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
Predict:1.0Illustration:evergreen
Web:http://lolpics.se/18552-stun-grenade-ar
Predict:1.0Illustration:evergreen
Web:http://www.xcelerationfitness.com/treadmills.html
Predict:1.0Illustration:evergreen
Web:http://www.bloomberg.com/news/2012-02-06/syria-s-assad-deploys-tactics-of-father-to-crush-revolt-threatening-reign.html
Predict:1.0Illustration:evergreen
Web:http://www.wired.com/gadgetlab/2011/12/stem-turns-lemons-and-limes-into-juicy-atomizers/
Predict:1.0Illustration:evergreen
Web:http://www.latimes.com/health/boostershots/la-heb-fat-tax-denmark-20111013,0,2603132.story
Predict:1.0Illustration:evergreen
Web:http://www.howlifeworks.com/a/a?AG_ID=1186&cid=7340ci
Predict:1.0Illustration:evergreen
Web:http://romancingthestoveblog.wordpress.com/2010/01/13/sweet-potato-ravioli-with-lemon-sage-brown-butter-sauce/
Predict:1.0Illustration:evergreen
Web:http://www.funniez.net/Funny-Pictures/turn-men-down.html
Predict:1.0Illustration:evergreen
Web:http://youfellasleepwatchingadvd.com/
Predict:1.0Illustration:evergreen