nlp_data_process

data_process.py

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+import string
+import jieba
+from tqdm import tqdm
+import json
+import numpy as np
+
+stop_words = ['！', '……', '？', '的', '了', '嗯', '哦', '啊', '我', '你',
+              '她', '他', '它', '在', '和', '吗', '呢', '可以', '，', '。',
+              '：', '；']
+
+
+def cut_sentence_by_word(sentence, stopwords):
+    """
+    按照单个字进行分词，需要处理单个英文字
+    :param sentence:
+    :param stopwords:
+    :return:word_list
+    """
+    continue_words = string.ascii_lowercase + string.digits
+    temp = ""
+    result = []
+    for word in sentence:
+        if word in continue_words:
+            temp += word
+            continue
+        if len(temp) > 0:
+            result.append(temp)
+            temp = ""
+        result.append(word)
+    if len(temp) > 0:
+        result.append(temp)
+    return [word for word in result if word not in stopwords]
+
+
+def cut_sentence(sentence, stopwords):
+    """
+    按照词语进行分词
+    :param sentence:
+    :param stopwords:
+    :return: words_list
+    """
+    return [word for word in jieba.lcut(sentence) if word not in stopwords]
+
+
+def transfer_file_to_list(file_path):
+    """
+
+    :param file_path:
+    :return:
+    """
+    with open(file_path, 'r', encoding='utf-8') as f:
+        lines = [line.rstrip().lower() for line in f]
+    return lines
+
+
+class TxtToIndex(object):
+    def __init__(self, train_txt_path, test_txt_path, cuf_fn):
+        """
+        初始化
+        :param train_txt_path: 原始训练数据地址
+        :param test_txt_path: 原始测试数据地址
+        :param cuf_fn: 分割函数，可以按字或者词分割
+        """
+        self.cut_fn = cuf_fn
+        self._origin_train_data = transfer_file_to_list(train_txt_path)
+        self._origin_test_data = transfer_file_to_list(test_txt_path)
+        self._labels = []
+        self._vocab = {}  # {"汀": 0, "哟": 1}
+        self._create_vocab_and_labels()
+
+    def _create_vocab_and_labels(self):
+        vocab_length = 0
+        label_set = set()  # 去重
+        for i in tqdm(range(0, len(self._origin_train_data)), desc='Initializing'):
+            sentence, label = self._origin_train_data[i].rsplit(sep=' ', maxsplit=1)
+            label_set.add(label)
+            word_list = self.cut_fn(sentence, stop_words)
+            for word in word_list:
+                if not self._vocab.get(word):
+                    self._vocab[word] = vocab_length
+                    vocab_length += 1
+
+        self._labels = list(label_set)
+        # 异常保护，如果原文中带<unk>或<pad>则用原文中的值
+        self._vocab['<unk>'] = vocab_length if not self._vocab.get('<unk>', None) else self._vocab.get('<unk>')
+        self._vocab['<pad>'] = vocab_length + 1 if not self._vocab.get('<unk>', None) else self._vocab.get('<unk>')
+
+    def save_labels(self, label_path):
+        """
+        包括所有标签
+        :param label_path: 保存地址
+        """
+        with open(label_path, 'w', encoding='utf-8') as f:
+            for label in self._labels:
+                f.writelines(label + '\n')
+
+    def save_vocabulary(self, vocab_path):
+        """
+        包括train和dev的字典
+        :param vocab_path: 保存地址
+        :return:
+        """
+        if not vocab_path.endswith('.json'):
+            print('vocabulary should be a json file')
+            return
+        with open(vocab_path, 'w', encoding='utf-8') as f:
+            json.dump(self._vocab, f, ensure_ascii=False)
+
+    def _save_labeled_data(self, save_to, description, txt_data):
+        if len(txt_data) == 0:
+            return
+        f = open(save_to, 'w', encoding='utf-8')
+        for i in tqdm(range(0, len(txt_data)), desc=description):
+            sentence, label = txt_data[i].rsplit(sep=' ', maxsplit=1)
+            word_list = self.cut_fn(sentence, stop_words)
+            label_idx = self._labels.index(label)
+            sentence_idx = [str(self._vocab.get(word, self._vocab['<unk>'])) for word in word_list]
+            f.writelines(' '.join(sentence_idx) + '\t' + str(label_idx) + '\n')
+        f.close()
+
+    def _save_non_labeled_data(self, save_to, description, txt_data):
+        f = open(save_to, 'w', encoding='utf-8')
+        for i in tqdm(range(0, len(txt_data)), desc=description):
+            word_list = self.cut_fn(txt_data[i], stop_words)
+            sentence_idx = list(map(str, [self._vocab.get(word, self._vocab['<unk>']) for word in word_list]))
+            f.writelines(' '.join(sentence_idx) + '\n')
+        f.close()
+
+    def split_and_save(self, train_idx_path, dev_idx_path=None, frac=0.4):
+        """
+        将训练数据按比例分为训练集和数据集，并保存为索引
+        :param train_idx_path: 训练集索引保存地址
+        :param dev_idx_path: 验证集索引保存地址
+        :param frac: 训练集和验证集比例
+        :return:
+        """
+        if frac <= 0 or frac > 1:
+            print('分割比例必须大于0 ���小于等于1')
+            return
+        if frac < 1 and not dev_idx_path:
+            print('分割比例小于1时，必须指定测试集路径')
+            return
+        if frac == 1 and dev_idx_path:
+            print('分割比例等于1时全部数据均用作训练集,测试集路径{}无效'.format(dev_idx_path))
+
+        np.random.shuffle(self._origin_train_data)  # 打乱数据，使每次保存的验证集和测试集均不同
+        split_point = int(len(self._origin_train_data) * frac)
+        self._save_labeled_data(train_idx_path, 'Saving Train Index', self._origin_train_data[:split_point])
+        self._save_labeled_data(dev_idx_path, 'Saving Dev Index', self._origin_train_data[split_point:])
+
+    def save_test_set(self, test_idx_path):
+        """
+        将测试数据保存为索引
+        :param test_idx_path:
+        """
+        self._save_non_labeled_data(test_idx_path, 'Saving Test Index', self._origin_test_data)
+
+
+if __name__ == '__main__':
+    txt_to_idx = TxtToIndex(
+        train_txt_path='data/myTrain.txt',
+        test_txt_path='data/myTest.txt',
+        cuf_fn=cut_sentence_by_word,
+    )
+    txt_to_idx.save_test_set('datatest/test_idx.txt')
+    txt_to_idx.save_labels('datatest/labels.txt')
+    txt_to_idx.save_vocabulary('datatest/vocab.json')
+    txt_to_idx.split_and_save('datatest/train_idx.txt', 'datatest/dev_idx.txt', frac=0.6)