Jiagu/jiagu/perceptron.py

# -*- coding:utf-8 -*-
import os
import gzip
import pickle
import random
from collections import defaultdict

class AveragedPerceptron(object):
    def __init__(self):
        # Each feature gets its own weight vector, so weights is a dict-of-dicts
        self.weights = {}
        self.classes = set()
        # The accumulated values, for the averaging. These will be keyed by
        # feature/clas tuples
        self._totals = defaultdict(int)
        # The last time the feature was changed, for the averaging. Also
        # keyed by feature/clas tuples
        # (tstamps is short for timestamps)
        self._tstamps = defaultdict(int)
        # Number of instances seen
        self.i = 0

    def predict(self, features):
        '''Dot-product the features and current weights and return the best label.'''
        scores = defaultdict(float)
        for feat, value in features.items():
            if feat not in self.weights or value == 0:
                continue
            weights = self.weights[feat]
            for label, weight in weights.items():
                scores[label] += value * weight
        # Do a secondary alphabetic sort, for stability
        return max(self.classes, key=lambda label: (scores[label], label))

    def update(self, truth, guess, features):
        '''Update the feature weights.'''
        def upd_feat(c, f, w, v):
            param = (f, c)
            self._totals[param] += (self.i - self._tstamps[param]) * w
            self._tstamps[param] = self.i
            self.weights[f][c] = w + v

        self.i += 1
        if truth == guess:
            return None
        for f in features:
            weights = self.weights.setdefault(f, {})
            upd_feat(truth, f, weights.get(truth, 0.0), 1.0)
            upd_feat(guess, f, weights.get(guess, 0.0), -1.0)
        return None

    def average_weights(self):
        '''Average weights from all iterations.'''
        for feat, weights in self.weights.items():
            new_feat_weights = {}
            for clas, weight in weights.items():
                param = (feat, clas)
                total = self._totals[param]
                total += (self.i - self._tstamps[param]) * weight
                averaged = round(total / float(self.i), 3)
                if averaged:
                    new_feat_weights[clas] = averaged
            self.weights[feat] = new_feat_weights
        return None

class Perceptron:
	def __init__(self, loc=None):
		self.START = ['-START-', '-START2-']
		self.END = ['-END-', '-END2-']
		self.model = AveragedPerceptron()
		
		if loc != None:
			self.load(loc)

	def predict(self, words):
		prev, prev2 = self.START
		labels = []
		context = self.START + words + self.END
		for i, word in enumerate(words):
			features = self._get_features(i, word, context, prev, prev2)
			tag = self.model.predict(features)
			labels.append(tag)
			prev2 = prev
			prev = tag
		return labels
		
	def train(self, sentences, save_loc=None, nr_iter=5, shuf=False):
		self._make_tagdict(sentences)
		for iter_ in range(nr_iter):
			c = 0
			n = 0
			for words, tags in sentences:
				prev, prev2 = self.START
				context = self.START + words + self.END
				for i, word in enumerate(words):
					feats = self._get_features(i, word, context, prev, prev2)
					guess = self.model.predict(feats)
					self.model.update(tags[i], guess, feats)

					prev2 = prev
					prev = guess
					c += guess == tags[i]
					n += 1
			if shuf == True:
				random.shuffle(sentences)
				
			print("Iter {0}: {1}/{2}={3}".format(iter_, c, n, (float(c) / n) * 100))
			self.save(save_loc)
			
		self.model.average_weights()
		self.save(save_loc)
		
	def save(self, loc='model/ap.model', zip=True):
		if zip == False:
			pickle.dump((self.model.weights, self.model.classes), open(loc, 'wb'))
		else:
			pickle.dump((self.model.weights, self.model.classes), gzip.open(loc, 'wb'))
			
	def load(self, loc='model/ap.model', zip=True):
		if zip == False:
			self.model.weights, self.model.classes = pickle.load(open(loc, 'rb'))
		else:
			self.model.weights, self.model.classes = pickle.load(gzip.open(loc,'rb'))
			
	def _get_features(self, i, word, context, prev, prev2):
		'''Map tokens into a feature representation, implemented as a
		{hashable: float} dict. If the features change, a new model must be
		trained.
		'''
		def add(name, *args):
			features[' '.join((name,) + tuple(args))] += 1

		i += len(self.START)
		features = defaultdict(int)
		# It's useful to have a constant feature, which acts sort of like a prior
		add('bias')
		add('i suffix', word[-3:])
		add('i pref1', word[0])
		add('i-1 tag', prev)
		add('i-2 tag', prev2)
		add('i tag+i-2 tag', prev, prev2)
		add('i word', context[i])
		add('i-1 tag+i word', prev, context[i])
		add('i-1 word', context[i - 1])
		add('i-1 suffix', context[i - 1][-3:])
		add('i-2 word', context[i - 2])
		add('i+1 word', context[i + 1])
		add('i+1 suffix', context[i + 1][-3:])
		add('i+2 word', context[i + 2])
		return features

	def _make_tagdict(self, sentences):
		'''Make a tag dictionary for single-tag words.'''
		for words, tags in sentences:
			for word, tag in zip(words, tags):
				self.model.classes.add(tag)
				
def train(filepath='data/train.txt', model='model/ap.model', nr_iter=1):
	tagger = Perceptron()
	print('Reading corpus...')
	training_data = []
	sentence = ([], [])
	fin = open(filepath, 'r', encoding='utf8')
	for index, line in enumerate(fin):
		line = line.strip()
		if line == '':
			training_data.append(sentence)
			sentence = ([], [])
		else:
			params = line.split()
			if len(params) != 2: continue
			sentence[0].append(params[0])
			sentence[1].append(params[1])
	fin.close()
	print('training corpus size : %d', len(training_data))
	print('Start training...')
	tagger.train(training_data, save_loc=model, nr_iter=nr_iter)

def eval(filepath='data/test.txt', model='model/ap.model'):
	tagger = Perceptron(model)
	
	print('Start testing...')
	right = 0.0
	total = 0.0
	sentence = ([], [])
	fin = open(filepath, 'r', encoding='utf8')
	for index, line in enumerate(fin):
		line = line.strip()
		if line == '':
			words = sentence[0]
			tags = sentence[1]
			outputs = tagger.predict(words)
			assert len(tags) == len(outputs)
			total += len(tags)
			for o, t in zip(outputs, tags):
				if o == t: right += 1
			sentence = ([], [])
		else:
			params = line.split()
			if len(params) != 2: continue
			sentence[0].append(params[0])
			sentence[1].append(params[1])
	fin.close()
	print("Precision : %f", right / total)
	
def predict(model='model/ap.model'):
	tagger = Perceptron(model)

	while True:
		text = input('>')
		words = list(text)
		labels = tagger.predict(words)
		
		for word, label in zip(words, labels):
			print(word, label)
			

if __name__ == '__main__':
	train()
	eval()
	# predict()