python spark2.0_Python+Spark2.0+hadoop学习笔记——Python Spark MLlib Naive Bayes二分类

weixin_39720865

240人浏览 · 2020-12-22 13:39:00

weixin_39720865 · 2020-12-22 13:39:00 发布

朴素贝叶斯是一种经典的分类方法，其原理在高中或大学的概率论部分学习了很多了，下面开始介绍在Spark环境下使用MLlib来使用Naive Bayes来对网站性质进行分类判断。

第一步：导入库函数

import sys

from time import time

import pandas as pd

import matplotlib.pyplot as plt

from pyspark import SparkConf, SparkContext

from pyspark.mllib.classification import NaiveBayes

from pyspark.mllib.regression import LabeledPoint

import numpy as np

from pyspark.mllib.evaluation import BinaryClassificationMetrics

from pyspark.mllib.feature import StandardScaler

第二步：数据准备

def get_mapping(rdd, idx):

return rdd.map(lambda fields: fields[idx]).distinct().zipWithIndex().collectAsMap()

def extract_label(record):

label=(record[-1])

return float(label)

def extract_features(field,categoriesMap,featureEnd):

categoryIdx = categoriesMap[field[3]]

categoryFeatures = np.zeros(len(categoriesMap))

categoryFeatures[categoryIdx] = 1

numericalFeatures=[convert_float(field) for field in field[4: featureEnd]]

return np.concatenate(( categoryFeatures, numericalFeatures))

def convert_float(x):

ret=(0 if x=="?" else float(x))

return(0 if ret<0 else ret)

def PrepareData(sc):

print("Data loading...")

rawDataWithHeader = sc.textFile(Path+"data/train.tsv")

header = rawDataWithHeader.first()

rawData = rawDataWithHeader.filter(lambda x:x !=header)

rData=rawData.map(lambda x: x.replace("\"", ""))

lines = rData.map(lambda x: x.split("\t"))

print("The number of data" + str(lines.count()))

print("Before normalization:")

categoriesMap = lines.map(lambda fields: fields[3]). \

distinct().zipWithIndex().collectAsMap()

labelRDD = lines.map(lambda r: extract_label(r))

featureRDD = lines.map(lambda r: extract_features(r,categoriesMap,len(r) - 1))

for i in featureRDD.first():

print (str(i)+","),

print( "After normalization:" )

stdScaler = StandardScaler(withMean=False, withStd=True).fit(featureRDD)

ScalerFeatureRDD=stdScaler.transform(featureRDD)

for i in ScalerFeatureRDD.first():

print (str(i)+","),

labelpoint=labelRDD.zip(ScalerFeatureRDD)

labelpointRDD=labelpoint.map(lambda r: LabeledPoint(r[0], r[1]))

(trainData, validationData, testData) = labelpointRDD.randomSplit([8, 1, 1])

print("trainData:" + str(trainData.count()) +

"validationData:" + str(validationData.count()) +

"testData:" + str(testData.count()))

return (trainData, validationData, testData, categoriesMap)

第三步：对模型进行训练

def PredictData(sc,model,categoriesMap):

print("Data loading...")

rawDataWithHeader = sc.textFile(Path+"data/test.tsv")

header = rawDataWithHeader.first()

rawData = rawDataWithHeader.filter(lambda x:x !=header)

rData=rawData.map(lambda x: x.replace("\"", ""))

lines = rData.map(lambda x: x.split("\t"))

print("The number of data" + str(lines.count()))

dataRDD = lines.map(lambda r: ( r[0] ,

extract_features(r,categoriesMap,len(r) )))

DescDict = {

0: "ephemeral",

1: "evergreen"

}

for data in dataRDD.take(10):

predictResult = model.predict(data[1])

print ("Web:" +str(data[0])+"\n" +\

"Predict:"+ str(predictResult)+ \

"Illustration:"+DescDict[predictResult] +"\n")

第四步：对模型进行评估(NB模型只需要调节一个参数lambda)

def evaluateModel(model, validationData):

score = model.predict(validationData.map(lambda p: p.features))

score = score.map(lambda score : float(score))

Labels = validationData.map(lambda p: p.label)

Labels = Labels.map(lambda Labels : float(Labels))

scoreAndLabels=score.zip(Labels)

metrics = BinaryClassificationMetrics(scoreAndLabels)

AUC=metrics.areaUnderROC

return(AUC)

def trainEvaluateModel(trainData,validationData,lambdaParam):

startTime = time()

model = NaiveBayes.train(trainData, lambdaParam)

AUC = evaluateModel(model, validationData)

duration = time() - startTime

print(" lambda="+str( lambdaParam) +\

" time="+str(duration) + \

" AUC = " + str(AUC) )

return (AUC,duration, lambdaParam,model)

def evalParameter(trainData, validationData, evalparm,

lambdaParamList):

metrics = [trainEvaluateModel(trainData, validationData,regParam )

for regParam in lambdaParamList]

evalparm="lambdaParam"

IndexList=lambdaParamList

df = pd.DataFrame(metrics,index=IndexList,

columns=['AUC', 'duration',' lambdaParam','model'])

showchart(df,evalparm,'AUC','duration',0.5,0.7 )

def showchart(df,evalparm ,barData,lineData,yMin,yMax):

ax = df[barData].plot(kind='bar', title =evalparm,figsize=(10,6),legend=True, fontsize=12)

ax.set_xlabel(evalparm,fontsize=12)

ax.set_ylim([yMin,yMax])

ax.set_ylabel(barData,fontsize=12)

ax2 = ax.twinx()

ax2.plot(df[[lineData ]].values, linestyle='-', marker='o', linewidth=2.0,color='r')

plt.show()

def evalAllParameter(training_RDD, validation_RDD, lambdaParamList):

metrics = [trainEvaluateModel(trainData, validationData, lambdaParam )

for lambdaParam in lambdaParamList ]

Smetrics = sorted(metrics, key=lambda k: k[0], reverse=True)

bestParameter=Smetrics[0]

print("lambdaParam:" + str(bestParameter[2]) +

"AUC = " + str(bestParameter[0]))

return bestParameter[3]

def parametersEval(trainData, validationData):

print("For evaluating lambdaParam")

evalParameter(trainData, validationData,"lambdaParam",

lambdaParamList=[1.0, 3.0, 5.0, 15.0, 25.0,30.0,35.0,40.0,45.0,50.0,60.0])

第五步：Spark相关设置

def SetLogger( sc ):

logger = sc._jvm.org.apache.log4j

logger.LogManager.getLogger("org"). setLevel( logger.Level.ERROR )

logger.LogManager.getLogger("akka").setLevel( logger.Level.ERROR )

logger.LogManager.getRootLogger().setLevel(logger.Level.ERROR)

def SetPath(sc):

global Path

if sc.master[0:5]=="local" :

Path="file:/home/jorlinlee/pythonsparkexample/PythonProject/"

else:

Path="hdfs://master:9000/user/jorlinlee/"

def CreateSparkContext():

sparkConf = SparkConf() \

.setAppName("NB") \

.set("spark.ui.showConsoleProgress", "false")

sc = SparkContext(conf = sparkConf)

print ("master="+sc.master)

SetLogger(sc)

SetPath(sc)

return (sc)

sc.stop()

第六步：运行主程序

if __name__ == "__main__":

print("NB")

sc=CreateSparkContext()

print("Preparing")

(trainData, validationData, testData, categoriesMap) =PrepareData(sc)

trainData.persist(); validationData.persist(); testData.persist()

print("Evaluating")

(AUC,duration, lambdaParam,model)= \

trainEvaluateModel(trainData, validationData, 60.0)

if (len(sys.argv) == 2) and (sys.argv[1]=="-e"):

parametersEval(trainData, validationData)

elif (len(sys.argv) == 2) and (sys.argv[1]=="-a"):

print("Best parameter")

model=evalAllParameter(trainData, validationData,

[1.0, 3.0, 5.0, 15.0, 25.0,30.0,35.0,40.0,45.0,50.0,60.0])

print("Test")

auc = evaluateModel(model, testData)

print("AUC:" + str(auc))

print("Predict")

PredictData(sc, model, categoriesMap)

结果：

Web:http://www.lynnskitchenadventures.com/2009/04/homemade-enchilada-sauce.html