import os
import re
import cPickle
import copy
import numpy
import torch
import nltk
from nltk.corpus import ptb
from nltk import Tree
word_tags = ['CC', 'CD', 'DT', 'EX', 'FW', 'IN', 'JJ', 'JJR', 'JJS', 'LS', 'MD', 'NN', 'NNS', 'NNP', 'NNPS', 'PDT',
'POS', 'PRP', 'PRP$', 'RB', 'RBR', 'RBS', 'RP', 'SYM', 'TO', 'UH', 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ',
'WDT', 'WP', 'WP$', 'WRB']
currency_tags_words = ['#', '$', 'C$', 'A$']
ellipsis = ['*', '*?*', '0', '*T*', '*ICH*', '*U*', '*RNR*', '*EXP*', '*PPA*', '*NOT*']
punctuation_tags = ['.', ',', ':', '-LRB-', '-RRB-', '\'\'', '``']
punctuation_words = ['.', ',', ':', '-LRB-', '-RRB-', '\'\'', '``', '--', ';', '-', '?', '!', '...', '-LCB-', '-RCB-']
file_ids = ptb.fileids()
train_file_ids = []
valid_file_ids = []
test_file_ids = []
rest_file_ids = []
for id in file_ids:
if 'WSJ/00/WSJ_0000.MRG' <= id <= 'WSJ/24/WSJ_2499.MRG':
train_file_ids.append(id)
elif 'WSJ/22/WSJ_2200.MRG' <= id <= 'WSJ/22/WSJ_2299.MRG':
valid_file_ids.append(id)
elif 'WSJ/23/WSJ_2300.MRG' <= id <= 'WSJ/23/WSJ_2399.MRG':
test_file_ids.append(id)
elif 'WSJ/00/WSJ_0000.MRG' <= id <= 'WSJ/01/WSJ_0199.MRG' or 'WSJ/24/WSJ_2400.MRG' <= id <= 'WSJ/24/WSJ_2499.MRG':
rest_file_ids.append(id)
data_path = '/misc/vlgscratch4/BowmanGroup/pmh330/datasets/'
train_files = data_path + 'all_nli/all_nli_train.jsonl'
valid_files = data_path + 'all_nli/all_nli_valid.jsonl'
test_files_snli = data_path + 'snli_1.0/snli_1.0_test.jsonl'
test_files_mnli_match = data_path + 'multinli_1.0/multinli_1.0_dev_matched.jsonl'
class Dictionary(object):
def __init__(self):
self.word2idx = {'<unk>': 0}
self.idx2word = ['<unk>']
self.word2frq = {}
def add_word(self, word):
if word not in self.word2idx:
self.idx2word.append(word)
self.word2idx[word] = len(self.idx2word) - 1
if word not in self.word2frq:
self.word2frq[word] = 1
else:
self.word2frq[word] += 1
return self.word2idx[word]
def __len__(self):
return len(self.idx2word)
def __getitem__(self, item):
if self.word2idx.has_key(item):
return self.word2idx[item]
else:
return self.word2idx['<unk>']
def rebuild_by_freq(self, thd=3):
self.word2idx = {'<unk>': 0}
self.idx2word = ['<unk>']
for k, v in self.word2frq.iteritems():
if v >= thd and (not k in self.idx2word):
self.idx2word.append(k)
self.word2idx[k] = len(self.idx2word) - 1
print 'Number of words:', len(self.idx2word)
return len(self.idx2word)
class Corpus(object):
def __init__(self, path):
dict_file_name = os.path.join(path, 'dict_nli.pkl')
if os.path.exists(dict_file_name):
self.dictionary = cPickle.load(open(dict_file_name, 'rb'))
print("loading: ", dict_file_name)
else:
self.dictionary = Dictionary()
self.add_words(train_files)
self.add_words(valid_files)
self.add_words(test_files_snli)
self.add_words(test_files_mnli_match)
self.dictionary.rebuild_by_freq()
cPickle.dump(self.dictionary, open(dict_file_name, 'wb'))
self.train, self.train_sens, self.train_trees = self.tokenize(train_files)
self.valid, self.valid_sens, self.valid_trees = self.tokenize(valid_files)
self.test_snli, self.test_snli_sens, self.test_snli_trees = self.tokenize(test_files_snli)
self.test_mnli, self.test_mnli_sens, self.test_mnli_trees = self.tokenize(test_files_mnli_match)
self.test, self.test_sens, self.test_trees = self.tokenize(test_file_ids)
def filter_words(self, tree):
words = []
for w, tag in tree.pos():
if tag in word_tags:
w = w.lower()
w = re.sub('[0-9]+', 'N', w)
# if tag == 'CD':
# w = 'N'
words.append(w)
return words
def add_words(self, file_name):
# Add words to the dictionary
f_in = open(file_name, 'r')
for line in f_in:
if line.strip() == '':
continue
data = eval(line)
sen_tree = Tree.fromstring(data['sentence1_parse'])
words = self.filter_words(sen_tree)
words = ['<s>'] + words + ['</s>']
for word in words:
self.dictionary.add_word(word)
sen_tree = Tree.fromstring(data['sentence2_parse'])
words = self.filter_words(sen_tree)
words = ['<s>'] + words + ['</s>']
for word in words:
self.dictionary.add_word(word)
f_in.close()
def tokenize(self, file_name):
def tree2list(tree):
if isinstance(tree, nltk.Tree):
if tree.label() in word_tags:
return tree.leaves()[0]
else:
root = []
for child in tree:
c = tree2list(child)
if c != []:
root.append(c)
if len(root) > 1:
return root
elif len(root) == 1:
return root[0]
return []
sens_idx = []
sens = []
sentences = []
trees = []
f_in = open(file_name, 'r')
for line in f_in:
if line.strip() == '':
continue
data = eval(line)
sentences = []
sentences.append(Tree.fromstring(data['sentence1_parse']))
sentences.append(Tree.fromstring(data['sentence2_parse']))
for sen_tree in sentences:
words = self.filter_words(sen_tree)
if not words:
continue
words = ['<s>'] + words + ['</s>']
# if len(words) > 50:
# continue
sens.append(words)
idx = []
for word in words:
idx.append(self.dictionary[word])
sens_idx.append(torch.LongTensor(idx))
trees.append(tree2list(sen_tree))
f_in.close()
return sens_idx, sens, trees
