def tfidf_knn(news, category):
c = idf_knn(news, category)
wordlist = c.keys()
tok = token__.tokenize2(news, category)
label = tok.keys()
matrix = {}
print 'hello'
for topic in label:
matrix[topic] = {}
for t in tok[topic].keys():
matrix[topic][t] = []
for i in range(len(c)):
matrix[topic][t].append(0)
print 'world'
for topic in label:
for i in tok[topic].keys():
for word in tok[topic][i]:
if word in wordlist:
matrix[topic][i][wordlist.index(i)] = tok[topic][i].count(i) * c[word]
print 'today'
#normalization
for topic in matrix:
for i in matrix[topic]:
normalize(matrix[topic][i])
return matrix
def idf_knn(news, category):
tok = token__.tokenize2(news, category)
text = []
for topic in tok:
for i in tok[topic]:
text += list(set(tok[topic][i]))
idf = {}
for word in text:
idf[word] = math.log(float(len(text)) / float(text.count(word))) / math.log(2)
print len(idf)
return idf
def classify(guess, p, category):
cl = {}
tok = token__.tokenize2(guess, category)
for i in range(len(tok)):
cl[i] = {}
for j in tok[i].keys():
cl[i][j] = {}
for k in range(len(category)):
cl[i][j][k] = 0
for word in tok[i][j]:
if word in p:
cl[i][j][k] += p[word][k]
result = {}
for i in range(len(cl)):
result[i] = {}
for j in cl[i].keys():
for k in cl[i][j]:
if cl[i][j][k] == max(cl[i][j].values()):
result[i][j] = k
for i in range(len(result)):
print category[i] + ':'
for j in result[i].keys():
print ' ' + category[result[i][j]] + ' ' + j
TP_ = 0 #overall TP
FP_ = 0 #overall FP
FN_ = 0 #overall FN
for i in range(len(result)):
TP = result[i].values().count(i)
FP = len(result[i].values()) - TP
FN = 0
for j in range(len(result)):
if j != i:
FN += result[j].values().count(i)
print category[i] + ': ' + 'TP = ' + str(TP) + ',FP = ' + str(FP) + ',FN = ' + str(FN)
print ' Precision = ' + str(float(TP) / float(TP + FP)) + ', Recall = ' + str(float(TP) / float(TP + FN))
print ' F1 = ' + str(float(2 * TP) / float(2 * TP + FP + FN))
TP_ += TP
FP_ += FP
FN_ += FN
print ' Microaveraged F1 = ' + str(float(2 * TP_) / float(2 * TP_ + FP_ + FN_))
def bayes(news, category):
tok = token__.tokenize2(news, category)
text = []
text_c = {}
s = []
for i in range(len(tok)):
tmp = 0
text_c[i] = []
for doc in tok[i]:
text += tok[i][doc]
text_c[i] += tok[i][doc]
tmp += len(tok[i][doc])
s.append(tmp)
text = list(set(text))
p = {}
for word in text:
p[word] = {}
for i in range(len(text_c)):
p[word][i] = math.log(float(text_c[i].count(word) + 1) / float(s[i] + len(text)))
return p
def not_naive(news, category):
tok = token__.tokenize2(news, category)
text = []
text_c = {}
text_raw = []
s = []
for i in range(len(tok)):
tmp = 0
text_c[i] = []
for doc in tok[i]:
text += tok[i][doc]
text_c[i] += tok[i][doc]
text_raw += tok[i][doc]
tmp += len(tok[i][doc])
s.append(tmp)
text = list(set(text))
p = {}
for word in text:
p[word] = {}
for i in range(len(text_c)):
p[word][i] = math.log(float(text_c[i].count(word) + 1) / float(s[i] + len(text)))
p_optimal = {}
for word in p:
for i in p[word].keys():
if p[word][i] == max(p[word].values()):
a = text_c[i].count(word)
c = text_raw.count(word) * 0.5
if a >= c:
p_optimal[word] = {}
for word in p_optimal:
for i in range(len(text_c)):
p_optimal[word][i] = p[word][i]
return p_optimal
