tnt.py
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# -*- coding: utf-8 -*-
'''
Implementation of 'TnT - A Statisical Part of Speech Tagger'
'''
from __future__ import unicode_literals
import sys
import gzip
import heapq
import marshal
from math import log
from . import frequency
class TnT(object):
def __init__(self, N=1000):
self.N = N
self.l1 = 0.0
self.l2 = 0.0
self.l3 = 0.0
self.status = set()
self.wd = frequency.AddOneProb()
self.eos = frequency.AddOneProb()
self.eosd = frequency.AddOneProb()
self.uni = frequency.NormalProb()
self.bi = frequency.NormalProb()
self.tri = frequency.NormalProb()
self.word = {}
self.trans = {}
def save(self, fname, iszip=True):
d = {}
for k, v in self.__dict__.items():
if isinstance(v, set):
d[k] = list(v)
elif hasattr(v, '__dict__'):
d[k] = v.__dict__
else:
d[k] = v
if sys.version_info[0] == 3:
fname = fname + '.3'
if not iszip:
marshal.dump(d, open(fname, 'wb'))
else:
f = gzip.open(fname, 'wb')
f.write(marshal.dumps(d))
f.close()
def load(self, fname, iszip=True):
if sys.version_info[0] == 3:
fname = fname + '.3'
if not iszip:
d = marshal.load(open(fname, 'rb'))
else:
try:
f = gzip.open(fname, 'rb')
d = marshal.loads(f.read())
except IOError:
f = open(fname, 'rb')
d = marshal.loads(f.read())
f.close()
for k, v in d.items():
if isinstance(self.__dict__[k], set):
self.__dict__[k] = set(v)
elif hasattr(self.__dict__[k], '__dict__'):
self.__dict__[k].__dict__ = v
else:
self.__dict__[k] = v
def tnt_div(self, v1, v2):
if v2 == 0:
return 0
return float(v1)/v2
def geteos(self, tag):
tmp = self.eosd.get(tag)
if not tmp[0]:
return log(1.0/len(self.status))
return log(self.eos.get((tag, 'EOS'))[1])-log(self.eosd.get(tag)[1])
def train(self, data):
for sentence in data:
now = ['BOS', 'BOS']
self.bi.add(('BOS', 'BOS'), 1)
self.uni.add('BOS', 2)
for word, tag in sentence:
now.append(tag)
self.status.add(tag)
self.wd.add((tag, word), 1)
self.eos.add(tuple(now[1:]), 1)
self.eosd.add(tag, 1)
self.uni.add(tag, 1)
self.bi.add(tuple(now[1:]), 1)
self.tri.add(tuple(now), 1)
if word not in self.word:
self.word[word] = set()
self.word[word].add(tag)
now.pop(0)
self.eos.add((now[-1], 'EOS'), 1)
tl1 = 0.0
tl2 = 0.0
tl3 = 0.0
for now in self.tri.samples():
c3 = self.tnt_div(self.tri.get(now)[1]-1,
self.bi.get(now[:2])[1]-1)
c2 = self.tnt_div(self.bi.get(now[1:])[1]-1,
self.uni.get(now[1])[1]-1)
c1 = self.tnt_div(self.uni.get(now[2])[1]-1, self.uni.getsum()-1)
if c3 >= c1 and c3 >= c2:
tl3 += self.tri.get(now)[1]
elif c2 >= c1 and c2 >= c3:
tl2 += self.tri.get(now)[1]
elif c1 >= c2 and c1 >= c3:
tl1 += self.tri.get(now)[1]
self.l1 = float(tl1)/(tl1+tl2+tl3)
self.l2 = float(tl2)/(tl1+tl2+tl3)
self.l3 = float(tl3)/(tl1+tl2+tl3)
for s1 in self.status | set(('BOS',)):
for s2 in self.status | set(('BOS',)):
for s3 in self.status:
uni = self.l1*self.uni.freq(s3)
bi = self.tnt_div(self.l2*self.bi.get((s2, s3))[1],
self.uni.get(s2)[1])
tri = self.tnt_div(self.l3*self.tri.get((s1, s2, s3))[1],
self.bi.get((s1, s2))[1])
self.trans[(s1, s2, s3)] = log(uni+bi+tri)
def tag(self, data):
now = [(('BOS', 'BOS'), 0.0, [])]
for w in data:
stage = {}
samples = self.status
if w in self.word:
samples = self.word[w]
for s in samples:
wd = log(self.wd.get((s, w))[1])-log(self.uni.get(s)[1])
for pre in now:
p = pre[1]+wd+self.trans[(pre[0][0], pre[0][1], s)]
if (pre[0][1], s) not in stage or p > stage[(pre[0][1],
s)][0]:
stage[(pre[0][1], s)] = (p, pre[2]+[s])
stage = list(map(lambda x: (x[0], x[1][0], x[1][1]), stage.items()))
now = heapq.nlargest(self.N, stage, key=lambda x: x[1])
now = heapq.nlargest(1, stage, key=lambda x: x[1]+self.geteos(x[0][1]))
return zip(data, now[0][2])