2020.02.15

1.数据导入

从文件中导入数据，并转化为 DataFrame。

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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; scala> import spark.implicits._ import spark.implicits._ scala> case class Adult(features: org.apache.spark.ml.linalg.Vector, label: String) <br>defined class Adult scala> val df=sc.textFile("adult.data.txt").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()<br>df:org.apache.spark.sql.DataFrame = [features: vector, label: string] scala> val test = sc.textFile("adult.test.txt").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() <br>test:org.apache.spark.sql.DataFrame = [features: vector, label: string]

2.进行主成分分析（PCA）

 对 6 个连续型的数值型变量进行主成分分析。PCA（主成分分析）是通过正交变换把一 组相关变量的观测值转化成一组线性无关的变量值，即主成分的一种方法。PCA 通过使用 主成分把特征向量投影到低维空间，实现对特征向量的降维。请通过 setK()方法将主成分数 量设置为 3，把连续型的特征向量转化成一个 3 维的主成分。

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scala> val pca = new PCA().setInputCol("features").setOutputCol("pcaFeatures").setK(3).fit(df) scala> val result = pca.transform(df) scala> val testdata = pca.transform(test) scala> result.show(false) scala> testdata.show(false)

3.训练分类模型并预测居民收入

在主成分分析的基础上，采用逻辑斯蒂回归，或者决策树模型预测居民收入是否超过 50K；对 Test 数据集进行验证。

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

4.超参数调优

利用 CrossValidator 确定最优的参数，包括最优主成分 PCA 的维数、分类器自身的参数 等。

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scala> val pca = new PCA().setInputCol("features").setOutputCol("pcaFeatures") scala> val labelIndexer = new StringIndexer().setInputCol("label").setOutputCol("indexedLabel").fit(df) scala> val featureIndexer = new VectorIndexer().setInputCol("pcaFeatures").setOutputCol("indexedFeatures") scala> val labelConverter = new IndexToString().setInputCol("prediction").setOutputCol("predictedLabel").setLabels(labelIndexer.l abels) scala> val lr = new LogisticRegression().setLabelCol("indexedLabel").setFeaturesCol("indexedFeatures").setMaxIter(1 00) scala> val lrPipeline = new Pipeline().setStages(Array(pca, labelIndexer, featureIndexer, lr, labelConverter)) scala> 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() scala> val cv = new CrossValidator().setEstimator(lrPipeline).setEvaluator(new MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setPredictionCol("prediction")).se tEstimatorParamMaps(paramGrid).setNumFolds(3) scala> val cvModel = cv.fit(df) scala> val lrPredictions=cvModel.transform(test) scala> val evaluator = new MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setPredictionCol("prediction") scala> val lrAccuracy = evaluator.evaluate(lrPredictions) scala> println("准确率为"+lrAccuracy) scala> val bestModel= cvModel.bestModel.asInstanceOf[PipelineModel] scala> val lrModel = bestModel.stages(3).asInstanceOf[LogisticRegressionModel] scala> println("Coefficients: " + lrModel.coefficientMatrix + "Intercept: "+lrModel.interceptVector+ "numClasses: "+lrModel.numClasses+"numFeatures: "+lrModel.numFeatures) scala> val pcaModel = bestModel.stages(0).asInstanceOf[PCAModel] scala> println("Primary Component: " + pcaModel.pc)