def main(args):
utility = Utility()
train_name = "random-split-1-train.examples"
dev_name = "random-split-1-dev.examples"
test_name = "pristine-unseen-tables.examples"
#load data
dat = wiki_data.WikiQuestionGenerator(train_name, dev_name, test_name, FLAGS.data_dir)
train_data, dev_data, test_data = dat.load()
utility.words = []
utility.word_ids = {}
utility.reverse_word_ids = {}
#construct vocabulary
data_utils.construct_vocab(train_data, utility)
data_utils.construct_vocab(dev_data, utility, True)
data_utils.construct_vocab(test_data, utility, True)
data_utils.add_special_words(utility)
data_utils.perform_word_cutoff(utility)
#convert data to int format and pad the inputs
train_data = data_utils.complete_wiki_processing(train_data, utility, True)
dev_data = data_utils.complete_wiki_processing(dev_data, utility, False)
test_data = data_utils.complete_wiki_processing(test_data, utility, False)
print "# train examples ", len(train_data)
print "# dev examples ", len(dev_data)
print "# test examples ", len(test_data)
print "running open source"
#construct TF graph and train or evaluate
master(train_data, dev_data, utility)
def main(args):
utility = Utility()
train_name = "random-split-1-train.examples"
dev_name = "random-split-1-dev.examples"
test_name = "pristine-unseen-tables.examples"
#load data
dat = wiki_data.WikiQuestionGenerator(train_name, dev_name, test_name,
FLAGS.data_dir)
train_data, dev_data, test_data = dat.load()
utility.words = []
utility.word_ids = {}
utility.reverse_word_ids = {}
#construct vocabulary
data_utils.construct_vocab(train_data, utility)
data_utils.construct_vocab(dev_data, utility, True)
data_utils.construct_vocab(test_data, utility, True)
data_utils.add_special_words(utility)
data_utils.perform_word_cutoff(utility)
#convert data to int format and pad the inputs
train_data = data_utils.complete_wiki_processing(train_data, utility, True)
dev_data = data_utils.complete_wiki_processing(dev_data, utility, False)
test_data = data_utils.complete_wiki_processing(test_data, utility, False)
print "# train examples ", len(train_data)
print "# dev examples ", len(dev_data)
print "# test examples ", len(test_data)
print "running open source"
#construct TF graph and train or evaluate
master(train_data, dev_data, utility)
def init_data(
data_dir,
preserve_vocab=False,
split_filenames={
'train': 'random-split-1-train.examples',
'dev': 'random-split-1-dev.examples',
'test': 'pristine-unseen-tables.examples'
},
annotated_filenames={
'train': 'training.annotated',
'test': 'pristine-unseen-tables.annotated'
}):
""" Load WikiTableQuestions data.
preserve_vocab is used when perturbed data is loaded,
in which case special words are given hard-coded ids
to match that of the unperturbed data case
"""
utility = Utility()
train_name = split_filenames['train']
dev_name = split_filenames['dev']
test_name = split_filenames['test']
# load data
dat = wiki_data.WikiQuestionGenerator(train_name, dev_name, test_name,
data_dir)
train_data, dev_data, test_data = dat.load(annotated_filenames)
utility.words = []
utility.word_ids = {}
utility.reverse_word_ids = {}
# construct vocabulary
data_utils.construct_vocab(train_data, utility)
data_utils.construct_vocab(dev_data, utility, True)
data_utils.construct_vocab(test_data, utility, True)
data_utils.add_special_words(utility)
# set absolute word_ids for special words
if preserve_vocab:
print("hardcoded ids for special words")
word_to_swap = utility.reverse_word_ids[9133]
word_id_to_swap = utility.word_ids[utility.entry_match_token]
utility.word_ids[word_to_swap] = utility.word_ids[
utility.entry_match_token]
utility.word_ids[utility.entry_match_token] = 9133
utility.entry_match_token_id = utility.word_ids[
utility.entry_match_token]
utility.reverse_word_ids[word_id_to_swap] = word_to_swap
utility.reverse_word_ids[9133] = utility.entry_match_token
word_to_swap = utility.reverse_word_ids[9134]
word_id_to_swap = utility.word_ids[utility.column_match_token]
utility.word_ids[word_to_swap] = utility.word_ids[
utility.column_match_token]
utility.word_ids[utility.column_match_token] = 9134
utility.column_match_token_id = utility.word_ids[
utility.column_match_token]
utility.reverse_word_ids[word_id_to_swap] = word_to_swap
utility.reverse_word_ids[9134] = utility.column_match_token
word_to_swap = utility.reverse_word_ids[9135]
word_id_to_swap = utility.word_ids[utility.dummy_token]
utility.word_ids[word_to_swap] = utility.word_ids[utility.dummy_token]
utility.word_ids[utility.dummy_token] = 9135
utility.dummy_token_id = utility.word_ids[utility.dummy_token]
utility.reverse_word_ids[word_id_to_swap] = word_to_swap
utility.reverse_word_ids[9135] = utility.dummy_token
word_to_swap = utility.reverse_word_ids[9136]
word_id_to_swap = utility.word_ids[utility.unk_token]
utility.word_ids[word_to_swap] = utility.word_ids[utility.unk_token]
utility.word_ids[utility.unk_token] = 9136
utility.unk_token_id = utility.word_ids[utility.unk_token]
utility.reverse_word_ids[word_id_to_swap] = word_to_swap
utility.reverse_word_ids[9136] = utility.unk_token
print(utility.entry_match_token_id, utility.column_match_token_id,
utility.dummy_token_id, utility.unk_token_id)
data_utils.perform_word_cutoff(utility)
unprocessed_dev_data = copy.deepcopy(dev_data)
# convert data to int format and pad the inputs
train_data = data_utils.complete_wiki_processing(train_data, utility, True)
dev_data = data_utils.complete_wiki_processing(dev_data, utility, False)
test_data = data_utils.complete_wiki_processing(test_data, utility, False)
print(("# train examples ", len(train_data)))
print(("# dev examples ", len(dev_data)))
print(("# test examples ", len(test_data)))
return train_data, dev_data, test_data, utility, unprocessed_dev_data
