自然语言处理简洁自用代码合集_自然语言处理代码样例

记录文字处理的各种简介的代码表示

1.快速去除中文标点（read的时候要以utf8格式）

def clean_str(string):
    string = re.sub("[^\u4e00-\u9fff]", " ", string)
    string = re.sub(r"\s{2,}", " ", string)#合并多个空格为一个
    return string.strip()
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2.快速分词,默认一行为一样本

def seperate_line(string):
    return ''.join([word + ' ' for word in jieba.cut(string)])

f=open("xxx",'r',encoding="utf8")
lines = list(f.readlines())
lines = [clean_str(seperate_line(line)) for line in lines]
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3.分行，使得一行为一句

for line in lines
  line.replace('\n','').replace('，','\n').replace('。','\n').replace('！','\n').replace('？','\n')
重新写入
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4.语料训练集生成

def load_positive_negative_data_files(positive_data_file_path, negative_data_file_path):
    positive_example_lists = read_and_clean_zh_file(positive_data_file_path)
    #positive_example_lists ---> 0维度上为样本有多少句句子，1维度上为每句的string，单词间空格隔开
    negative_example_lists = read_and_clean_zh_file(negative_data_file_path)
    #positive_example_lists ---> 形式同上
    # Combine data
    x_text = positive_example_lists + negative_example_lists
    # Generate labels
    positive_labels = [[1] for _ in positive_example_lists]
    negative_labels = [[0] for _ in negative_example_lists]
    y = np.concatenate([positive_labels, negative_labels], 0)
    return [x_text, y]
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5.句子填充

def padding_sentences(input_sentences, padding_token, padding_sentence_length = None):
    sentences = [sentence.split(' ') for sentence in input_sentences]
    if padding_sentence_length !=None:
        max_sentence_length=padding_sentence_length
    else:
        max_sentence_length=max([len(sentence) for sentence in sentences])
    for i,sentence in generate(sentences):
        if len(sentence) > max_sentence_length:
            sentences[i] = sentence[:max_sentence_length]
        else:
            sentence.extend([padding_token] * (max_sentence_length - len(sentence)))
    return (sentences, max_sentence_length)
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6.从gensim训练模型拿词向量

model加载
all_vectors = []
embeddingDim = w2vModel.vector_size
embeddingUnknown = [0 for i in range(embeddingDim)]
for sentence in sentences:
    this_vector = []
    for word in sentence:
        if word in w2vModel.wv.vocab:
            this_vector.append(w2vModel[word])
        else:
            this_vector.append(embeddingUnknown)
    all_vectors.append(this_vector)
return all_vectors
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7.打乱np矩阵的方法

x=[0,1,2,3,4,5,6]
x=np.array(x)
np.random.seed(10)
shuffle_indices = np.random.permutation(np.arange(len(x)))
print(shuffle_indices)
x_shuffled = x[shuffle_indices]
print(x_shuffled)

输出
[2 6 0 3 4 5 1]
[2 6 0 3 4 5 1]
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8.分离部分样本为训练集和验证集

1.打乱样本顺序（参考上面代码）
2.按比例截断
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