Spark MLlib快速入门(1)逻辑回归、Kmeans、决策树、Pipeline、交叉验证

Spark MLlib快速入门(1)逻辑回归、Kmeans、决策树案例

除了scikit-learn外，在spark中也提供了机器学习库，即Spark MLlib。

在Spark MLlib机器学习库提供两套算法实现的API：基于RDD API和基于DataFrame API。今天，主要介绍下DataFrame API的使用，不涉及算法的原理。

主要提供的算法如下：

• 分类

  • 逻辑回归、贝叶斯支持向量机

• 聚类

  • K-均值

• 推荐

  • 交替最小二乘法

• 回归

  • 线性回归

• 树

  • 决策树、随机森林

1 Spark MLlib中逻辑回归在鸢尾花数据集上的应用

鸢尾花数据集，总共150条数据，分为三种类别的鸢尾花。

鸢尾花数据集属于分类算法，构建分类模型，此处使用逻辑回归分类算法构建分类模型，进行预测。

全部基于DataFrame API算法库和特征工程函数使用。

使用的spark版本为2.3。

1.1 读取数据

package com.yyds.tags.ml.classification

import org.apache.spark.ml.classification.{
    
    LogisticRegression, LogisticRegressionModel}
import org.apache.spark.ml.feature.{
    
    Normalizer, StringIndexer, StringIndexerModel, VectorAssembler}
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.sql.{
    
    DataFrame, SparkSession}
import org.apache.spark.sql.types.{
    
    DoubleType, StringType, StructType}
import org.apache.spark.storage.StorageLevel

object IrisClassification {
    
    

  def main(args: Array[String]): Unit = {
    
    

    // 构建SparkSession实例对象
    val spark: SparkSession = SparkSession.builder()
      .appName(this.getClass.getSimpleName.stripSuffix("$"))
      .master("local[4]")
      .config("spark.sql.shuffle.partitions",4)
      .getOrCreate()

    import spark.implicits._

    // TODO step1 -> 读取数据
    val isrsSchema: StructType = new StructType()
      .add("sepal_length",DoubleType,nullable = true)
      .add("sepal_width",DoubleType,nullable = true)
      .add("petal_length",DoubleType,nullable = true)
      .add("petal_width",DoubleType,nullable = true)
      .add("category",StringType, nullable = true)

    val rawIrisDF: DataFrame =  spark.read
      .option("sep",",")
      // 当首行不是列名称时候，需要自动设置schema
      .option("header","false")
      .option("inferSchema","false")
      .schema(isrsSchema)
      .csv("datas/iris/iris.data")

    rawIrisDF.printSchema()
    rawIrisDF.show(10,truncate = false)

  }

}

root
 |-- sepal_length: double (nullable = true)
 |-- sepal_width: double (nullable = true)
 |-- petal_length: double (nullable = true)
 |-- petal_width: double (nullable = true)
 |-- category: string (nullable = true)
 
 
+------------+-----------+------------+-----------+-----------+
|sepal_length|sepal_width|petal_length|petal_width|category   |
+------------+-----------+------------+-----------+-----------+
|5.1         |3.5        |1.4         |0.2        |Iris-setosa|
|4.9         |3.0        |1.4         |0.2        |Iris-setosa|
|4.7         |3.2        |1.3         |0.2        |Iris-setosa|
|4.6         |3.1        |1.5         |0.2        |Iris-setosa|
|5.0         |3.6        |1.4         |0.2        |Iris-setosa|
|5.4         |3.9        |1.7         |0.4        |Iris-setosa|
|4.6         |3.4        |1.4         |0.3        |Iris-setosa|
|5.0         |3.4        |1.5         |0.2        |Iris-setosa|
|4.4         |2.9        |1.4         |0.2        |Iris-setosa|
|4.9         |3.1        |1.5         |0.1        |Iris-setosa|
+------------+-----------+------------+-----------+-----------+

1.2 特征工程

    // TODO step2 -> 特征工程
    /*
      1、类别转换数值类型
         类别特征索引化 -> label
      2、组合特征值
         features: Vector
    */
    // 1、类别特征转换 StringIndexer
    val indexerModel: StringIndexerModel = new StringIndexer()
      .setInputCol("category")
      .setOutputCol("label")
      .fit(rawIrisDF)

    val df1: DataFrame = indexerModel.transform(rawIrisDF)

    // 2、组合特征值 VectorAssembler
    val assembler: VectorAssembler = new VectorAssembler()
      // 设置特征列名称
      .setInputCols(rawIrisDF.columns.dropRight(1))
      .setOutputCol("raw_features")

    val rawFeaturesDF: DataFrame = assembler.transform(df1)

    
    // 3、特征值正则化，使用L2正则
    val normalizer: Normalizer = new Normalizer()
      .setInputCol("raw_features")
      .setOutputCol("features")
      .setP(2.0)

    val featuresDF: DataFrame = normalizer.transform(rawFeaturesDF)
    
    // 将数据集缓存，LR算法属于迭代算法，使用多次
    featuresDF.persist(StorageLevel.MEMORY_AND_DISK).count()

    featuresDF.printSchema()
    featuresDF.show(10, truncate = false)

root
 |-- sepal_length: double (nullable = true)
 |-- sepal_width: double (nullable = true)
 |-- petal_length: double (nullable = true)
 |-- petal_width: double (nullable = true)
 |-- category: string (nullable = true)
 |-- label: double (nullable = true)
 |-- raw_features: vector (nullable = true)
 |-- features: vector (nullable = true)

1.3 训练模型

    // TODO step3 -> 模型训练
    val lr: LogisticRegression = new LogisticRegression()
      // 设置列名称
      .setLabelCol("label")
      .setFeaturesCol("features")
      .setPredictionCol("prediction")
      // 设置迭代次数
      .setMaxIter(10)
      .setRegParam(0.3) // 正则化参数
      .setElasticNetParam(0.8) // 弹性网络参数：L1正则和L2正则联合使用



    val lrModel: LogisticRegressionModel = lr.fit(featuresDF)

1.4 模型预测

    // TODO step4 -> 使用模型预测
    val predictionDF: DataFrame = lrModel.transform(featuresDF)


    predictionDF
       // 获取真实标签类别和预测标签类别
      .select("label", "prediction")
      .show(10)

1.5 模型评估

 // TODO step5 -> 模型评估：准确度 = 预测正确的样本数 / 所有的样本数
    import  org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator

    val evaluator = new MulticlassClassificationEvaluator()
      .setLabelCol("label")
      .setPredictionCol("prediction")
      .setMetricName("accuracy")

    # accuracy = 0.9466666666666667
    println(s"accuracy = ${
      
      evaluator.evaluate(predictionDF)}")

1.6 模型的保存与加载

   // TODO step6 ->  模型调优，此处省略


    // TODO step7 ->  模型保存与加载
    val modelPath = s"datas/models/lrModel-${
      
      System.currentTimeMillis()}"
    // 保存模型
    lrModel.save(modelPath)
    // 加载模型
    val loadLrModel = LogisticRegressionModel.load(modelPath)
    // 模型预测
    loadLrModel.transform(
      Seq(
        Vectors.dense(Array(5.1,3.5,1.4,0.2))
      )
        .map(x => Tuple1.apply(x))
        .toDF("features")
    ).show(10, truncate = false)

    // 应用结束，关闭资源
    spark.stop()

2 Spark MLlib中KMeans在鸢尾花数据集上的应用

2.1 读取数据集

iris_kmeans.txt数据如下

1 1:5.1 2:3.5 3:1.4 4:0.2
1 1:4.9 2:3.0 3:1.4 4:0.2
1 1:4.7 2:3.2 3:1.3 4:0.2
1 1:4.6 2:3.1 3:1.5 4:0.2
1 1:5.0 2:3.6 3:1.4 4:0.2
1 1:5.4 2:3.9 3:1.7 4:0.4
1 1:4.6 2:3.4 3:1.4 4:0.3
1 1:5.0 2:3.4 3:1.5 4:0.2
1 1:4.4 2:2.9 3:1.4 4:0.2
1 1:4.9 2:3.1 3:1.5 4:0.1
1 1:5.4 2:3.7 3:1.5 4:0.2
1 1:4.8 2:3.4 3:1.6 4:0.2
1 1:4.8 2:3.0 3:1.4 4:0.1
1 1:4.3 2:3.0 3:1.1 4:0.1
1 1:5.8 2:4.0 3:1.2 4:0.2
1 1:5.7 2:4.4 3:1.5 4:0.4
1 1:5.4 2:3.9 3:1.3 4:0.4
1 1:5.1 2:3.5 3:1.4 4:0.3
1 1:5.7 2:3.8 3:1.7 4:0.3
1 1:5.1 2:3.8 3:1.5 4:0.3
1 1:5.4 2:3.4 3:1.7 4:0.2
1 1:5.1 2:3.7 3:1.5 4:0.4
1 1:4.6 2:3.6 3:1.0 4:0.2
1 1:5.1 2:3.3 3:1.7 4:0.5
1 1:4.8 2:3.4 3:1.9 4:0.2
1 1:5.0 2:3.0 3:1.6 4:0.2
1 1:5.0 2:3.4 3:1.6 4:0.4
1 1:5.2 2:3.5 3:1.5 4:0.2
1 1:5.2 2:3.4 3:1.4 4:0.2
1 1:4.7 2:3.2 3:1.6 4:0.2
1 1:4.8 2:3.1 3:1.6 4:0.2
1 1:5.4 2:3.4 3:1.5 4:0.4
1 1:5.2 2:4.1 3:1.5 4:0.1
1 1:5.5 2:4.2 3:1.4 4:0.2
1 1:4.9 2:3.1 3:1.5 4:0.1
1 1:5.0 2:3.2 3:1.2 4:0.2
1 1:5.5 2:3.5 3:1.3 4:0.2
1 1:4.9 2:3.1 3:1.5 4:0.1
1 1:4.4 2:3.0 3:1.3 4:0.2
1 1:5.1 2:3.4 3:1.5 4:0.2
1 1:5.0 2:3.5 3:1.3 4:0.3
1 1:4.5 2:2.3 3:1.3 4:0.3
1 1:4.4 2:3.2 3:1.3 4:0.2
1 1:5.0 2:3.5 3:1.6 4:0.6
1 1:5.1 2:3.8 3:1.9 4:0.4
1 1:4.8 2:3.0 3:1.4 4:0.3
1 1:5.1 2:3.8 3:1.6 4:0.2
1 1:4.6 2:3.2 3:1.4 4:0.2
1 1:5.3 2:3.7 3:1.5 4:0.2
1 1:5.0 2:3.3 3:1.4 4:0.2
2 1:7.0 2:3.2 3:4.7 4:1.4
2 1:6.4 2:3.2 3:4.5 4:1.5
2 1:6.9 2:3.1 3:4.9 4:1.5
2 1:5.5 2:2.3 3:4.0 4:1.3
2 1:6.5 2:2.8 3:4.6 4:1.5
2 1:5.7 2:2.8 3:4.5 4:1.3
2 1:6.3 2:3.3 3:4.7 4:1.6
2 1:4.9 2:2.4 3:3.3 4:1.0
2 1:6.6 2:2.9 3:4.6 4:1.3
2 1:5.2 2:2.7 3:3.9 4:1.4
2 1:5.0 2:2.0 3:3.5 4:1.0
2 1:5.9 2:3.0 3:4.2 4:1.5
2 1:6.0 2:2.2 3:4.0 4:1.0
2 1:6.1 2:2.9 3:4.7 4:1.4
2 1:5.6 2:2.9 3:3.6 4:1.3
2 1:6.7 2:3.1 3:4.4 4:1.4
2 1:5.6 2:3.0 3:4.5 4:1.5
2 1:5.8 2:2.7 3:4.1 4:1.0
2 1:6.2 2:2.2 3:4.5 4:1.5
2 1:5.6 2:2.5 3:3.9 4:1.1
2 1:5.9 2:3.2 3:4.8 4:1.8
2 1:6.1 2:2.8 3:4.0 4:1.3
2 1:6.3 2:2.5 3:4.9 4:1.5
2 1:6.1 2:2.8 3:4.7 4:1.2
2 1:6.4 2:2.9 3:4.3 4:1.3
2 1:6.6 2:3.0 3:4.4 4:1.4
2 1:6.8 2:2.8 3:4.8 4:1.4
2 1:6.7 2:3.0 3:5.0 4:1.7
2 1:6.0 2:2.9 3:4.5 4:1.5
2 1:5.7 2:2.6 3:3.5 4:1.0
2 1:5.5 2:2.4 3:3.8 4:1.1
2 1:5.5 2:2.4 3:3.7 4:1.0
2 1:5.8 2:2.7 3:3.9 4:1.2
2 1:6.0 2:2.7 3:5.1 4:1.6
2 1:5.4 2:3.0 3:4.5 4:1.5
2 1:6.0 2:3.4 3:4.5 4:1.6
2 1:6.7 2:3.1 3:4.7 4:1.5
2 1:6.3 2:2.3 3:4.4 4:1.3
2 1:5.6 2:3.0 3:4.1 4:1.3
2 1:5.5 2:2.5 3:4.0 4:1.3
2 1:5.5 2:2.6 3:4.4 4:1.2
2 1:6.1 2:3.0 3:4.6 4:1.4
2 1:5.8 2:2.6 3:4.0 4:1.2
2 1:5.0 2:2.3 3:3.3 4:1.0
2 1:5.6 2:2.7 3:4.2 4:1.3
2 1:5.7 2:3.0 3:4.2 4:1.2
2 1:5.7 2:2.9 3:4.2 4:1.3
2 1:6.2 2:2.9 3:4.3 4:1.3
2 1:5.1 2:2.5 3:3.0 4:1.1
2 1:5.7 2:2.8 3:4.1 4:1.3
3 1:6.3 2:3.3 3:6.0 4:2.5
3 1:5.8 2:2.7 3:5.1 4:1.9
3 1:7.1 2:3.0 3:5.9 4:2.1
3 1:6.3 2:2.9 3:5.6 4:1.8
3 1:6.5 2:3.0 3:5.8 4:2.2
3 1:7.6 2:3.0 3:6.6 4:2.1
3 1:4.9 2:2.5 3:4.5 4:1.7
3 1:7.3 2:2.9 3:6.3 4:1.8
3 1:6.7 2:2.5 3:5.8 4:1.8
3 1:7.2 2:3.6 3:6.1 4:2.5
3 1:6.5 2:3.2 3:5.1 4:2.0
3 1:6.4 2:2.7 3:5.3 4:1.9
3 1:6.8 2:3.0 3:5.5 4:2.1
3 1:5.7 2:2.5 3:5.0 4:2.0
3 1:5.8 2:2.8 3:5.1 4:2.4
3 1:6.4 2:3.2 3:5.3 4:2.3
3 1:6.5 2:3.0 3:5.5 4:1.8
3 1:7.7 2:3.8 3:6.7 4:2.2
3 1:7.7 2:2.6 3:6.9 4:2.3
3 1:6.0 2:2.2 3:5.0 4:1.5
3 1:6.9 2:3.2 3:5.7 4:2.3
3 1:5.6 2:2.8 3:4.9 4:2.0
3 1:7.7 2:2.8 3:6.7 4:2.0
3 1:6.3 2:2.7 3:4.9 4:1.8
3 1:6.7 2:3.3 3:5.7 4:2.1
3 1:7.2 2:3.2 3:6.0 4:1.8
3 1:6.2 2:2.8 3:4.8 4:1.8
3 1:6.1 2:3.0 3:4.9 4:1.8
3 1:6.4 2:2.8 3:5.6 4:2.1
3 1:7.2 2:3.0 3:5.8 4:1.6
3 1:7.4 2:2.8 3:6.1 4:1.9
3 1:7.9 2:3.8 3:6.4 4:2.0
3 1:6.4 2:2.8 3:5.6 4:2.2
3 1:6.3 2:2.8 3:5.1 4:1.5
3 1:6.1 2:2.6 3:5.6 4:1.4
3 1:7.7 2:3.0 3:6.1 4:2.3
3 1:6.3 2:3.4 3:5.6 4:2.4
3 1:6.4 2:3.1 3:5.5 4:1.8
3 1:6.0 2:3.0 3:4.8 4:1.8
3 1:6.9 2:3.1 3:5.4 4:2.1
3 1:6.7 2:3.1 3:5.6 4:2.4
3 1:6.9 2:3.1 3:5.1 4:2.3
3 1:5.8 2:2.7 3:5.1 4:1.9
3 1:6.8 2:3.2 3:5.9 4:2.3
3 1:6.7 2:3.3 3:5.7 4:2.5
3 1:6.7 2:3.0 3:5.2 4:2.3
3 1:6.3 2:2.5 3:5.0 4:1.9
3 1:6.5 2:3.0 3:5.2 4:2.0
3 1:6.2 2:3.4 3:5.4 4:2.3
3 1:5.9 2:3.0 3:5.1 4:1.8

package com.yyds.tags.ml.clustering

import org.apache.spark.ml.clustering.{
    
    KMeans, KMeansModel}
import org.apache.spark.sql.{
    
    DataFrame, SparkSession}

/**
 * 使用KMeans算法对鸢尾花数据进行聚类操作
 */
object IrisClusterTest {
    
    
  def main(args: Array[String]): Unit = {
    
    

    val spark = SparkSession.builder()
      .appName(this.getClass.getSimpleName.stripSuffix("$"))
      .master("local[2]")
      .config("spark.sql.shuffle.partitions", "2")
      .getOrCreate()

    import org.apache.spark.sql.functions._
    import spark.implicits._


    // 1. 读取鸢尾花数据集
    val irisDF: DataFrame = spark.read
      .format("libsvm")
      .load("datas/iris/iris_kmeans.txt")
    irisDF.printSchema()
    irisDF.show(10, truncate = false)
  }

}

root
 |-- label: double (nullable = true)
 |-- features: vector (nullable = true)



+-----+-------------------------------+
|label|features                       |
+-----+-------------------------------+
|1.0  |(4,[0,1,2,3],[5.1,3.5,1.4,0.2])|
|1.0  |(4,[0,1,2,3],[4.9,3.0,1.4,0.2])|
|1.0  |(4,[0,1,2,3],[4.7,3.2,1.3,0.2])|
|1.0  |(4,[0,1,2,3],[4.6,3.1,1.5,0.2])|
|1.0  |(4,[0,1,2,3],[5.0,3.6,1.4,0.2])|
|1.0  |(4,[0,1,2,3],[5.4,3.9,1.7,0.4])|
|1.0  |(4,[0,1,2,3],[4.6,3.4,1.4,0.3])|
|1.0  |(4,[0,1,2,3],[5.0,3.4,1.5,0.2])|
|1.0  |(4,[0,1,2,3],[4.4,2.9,1.4,0.2])|
|1.0  |(4,[0,1,2,3],[4.9,3.1,1.5,0.1])|
+-----+-------------------------------+
only showing top 10 rows

2.2 模型训练

// 2. 构建KMeans算法
    val kmeans: KMeans = new KMeans()
      // 设置输入特征列名称和输出列的名名称
      .setFeaturesCol("features")
      .setPredictionCol("prediction")
      // 设置K值为3
      .setK(3)
      // 设置最大的迭代次数
      .setMaxIter(20)


    // 3. 应用数据集训练模型, 获取转换器
    val kMeansModel: KMeansModel = kmeans.fit(irisDF)

    // 获取聚类的簇中心点
    kMeansModel.clusterCenters.foreach(println)

[5.88360655737705,2.7409836065573776,4.388524590163936,1.4344262295081969]
[5.005999999999999,3.4180000000000006,1.4640000000000002,0.2439999999999999]
[6.853846153846153,3.0769230769230766,5.715384615384615,2.053846153846153]

2.3 模型评估和预测

   // 4. 模型评估
    val wssse: Double = kMeansModel.computeCost(irisDF)
    println(s"WSSSE = ${
      
      wssse}")


    // 5. 使用模型预测
    val predictionDF: DataFrame = kMeansModel.transform(irisDF)

    predictionDF.show(10, truncate = false)

    // 应用结束，关闭资源
    spark.stop()

+-----+-------------------------------+----------+
|label|features                       |prediction|
+-----+-------------------------------+----------+
|1.0  |(4,[0,1,2,3],[5.1,3.5,1.4,0.2])|1         |
|1.0  |(4,[0,1,2,3],[4.9,3.0,1.4,0.2])|1         |
|1.0  |(4,[0,1,2,3],[4.7,3.2,1.3,0.2])|1         |
|1.0  |(4,[0,1,2,3],[4.6,3.1,1.5,0.2])|1         |
|1.0  |(4,[0,1,2,3],[5.0,3.6,1.4,0.2])|1         |
|1.0  |(4,[0,1,2,3],[5.4,3.9,1.7,0.4])|1         |
|1.0  |(4,[0,1,2,3],[4.6,3.4,1.4,0.3])|1         |
|1.0  |(4,[0,1,2,3],[5.0,3.4,1.5,0.2])|1         |
|1.0  |(4,[0,1,2,3],[4.4,2.9,1.4,0.2])|1         |
|1.0  |(4,[0,1,2,3],[4.9,3.1,1.5,0.1])|1         |
+-----+-------------------------------+----------+

3 Spark MLlib中决策树入门案例

决策树学习采用的是 自顶向下 的递归方法 ，其基本思想是以信息熵为度量构造一颗熵值下降最快的树，到叶子节点处，熵值为0。其具有可读性、分类速度快的优点，是一种有监督学习。

最早提及决策树思想的是Quinlan在1986年提出的ID3算法和1993年提出的C4.5算法，以及Breiman等人在1984年提出的CART算法。

决策树算法是机器学习算法中非常重要的算法之一，既可以分类又可以回归，其中还可以构建出集成学习算法。

由于决策树分类模型 DecisionTreeClassificationModel 属于概率分类模型ProbabilisticClassificationModel ，所以构建模型时要求数据集中标签label必须从0开始。

上述数据集中特征：退款和婚姻状态，都是类别类型特征，需要将其转换为数值特征，数值从0开始计算。

针对 特征：退款 来说，将其转换为【0,1】两个值，不能是【1,2】数值。

3.1 读取数据

package com.yyds.tags.ml.classification

import org.apache.spark.ml.classification.{
    
    DecisionTreeClassificationModel, DecisionTreeClassifier}
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import org.apache.spark.ml.feature.{
    
    StringIndexer, StringIndexerModel, VectorIndexer, VectorIndexerModel}
import org.apache.spark.sql.{
    
    DataFrame, SparkSession}

object DecisionTreeTest {
    
    

  def main(args: Array[String]): Unit = {
    
    

    val spark = SparkSession.builder()
      .appName(this.getClass.getSimpleName.stripSuffix("$"))
      .master("local[4]")
      .getOrCreate()


    import org.apache.spark.sql.functions._
    import spark.implicits._

    // 1. 加载数据
    val dataframe: DataFrame = spark.read
      .format("libsvm")
      .load("datas/iris/sample_libsvm_data.txt")


    dataframe.printSchema()
    dataframe.show(10, truncate = false)

    spark.stop()
  }

}

3.2 特征工程

    // 2. 特征工程：特征提取、特征转换及特征选择

    // a. 将标签值label，转换为索引，从0开始，到 K-1
    val labelIndexer: StringIndexerModel = new StringIndexer()
      .setInputCol("label")
      .setOutputCol("index_label")
      .fit(dataframe)
    val df1: DataFrame = labelIndexer.transform(dataframe)

    // b. 对类别特征数据进行特殊处理, 当每列的值的个数小于设置K，那么此列数据被当做类别特征，自动进行索引转换
    val featureIndexer: VectorIndexerModel = new VectorIndexer()
      .setInputCol("features")
      .setOutputCol("index_features")
      .setMaxCategories(4)
      .fit(df1)


    val df2: DataFrame = featureIndexer.transform(df1)

    df2.printSchema()
    df2.show(10, truncate = false)

root
 |-- label: double (nullable = true)
 |-- features: vector (nullable = true)
 |-- index_label: double (nullable = true)
 |-- index_features: vector (nullable = true)

3.3 训练模型

    // 3. 划分数据集：训练数据和测试数据
    val Array(trainingDF, testingDF) = df2.randomSplit(Array(0.8, 0.2))


    // 4. 使用决策树算法构建分类模型
    val dtc: DecisionTreeClassifier = new DecisionTreeClassifier()
      .setLabelCol("index_label")
      .setFeaturesCol("index_features")
      // 设置决策树算法相关超参数
      .setMaxDepth(5)
      .setMaxBins(32)       // 此值必须大于等于类别特征类别个数
      .setImpurity("gini")  // 也可以是香农熵：entropy


    val dtcModel: DecisionTreeClassificationModel = dtc.fit(trainingDF)

    println(dtcModel.toDebugString)

DecisionTreeClassificationModel (uid=dtc_338073100075) of depth 1 with 3 nodes
  If (feature 406 <= 72.0)
   Predict: 1.0
  Else (feature 406 > 72.0)
   Predict: 0.0

3.4 模型评估

    // 5. 模型评估，计算准确度
    val predictionDF: DataFrame = dtcModel.transform(testingDF)
    predictionDF.printSchema()
    predictionDF
      .select($"label", $"index_label", $"probability", $"prediction")
      .show(10, truncate = false)


    val evaluator = new MulticlassClassificationEvaluator()
      .setLabelCol("index_label")
      .setPredictionCol("prediction")
      .setMetricName("accuracy")


    val accuracy: Double = evaluator.evaluate(predictionDF)
    println(s"Accuracy = $accuracy")

Accuracy = 0.8823529411764706

4、ML Pipeline

管道 Pipeline 概念：将多个Transformer转换器和Estimators模型学习器按照 依赖顺序 组工作流WorkFlow形式，方面数据集的特征转换和模型训练及预测。

将上面的决策树分类代码，改为使用 Pipeline 构建模型与预测。

package com.yyds.tags.ml.classification


import org.apache.spark.ml.{
    
    Pipeline, PipelineModel}
import org.apache.spark.ml.classification.{
    
    DecisionTreeClassificationModel, DecisionTreeClassifier}
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import org.apache.spark.ml.feature.{
    
    StringIndexer, StringIndexerModel, VectorIndexer, VectorIndexerModel}
import org.apache.spark.sql.{
    
    DataFrame, SparkSession}


object PipelineTest {
    
    

  def main(args: Array[String]): Unit = {
    
    

    val spark = SparkSession.builder()
      .appName(this.getClass.getSimpleName.stripSuffix("$"))
      .master("local[4]")
      .getOrCreate()

    import org.apache.spark.sql.functions._
    import spark.implicits._


    // 1. 加载数据
    val dataframe: DataFrame = spark.read
      .format("libsvm")
      .load("datas/iris/sample_libsvm_data.txt")
    
    //dataframe.printSchema()
    //dataframe.show(10, truncate = false)


    // 划分数据集：训练集和测试集
    val Array(trainingDF, testingDF) = dataframe.randomSplit(Array(0.8, 0.2))

    // 2. 构建管道Pipeline

    // a. 将标签值label，转换为索引，从0开始，到 K-1
    val labelIndexer = new StringIndexer()
      .setInputCol("label")
      .setOutputCol("index_label")
      .fit(dataframe)

    // b. 对类别特征数据进行特殊处理, 当每列的值的个数小于设置K，那么此列数据被当做类别特征，自动进行索引转换
    val featureIndexer = new VectorIndexer()
      .setInputCol("features")
      .setOutputCol("index_features")
      .setMaxCategories(4)
      .fit(dataframe)

    // c. 使用决策树算法构建分类模型
    val dtc: DecisionTreeClassifier = new DecisionTreeClassifier()
      .setLabelCol("index_label")
      .setFeaturesCol("index_features")
      // 设置决策树算法相关超参数
      .setMaxDepth(5)
      .setMaxBins(32) // 此值必须大于等于类别特征类别个数
      .setImpurity("gini")

    // d. 创建Pipeline，设置Stage（转换器和模型学习器）
    val pipeline: Pipeline = new Pipeline().setStages(
      Array(labelIndexer, featureIndexer, dtc)
    )


    // 3. 训练模型
    val pipelineModel: PipelineModel = pipeline.fit(trainingDF)

    // 获取决策树分类模型
    val dtcModel: DecisionTreeClassificationModel =
         pipelineModel.stages(2)
        .asInstanceOf[DecisionTreeClassificationModel]

    println(dtcModel.toDebugString)


    // 4. 模型评估
    val predictionDF: DataFrame = pipelineModel.transform(testingDF)

    predictionDF.printSchema()

    predictionDF
      .select($"label", $"index_label", $"probability", $"prediction")
      .show(20, truncate = false)


    val evaluator = new MulticlassClassificationEvaluator()
      .setLabelCol("index_label")
      .setPredictionCol("prediction")
      .setMetricName("accuracy")

    val accuracy: Double = evaluator.evaluate(predictionDF)

    println(s"Accuracy = $accuracy")

    // 应用结束，关闭资源
    spark.stop()

  }

}

5、模型调优

使用决策树算法训练模型时，可以调整相关超参数，结合训练验证（Train-Validation Split）或交叉验证（Cross-Validation），获取最佳模型。

5.1 训练验证

将数据集划分为两个部分 ，静态的划分，一个用于训练模型，一个用于验证模型

通过评估指标，获取最佳模型，超参数设置比较好。

// 无论使用何种验证方式通过调整算法超参数来进行模型调优，需要使用工具类ParamGridBuilder 将 超参数封装到Map集合中
import org.apache.spark.ml.tuning.ParamGridBuilder


val paramGrid: Array[ParamMap] = new ParamGridBuilder()
            .addGrid(lr.regParam, Array(0.1, 0.01))
            .addGrid(lr.elasticNetParam, Array(0.0, 0.5, 1.0))
            .build()

// 使用训练验证 TrainValidationSplit 方式获取最佳模型
val trainValidationSplit = new TrainValidationSplit()
        .setEstimator(lr)                      // 也可以是pipeline
        .setEvaluator(new RegressionEvaluator) // 评估器
        .setEstimatorParamMaps(paramGrid)      // 超参数
        // 80% of the data will be used for training and the remaining 20% for validation.
        .setTrainRatio(0.8)

训练验证的使用

package com.yyds.tags.ml.classification

import org.apache.spark.ml.{
    
    Pipeline, PipelineModel}
import org.apache.spark.ml.classification.{
    
    DecisionTreeClassificationModel, DecisionTreeClassifier}
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import org.apache.spark.ml.feature.{
    
    VectorAssembler, VectorIndexer}
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.ml.tuning.{
    
    ParamGridBuilder, TrainValidationSplit, TrainValidationSplitModel}
import org.apache.spark.sql.{
    
    DataFrame, SparkSession}

object HPO {
    
    

  /**
   * 调整算法超参数，找出最优模型
   * @param dataframe 数据集
   * @return
   */
  def trainBestModel(dataframe: DataFrame): PipelineModel = {
    
    
    // a. 特征向量化
    val assembler: VectorAssembler = new VectorAssembler()
      .setInputCols(Array("color", "product"))
      .setOutputCol("raw_features")

    // b. 类别特征进行索引
    val indexer: VectorIndexer = new VectorIndexer()
      .setInputCol("raw_features")
      .setOutputCol("features")
      .setMaxCategories(30)
    // .fit(dataframe)

    // c. 构建决策树分类器
    val dtc: DecisionTreeClassifier = new DecisionTreeClassifier()
      .setFeaturesCol("features")
      .setLabelCol("label")
      .setPredictionCol("prediction")

    // d. 构建Pipeline管道流实例对象
    val pipeline: Pipeline = new Pipeline().setStages(
      Array(assembler, indexer, dtc)
    )

    // e. 构建参数网格，设置超参数的值
    val paramGrid: Array[ParamMap] = new ParamGridBuilder()
      .addGrid(dtc.maxDepth, Array(5, 10))
      .addGrid(dtc.impurity, Array("gini", "entropy"))
      .addGrid(dtc.maxBins, Array(32, 64))
      .build()

    // f. 多分类评估器
    val evaluator = new MulticlassClassificationEvaluator()
      .setLabelCol("label")
      .setPredictionCol("prediction")
      // 指标名称，支持：f1、weightedPrecision、weightedRecall、accuracy
      .setMetricName("accuracy")

    // g. 训练验证
    val trainValidationSplit = new TrainValidationSplit()
      .setEstimator(pipeline)
      .setEvaluator(evaluator)
      .setEstimatorParamMaps(paramGrid)

      // 80% of the data will be used for training and the remaining 20% for validation.
      .setTrainRatio(0.8)


    // h. 训练模型
    val model: TrainValidationSplitModel =
      trainValidationSplit.fit(dataframe)
    // i. 获取最佳模型返回
    model.bestModel.asInstanceOf[PipelineModel]
  }


}

5.2 交叉验证(K折)

将数据集划分为两个部分 ，动态的划分为K个部分数据集，其中1份数据集为验证数据集，其他K-1分数据为训练数据集，调整参数训练模型。

/**
   * 采用K-Fold交叉验证方式，调整超参数获取最佳PipelineModel模型
   * @param dataframe 数据集
   * @return
   */
  def trainBestPipelineModel(dataframe: DataFrame): PipelineModel = {
    
    
    // a. 特征向量化
    val assembler: VectorAssembler = new VectorAssembler()
      .setInputCols(Array("color", "product"))
      .setOutputCol("raw_features")
    
    
    // b. 类别特征进行索引
    val indexer: VectorIndexer = new VectorIndexer()
      .setInputCol("raw_features")
      .setOutputCol("features")
      .setMaxCategories(30)
    // .fit(dataframe)
    
    
    // c. 构建决策树分类器
    val dtc: DecisionTreeClassifier = new DecisionTreeClassifier()
      .setFeaturesCol("features")
      .setLabelCol("label")
      .setPredictionCol("prediction")
    
    
    // d. 构建Pipeline管道流实例对象
    val pipeline: Pipeline = new Pipeline().setStages(
      Array(assembler, indexer, dtc)
    )
    
    
    // e. 构建参数网格，设置超参数的值
    val paramGrid: Array[ParamMap] = new ParamGridBuilder()
      .addGrid(dtc.maxDepth, Array(5, 10))
      .addGrid(dtc.impurity, Array("gini", "entropy"))
      .addGrid(dtc.maxBins, Array(32, 64))
      .build()
    
    
    // f. 多分类评估器
    val evaluator = new MulticlassClassificationEvaluator()
      .setLabelCol("label")
      .setPredictionCol("prediction")
      // 指标名称，支持：f1、weightedPrecision、weightedRecall、accuracy
      .setMetricName("accuracy")
    // g. 构建交叉验证实例对象
    val crossValidator: CrossValidator = new CrossValidator()
      .setEstimator(pipeline)
      .setEvaluator(evaluator)
      .setEstimatorParamMaps(paramGrid)
      .setNumFolds(3)

    // h. 训练模式
    val crossValidatorModel: CrossValidatorModel =  crossValidator.fit(dataframe)
    
    // i. 获取最佳模型
    val pipelineModel: PipelineModel = crossValidatorModel.bestModel.asInstanceOf[PipelineModel]
    
    
    // j. 返回模型
    pipelineModel

  }