complete spam test classification in Naive Bayes

Bayes/NativeBayes.py

@@ -1,13 +1,111 @@
-__author__ = 'wangjiewen'
+# coding=utf-8
 
-import numpy
+import numpy as np
 
-class NativeBayes(object):
-    def test(self):
-        print 'test'
+def createVocabList(dataSet):
+    vocabSet = set([])
+    for document in dataSet:
+        vocabSet = vocabSet | set(document)
+    return list(vocabSet)
+
+
+def bagOfWords2VecMN(vocabList, inputSet):
+    returnVec = [0] * len(vocabList)
+    for word in inputSet:
+        if word in vocabList:
+            returnVec[vocabList.index(word)] += 1
+    return returnVec
+
+
+def trainNB(trainMat, labelMat):
+    numTrainDocs = len(trainMat)
+    numWords = len(trainMat[0])
+    # in labelMat, 1 stand for abusive; 0 for normal
+    pClass1 = sum(labelMat) / float(numTrainDocs)
+    p0Num = np.ones(numWords)
+    p1Num = np.ones(numWords)
+    p0Denom = 2.0
+    p1Denom = 2.0
+    for i in range(numTrainDocs):
+        if labelMat[i] == 1:
+            p1Num += trainMat[i]
+            p1Denom += sum(trainMat[i])
+        else:
+            p0Num += trainMat[i]
+            p0Denom += sum(trainMat[i])
+    # regarding the accuracy of float in python
+    # change it to log
+    p0Vec = np.log(p0Num / p0Denom)
+    p1Vec = np.log(p1Num / p1Denom)
+    return p0Vec, p1Vec, pClass1
+
+
+def classifyBN(vec2Classify, p0Vec, p1Vec, pClass1):
+    p0 = sum(vec2Classify * p0Vec) + np.log(1 - pClass1)
+    p1 = sum(vec2Classify * p1Vec) + np.log(pClass1)
+    if p0 > p1:
+        return 0
+    else:
+        return 1
+
+
+def textParse(bigString):
+    """
+    " filter too short words
+    " input is big string, output is a list
+    """
+    import re
+    listOfTokens = re.split(r'\W*', bigString)
+    return [tok.lower() for tok in listOfTokens if len(tok) > 2]
+
+
+def spamTest():
+    docList = []
+    classList = []
+    for i in range(1, 26):
+        wordList = textParse(open('email/spam/%d.txt' % i).read())
+        docList.append(wordList)
+        classList.append(1)
+
+        wordList = textParse(open('email/ham/%d.txt' % i).read())
+        docList.append(wordList)
+        classList.append(0)
+
+    vocabList = createVocabList(docList)
+    trainingSet = range(50)
+    testSet = []
+    for i in range(10):
+        randIndex = int(np.random.uniform(0, len(trainingSet)))
+        testSet.append(trainingSet[randIndex])
+        del(trainingSet[randIndex])
+
+    trainMat = []
+    labelMat = []
+    for docId in trainingSet:
+        trainMat.append(bagOfWords2VecMN(vocabList, docList[docId]))
+        labelMat.append(classList[docId])
+
+    p0Vec, p1Vec, pSpam = trainNB(np.array(trainMat), np.array(labelMat))
+    errorCount = 0
+    for docId in testSet:
+        wordVec = bagOfWords2VecMN(vocabList, docList[docId])
+        if classifyBN(wordVec, p0Vec, p1Vec, pSpam) != classList[docId]:
+            errorCount += 1
+            print "classfiy wrongly", docList[docId]
+
+    print 'the error rate is: ', float(errorCount) / len(testSet)
+
+
+
+
+
+
+
+
+
+if __name__ == '__main__':
+    print 'begin'
+    spamTest()
+    print 'end'
 
-    def __init__(self):
-        print 'init'
 
-    def loadData(self):
-        print 'load data'