Spark 2.0 机器学习 ML 库：常见的机器学习模型（Scala 版）_spark ml scala

作者：盐析白兔 | 2024-06-19 17:00:45

踩

spark ml scala

一、前言

机器学习中，人为地设计算法，需要一定的知识积淀。
而使用别人设计好的机器学习库如 Spark 2.0 ML，那是基本不需要什么基础的，开箱即用。
首先，看一个简单、完整、规范的案例，无疑是最好的方式。

之前的文章（内含短小精悍的案例）：
Spark 2.0 机器学习 ML 库：特征提取、转化、选取（Scala 版）
Spark 2.0 机器学习 ML 库：机器学习工作流、交叉 - 验证方法（Scala 版）
Spark 2.0 机器学习 ML 库：数据分析方法（Scala 版）

二、代码

下面的代码，来自网上，挺好的，笔者加以细化

1.线性回归

package change

import org.apache.spark.ml.regression.{LinearRegression, LinearRegressionModel}
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.sql._
import org.apache.spark.sql.SparkSession

/**
  * 线性回归
  */
object linearTest {

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

    // 0.构建 Spark 对象
    val spark = SparkSession
      .builder()
      .master("local") // 本地测试，否则报错 A master URL must be set in your configuration at org.apache.spark.SparkContext.
      .appName("test")
      .enableHiveSupport()
      .getOrCreate() // 有就获取无则创建

    spark.sparkContext.setCheckpointDir("C:\\LLLLLLLLLLLLLLLLLLL\\BigData_AI\\sparkmlTest") //设置文件读取、存储的目录，HDFS最佳
    import spark.implicits._

    //1 训练样本准备
    val training =  spark.createDataFrame(Seq(
      (5.601801561245534, Vectors.sparse(10, Array(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), Array(0.6949189734965766, -0.32697929564739403, -0.15359663581829275, -0.8951865090520432, 0.2057889391931318, -0.6676656789571533, -0.03553655732400762, 0.14550349954571096, 0.034600542078191854, 0.4223352065067103))),
      (0.2577820163584905, Vectors.sparse(10, Array(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), Array(0.8386555657374337, -0.1270180511534269, 0.499812362510895, -0.22686625128130267, -0.6452430441812433, 0.18869982177936828, -0.5804648622673358, 0.651931743775642, -0.6555641246242951, 0.17485476357259122))),
      (1.5299675726687754, Vectors.sparse(10, Array(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), Array(-0.13079299081883855, 0.0983382230287082, 0.15347083875928424, 0.45507300685816965, 0.1921083467305864, 0.6361110540492223, 0.7675261182370992, -0.2543488202081907, 0.2927051050236915, 0.680182444769418))))).toDF("label", "features")
    training.show(false)

    //2 建立逻辑回归模型
    val lr = new LinearRegression()
      .setMaxIter(100)
      .setRegParam(0.1)
      .setElasticNetParam(0.5)

    //2 根据训练样本进行模型训练
    val lrModel = lr.fit(training)

    //2 打印模型信息
    println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}")

    /**
      * Coefficients: [0.0,-0.8840148895400428,-4.451571521834594,-0.42090140779272434,0.857395634491616,-1.237347818637769,0.0,0.0,0.0,0.0] Intercept: 3.1417724655192645
      */

    println(s"Intercept: ${lrModel.intercept}")

    /**
      * Intercept: 3.1417724655192645
      */

    //4 测试样本
    val test = spark.createDataFrame(Seq(
      (5.601801561245534, Vectors.sparse(10, Array(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), Array(0.6949189734965766, -0.32697929564739403, -0.15359663581829275, -0.8951865090520432, 0.2057889391931318, -0.6676656789571533, -0.03553655732400762, 0.14550349954571096, 0.034600542078191854, 0.4223352065067103))),
      (0.2577820163584905, Vectors.sparse(10, Array(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), Array(0.8386555657374337, -0.1270180511534269, 0.499812362510895, -0.22686625128130267, -0.6452430441812433, 0.18869982177936828, -0.5804648622673358, 0.651931743775642, -0.6555641246242951, 0.17485476357259122))),
      (1.5299675726687754, Vectors.sparse(10, Array(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), Array(-0.13079299081883855, 0.0983382230287082, 0.15347083875928424, 0.45507300685816965, 0.1921083467305864, 0.6361110540492223, 0.7675261182370992, -0.2543488202081907, 0.2927051050236915, 0.680182444769418))))).toDF("label", "features")
    test.show(false)

    /**
      * +------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
      * |label             |features                                                                                                                                                                                                                             |
      * +------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
      * |5.601801561245534 |(10,[0,1,2,3,4,5,6,7,8,9],[0.6949189734965766,-0.32697929564739403,-0.15359663581829275,-0.8951865090520432,0.2057889391931318,-0.6676656789571533,-0.03553655732400762,0.14550349954571096,0.034600542078191854,0.4223352065067103])|
      * |0.2577820163584905|(10,[0,1,2,3,4,5,6,7,8,9],[0.8386555657374337,-0.1270180511534269,0.499812362510895,-0.22686625128130267,-0.6452430441812433,0.18869982177936828,-0.5804648622673358,0.651931743775642,-0.6555641246242951,0.17485476357259122])     |
      * |1.5299675726687754|(10,[0,1,2,3,4,5,6,7,8,9],[-0.13079299081883855,0.0983382230287082,0.15347083875928424,0.45507300685816965,0.1921083467305864,0.6361110540492223,0.7675261182370992,-0.2543488202081907,0.2927051050236915,0.680182444769418])       |
      * +------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
      */

    //5 对模型进行测试
    val test_predict = lrModel.transform(test)
    test_predict
      .select("label","prediction","features")
      .show(false)

    /**
      * +------------------+-------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
      * |label             |prediction         |features                                                                                                                                                                                                                             |
      * +------------------+-------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
      * |5.601801561245534 |5.493935912726037  |(10,[0,1,2,3,4,5,6,7,8,9],[0.6949189734965766,-0.32697929564739403,-0.15359663581829275,-0.8951865090520432,0.2057889391931318,-0.6676656789571533,-0.03553655732400762,0.14550349954571096,0.034600542078191854,0.4223352065067103])|
      * |0.2577820163584905|0.33788027718672575|(10,[0,1,2,3,4,5,6,7,8,9],[0.8386555657374337,-0.1270180511534269,0.499812362510895,-0.22686625128130267,-0.6452430441812433,0.18869982177936828,-0.5804648622673358,0.651931743775642,-0.6555641246242951,0.17485476357259122])     |
      * |1.5299675726687754|1.557734960360036  |(10,[0,1,2,3,4,5,6,7,8,9],[-0.13079299081883855,0.0983382230287082,0.15347083875928424,0.45507300685816965,0.1921083467305864,0.6361110540492223,0.7675261182370992,-0.2543488202081907,0.2927051050236915,0.680182444769418])       |
      * +------------------+-------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
      */

    //6 模型摘要
    val trainingSummary = lrModel.summary

    // 每次迭代目标值
    val objectiveHistory = trainingSummary.objectiveHistory
    println(s"numIterations: ${trainingSummary.totalIterations}")

    /**
      * numIterations: 101
      */

    println(s"objectiveHistory: [${trainingSummary.objectiveHistory.mkString(",")}]")

    /**
      * objectiveHistory: [0.5,0.3343138710353481,0.03498698228406803,0.034394702527331365,0.03361752133820051,0.033440576313009396,0.032492999827506586,0.03209249818672103,0.03201118276878801,0.0318030335506653,0.031556141484809515,0.03146914334471842,0.03132368104987874,0.030906857778152226,0.030829631969772512,0.030792601096269995,0.03075807300477159,0.03064409361649658,0.03057645418974434,0.03048720940080922,0.030450452329432418,0.0303403006892938,0.03022336621283447,0.030105231797686347,0.03005248564337978,0.029952523828252434,0.029901762708870988,0.029901114112460842,0.029897992643680316,0.029897097909156505,0.029892358780083193,0.029890487541861296,0.029883508098656905,0.02986342331315129,0.029846157576330717,0.02983921669719768,0.029837621981381814,0.029832343881027193,0.029818011565517288,0.0298174329753425,0.029816619127868163,0.029815897918569062,0.029815813156609985,0.029815635355907394,0.029814914126549,0.029813735638819686,0.02981357400967502,0.02981340129452729,0.029813363218666296,0.029813104482615992,0.029813066188642295,0.02981290111924657,0.029812867201451012,0.029812730285385426,0.029812706953398726,0.02981259780704471,0.02981258478371474,0.02981249810105761,0.029812492058484363,0.029812414896583955,0.02981239284306545,0.02981217952516655,0.029812093354005524,0.029812078847204722,0.02981204606864486,0.029812029284085127,0.029812008170753846,0.029812001127453244,0.02981198610905457,0.029811978179336476,0.029811968590860403,0.029811960922339894,0.02981195510843637,0.029811951516538388,0.02981194560589678,0.029811931971338676,0.029811927559300986,0.02981192583464405,0.029811923533256,0.02981192147493291,0.029811919101372975,0.0298119178536648,0.029811915692737362,0.02981191417259256,0.029811912340872517,0.02981191111669305,0.02981190922210416,0.029811908328486812,0.029811906376022823,0.029811905682559023,0.02981190386743857,0.029811903165691635,0.02981190159751578,0.029811901021202986,0.02981189985181355,0.02981189892054736,0.029811897724408266,0.02981189698790617,0.02981189562974597,0.029811894938092554,0.029811894064851477]
      */

    trainingSummary.residuals.show(false)

    /**
      * +---------------------+
      * |residuals            |
      * +---------------------+
      * |0.1078656485194962   |
      * |-0.08009826082823523 |
      * |-0.027767387691260526|
      * +---------------------+
      */

    println(s"RMSE: ${trainingSummary.rootMeanSquaredError}")

    /**
      * RMSE: 0.07920807479341203
      */

    println(s"r2: ${trainingSummary.r2}")

    /**
      * r2: 0.998792363204057
      */

    //7 模型保存与加载（发布到服务器 django 时，View 加入如下代码 + 文件）
    lrModel.save("C:\\LLLLLLLLLLLLLLLLLLL\\BigData_AI\\sparkmlTest\\lrmodel2")
    val load_lrModel = LinearRegressionModel.load("C:\\LLLLLLLLLLLLLLLLLLL\\BigData_AI\\sparkmlTest\\lrmodel2")

  }

}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136

这里写图片描述

2.逻辑回归

package change

import org.apache.spark.ml.classification.{BinaryLogisticRegressionSummary, LogisticRegression, LogisticRegressionModel}
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.sql.functions._
import org.apache.spark.sql.SparkSession

object logicTest {

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

    // 0.构建 Spark 对象
    val spark = SparkSession
      .builder()
      .master("local") // 本地测试，否则报错 A master URL must be set in your configuration at org.apache.spark.SparkContext.
      .appName("test")
      .enableHiveSupport()
      .getOrCreate() // 有就获取无则创建

    spark.sparkContext.setCheckpointDir("C:\\LLLLLLLLLLLLLLLLLLL\\BigData_AI\\sparkmlTest") //设置文件读取、存储的目录，HDFS最佳
    import spark.implicits._

    //1 训练样本准备
    val training = spark.createDataFrame(Seq(
      (1.0, Vectors.sparse(692, Array(10, 20, 30), Array(-1.0, 1.5, 1.3))),
      (0.0, Vectors.sparse(692, Array(45, 175, 500), Array(-1.0, 1.5, 1.3))),
      (1.0, Vectors.sparse(692, Array(100, 200, 300), Array(-1.0, 1.5, 1.3))))).toDF("label", "features")
    training.show(false)

    /**
      * +-----+----------------------------------+
      * |label|features                          |
      * +-----+----------------------------------+
      * |1.0  |(692,[10,20,30],[-1.0,1.5,1.3])   |
      * |0.0  |(692,[45,175,500],[-1.0,1.5,1.3]) |
      * |1.0  |(692,[100,200,300],[-1.0,1.5,1.3])|
      * +-----+----------------------------------+
      */

    //2 建立逻辑回归模型
    val lr = new LogisticRegression().setMaxIter(10).setRegParam(0.3).setElasticNetParam(0.8)

    //2 根据训练样本进行模型训练
    val lrModel = lr.fit(training)

    //2 打印模型信息
    println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}")

    /**
      * Coefficients: (692,[45,175,500],[0.48944928041408226,-0.32629952027605463,-0.37649944647237077]) Intercept: 1.251662793530725
      */

    println(s"Intercept: ${lrModel.intercept}")

    /**
      * Intercept: 1.251662793530725
      */

    //3 建立多元回归模型
    val mlr = new LogisticRegression().setMaxIter(10).setRegParam(0.3).setElasticNetParam(0.8).setFamily("multinomial")

    //3 根据训练样本进行模型训练
    val mlrModel = mlr.fit(training)

    //3 打印模型信息
    println(s"Multinomial coefficients: ${mlrModel.coefficientMatrix}")

    /**
      * Multinomial coefficients: 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ... (692 total)
      * 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...
      */

    println(s"Multinomial intercepts: ${mlrModel.interceptVector}")

    /**
      * Multinomial intercepts: [-0.6449310568167714,0.6449310568167714]
      */

    //4 测试样本
    val test = spark.createDataFrame(Seq(
      (1.0, Vectors.sparse(692, Array(10, 20, 30), Array(-1.0, 1.5, 1.3))),
      (0.0, Vectors.sparse(692, Array(45, 175, 500), Array(-1.0, 1.5, 1.3))),
      (1.0, Vectors.sparse(692, Array(100, 200, 300), Array(-1.0, 1.5, 1.3))))).toDF("label", "features")
    test.show(false)

    /**
      * +-----+----------------------------------+
      * |label|features                          |
      * +-----+----------------------------------+
      * |1.0  |(692,[10,20,30],[-1.0,1.5,1.3])   |
      * |0.0  |(692,[45,175,500],[-1.0,1.5,1.3]) |
      * |1.0  |(692,[100,200,300],[-1.0,1.5,1.3])|
      * +-----+----------------------------------+
      */

    //5 对模型进行测试
    val test_predict = lrModel.transform(test)
    test_predict
      .select("label", "prediction", "probability", "features")
      .show(false)

    /**
      * +-----+----------+----------------------------------------+----------------------------------+
      * |label|prediction|probability                             |features                          |
      * +-----+----------+----------------------------------------+----------------------------------+
      * |1.0  |1.0       |[0.22241243403014824,0.7775875659698517]|(692,[10,20,30],[-1.0,1.5,1.3])   |
      * |0.0  |0.0       |[0.5539602964649871,0.44603970353501293]|(692,[45,175,500],[-1.0,1.5,1.3]) |
      * |1.0  |1.0       |[0.22241243403014824,0.7775875659698517]|(692,[100,200,300],[-1.0,1.5,1.3])|
      * +-----+----------+----------------------------------------+----------------------------------+
      */

    //6 模型摘要
    val trainingSummary = lrModel.summary

    //6 每次迭代目标值
    val objectiveHistory = trainingSummary.objectiveHistory
    println("objectiveHistory:")
    objectiveHistory.foreach(loss => println(loss))

    /**
      * objectiveHistory:
      * 0.6365141682948128
      * 0.6212055977633174
      * 0.5894552698389314
      * 0.5844805633573479
      * 0.5761098112571359
      * 0.575517297029231
      * 0.5754098875805627
      * 0.5752562156795122
      * 0.5752506337221737
      * 0.5752406742715199
      * 0.5752404945106846
      */

    //6 计算模型指标数据
    val binarySummary = trainingSummary.asInstanceOf[BinaryLogisticRegressionSummary]

    //6 AUC指标
    val roc = binarySummary.roc
    roc.show(false)

    /**
      * +---+---+
      * |FPR|TPR|
      * +---+---+
      * |0.0|0.0|
      * |0.0|1.0|
      * |1.0|1.0|
      * |1.0|1.0|
      * +---+---+
      */

    val AUC = binarySummary.areaUnderROC
    println(s"areaUnderROC: ${binarySummary.areaUnderROC}")

    //6 设置模型阈值
    //不同的阈值，计算不同的F1，然后通过最大的F1找出并重设模型的最佳阈值。
    val fMeasure = binarySummary.fMeasureByThreshold
    fMeasure.show(false)

    /**
      * +-------------------+---------+
      * |threshold          |F-Measure|
      * +-------------------+---------+
      * |0.7775875659698517 |1.0      |
      * |0.44603970353501293|0.8      |
      * +-------------------+---------+
      */

    //获得最大的F1值
    val maxFMeasure = fMeasure.select(max("F-Measure")).head().getDouble(0)
    //找出最大F1值对应的阈值（最佳阈值）
    val bestThreshold = fMeasure.where($"F-Measure" === maxFMeasure).select("threshold").head().getDouble(0)
    //并将模型的Threshold设置为选择出来的最佳分类阈值
    lrModel.setThreshold(bestThreshold)

    //7 模型保存与加载（发布到服务器 django 时，View 加入如下代码 + 文件）
    lrModel.save("C:\\LLLLLLLLLLLLLLLLLLL\\BigData_AI\\sparkmlTest\\lrmodel")
    val load_lrModel = LogisticRegressionModel.load("C:\\LLLLLLLLLLLLLLLLLLL\\BigData_AI\\sparkmlTest\\lrmodel")

  }

}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184

保存好的模型
这里写图片描述

3.决策树、随机森林、GBDT

比较容易理解的一个算法

package tree

import org.apache.spark.ml.feature._
import org.apache.spark.ml.classification.RandomForestClassifier
import org.apache.spark.ml.classification.{ DecisionTreeClassifier, DecisionTreeClassificationModel }
import org.apache.spark.ml.classification.GBTClassifier
import org.apache.spark.ml.evaluation.{ MulticlassClassificationEvaluator, BinaryClassificationEvaluator }
import org.apache.spark.ml.{ Pipeline, PipelineModel }
import org.apache.spark.sql.SparkSession

object tree {

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

    // 0.构建 Spark 对象
    val spark = SparkSession
      .builder()
      .master("local") // 本地测试，否则报错 A master URL must be set in your configuration at org.apache.spark.SparkContext.
      .appName("test")
      .enableHiveSupport()
      .getOrCreate() // 有就获取无则创建

    spark.sparkContext.setCheckpointDir("C:\\LLLLLLLLLLLLLLLLLLL\\BigData_AI\\sparkmlTest") //设置文件读取、存储的目录，HDFS最佳
    import spark.implicits._

    //1 训练样本准备
    val data = spark.read.format("libsvm").load("C:\\LLLLLLLLLLLLLLLLLLL\\BigData_AI\\sparkmlTest\\sample_libsvm_data.txt")
    data.show

    //2 标签进行索引编号
    val labelIndexer = new StringIndexer().
      setInputCol("label").
      setOutputCol("indexedLabel").
      fit(data)

    // 对离散特征进行标记索引，以用来确定哪些特征是离散特征
    // 如果一个特征的值超过4个以上，该特征视为连续特征，否则将会标记得离散特征并进行索引编号
    val featureIndexer = new VectorIndexer().
      setInputCol("features").
      setOutputCol("indexedFeatures").
      setMaxCategories(4).
      fit(data)

    //3 样本划分
    val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))

    //4 训练决策树模型
    val dt = new DecisionTreeClassifier().
      setLabelCol("indexedLabel").
      setFeaturesCol("indexedFeatures")

    //4 训练随机森林模型
    val rf = new RandomForestClassifier()
      .setLabelCol("indexedLabel")
      .setFeaturesCol("indexedFeatures")
      .setNumTrees(10)

    //4 训练GBDT模型
    val gbt = new GBTClassifier()
      .setLabelCol("indexedLabel")
      .setFeaturesCol("indexedFeatures")
      .setMaxIter(10)

    //5 将索引的标签转回原始标签
    val labelConverter = new IndexToString().
      setInputCol("prediction").
      setOutputCol("predictedLabel").
      setLabels(labelIndexer.labels)

    //6 构建Pipeline
    val pipeline1 = new Pipeline().
      setStages(Array(labelIndexer, featureIndexer, dt, labelConverter))

    val pipeline2 = new Pipeline().
      setStages(Array(labelIndexer, featureIndexer, rf, labelConverter))

    val pipeline3 = new Pipeline().
      setStages(Array(labelIndexer, featureIndexer, gbt, labelConverter))

    //7 Pipeline开始训练
    val model1 = pipeline1.fit(trainingData)

    val model2 = pipeline2.fit(trainingData)

    val model3 = pipeline3.fit(trainingData)

    //8 模型测试
    val predictions = model1.transform(testData)
    predictions.show(5)

    //8 测试结果
    predictions.select("predictedLabel", "label", "features").show(5)

    //9 分类指标
    // 正确率
    val evaluator1 = new MulticlassClassificationEvaluator().
      setLabelCol("indexedLabel").
      setPredictionCol("prediction").
      setMetricName("accuracy")
    val accuracy = evaluator1.evaluate(predictions)
    println("Test Error = " + (1.0 - accuracy))

    // f1
    val evaluator2 = new MulticlassClassificationEvaluator().
      setLabelCol("indexedLabel").
      setPredictionCol("prediction").
      setMetricName("f1")
    val f1 = evaluator2.evaluate(predictions)
    println("f1 = " + f1)

    // Precision
    val evaluator3 = new MulticlassClassificationEvaluator().
      setLabelCol("indexedLabel").
      setPredictionCol("prediction").
      setMetricName("weightedPrecision")
    val Precision = evaluator3.evaluate(predictions)
    println("Precision = " + Precision)

    // Recall
    val evaluator4 = new MulticlassClassificationEvaluator().
      setLabelCol("indexedLabel").
      setPredictionCol("prediction").
      setMetricName("weightedRecall")
    val Recall = evaluator4.evaluate(predictions)
    println("Recall = " + Recall)

    // AUC
    val evaluator5 = new BinaryClassificationEvaluator().
      setLabelCol("indexedLabel").
      setRawPredictionCol("prediction").
      setMetricName("areaUnderROC")
    val AUC = evaluator5.evaluate(predictions)
    println("Test AUC = " + AUC)

    // aupr
    val evaluator6 = new BinaryClassificationEvaluator().
      setLabelCol("indexedLabel").
      setRawPredictionCol("prediction").
      setMetricName("areaUnderPR")
    val aupr = evaluator6.evaluate(predictions)
    println("Test aupr = " + aupr)

    //10 决策树打印
    val treeModel = model1.stages(2).asInstanceOf[DecisionTreeClassificationModel]
    println("Learned classification tree model:\n" + treeModel.toDebugString)

    //11 模型保存与加载
    model1.save("C:\\LLLLLLLLLLLLLLLLLLL\\BigData_AI\\sparkmlTest\\dtmodel")
    val load_treeModel = PipelineModel.load("C:\\LLLLLLLLLLLLLLLLLLL\\BigData_AI\\sparkmlTest\\dtmodel")

  }

}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154

4.KMeans

和 KNN 算法有点像

package juhe

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

object Kmeans {

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

    // 0.构建 Spark 对象
    val spark = SparkSession
      .builder()
      .master("local") // 本地测试，否则报错 A master URL must be set in your configuration at org.apache.spark.SparkContext.
      .appName("test")
      .enableHiveSupport()
      .getOrCreate() // 有就获取无则创建

    spark.sparkContext.setCheckpointDir("C:\\LLLLLLLLLLLLLLLLLLL\\BigData_AI\\sparkmlTest") //设置文件读取、存储的目录，HDFS最佳
    import spark.implicits._

    // 读取样本
    val dataset = spark.read.format("libsvm").load("C:\\LLLLLLLLLLLLLLLLLLL\\BigData_AI\\sparkmlTest\\sample_kmeans_data.txt")
    dataset.show()

    // 训练 a k-means model.
    val kmeans = new KMeans().setK(2).setSeed(1L)
    val model = kmeans.fit(dataset)

    // 模型指标计算.
    val WSSSE = model.computeCost(dataset)
    println(s"Within Set Sum of Squared Errors = $WSSSE")

    // 结果显示.
    println("Cluster Centers: ")
    model.clusterCenters.foreach(println)

    // 模型保存与加载
    model.save("C:\\LLLLLLLLLLLLLLLLLLL\\BigData_AI\\sparkmlTest\\kmmodel")
    val load_treeModel = KMeansModel.load("C:\\LLLLLLLLLLLLLLLLLLL\\BigData_AI\\sparkmlTest\\kmmodel")
    spark.stop()


  }

}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46

5.LDA

可以用于文章主题分类

package juhe

import org.apache.spark.ml.clustering.{LDA, LDAModel}
import org.apache.spark.sql.SparkSession

object ldaTest {

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

    // 0.构建 Spark 对象
    val spark = SparkSession
      .builder()
      .master("local") // 本地测试，否则报错 A master URL must be set in your configuration at org.apache.spark.SparkContext.
      .appName("test")
      .enableHiveSupport()
      .getOrCreate() // 有就获取无则创建

    // 1.读取样本
    val dataset = spark.read.format("libsvm").load("C:\\LLLLLLLLLLLLLLLLLLL\\BigData_AI\\data.txt")
    dataset.show()

    // 2.训练 LDA model.
    val lda = new LDA().setK(10).setMaxIter(10)
    val model = lda.fit(dataset)

    val ll = model.logLikelihood(dataset)
    val lp = model.logPerplexity(dataset)
    println(s"The lower bound on the log likelihood of the entire corpus: $ll")
    println(s"The upper bound on perplexity: $lp")

    // 3.主题 topics.
    val topics = model.describeTopics(3)
    println("The topics described by their top-weighted terms:")
    topics.show(false)

    val aa = model.topicsMatrix
    model.estimatedDocConcentration
    model.getTopicConcentration

    // 4.测试结果.
    val transformed = model.transform(dataset)
    transformed.show(false)
    transformed.columns

    // 5.模型保存与加载
    model.save("C:\\LLLLLLLLLLLLLLLLLLL\\BigData_AI\\ldamodel")

    spark.stop()

  }

}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53

本文内容由网友自发贡献，转载请注明出处：https://www.wpsshop.cn/w/盐析白兔/article/detail/737115