def CreatePredictSample(self, src):
print src
if (not self.trained):
print "train Classifier Matrix before predict"
#split sentence
#if src is read from utf-8 file directly,
# should using CreatePredictSample(src.decode("utf-8"))
wordList = mmseg.seg_txt(src)
wordList = [word for word in wordList if len(word.decode('utf8'))>1]
cols = []
vals = []
#fill partCols, and create csr
partCols = []
termFreqs = {}
for word in wordList:
if (GlobalInfo.termToId.has_key(word)):
termId = GlobalInfo.termToId[word]
partCols.append(termId)
if (not termFreqs.has_key(termId)):
termFreqs[termId] = 1
else:
termFreqs[termId] += 1
partCols = set(partCols)
partCols = list(partCols)
partCols.sort()
for col in partCols:
cols.append(col)
vals.append(termFreqs[col])
return [cols, vals]
def CreatePredictMatrix(self, path = ""):
if (not self.trained):
print "train ClassifierMatrix before predict"
return False
#get input path
inputPath = path
if (inputPath == ""):
inputPath = self.curNode.GetChild("test_input")
f = open(inputPath, "r")
rows = [0]
cols = []
vals = []
y = []
for line in f:
vec = line.split("\t")
line = vec[0]
y.append(int(vec[1]))
#split sentence
wordList = mmseg.seg_txt(line)
wordList = [word for word in wordList if len(word.decode('utf8'))>1]
#fill partCols, and create csr
partCols = []
termFreqs = {}
curWordCount = 0
for word in wordList:
curWordCount += 1
if (GlobalInfo.termToId.has_key(word)):
termId = GlobalInfo.termToId[word]
partCols.append(termId)
if (not termFreqs.has_key(termId)):
termFreqs[termId] = 1
else:
termFreqs[termId] += 1
partCols = set(partCols)
partCols = list(partCols)
partCols.sort()
for col in partCols:
cols.append(col)
vals.append(termFreqs[col])
rows.append(rows[len(rows) - 1] + \
len(partCols))
#close file
f.close()
return [Matrix(rows, cols, vals), y]
def CreateTrainMatrix(self):
uid = 0
rows = [0]
cols = []
vals = []
y = []
target = -1
uids = []
#fill the matrix's cols and rows
for doc in self.db[self.collection].find():
if not doc.has_key(self.field):
continue
line = doc.get(self.field)
user_id = doc['uid'].encode('utf-8')
uids.append(user_id)
target += 1
y.append(target)
wordList = mmseg.seg_txt(line.encode('utf-8'))
wordList = [word for word in wordList if len(word.decode('utf8'))>1]
#store current row's cols
partCols = []
#create dicts and fill partCol
#calculate term-frequent in this loop
curWordCount = 0
termFres = {}
for word in wordList:
curWordCount += 1
if (not GlobalInfo.termToId.has_key(word)):
GlobalInfo.termToId[word] = uid
GlobalInfo.idToTerm[uid] = word
uid += 1
termId = GlobalInfo.termToId[word]
partCols.append(termId)
if (not termFres.has_key(termId)):
termFres[termId] = 1
else:
termFres[termId] += 1
#fill partCol: delete duplicate and sort
partCols = set(partCols)
partCols = list(partCols)
partCols.sort()
#fill cols and vals, fill termToDocCount
for col in partCols:
cols.append(col)
#fill vals with termFrequent
vals.append(termFres[col])
#fill idToDocCount
if (not GlobalInfo.idToDocCount.has_key(col)):
GlobalInfo.idToDocCount[col] = 1
else:
GlobalInfo.idToDocCount[col] += 1
#fill rows
rows.append(rows[len(rows) - 1] + len(partCols))
#fill classToDocCount
if (not GlobalInfo.classToDocCount.has_key(target)):
GlobalInfo.classToDocCount[target] = 1
else:
GlobalInfo.classToDocCount[target] += 1
#fill GlobalInfo's idToIdf
for termId in GlobalInfo.idToTerm.keys():
GlobalInfo.idToIdf[termId] = math.log(float(len(rows) - 1) / (GlobalInfo.idToDocCount[termId] + 1))
#NOTE: now, not mul idf to vals, because not all algorithms need tf * idf
#change matrix's vals using tf-idf represent
#for r in range(len(rows) - 1):
# for c in range(rows[r], rows[r + 1]):
# termId = cols[c]
# #idf(i) = log(|D| / |{d (ti included)}| + 1
# vals[c] = vals[c] * GlobalInfo.idToIdf[termId]
#write dicts out
GlobalInfo.Write()
self.trained = True
return [Matrix(rows, cols, vals), y, uids]
