (10, 1, 1, 0, 0, 0, 25, 85, 13, 0),
      (2, 1, 3, 1, 0, 0, 20, 84, 12, 1),
      (2, 1, 3, 0, 1, 1, 24, 84, 12, 0),
      (64, 1, 3, 1, 0, 0, 21, 80, 11, 0),
      (8, 1, 2, 1, 0, 0, 21, 84, 12, 0),
      (48, 1, 2, 1, 1, 0, 21, 81, 12, 0),
      (28, 1, 3, 0, 0, 0, 21, 80, 11, 1),
      (24, 1, 3, 0, 0, 0, 18, 79, 10, 0),
      (18, 1, 2, 1, 0, 0, 23, 82, 11, 0),
      (6, 1, 3, 0, 0, 0, 21, 85, 12, 0),
      (3, 1, 2, 1, 1, 0, 19, 82, 11, 1),
      (56, 1, 3, 0, 0, 0, 26, 84, 15, 1),
      (18, 1, 1, 0, 0, 0, 24, 83, 13, 0),
      (2, 1, 3, 0, 0, 0, 21, 81, 9, 0),
      (6, 1, 3, 0, 0, 0, 20, 79, 6, 1),
      (12, 1, 3, 0, 0, 0, 24, 82, 14, 0),
      (32, 1, 1, 0, 1, 0, 19, 82, 9, 0),
      (3, 1, 3, 0, 0, 0, 21, 83, 12, 1),
      (72, 1, 1, 0, 0, 0, 19, 83, 11, 0),
      (5, 1, 1, 1, 1, 0, 19, 84, 7, 0),
      (12, 1, 2, 0, 0, 0, 20, 80, 13, 1),
      (8, 1, 3, 0, 0, 0, 23, 82, 12, 0),
      (8, 1, 1, 0, 1, 0, 23, 81, 13, 0),
      (16, 1, 1, 0, 1, 0, 21, 78, 14, 0),
      (2, 0, 1, 0, 0, 0, 26, 85, 13, 0),
      (3, 0, 1, 0, 0, 0, 27, 85, 16, 0),
      (12, 1, 2, 1, 0, 0, 17, 79, 10, 0),
      (40, 1, 2, 1, 0, 0, 18, 81, 12, 0),
      (96, 1, 1, 0, 0, 0, 25, 83, 14, 0),
      (4, 1, 1, 0, 1, 1, 22, 81, 12, 0),
      (3, 1, 1, 0, 1, 0, 20, 84, 12, 1),
      (12, 1, 1, 0, 0, 0, 25, 83, 16, 0),
      (28, 1, 1, 0, 1, 0, 25, 83, 16, 0),
      (7, 1, 1, 1, 1, 1, 22, 84, 11, 0),
      (1, 1, 2, 1, 0, 0, 22, 85, 15, 0),
      (1, 1, 1, 0, 1, 0, 23, 83, 14, 0),
      (4, 1, 3, 0, 0, 0, 26, 83, 9, 0),
      (3, 1, 3, 0, 0, 0, 25, 84, 14, 1),
      (12, 1, 3, 1, 0, 0, 24, 84, 12, 0),
      (1, 1, 2, 0, 0, 0, 19, 83, 12, 0),
      (14, 1, 1, 0, 0, 1, 26, 84, 15, 0),
      (12, 1, 1, 0, 0, 0, 20, 79, 12, 0),
      (8, 1, 2, 1, 0, 0, 19, 81, 12, 0),
      (12, 1, 3, 0, 0, 0, 25, 82, 12, 0),
      (1, 1, 3, 0, 0, 0, 22, 85, 11, 0),
      (8, 1, 1, 1, 0, 0, 17, 81, 10, 0),
      (8, 1, 1, 1, 1, 0, 24, 85, 9, 1),
      (8, 1, 1, 1, 1, 0, 15, 80, 9, 0),
      (7, 1, 3, 0, 1, 0, 23, 84, 13, 0),
      (12, 1, 3, 0, 0, 0, 21, 82, 12, 0),
      (6, 1, 3, 0, 0, 0, 24, 84, 12, 0),
      (2, 1, 2, 0, 0, 0, 23, 85, 14, 0),
      (16, 1, 3, 1, 0, 0, 20, 81, 11, 1),
      (24, 1, 1, 0, 0, 0, 18, 80, 9, 1),
      (24, 1, 3, 0, 0, 0, 20, 79, 10, 0),
      (12, 1, 3, 0, 1, 1, 24, 83, 9, 0),
      (72, 1, 1, 1, 0, 0, 16, 81, 9, 1),
      (6, 1, 1, 0, 1, 0, 23, 83, 12, 0),
      (1, 1, 2, 0, 0, 0, 25, 82, 13, 0),
      (48, 1, 1, 0, 0, 0, 20, 80, 12, 0),
      (3, 1, 1, 0, 1, 0, 22, 83, 12, 0),
      (24, 1, 1, 0, 0, 0, 23, 81, 12, 0),
      (12, 1, 1, 0, 0, 0, 23, 82, 12, 0),
      (26, 1, 1, 0, 0, 0, 22, 80, 12, 0),
      (3, 1, 1, 0, 1, 0, 23, 83, 12, 0),
      (24, 1, 1, 0, 0, 0, 20, 80, 12, 0),
      (1, 1, 1, 0, 0, 1, 20, 78, 12, 0),
      (5, 1, 1, 0, 1, 0, 24, 83, 12, 0),
      (2, 1, 1, 0, 1, 0, 21, 79, 12, 0),
      (12, 1, 1, 0, 0, 0, 19, 80, 12, 0),
      (48, 1, 1, 0, 0, 0, 23, 81, 12, 0),
      (3, 1, 2, 0, 1, 1, 23, 81, 13, 0),
      (4, 1, 1, 0, 1, 0, 22, 81, 12, 0),
      (6, 1, 1, 0, 1, 0, 22, 81, 12, 0),
      (4, 1, 1, 0, 1, 1, 24, 82, 13, 0),
      (52, 1, 1, 1, 0, 0, 22, 81, 12, 0),
      (8, 1, 2, 1, 0, 0, 23, 81, 12, 0),
      (20, 1, 1, 1, 0, 0, 21, 81, 12, 0),
      (20, 1, 1, 0, 0, 0, 23, 82, 12, 0),
      (28, 1, 1, 0, 0, 0, 22, 83, 12, 0),
      (4, 1, 1, 0, 0, 0, 24, 82, 12, 1),
      (16, 1, 1, 1, 0, 0, 19, 78, 12, 0),
      (8, 1, 2, 0, 0, 0, 19, 79, 12, 0),
      (5, 1, 1, 0, 0, 0, 21, 80, 12, 0),
      (11, 1, 1, 0, 1, 0, 24, 83, 12, 0),
      (4, 1, 2, 0, 0, 0, 24, 83, 12, 0),
      (3, 1, 1, 0, 0, 0, 22, 81, 12, 0),
      (2, 1, 1, 0, 0, 0, 23, 81, 12, 0),
      (2, 1, 1, 0, 0, 0, 21, 80, 12, 0),
      (6, 1, 1, 0, 0, 0, 21, 80, 13, 0),
      (8, 1, 1, 1, 1, 0, 21, 79, 12, 0),
      (1, 1, 1, 0, 1, 1, 18, 79, 12, 1),
      (48, 1, 1, 0, 1, 0, 19, 79, 12, 0),
      (32, 1, 1, 0, 0, 0, 20, 79, 13, 0),
      (32, 1, 1, 0, 0, 1, 20, 79, 12, 0),
      (6, 1, 1, 0, 1, 0, 22, 81, 12, 0),
      (6, 1, 1, 0, 0, 0, 25, 82, 12, 0),
      (24, 1, 1, 0, 0, 1, 20, 79, 12, 0),
      (4, 1, 3, 0, 0, 0, 20, 79, 14, 0),
      (16, 1, 1, 0, 0, 0, 22, 82, 12, 0),
      (12, 1, 1, 0, 0, 0, 21, 80, 12, 0),
      (2, 1, 1, 0, 1, 0, 24, 83, 12, 0),
      (1, 1, 1, 0, 0, 0, 21, 81, 12, 0),
      (3, 1, 1, 0, 1, 0, 22, 82, 12, 0),
      (7, 1, 1, 1, 1, 1, 25, 83, 12, 0),
      (56, 1, 1, 0, 0, 0, 24, 81, 12, 0),
      (1, 1, 1, 0, 0, 0, 22, 80, 12, 0),
      (24, 1, 1, 0, 0, 0, 21, 81, 12, 0),
      (16, 1, 1, 0, 1, 0, 19, 78, 12, 0),
      (20, 1, 1, 0, 1, 0, 22, 82, 12, 0),
      (1, 1, 1, 0, 0, 0, 22, 81, 13, 0),
      (120, 1, 3, 0, 0, 0, 22, 80, 12, 0),
      (44, 1, 1, 0, 0, 0, 22, 81, 14, 0),
      (3, 1, 1, 0, 0, 0, 25, 83, 12, 0),
      (32, 1, 1, 0, 0, 0, 20, 80, 12, 0),
      (6, 1, 1, 0, 0, 0, 22, 82, 12, 0),
      (52, 1, 1, 0, 0, 0, 22, 79, 12, 0),
      (24, 1, 2, 0, 0, 1, 23, 81, 12, 0),
      (72, 1, 1, 0, 0, 0, 21, 81, 12, 0),
      (24, 1, 2, 0, 0, 0, 21, 81, 12, 0),
      (3, 1, 1, 0, 1, 0, 22, 82, 10, 0),
      (6, 1, 1, 0, 1, 0, 21, 79, 12, 0),
      (24, 1, 1, 0, 1, 0, 24, 82, 12, 0),
      (16, 1, 2, 0, 0, 0, 23, 83, 12, 0),
      (5, 1, 1, 0, 1, 0, 25, 83, 12, 0),
      (48, 1, 1, 0, 0, 0, 20, 79, 12, 0),
      (10, 1, 1, 0, 1, 0, 23, 82, 12, 0),
      (48, 1, 1, 0, 0, 0, 23, 82, 13, 0),
      (2, 1, 1, 1, 1, 0, 24, 82, 12, 0),
      (6, 1, 1, 0, 0, 0, 24, 82, 14, 0),
      (36, 1, 2, 0, 0, 0, 22, 82, 14, 0),
      (12, 1, 1, 0, 0, 0, 22, 83, 12, 0),
      (2, 1, 1, 0, 1, 0, 21, 85, 12, 0),
      (4, 1, 1, 0, 1, 0, 18, 80, 12, 1),
      (24, 1, 1, 0, 0, 0, 23, 82, 13, 0),
      (1, 1, 1, 0, 0, 0, 23, 83, 11, 0),
      (20, 1, 1, 0, 0, 0, 21, 81, 13, 0),
      (76, 1, 1, 0, 0, 0, 21, 83, 14, 0),
      (8, 1, 1, 0, 0, 0, 20, 79, 12, 0),
      (4, 1, 3, 0, 0, 0, 24, 81, 11, 0),
      (36, 1, 1, 0, 1, 0, 20, 80, 12, 0),
      (6, 1, 1, 0, 0, 0, 21, 82, 12, 0),
      (32, 1, 1, 0, 0, 0, 19, 83, 14, 0),
      (4, 1, 1, 0, 0, 0, 20, 81, 12, 0),
      (4, 1, 1, 0, 1, 0, 21, 79, 12, 0),
      (40, 1, 1, 1, 1, 0, 22, 85, 12, 0),
      (3, 1, 3, 0, 0, 0, 20, 81, 9, 0),
      (8, 1, 1, 1, 0, 1, 23, 83, 10, 1),
      (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
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  • 原文地址:https://www.cnblogs.com/itboys/p/8228335.html
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