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实验七：Spark机器学习库Mtlib编程实践

发布时间 2024-01-13 16:43:20作者: yesyes1

1、数据导入

导入相关的jar包：

import org.apache.spark.ml.feature.PCA
import org.apache.spark.sql.Row
import org.apache.spark.ml.linalg.{Vector,Vectors}
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
import org.apache.spark.ml.{Pipeline,PipelineModel}
import org.apache.spark.ml.feature.{IndexToString,StringIndexer,VectorIndexer,HashingTF,Tokenizer}
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.classification.LogisticRegressionModel
import org.apache.spark.ml.classification.{BinaryLogisticRegressionSummary,LogisticRegression}
import org.apache.spark.sql.functions
import org.apache.spark.ml.tuning.{CrossValidator,CrossValidatorModel,ParamGridBuilder}

导入数据的代码：

import spark.implicits._
case class Adult(features: org.apache.spark.ml.linalg.Vector,label: String)
val df = sc.textFile("file:///data/adult.data").map(_.split(",")).map(p => Adult(Vectors.dense(p(0).toDouble,p(2).toDouble,p(4).toDouble, p(10).toDouble, p(11).toDouble, p(12).toDouble),p(14).toString())).toDF()

2、读取数据集信息

读取数据集代码：

val test = sc.textFile("file:///data/adult.test").map(_.split(",")).map(p => Adult(Vectors.dense(p(0).toDouble,p(2).toDouble,p(4).toDouble, p(10).toDouble, p(11).toDouble, p(12).toDouble), p(14).toString())).toDF()
val pca = new PCA().setInputCol("features").setOutputCol("pcaFeatures").setK(3).fit(df)
val result = pca.transform(df)
val testdata = pca.transform(test)
result.show(false)
testdata.show(false)

3、训练分类模型预测居民收入

预测居民收入：

val labelIndexer = new StringIndexer().setInputCol("label").setOutputCol("indexedLabel").fit(result)
labelIndexer.labels.foreach(println)
val featureIndexer = new VectorIndexer().setInputCol("pcaFeatures").setOutputCol("indexedFeatures").fit(result)
println(featureIndexer.numFeatures)
val labelConverter = new IndexToString().setInputCol("prediction").setOutputCol("predictedLabel").setLabels(labelIndexer.labels)
val lr = new LogisticRegression().setLabelCol("indexedLabel").setFeaturesCol("indexedFeatures").setMaxIter(100)
val lrPipeline = new Pipeline().setStages(Array(labelIndexer, featureIndexer, lr, labelConverter))
val lrPipelineModel = lrPipeline.fit(result)
val lrModel = lrPipelineModel.stages(2).asInstanceOf[LogisticRegressionModel]
println("Coefficients:" + lrModel.coefficientMatrix+"Intercept:"+lrModel.interceptVector+"numClasses:"+lrModel.numClasses+"numFeatures: "+lrModel.numFeatures)
val lrPredictions = lrPipelineModel.transform(testdata)
val evaluator = new MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setPredictionCol("prediction")
val lrAccuracy = evaluator.evaluate(lrPredictions)
println("Test Error = " + (1.0 - lrAccuracy))

4、超参数调优

代码：

val pca = new PCA().setInputCol("features").setOutputCol("pcaFeatures")
val labelIndexer = new StringIndexer().setInputCol("label").setOutputCol("indexedLabel").fit(df)
val featureIndexer = new VectorIndexer().setInputCol("pcaFeatures").setOutputCol("indexedFeatures")
val labelConverter = new IndexToString().setInputCol("prediction").setOutputCol("predictedLabel").setLabels(labelIndexer.labels)
val lr = new LogisticRegression().setLabelCol("indexedLabel").setFeaturesCol("indexedFeatures").setMaxIter(100)
val lrPipeline = new Pipeline().setStages(Array(pca, labelIndexer, featureIndexer, lr, labelConverter))
val paramGrid = new ParamGridBuilder().addGrid(pca.k, Array(1,2,3,4,5,6)).addGrid(lr.elasticNetParam, Array(0.2,0.8)).addGrid(lr.regParam, Array(0.01, 0.1, 0.5)).build()