160 lines
6.6 KiB
Python
160 lines
6.6 KiB
Python
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import json
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import os
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import random
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import itertools
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import math
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import nltk
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from my.nltk_utils import load_compressed_tree
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from my.utils import index
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class DataSet(object):
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def __init__(self, data, data_type, shared=None, valid_idxs=None):
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total_num_examples = len(next(iter(data.values())))
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self.data = data # e.g. {'X': [0, 1, 2], 'Y': [2, 3, 4]}
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self.data_type = data_type
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self.shared = shared
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self.valid_idxs = range(total_num_examples) if valid_idxs is None else valid_idxs
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self.num_examples = len(self.valid_idxs)
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def get_batches(self, batch_size, num_batches=None, shuffle=False):
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num_batches_per_epoch = int(math.ceil(self.num_examples / batch_size))
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if num_batches is None:
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num_batches = num_batches_per_epoch
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num_epochs = int(math.ceil(num_batches / num_batches_per_epoch))
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idxs = itertools.chain.from_iterable(random.sample(self.valid_idxs, len(self.valid_idxs))
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if shuffle else self.valid_idxs
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for _ in range(num_epochs))
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for _ in range(num_batches):
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batch_idxs = tuple(itertools.islice(idxs, batch_size))
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batch_data = {}
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for key, val in self.data.items():
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if key.startswith('*'):
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assert self.shared is not None
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shared_key = key[1:]
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batch_data[shared_key] = [index(self.shared[shared_key], val[idx]) for idx in batch_idxs]
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else:
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batch_data[key] = list(map(val.__getitem__, batch_idxs))
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batch_ds = DataSet(batch_data, self.data_type, shared=self.shared)
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yield batch_idxs, batch_ds
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class SquadDataSet(DataSet):
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def __init__(self, data, data_type, shared=None, valid_idxs=None):
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super(SquadDataSet, self).__init__(data, data_type, shared=shared, valid_idxs=valid_idxs)
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def load_metadata(config, data_type):
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metadata_path = os.path.join(config.data_dir, "metadata_{}.json".format(data_type))
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with open(metadata_path, 'r') as fh:
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metadata = json.load(fh)
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for key, val in metadata.items():
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config.__setattr__(key, val)
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return metadata
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def read_data(config, data_type, ref, data_filter=None):
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data_path = os.path.join(config.data_dir, "data_{}.json".format(data_type))
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shared_path = os.path.join(config.data_dir, "shared_{}.json".format(data_type))
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with open(data_path, 'r') as fh:
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data = json.load(fh)
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with open(shared_path, 'r') as fh:
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shared = json.load(fh)
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num_examples = len(next(iter(data.values())))
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if data_filter is None:
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valid_idxs = range(num_examples)
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else:
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mask = []
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keys = data.keys()
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values = data.values()
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for vals in zip(*values):
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each = {key: val for key, val in zip(keys, vals)}
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mask.append(data_filter(each, shared))
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valid_idxs = [idx for idx in range(len(mask)) if mask[idx]]
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print("Loaded {}/{} examples from {}".format(len(valid_idxs), num_examples, data_type))
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shared_path = os.path.join(config.out_dir, "shared.json")
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if not ref:
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word_counter = shared['lower_word_counter'] if config.lower_word else shared['word_counter']
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char_counter = shared['char_counter']
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pos_counter = shared['pos_counter']
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shared['word2idx'] = {word: idx + 2 for idx, word in
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enumerate(word for word, count in word_counter.items()
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if count > config.word_count_th)}
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shared['char2idx'] = {char: idx + 2 for idx, char in
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enumerate(char for char, count in char_counter.items()
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if count > config.char_count_th)}
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shared['pos2idx'] = {pos: idx + 2 for idx, pos in enumerate(pos_counter.keys())}
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NULL = "-NULL-"
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UNK = "-UNK-"
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shared['word2idx'][NULL] = 0
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shared['word2idx'][UNK] = 1
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shared['char2idx'][NULL] = 0
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shared['char2idx'][UNK] = 1
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shared['pos2idx'][NULL] = 0
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shared['pos2idx'][UNK] = 1
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json.dump({'word2idx': shared['word2idx'], 'char2idx': shared['char2idx'],
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'pos2idx': shared['pos2idx']}, open(shared_path, 'w'))
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else:
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new_shared = json.load(open(shared_path, 'r'))
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for key, val in new_shared.items():
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shared[key] = val
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data_set = DataSet(data, data_type, shared=shared, valid_idxs=valid_idxs)
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return data_set
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def get_squad_data_filter(config):
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def data_filter(data_point, shared):
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assert shared is not None
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rx, rcx, q, cq, y = (data_point[key] for key in ('*x', '*cx', 'q', 'cq', 'y'))
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x, cx, stx = shared['x'], shared['cx'], shared['stx']
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if len(q) > config.ques_size_th:
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return False
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xi = x[rx[0]][rx[1]]
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if len(xi) > config.num_sents_th:
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return False
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if any(len(xij) > config.sent_size_th for xij in xi):
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return False
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stxi = stx[rx[0]][rx[1]]
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if any(nltk.tree.Tree.fromstring(s).height() > config.tree_height_th for s in stxi):
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return False
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return True
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return data_filter
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def update_config(config, data_sets):
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config.max_num_sents = 0
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config.max_sent_size = 0
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config.max_ques_size = 0
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config.max_word_size = 0
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config.max_tree_height = 0
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for data_set in data_sets:
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data = data_set.data
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shared = data_set.shared
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for idx in data_set.valid_idxs:
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rx = data['*x'][idx]
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q = data['q'][idx]
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sents = shared['x'][rx[0]][rx[1]]
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trees = map(nltk.tree.Tree.fromstring, shared['stx'][rx[0]][rx[1]])
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config.max_tree_height = max(config.max_tree_height, max(tree.height() for tree in trees))
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config.max_num_sents = max(config.max_num_sents, len(sents))
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config.max_sent_size = max(config.max_sent_size, max(map(len, sents)))
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config.max_word_size = max(config.max_word_size, max(len(word) for sent in sents for word in sent))
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if len(q) > 0:
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config.max_ques_size = max(config.max_ques_size, len(q))
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config.max_word_size = max(config.max_word_size, max(len(word) for word in q))
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config.max_word_size = min(config.max_word_size, config.word_size_th)
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config.char_vocab_size = len(data_sets[0].shared['char2idx'])
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config.word_emb_size = len(next(iter(data_sets[0].shared['word2vec'].values())))
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config.word_vocab_size = len(data_sets[0].shared['word2idx'])
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config.pos_vocab_size = len(data_sets[0].shared['pos2idx'])
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