preprocess.py
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import random
from argparse import ArgumentParser
import common
import pickle
def save_dictionaries(dataset_name, word_to_count, path_to_count, target_to_count,
num_training_examples):
save_dict_file_path = '{}.dict.c2v'.format(dataset_name)
with open(save_dict_file_path, 'wb') as file:
pickle.dump(word_to_count, file)
pickle.dump(path_to_count, file)
pickle.dump(target_to_count, file)
pickle.dump(num_training_examples, file)
print('Dictionaries saved to: {}'.format(save_dict_file_path))
def process_file(file_path, data_file_role, dataset_name, word_to_count, path_to_count, max_contexts):
sum_total = 0
sum_sampled = 0
total = 0
empty = 0
max_unfiltered = 0
output_path = '{}.{}.c2v'.format(dataset_name, data_file_role)
with open(output_path, 'w') as outfile:
with open(file_path, 'r') as file:
for line in file:
parts = line.rstrip('\n').split(' ')
target_name = parts[0]
contexts = parts[1:]
if len(contexts) > max_unfiltered:
max_unfiltered = len(contexts)
sum_total += len(contexts)
if len(contexts) > max_contexts:
context_parts = [c.split(',') for c in contexts]
full_found_contexts = [c for i, c in enumerate(contexts)
if context_full_found(context_parts[i], word_to_count, path_to_count)]
partial_found_contexts = [c for i, c in enumerate(contexts)
if context_partial_found(context_parts[i], word_to_count, path_to_count)
and not context_full_found(context_parts[i], word_to_count,
path_to_count)]
if len(full_found_contexts) > max_contexts:
contexts = random.sample(full_found_contexts, max_contexts)
elif len(full_found_contexts) <= max_contexts \
and len(full_found_contexts) + len(partial_found_contexts) > max_contexts:
contexts = full_found_contexts + \
random.sample(partial_found_contexts, max_contexts - len(full_found_contexts))
else:
contexts = full_found_contexts + partial_found_contexts
if len(contexts) == 0:
empty += 1
continue
sum_sampled += len(contexts)
csv_padding = " " * (max_contexts - len(contexts))
outfile.write(target_name + ' ' + " ".join(contexts) + csv_padding + '\n')
total += 1
print('File: ' + file_path)
print('Average total contexts: ' + str(float(sum_total) / total))
print('Average final (after sampling) contexts: ' + str(float(sum_sampled) / total))
print('Total examples: ' + str(total))
print('Empty examples: ' + str(empty))
print('Max number of contexts per word: ' + str(max_unfiltered))
return total
def context_full_found(context_parts, word_to_count, path_to_count):
return context_parts[0] in word_to_count \
and context_parts[1] in path_to_count and context_parts[2] in word_to_count
def context_partial_found(context_parts, word_to_count, path_to_count):
return context_parts[0] in word_to_count \
or context_parts[1] in path_to_count or context_parts[2] in word_to_count
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument("-trd", "--train_data", dest="train_data_path",
help="path to training data file", required=True)
parser.add_argument("-ted", "--test_data", dest="test_data_path",
help="path to test data file", required=True)
parser.add_argument("-vd", "--val_data", dest="val_data_path",
help="path to validation data file", required=True)
parser.add_argument("-mc", "--max_contexts", dest="max_contexts", default=200,
help="number of max contexts to keep", required=False)
parser.add_argument("-wvs", "--word_vocab_size", dest="word_vocab_size", default=1301136,
help="Max number of origin word in to keep in the vocabulary", required=False)
parser.add_argument("-pvs", "--path_vocab_size", dest="path_vocab_size", default=911417,
help="Max number of paths to keep in the vocabulary", required=False)
parser.add_argument("-tvs", "--target_vocab_size", dest="target_vocab_size", default=261245,
help="Max number of target words to keep in the vocabulary", required=False)
parser.add_argument("-wh", "--word_histogram", dest="word_histogram",
help="word histogram file", metavar="FILE", required=True)
parser.add_argument("-ph", "--path_histogram", dest="path_histogram",
help="path_histogram file", metavar="FILE", required=True)
parser.add_argument("-th", "--target_histogram", dest="target_histogram",
help="target histogram file", metavar="FILE", required=True)
parser.add_argument("-o", "--output_name", dest="output_name",
help="output name - the base name for the created dataset", metavar="FILE", required=True,
default='data')
args = parser.parse_args()
train_data_path = args.train_data_path
test_data_path = args.test_data_path
val_data_path = args.val_data_path
word_histogram_path = args.word_histogram
path_histogram_path = args.path_histogram
word_histogram_data = common.common.load_vocab_from_histogram(word_histogram_path, start_from=1,
max_size=int(args.word_vocab_size),
return_counts=True)
_, _, _, word_to_count = word_histogram_data
_, _, _, path_to_count = common.common.load_vocab_from_histogram(path_histogram_path, start_from=1,
max_size=int(args.path_vocab_size),
return_counts=True)
_, _, _, target_to_count = common.common.load_vocab_from_histogram(args.target_histogram, start_from=1,
max_size=int(args.target_vocab_size),
return_counts=True)
num_training_examples = 0
for data_file_path, data_role in zip([test_data_path, val_data_path, train_data_path], ['test', 'val', 'train']):
num_examples = process_file(file_path=data_file_path, data_file_role=data_role, dataset_name=args.output_name,
word_to_count=word_to_count, path_to_count=path_to_count,
max_contexts=int(args.max_contexts))
if data_role == 'train':
num_training_examples = num_examples
save_dictionaries(dataset_name=args.output_name, word_to_count=word_to_count,
path_to_count=path_to_count, target_to_count=target_to_count,
num_training_examples=num_training_examples)