# 這個資料集來自www.kaggle.com/c/stumbleupon/data, 用於預測網頁是短暫存在還是長時間流行,這裡我把它存放在hdfs檔案系統內的/user/yy/stumbleupon/目錄裡
rawData = sc.textFile("hdfs:///user/yy/stumbleupon/train_noheader.tsv")
records = rawData.map(lambda line: line.split('\t'))
records.first()
# 每一條樣本是一個網頁的資訊

[u’”http://www.bloomberg.com/news/2010-12-23/ibm-predicts-holographic-calls-air-breathing-batteries-by-2015.html

“’,
u‘“4042”’,
u’”{“”title”“:”“IBM Sees Holographic Calls Air Breathing Batteries ibm sees holographic calls, air-breathing batteries”“,”“body”“:”“A sign stands outside the International Business Machines Corp IBM Almaden Research Center campus in San Jose California Photographer Tony Avelar Bloomberg Buildings stand at the International Business Machines Corp IBM Almaden Research Center campus in the Santa Teresa Hills of San Jose California Photographer Tony Avelar Bloomberg By 2015 your mobile phone will project a 3 D image of anyone who calls and your laptop will be powered by kinetic energy At least that s what International Business Machines Corp sees in its crystal ball The predictions are part of an annual tradition for the Armonk New York based company which surveys its 3 000 researchers to find five ideas expected to take root in the next five years IBM the world s largest provider of computer services looks to Silicon Valley for input gleaning many ideas from its Almaden research center in San Jose California Holographic conversations projected from mobile phones lead this year s list The predictions also include air breathing batteries computer programs that can tell when and where traffic jams will take place environmental information generated by sensors in cars and phones and cities powered by the heat thrown off by computer servers These are all stretch goals and that s good said Paul Saffo managing director of foresight at the investment advisory firm Discern in San Francisco In an era when pessimism is the new black a little dose of technological optimism is not a bad thing For IBM it s not just idle speculation The company is one of the few big corporations investing in long range research projects and it counts on innovation to fuel growth Saffo said Not all of its predictions pan out though IBM was overly optimistic about the spread of speech technology for instance When the ideas do lead to products they can have broad implications for society as well as IBM s bottom line he said Research Spending They have continued to do research when all the other grand research organizations are gone said Saffo who is also a consulting associate professor at Stanford University IBM invested 5 8 billion in research and development last year 6 1 percent of revenue While that s down from about 10 percent in the early 1990s the company spends a bigger share on research than its computing rivals Hewlett Packard Co the top maker of personal computers spent 2 4 percent last year At Almaden scientists work on projects that don t always fit in with IBM s computer business The lab s research includes efforts to develop an electric car battery that runs 500 miles on one charge a filtration system for desalination and a program that shows changes in geographic data IBM rose 9 cents to 146 04 at 11 02 a m in New York Stock Exchange composite trading The stock had gained 11 percent this year before today Citizen Science The list is meant to give a window into the company s innovation engine said Josephine Cheng a vice president at IBM s Almaden lab All this demonstrates a real culture of innovation at IBM and willingness to devote itself to solving some of the world s biggest problems she said Many of the predictions are based on projects that IBM has in the works One of this year s ideas that sensors in cars wallets and personal devices will give scientists better data about the environment is an expansion of the company s citizen science initiative Earlier this year IBM teamed up with the California State Water Resources Control Board and the City of San Jose Environmental Services to help gather information about waterways Researchers from Almaden created an application that lets smartphone users snap photos of streams and creeks and report back on conditions The hope is that these casual observations will help local and state officials who don t have the resources to do the work themselves Traffic Predictors IBM also sees data helping shorten commutes in the next five years Computer programs will use algorithms and real time traffic information to predict which roads will have backups and how to avoid getting stuck Batteries may last 10 times longer in 2015 than today IBM says Rather than using the current lithium ion technology new models could rely on energy dense metals that only need to interact with the air to recharge Some electronic devices might ditch batteries altogether and use something similar to kinetic wristwatches which only need to be shaken to generate a charge The final prediction involves recycling the heat generated by computers and data centers Almost half of the power used by data centers is currently spent keeping the computers cool IBM scientists say it would be better to harness that heat to warm houses and offices In IBM s first list of predictions compiled at the end of 2006 researchers said instantaneous speech translation would become the norm That hasn t happened yet While some programs can quickly translate electronic documents and instant messages and other apps can perform limited speech translation there s nothing widely available that acts like the universal translator in Star Trek Second Life The company also predicted that online immersive environments such as Second Life would become more widespread While immersive video games are as popular as ever Second Life s growth has slowed Internet users are flocking instead to the more 2 D environments of Facebook Inc and Twitter Inc Meanwhile a 2007 prediction that mobile phones will act as a wallet ticket broker concierge bank and shopping assistant is coming true thanks to the explosion of smartphone applications Consumers can pay bills through their banking apps buy movie tickets and get instant feedback on potential purchases all with a few taps on their phones The nice thing about the list is that it provokes thought Saffo said If everything came true they wouldn t be doing their job To contact the reporter on this story Ryan Flinn in San Francisco at rflinn bloomberg net To contact the editor responsible for this story Tom Giles at tgiles5 bloomberg net by 2015, your mobile phone will project a 3-d image of anyone who calls and your laptop will be powered by kinetic energy. at least that\u2019s what international business machines corp. sees in its crystal ball.”“,”“url”“:”“bloomberg news 2010 12 23 ibm predicts holographic calls air breathing batteries by 2015 html”“}”’,
u‘“business”’,
u‘“0.789131”’,
u‘“2.055555556”’,
u‘“0.676470588”’,
u‘“0.205882353”’,
u‘“0.047058824”’,
u‘“0.023529412”’,
u‘“0.443783175”’,
u‘“0”’,
u‘“0”’,
u‘“0.09077381”’,
u‘“0”’,
u‘“0.245831182”’,
u‘“0.003883495”’,
u‘“1”’,
u‘“1”’,
u‘“24”’,
u‘“0”’,
u‘“5424”’,
u‘“170”’,
u‘“8”’,
u‘“0.152941176”’,
u‘“0.079129575”’,
u‘“0”’]

from pyspark.mllib.regression import LabeledPoint # 標註點型別，（label, feature）通常feature是Vectors.dense型別
from pyspark.mllib.linalg import Vectors

# DenseVector型別的好處,這2行只是一個演示而已
a = sc.parallelize([Vectors.dense(1,2),Vectors.dense(3,4)])
a.sum() # 裝著DenseVector型別向量的rdd,可以按列求值

# def records_processing(record):
 

#     trimmed = record.map(lambda e: e.replaceAll('\"', ''))
#     label = int(trimmed[record.size - 1])
#     features = trimmed[4 : record.size - 1].map(lambda d: 0.0 if d == '?' else float(d))
#     return LabeledPoint(label, Vectors.dense(features))

sizeAndData = records.map(lambda record : (len(record), map(lambda e: e.replace('\"', ''), record)))
labelAndFeature = sizeAndData.map(lambda (size, data): (int(data[size-1]), map(lambda d: 0.0 if d == '?' else float(d), data[4: size-1])))
data = labelAndFeature.map(lambda (label, feature): LabeledPoint(label, Vectors.dense(map(lambda d: 0.0 if d < 0 else d, feature))))
data.first()

LabeledPoint(0.0, [0.789131,2.055555556,0.676470588,0.205882353,0.047058824,0.023529412,0.443783175,0.0,0.0,0.09077381,0.0,0.245831182,0.003883495,1.0,1.0,24.0,0.0,5424.0,170.0,8.0,0.152941176,0.079129575])

data.cache()
data.count()

7395

from pyspark.mllib.classification import LogisticRegressionWithSGD
from pyspark.mllib.classification import SVMWithSGD
from pyspark.mllib.classification import NaiveBayes
from pyspark.mllib.tree import DecisionTree
# from pyspark.mllib.tree.configuration import Algo 
# from pyspark.mllib.tree.impurity import Entropy

numIterations = 10
maxTreeDepth = 5
dataPoint = data.first()
dataPoint

LabeledPoint(0.0, [0.789131,2.055555556,0.676470588,0.205882353,0.047058824,0.023529412,0.443783175,0.0,0.0,0.09077381,0.0,0.245831182,0.003883495,1.0,1.0,24.0,0.0,5424.0,170.0,8.0,0.152941176,0.079129575])

# 訓練邏輯迴歸模型
lrModel = LogisticRegressionWithSGD.train(data, numIterations)

# 使用邏輯歸回模型
prediction = lrModel.predict(dataPoint.features)
print "預測值：%d, 真實值：%d" % (prediction, dataPoint.label)

# 看看邏輯迴歸整體的預測結果
predictionsAndRealities = data.map(lambda point: lrModel.predict(point.features) == point.label)

# 計算邏輯迴歸的準確率
lrTotalCorrect = data.map(lambda point :
                         1 if lrModel.predict(point.features) == point.label else 0
                         ).sum()
lrAccuracy = float(lrTotalCorrect) / data.count()
print "邏輯迴歸準確率：", lrAccuracy

# 計算PR(準確率-召回率)曲線下面積， 以及ROC(受試者工作特徵曲線， 真陽性率-假陽性率)曲線下面積即AUC
from pyspark.mllib.evaluation import BinaryClassificationMetrics
scoreAndLabels = data.map(lambda point: (float(lrModel.predict(point.features)), point.label))

metrics = BinaryClassificationMetrics(scoreAndLabels)
print 'Area under PR: %2.4f%%, Area under ROC: %2.4f%%' % \
    (metrics.areaUnderPR * 100, metrics.areaUnderROC * 100)

預測值：1, 真實值：0

邏輯迴歸準確率： 0.514672075727

Area under PR: 75.6759%, Area under ROC: 50.1418%

# 訓練SVM模型
svmModel = SVMWithSGD.train(data, numIterations)

# 使用SVM模型
prediction = svmModel.predict(dataPoint.features)
print "預測值：%d, 真實值：%d" % (prediction, dataPoint.label)

# 看看SVM整體的預測結果
predictionsAndRealities = data.map(lambda point: svmModel.predict(point.features) == point.label)

# 計算SVM的準確率
svmTotalCorrect = data.map(lambda point :
                         1 if svmModel.predict(point.features) == point.label else 0
                         ).sum()
svmAccuracy = float(svmTotalCorrect) / data.count()
print "SVM準確率：", svmAccuracy

# 計算PR(準確率-召回率)曲線下面積， 以及ROC(受試者工作特徵曲線， 真陽性率-假陽性率)曲線下面積即AUC
scoreAndLabels = data.map(lambda point: (float(svmModel.predict(point.features)), point.label))

metrics = BinaryClassificationMetrics(scoreAndLabels)
print 'Area under PR: %2.4f%%, Area under ROC: %2.4f%%' % \
    (metrics.areaUnderPR * 100, metrics.areaUnderROC * 100)

預測值：1, 真實值：0

SVM準確率： 0.514672075727

Area under PR: 75.6759%, Area under ROC: 50.1418%

# 訓練樸素貝葉斯模型
nbModel = NaiveBayes.train(data)

# 使用樸素貝葉斯模型
prediction = nbModel.predict(dataPoint.features)
print "預測值：%d, 真實值：%d" % (prediction, dataPoint.label)

# 看看樸素貝葉斯整體的預測結果
predictionsAndRealities = data.map(lambda point: nbModel.predict(point.features) == point.label)

# 計算樸素貝葉斯的準確率
nbTotalCorrect = data.map(lambda point :
                         1 if nbModel.predict(point.features) == point.label else 0
                         ).sum()
nbAccuracy = float(nbTotalCorrect) / data.count()
print "樸素貝葉斯準確率：", nbAccuracy

# 計算PR(準確率-召回率)曲線下面積， 以及ROC(受試者工作特徵曲線， 真陽性率-假陽性率)曲線下面積即AUC
from pyspark.mllib.evaluation import BinaryClassificationMetrics
scoreAndLabels = data.map(lambda point: (float(nbModel.predict(point.features)), point.label))

metrics = BinaryClassificationMetrics(scoreAndLabels)
print 'Area under PR: %2.4f%%, Area under ROC: %2.4f%%' % \
    (metrics.areaUnderPR * 100, metrics.areaUnderROC * 100)

預測值：1, 真實值：0

樸素貝葉斯準確率： 0.580392156863

Area under PR: 68.0851%, Area under ROC: 58.3559%

# 訓練決策樹模型
dtModel = DecisionTree.trainClassifier(data, 2, {})

# 使用決策樹模型
prediction = dtModel.predict(dataPoint.features)
print "預測值：%d, 真實值：%d" % (prediction, dataPoint.label)

# 看看決策樹整體的預測結果, 決策樹模型只能在主節點上使用
predictions = dtModel.predict(data.map(lambda point: point.features))
results = predictions.zip(data.map(lambda point: point.label))

# 計算決策樹的準確率
dtTotalCorrect = results.filter(lambda (real, pred): pred == real).count()
dtAccuracy = float(dtTotalCorrect) / data.count()
print "決策樹準確率：", dtAccuracy

# 計算PR(準確率-召回率)曲線下面積， 以及ROC(受試者工作特徵曲線， 真陽性率-假陽性率)曲線下面積即AUC
metrics = BinaryClassificationMetrics(results)
print 'Area under PR: %2.4f%%, Area under ROC: %2.4f%%' % \
    (metrics.areaUnderPR * 100, metrics.areaUnderROC * 100)

預測值：0, 真實值：0

決策樹準確率： 0.648275862069

Area under PR: 74.2894%, Area under ROC: 64.8916%

# 上面的結果跟隨機差不多，原因是我們僅僅只是把資料送入模型
# 改進模型效能以及引數調優

# from pyspark.mllib.linalg.distributed import RowMatrix

matrix = data.map(lambda point: point.features)
print "矩陣的第一行：\n", matrix.first()

result = matrix.stats()
print "矩陣各列的平均值：\n", result.mean()
print "矩陣各列的最小值：\n", result.min()
print "矩陣各列的最大值：\n", result.max()
print "矩陣各列的標準差：\n", result.stdev()
print "矩陣各列的方差：\n", result.variance()

矩陣的第一行：
[0.789131,2.055555556,0.676470588,0.205882353,0.047058824,0.023529412,0.443783175,0.0,0.0,0.09077381,0.0,0.245831182,0.003883495,1.0,1.0,24.0,0.0,5424.0,170.0,8.0,0.152941176,0.079129575]

矩陣各列的平均值：
[0.412258052995,2.76182319199,0.468230473286,0.214079926384,0.092062360719,0.0492621604391,2.25510345221,0.000914886880189,0.0,0.0564227449842,0.02123056119,0.233778176655,0.369015325398,0.615551048005,0.66031102096,30.0770791075,0.0397565922921,5716.59824206,178.754563895,4.96064908722,0.17286405047,0.101220791893]

矩陣各列的最小值：

[ 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.04556422 0. 0.
0. 0. 0. 0. 1. 0. 0.
0. ]

矩陣各列的最大值：

[ 9.99426000e-01 3.63000000e+02 1.00000000e+00 1.00000000e+00
9.80392157e-01 9.80392157e-01 2.10000000e+01 2.50000000e-01
0.00000000e+00 4.44444444e-01 1.00000000e+00 7.16883117e-01
1.13333333e+02 1.00000000e+00 1.00000000e+00 1.00000000e+02
1.00000000e+00 2.07952000e+05 4.99700000e+03 2.20000000e+01
1.00000000e+00 1.00000000e+00]

矩陣各列的標準差：

[ 3.31251568e-01 8.61920979e+00 2.03119645e-01 1.46732834e-01
9.59717238e-02 7.26238264e-02 5.70392733e+00 7.42829375e-03
0.00000000e+00 4.14428371e-02 1.44152088e-01 5.24830153e-02
1.87878356e+00 4.86464752e-01 4.73603607e-01 2.03917219e+01
1.95386810e-01 8.87483231e+03 1.79454063e+02 3.23289275e+00
1.83273269e-01 7.92255262e-02]

矩陣各列的方差：
[0.109727601589,74.2907773273,0.041257590017,0.0215305244407,0.00921057176901,0.00527422015895,32.5347869854,5.51795480353e-05,0.0,0.00171750875055,0.0207798244616,0.00275446689512,3.52982764865,0.236647955305,0.224300376559,415.822321151,0.0381760056614,78762648.5733,32203.7608565,10.4515955219,0.0335890912886,0.00627668399539]

from pyspark.mllib.feature import StandardScaler

# 第一個True表示均值正則化（每個值減去均值），第二個True表示正則化標準差（每個值除以標準差進行縮放）
scaler = StandardScaler(withMean=True, withStd=True).fit(matrix)
scaledData = scaler.transform(matrix)
scaledData = data.map(lambda lp: lp.label).zip(scaledData).map(lambda (label, feature): LabeledPoint(label, feature))

print "標準化前：\n", data.first().features
print "標準化後：\n", scaledData.first().features

import numpy as np
vector = np.array(matrix.first())
mean = np.array(result.mean())
stdev = np.array(result.stdev())
print "手動標準化：\n", (vector - mean) / stdev

標準化前：
[0.789131,2.055555556,0.676470588,0.205882353,0.047058824,0.023529412,0.443783175,0.0,0.0,0.09077381,0.0,0.245831182,0.003883495,1.0,1.0,24.0,0.0,5424.0,170.0,8.0,0.152941176,0.079129575]

標準化後：
[1.1376473365,-0.0819355716929,1.02513981289,-0.0558635644254,-0.468893253129,-0.354305326308,-0.317535217236,-0.123154125351,0.0,0.828822173315,-0.147268943346,0.229639823578,-0.194331667814,0.790238049918,0.717194729453,-0.297996816496,-0.20346257793,-0.0329672096969,-0.0487811297558,0.940069975117,-0.108698488525,-0.278820782314]

手動標準化：

[ 1.13772426 -0.08194111 1.02520913 -0.05586734 -0.46892496 -0.35432928
-0.31755669 -0.12316245 nan 0.82887822 -0.1472789 0.22965535
-0.19434481 0.79029149 0.71724323 -0.29801697 -0.20347634 -0.03296944
-0.04878443 0.94013354 -0.10870584 -0.27883964]

# 用標準化後的資料重新訓練模型，不訓練決策樹和樸素貝葉斯，因為他倆不受特徵標準化的影響

# 訓練邏輯迴歸模型
lrModelScaled = LogisticRegressionWithSGD.train(scaledData, numIterations)

# 看看邏輯迴歸整體的預測結果
lrTotalCorrectScaled = scaledData.filter(lambda point: lrModelScaled.predict(point.features) == point.label).count()
print "預測正確的樣本個數", lrTotalCorrectScaled

# 計算邏輯迴歸的準確率
lrAccuracyScaled = float(lrTotalCorrectScaled) / data.count()
print "邏輯迴歸準確率：", lrAccuracyScaled

# 計算PR(準確率-召回率)曲線下面積， 以及ROC(受試者工作特徵曲線， 真陽性率-假陽性率)曲線下面積即AUC
lrPredictionsVsTrue = scaledData.map(lambda point: (float(lrModelScaled.predict(point.features)), point.label))
lrMetricsScaled = BinaryClassificationMetrics(lrPredictionsVsTrue)
print 'Area under PR: %2.4f%%, Area under ROC: %2.4f%%' % \
    (lrMetricsScaled.areaUnderPR * 100, lrMetricsScaled.areaUnderROC * 100)

預測正確的樣本個數 4609

邏輯迴歸準確率： 0.623258958756

Area under PR: 73.0204%, Area under ROC: 62.2292%

# 考慮網頁的類別特徵，比如這個網頁是屬於sports類還是business類
categories = dict(records.map(lambda record: record[3].replace('\"', '')).distinct().zipWithIndex().collect())
print categories
numCategories = len(categories)
print numCategories

categories = sc.broadcast(categories)
numCategories = sc.broadcast(numCategories)

{u’gaming’: 7, u’recreation’: 0, u’business’: 1, u’computer_internet’: 2, u’unknown’: 8, u’culture_politics’: 3, u’science_technology’: 9, u’law_crime’: 4, u’sports’: 10, u’religion’: 11, u’weather’: 12, u’health’: 5, u’?’: 6, u’arts_entertainment’: 13}
14

def buildCategoriesVector(data):
    vector = [0] * numCategories.value
    vector[categories.value[data[3]]] = 1
    return vector + data[4:-1]

sizeAndData = records.map(lambda record : (len(record), map(lambda e: e.replace('\"', ''), record)))
labelAndFeature = sizeAndData.map(lambda (size, data): (int(data[-1]), map(lambda d: 0.0 if d == '?' else float(d), buildCategoriesVector(data))))
dataCategories = labelAndFeature.map(lambda (label, feature): LabeledPoint(label, Vectors.dense(map(lambda d: 0.0 if d < 0 else d, feature))))
dataPointCats = dataCategories.first()
print labelAndFeature.first()
print dataPointCats 

(0, [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.789131, 2.055555556, 0.676470588, 0.205882353, 0.047058824, 0.023529412, 0.443783175, 0.0, 0.0, 0.09077381, 0.0, 0.245831182, 0.003883495, 1.0, 1.0, 24.0, 0.0, 5424.0, 170.0, 8.0, 0.152941176, 0.079129575])

(0.0,[0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.789131,2.055555556,0.676470588,0.205882353,0.047058824,0.023529412,0.443783175,0.0,0.0,0.09077381,0.0,0.245831182,0.003883495,1.0,1.0,24.0,0.0,5424.0,170.0,8.0,0.152941176,0.079129575])

# 標準化轉換
labels = dataCategories.map(lambda lp: lp.label)
featuresMatrix = dataCategories.map(lambda lp: lp.features)
scalerCats = StandardScaler(withMean=True, withStd=True).fit(featuresMatrix)
scaledDataCats = scalerCats.transform(featuresMatrix).zip(labels).map(lambda (features, label): LabeledPoint(label, features))
scaledDataCats.first()

LabeledPoint(0.0, [-0.446421204794,2.72073665645,-0.204182210579,-0.220526884579,-0.0648775723926,-0.270999069693,-0.680752790425,-0.101894690972,-0.028494000387,-0.201654052319,-0.232727977095,-0.0991499193088,-0.0232621058984,-0.381813223243,1.1376473365,-0.0819355716929,1.02513981289,-0.0558635644254,-0.468893253129,-0.354305326308,-0.317535217236,-0.123154125351,0.0,0.828822173315,-0.147268943346,0.229639823578,-0.194331667814,0.790238049918,0.717194729453,-0.297996816496,-0.20346257793,-0.0329672096969,-0.0487811297558,0.940069975117,-0.108698488525,-0.278820782314])

lrModelScaledCats = LogisticRegressionWithSGD.train(scaledDataCats, numIterations)

# 看看邏輯迴歸整體的預測結果
lrTotalCorrectScaledCats = scaledDataCats.filter(lambda point: lrModelScaledCats.predict(point.features) == point.label).count()
print "預測正確的樣本個數", lrTotalCorrectScaledCats

# 計算邏輯迴歸的準確率
lrAccuracyScaledCats = float(lrTotalCorrectScaledCats) / scaledDataCats.count()
print "邏輯迴歸準確率：", lrAccuracyScaledCats

# 計算PR(準確率-召回率)曲線下面積， 以及ROC(受試者工作特徵曲線， 真陽性率-假陽性率)曲線下面積即AUC
lrPredictionsVsTrue = scaledDataCats.map(lambda point: (float(lrModelScaledCats.predict(point.features)), point.label))
lrMetricsScaledCats = BinaryClassificationMetrics(lrPredictionsVsTrue)
print 'Area under PR: %2.4f%%, Area under ROC: %2.4f%%' % \
    (lrMetricsScaledCats.areaUnderPR * 100, lrMetricsScaledCats.areaUnderROC * 100)

預測正確的樣本個數 4928

邏輯迴歸準確率： 0.666396213658

Area under PR: 75.8535%, Area under ROC: 66.6127%

# 只使用1-of-k編碼的類別特徵
def onlyCategoriesVector(data):
    vector = [0] * numCategories.value
    vector[categories.value[data[3]]] = 1
    return vector

sizeAndData = records.map(lambda record : (len(record), map(lambda e: e.replace('\"', ''), record)))
labelAndFeature = sizeAndData.map(lambda (size, data): \
                                  (int(data[-1]), map(lambda d: 0.0 if d == '?' else float(d), onlyCategoriesVector(data))))
dataNB = labelAndFeature.map(lambda (label, feature): \
                             LabeledPoint(label, Vectors.dense(map(lambda d: 0.0 if d < 0 else d, feature))))

# 訓練樸素貝葉斯模型
nbModelCats = NaiveBayes.train(dataNB)

# 計算樸素貝葉斯的準確率
nbTotalCorrectCats = dataNB.map(lambda point :
                         1 if nbModelCats.predict(point.features) == point.label else 0
                         ).sum()
nbAccuracyCats = float(nbTotalCorrectCats) / dataNB.count()
print "樸素貝葉斯準確率：", nbAccuracyCats

nbPredictionsVsTrueCats = dataNB.map(lambda point: (float(nbModelCats.predict(point.features)), point.label))
nbMetricsCats = BinaryClassificationMetrics(nbPredictionsVsTrueCats)
print 'Area under PR: %2.4f%%, Area under ROC: %2.4f%%' % \
    (nbMetricsCats.areaUnderPR * 100, nbMetricsCats.areaUnderROC * 100)

樸素貝葉斯準確率： 0.609601081812

Area under PR: 74.0522%, Area under ROC: 60.5138%