source_code_classification/code2vec/preprocess.py

import random
from argparse import ArgumentParser
import common
import pickle

'''
This script preprocesses the data from MethodPaths. It truncates methods with too many contexts,
and pads methods with less paths with spaces.
'''


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: ' + data_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)