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96 lines
3.6 KiB
Python
96 lines
3.6 KiB
Python
# 读取数据集
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from re import S
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with open('data/ThePeoplesDaily/raw/source_BIO_2014_cropus.txt', 'r') as source_file:
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source = source_file.read().split('\n')
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with open('data/ThePeoplesDaily/raw/target_BIO_2014_cropus.txt', 'r') as target_file:
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target = target_file.read().split('\n')
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# 统计每个样本的句长
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max_len = 0
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seq_len_dict = {}
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for sentence in source:
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seq_len = (len(sentence) + 1) / 2
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if max_len < seq_len:
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max_len = seq_len
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if seq_len in seq_len_dict: seq_len_dict[seq_len] += 1
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else: seq_len_dict[seq_len] = 1
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print(max_len)
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seq_len_dict = sorted(seq_len_dict.items(), key=lambda x: x[0], reverse=True)
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print(seq_len_dict)
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# # 统计样本数量
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# assert len(source) == len(target)
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# sample_count = len(source)
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# print('行数：', sample_count)
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# # 统计字数 字典 标签种类
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# vocabulary = dict()
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# labels = dict()
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# char_count = 0
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# for sentence, tags in zip(source, target):
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# char_count += round( (len(sentence)+0.49)/2 )
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# for char, tag in zip(sentence.split(' '), tags.split(' ')):
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# if char in vocabulary: vocabulary[char] += 1
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# else: vocabulary[char] = 1
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# if tag in labels: labels[tag] += 1
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# else: labels[tag] = 1
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# print('字数：', char_count)
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# # 根据数量 降序 并写入vocabulary.txt和labels.txt文件
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# vocabulary = dict(sorted(vocabulary.items(), key=lambda x: x[1], reverse=True))
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# labels = dict(sorted(labels.items(), key=lambda x: x[1], reverse=True))
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# with open('data/ThePeoplesDaily/vocabulary.txt', 'w') as vocabulary_file:
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# vocabulary_file.write('\n'.join(vocabulary))
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# with open('data/ThePeoplesDaily/labels.txt', 'w') as labels_file:
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# labels_file.write('\n'.join(labels))
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# # 划分 训练集 验证集 测试集
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# train_data_size = 0.7
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# dev_data_size = 0.2
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# test_data_size = 0.1
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# train_source, train_target = source[0 : round(train_data_size*sample_count)], target[0 : round(train_data_size*sample_count)]
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# with open('data/ThePeoplesDaily/train.txt', 'w') as train_file:
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# for sentence, tags in zip(train_source, train_target):
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# for char, tag in zip(sentence.split(' '), tags.split(' ')):
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# train_file.write(char + ' ' + tag + '\n')
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# train_file.write('\n')
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# print('train_data_sample_size: ', len(train_source))
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# dev_source, dev_target = source[round(train_data_size*sample_count) : round((train_data_size+dev_data_size)*sample_count)], target[round(train_data_size*sample_count) : round((train_data_size+dev_data_size)*sample_count)]
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# with open('data/ThePeoplesDaily/dev.txt', 'w') as dev_file:
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# for sentence, tags in zip(dev_source, dev_target):
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# for char, tag in zip(sentence.split(' '), tags.split(' ')):
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# dev_file.write(char + ' ' + tag + '\n')
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# dev_file.write('\n')
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# print('dev_data_sample_size: ', len(dev_source))
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# test_source, test_target = source[round((train_data_size+dev_data_size)*sample_count) : -1], target[round((train_data_size+dev_data_size)*sample_count) : -1]
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# with open('data/ThePeoplesDaily/test.txt', 'w') as test_file:
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# for sentence, tags in zip(test_source, test_target):
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# for char, tag in zip(sentence.split(' '), tags.split(' ')):
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# test_file.write(char + ' ' + tag + '\n')
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# test_file.write('\n')
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# print('test_data_sample_size: ', len(test_source))
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with open('data/ThePeoplesDaily/raw/source_BIO.txt', 'w') as file:
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for sentence, tags in zip(source, target):
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for char, tag in zip(sentence.split(' '), tags.split(' ')):
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file.write(char + ' ' + tag + '\n')
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file.write('\n')
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print('data_sample_size: ', len(source)) |