/
data_utils.py
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/
data_utils.py
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# encoding: utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import re
import random
import importlib
from tensorflow.python.platform import gfile
import tensorflow as tf
from knowledge_graph import *
import args
FLAGS = args.FLAGS
# Special vocabulary symbols
_PAD = b"_PAD"
_GO = b"_GO"
_EOS = b"_EOS"
_UNK = b"_UNK"
_KB = b"_KB"
_START_VOCAB = [_PAD, _GO, _EOS, _UNK, _KB]
read_kb = importlib.import_module('.'.join(FLAGS.data_dir.split('/'))+'.read_kb')
KG = KGraph(FLAGS.data_dir, lambda x: read_kb.read_in_graph(x))
nodes = KG.get_vocab_nodes()
str_nodes = KG.get_nodes()
edge_types = KG.get_edge_types()
print(edge_types)
print(len(nodes))
kbstart = 0
kbend = len(nodes) - 1
kdim = len(nodes)
edim = len(edge_types)
print(edim)
################################
import numpy as np
def load_embed(f):
s_arr = [line.strip().split('\t') for line in f.readlines()]
vec_arr = [[float(s) for s in line[1][1:-1].split(', ')] for line in s_arr]
name_arr = [line[0] for line in s_arr]
embed_dict = {}
for name, vec in zip(name_arr, vec_arr):
embed_dict[name] = vec
return embed_dict
with open(FLAGS.data_dir+'/transE/entityVector.txt', 'r') as fn, \
open(FLAGS.data_dir+'/transE/relationVector.txt', 'r') as fe:
node_dict = load_embed(fn)
edge_type_dict = load_embed(fe)
kbembed_size = len(node_dict[str_nodes[0]])*3
triple_num = FLAGS.fact_size
################################
PAD_ID = 0 + len(nodes)
GO_ID = 1 + len(nodes)
EOS_ID = 2 + len(nodes)
UNK_ID = 3 + len(nodes)
KB_ID = 4 + len(nodes)
# Regular expressions used to tokenize.
#_WORD_SPLIT = re.compile(b"([.,!?\"':;)(])")
_DIGIT_RE = re.compile(br"\d")
def basic_tokenizer(sentence):
"""Very basic tokenizer: split the sentence into a list of tokens."""
words = []
for space_separated_fragment in sentence.strip().split():
"""Plz costomize the tokenizer for different dataset"""
#TODO words.extend(_WORD_SPLIT.split(space_separated_fragment))
words.append(space_separated_fragment)
return [w for w in words if w]
def get_split_path(data_path):
data_dir, file_name = data_path.rsplit('/',1)
train_path = os.path.join(data_dir, 'train_' + file_name)
dev_path = os.path.join(data_dir, 'dev_' + file_name)
if not (gfile.Exists(train_path)) and ( not gfile.Exists(dev_path) ):
if not (gfile.Exists(data_path)):
raise ValueError("Source file %s not found.", data_path)
raise ValueError("Train file or development file not found.")
return (train_path, dev_path)
def create_vocabulary(vocabulary_path, data_path, max_vocabulary_size,
tokenizer=None, normalize_digits=True):
if not gfile.Exists(vocabulary_path):
print("Creating vocabulary %s from data %s" % (vocabulary_path, data_path))
vocab = {}
with gfile.GFile(data_path, mode="rb") as f:
counter = 0
for line in f:
counter += 1
if counter % 100000 == 0:
print(" processing line %d" % counter)
line = tf.compat.as_bytes(line)
tokens = tokenizer(line) if tokenizer else basic_tokenizer(line)
for w in tokens:
word = _DIGIT_RE.sub(b"0", w) if normalize_digits else w
if word in vocab and word not in nodes:
vocab[word] += 1
elif word not in nodes:
vocab[word] = 1
vocab_list = nodes + _START_VOCAB + sorted(vocab, key=vocab.get, reverse=True)
if len(vocab_list) > max_vocabulary_size:
vocab_list = vocab_list[:max_vocabulary_size]
with gfile.GFile(vocabulary_path, mode="wb") as vocab_file:
for w in vocab_list:
vocab_file.write(w + b"\n")
def initialize_vocabulary(vocabulary_path):
if gfile.Exists(vocabulary_path):
rev_vocab = []
with gfile.GFile(vocabulary_path, mode="r") as f:
rev_vocab.extend(f.readlines())
rev_vocab = [line.strip().encode('utf-8') for line in rev_vocab]
vocab = dict([(x, y) for (y, x) in enumerate(rev_vocab)])
print(len(vocab))
print(len(rev_vocab))
return vocab, rev_vocab
else:
raise ValueError("Vocabulary file %s not found.", vocabulary_path)
def sentence_to_token_ids(sentence, vocabulary,
tokenizer=None, normalize_digits=True):
if tokenizer:
words = tokenizer(sentence)
else:
words = basic_tokenizer(sentence)
if not normalize_digits:
return [vocabulary.get(w, UNK_ID) for w in words]
# Normalize digits by 0 before looking words up in the vocabulary.
return [vocabulary.get(_DIGIT_RE.sub(b"0", w), UNK_ID) for w in words]
def data_to_token_ids(data_path, target_path, vocabulary_path,
tokenizer=None, normalize_digits=True):
if not gfile.Exists(target_path):
print("Tokenizing data in %s" % data_path)
vocab, _ = initialize_vocabulary(vocabulary_path)
with gfile.GFile(data_path, mode="rb") as data_file:
with gfile.GFile(target_path, mode="w") as tokens_file:
counter = 0
for line in data_file:
counter += 1
if counter % 100000 == 0:
print(" tokenizing line %d" % counter)
token_ids = sentence_to_token_ids(tf.compat.as_bytes(line), vocab,
tokenizer, normalize_digits)
tokens_file.write(" ".join([str(tok) for tok in token_ids]) + "\n")
def prepare_data(data_dir, data_path, vocabulary_size, tokenizer=None):
train_path, dev_path = get_split_path(data_path)
vocab_path = os.path.join(data_dir, "vocab%d" % vocabulary_size)
create_vocabulary(vocab_path, train_path, vocabulary_size, tokenizer, normalize_digits=False)
train_ids_path = train_path + (".ids%d" % vocabulary_size)
data_to_token_ids(train_path, train_ids_path, vocab_path, tokenizer, normalize_digits=False)
dev_ids_path = dev_path + (".ids%d" % vocabulary_size)
data_to_token_ids(dev_path, dev_ids_path, vocab_path, tokenizer, normalize_digits=False)
return (train_ids_path, dev_ids_path, vocab_path)
def prepare_info(data_dir, data_path):
train_path, dev_path = get_split_path(data_path)
train_info_path = train_path.rsplit('.',1)[0] + (".info")
dev_info_path = dev_path.rsplit('.',1)[0] + (".info")
return (train_info_path, dev_info_path)
def prepare_kb(data_dir, data_path):
train_path, dev_path = get_split_path(data_path)
train_kb_path = train_path.rsplit('.',1)[0] + (".sp5")
dev_kb_path = dev_path.rsplit('.',1)[0] + (".sp5")
return (train_kb_path, dev_kb_path)
def test_info_data(data_dir, data_path, vocabulary_size, tokenizer=None):
data_dir, file_name = data_path.rsplit('/',1)
test_path = os.path.join(data_dir, 'test_' + file_name)
test_ids_path = test_path + (".ids%d" % vocabulary_size)
vocab_path = os.path.join(data_dir, "vocab%d" % vocabulary_size)
data_to_token_ids(test_path, test_ids_path, vocab_path, tokenizer, normalize_digits=False)
test_info_path = test_path.rsplit('.',1)[0] + (".info")
test_kb_path = test_path.rsplit('.',1)[0] + (".sp5")
return (test_ids_path, test_info_path, test_kb_path)