import json
import os
import random
import itertools
import math
from collections import defaultdict
import numpy as np
from cnn_dm.prepro import para2sents
from my.tensorflow import grouper
from my.utils import index
class Data(object):
def get_size(self):
raise NotImplementedError()
def get_by_idxs(self, idxs):
"""
Efficient way to obtain a batch of items from filesystem
:param idxs:
:return dict: {'X': [,], 'Y', }
"""
data = defaultdict(list)
for idx in idxs:
each_data = self.get_one(idx)
for key, val in each_data.items():
data[key].append(val)
return data
def get_one(self, idx):
raise NotImplementedError()
def get_empty(self):
raise NotImplementedError()
def __add__(self, other):
raise NotImplementedError()
class MyData(Data):
def __init__(self, config, root_dir, file_names):
self.root_dir = root_dir
self.file_names = file_names
self.config = config
def get_one(self, idx):
file_name = self.file_names[idx]
with open(os.path.join(self.root_dir, file_name), 'r') as fh:
url = fh.readline().strip()
_ = fh.readline()
para = fh.readline().strip()
_ = fh.readline()
ques = fh.readline().strip()
_ = fh.readline()
answer = fh.readline().strip()
_ = fh.readline()
cands = list(line.strip() for line in fh)
cand_ents = list(cand.split(":")[0] for cand in cands)
wordss = para2sents(para, self.config.width)
ques_words = ques.split(" ")
x = wordss
cx = [[list(word) for word in words] for words in wordss]
q = ques_words
cq = [list(word) for word in ques_words]
y = answer
c = cand_ents
data = {'x': x, 'cx': cx, 'q': q, 'cq': cq, 'y': y, 'c': c, 'ids': file_name}
return data
def get_empty(self):
return MyData(self.config, self.root_dir, [])
def __add__(self, other):
file_names = self.file_names + other.file_names
return MyData(self.config, self.root_dir, file_names)
def get_size(self):
return len(self.file_names)
class DataSet(object):
def __init__(self, data, data_type, shared=None, valid_idxs=None):
self.data = data # e.g. {'X': [0, 1, 2], 'Y': [2, 3, 4]}
self.data_type = data_type
self.shared = shared
total_num_examples = self.get_data_size()
self.valid_idxs = range(total_num_examples) if valid_idxs is None else valid_idxs
self.num_examples = total_num_examples
def _sort_key(self, idx):
rx = self.data['*x'][idx]
x = self.shared['x'][rx[0]][rx[1]]
return max(map(len, x))
def get_data_size(self):
if isinstance(self.data, dict):
return len(next(iter(self.data.values())))
elif isinstance(self.data, Data):
return self.data.get_size()
raise Exception()
def get_by_idxs(self, idxs):
if isinstance(self.data, dict):
out = defaultdict(list)
for key, val in self.data.items():
out[key].extend(val[idx] for idx in idxs)
return out
elif isinstance(self.data, Data):
return self.data.get_by_idxs(idxs)
raise Exception()
def get_one(self, idx):
if isinstance(self.data, dict):
out = {key: [val[idx]] for key, val in self.data.items()}
return out
elif isinstance(self.data, Data):
return self.data.get_one(idx)
def get_batches(self, batch_size, num_batches=None, shuffle=False, cluster=False):
"""
:param batch_size:
:param num_batches:
:param shuffle:
:param cluster: cluster examples by their lengths; this might give performance boost (i.e. faster training).
:return:
"""
num_batches_per_epoch = int(math.ceil(self.num_examples / batch_size))
if num_batches is None:
num_batches = num_batches_per_epoch
num_epochs = int(math.ceil(num_batches / num_batches_per_epoch))
if shuffle:
random_idxs = random.sample(self.valid_idxs, len(self.valid_idxs))
if cluster:
sorted_idxs = sorted(random_idxs, key=self._sort_key)
sorted_grouped = lambda: list(grouper(sorted_idxs, batch_size))
grouped = lambda: random.sample(sorted_grouped(), num_batches_per_epoch)
else:
random_grouped = lambda: list(grouper(random_idxs, batch_size))
grouped = random_grouped
else:
raw_grouped = lambda: list(grouper(self.valid_idxs, batch_size))
grouped = raw_grouped
batch_idx_tuples = itertools.chain.from_iterable(grouped() for _ in range(num_epochs))
for _ in range(num_batches):
batch_idxs = tuple(i for i in next(batch_idx_tuples) if i is not None)
batch_data = self.get_by_idxs(batch_idxs)
shared_batch_data = {}
for key, val in batch_data.items():
if key.startswith('*'):
assert self.shared is not None
shared_key = key[1:]
shared_batch_data[shared_key] = [index(self.shared[shared_key], each) for each in val]
batch_data.update(shared_batch_data)
batch_ds = DataSet(batch_data, self.data_type, shared=self.shared)
yield batch_idxs, batch_ds
def get_multi_batches(self, batch_size, num_batches_per_step, num_steps=None, shuffle=False, cluster=False):
batch_size_per_step = batch_size * num_batches_per_step
batches = self.get_batches(batch_size_per_step, num_batches=num_steps, shuffle=shuffle, cluster=cluster)
multi_batches = (tuple(zip(grouper(idxs, batch_size, shorten=True, num_groups=num_batches_per_step),
data_set.divide(num_batches_per_step))) for idxs, data_set in batches)
return multi_batches
def get_empty(self):
if isinstance(self.data, dict):
data = {key: [] for key in self.data}
elif isinstance(self.data, Data):
data = self.data.get_empty()
else:
raise Exception()
return DataSet(data, self.data_type, shared=self.shared)
def __add__(self, other):
if isinstance(self.data, dict):
data = {key: val + other.data[key] for key, val in self.data.items()}
elif isinstance(self.data, Data):
data = self.data + other.data
else:
raise Exception()
valid_idxs = list(self.valid_idxs) + [valid_idx + self.num_examples for valid_idx in other.valid_idxs]
return DataSet(data, self.data_type, shared=self.shared, valid_idxs=valid_idxs)
def divide(self, integer):
batch_size = int(math.ceil(self.num_examples / integer))
idxs_gen = grouper(self.valid_idxs, batch_size, shorten=True, num_groups=integer)
data_gen = (self.get_by_idxs(idxs) for idxs in idxs_gen)
ds_tuple = tuple(DataSet(data, self.data_type, shared=self.shared) for data in data_gen)
return ds_tuple
class MyDataSet(DataSet):
def __init__(self, data, data_type, shared=None, valid_idxs=None):
super(MyDataSet, self).__init__(data, data_type, shared=shared, valid_idxs=valid_idxs)
shared['max_num_sents'] = len(self.get_one(self.num_examples-1)['x'])
def _sort_key(self, idx):
return idx
def read_data(config, data_type, ref, data_filter=None):
shared_path = os.path.join(config.data_dir, "shared_{}.json".format(data_type))
with open(shared_path, 'r') as fh:
shared = json.load(fh)
paths = shared['sorted']
if config.filter_ratio < 1.0:
stop = int(round(len(paths) * config.filter_ratio))
paths = paths[:stop]
num_examples = len(paths)
valid_idxs = range(num_examples)
print("Loaded {}/{} examples from {}".format(len(valid_idxs), num_examples, data_type))
shared_path = config.shared_path or os.path.join(config.out_dir, "shared.json")
if not ref:
word2vec_dict = shared['lower_word2vec'] if config.lower_word else shared['word2vec']
word_counter = shared['lower_word_counter'] if config.lower_word else shared['word_counter']
char_counter = shared['char_counter']
if config.finetune:
shared['word2idx'] = {word: idx + 3 for idx, word in
enumerate(word for word, count in word_counter.items()
if count > config.word_count_th or (config.known_if_glove and word in word2vec_dict))}
else:
assert config.known_if_glove
assert config.use_glove_for_unk
shared['word2idx'] = {word: idx + 3 for idx, word in
enumerate(word for word, count in word_counter.items()
if count > config.word_count_th and word not in word2vec_dict)}
shared['char2idx'] = {char: idx + 2 for idx, char in
enumerate(char for char, count in char_counter.items()
if count > config.char_count_th)}
NULL = "-NULL-"
UNK = "-UNK-"
ENT = "-ENT-"
shared['word2idx'][NULL] = 0
shared['word2idx'][UNK] = 1
shared['word2idx'][ENT] = 2
shared['char2idx'][NULL] = 0
shared['char2idx'][UNK] = 1
json.dump({'word2idx': shared['word2idx'], 'char2idx': shared['char2idx']}, open(shared_path, 'w'))
else:
new_shared = json.load(open(shared_path, 'r'))
for key, val in new_shared.items():
shared[key] = val
if config.use_glove_for_unk:
# create new word2idx and word2vec
word2vec_dict = shared['lower_word2vec'] if config.lower_word else shared['word2vec']
new_word2idx_dict = {word: idx for idx, word in enumerate(word for word in word2vec_dict.keys() if word not in shared['word2idx'])}
shared['new_word2idx'] = new_word2idx_dict
offset = len(shared['word2idx'])
word2vec_dict = shared['lower_word2vec'] if config.lower_word else shared['word2vec']
new_word2idx_dict = shared['new_word2idx']
idx2vec_dict = {idx: word2vec_dict[word] for word, idx in new_word2idx_dict.items()}
# print("{}/{} unique words have corresponding glove vectors.".format(len(idx2vec_dict), len(word2idx_dict)))
new_emb_mat = np.array([idx2vec_dict[idx] for idx in range(len(idx2vec_dict))], dtype='float32')
shared['new_emb_mat'] = new_emb_mat
data = MyData(config, os.path.join(config.root_dir, data_type), paths)
data_set = MyDataSet(data, data_type, shared=shared, valid_idxs=valid_idxs)
return data_set
def get_cnn_data_filter(config):
return True
def update_config(config, data_sets):
config.max_num_sents = 0
config.max_sent_size = 0
config.max_ques_size = 0
config.max_word_size = 0
for data_set in data_sets:
shared = data_set.shared
config.max_sent_size = max(config.max_sent_size, shared['max_sent_size'])
config.max_ques_size = max(config.max_ques_size, shared['max_ques_size'])
config.max_word_size = max(config.max_word_size, shared['max_word_size'])
config.max_num_sents = max(config.max_num_sents, shared['max_num_sents'])
config.max_word_size = min(config.max_word_size, config.word_size_th)
config.char_vocab_size = len(data_sets[0].shared['char2idx'])
config.word_emb_size = len(next(iter(data_sets[0].shared['word2vec'].values())))
config.word_vocab_size = len(data_sets[0].shared['word2idx'])
