【NLP项目-文本分类】划分测试集，训练集，验证集一、不分词划分数据集二、分词划分数据集

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我是靠谱客的博主如意戒指，最近开发中收集的这篇文章主要介绍【NLP项目-文本分类】划分测试集，训练集，验证集一、不分词划分数据集二、分词划分数据集，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

目录

一、不分词划分数据集
1.划分数据集
2.将各数据集写入txt文件

二、分词划分数据集
1.分词
2.完整代码

本篇文章的主要任务是将自己的数据集使用在Chinese-Text-Classification-PyTorch项目中
github地址: Chinese-Text-Classification

数据集：二分类的文本数据，做情感分析，review为评论内容，label分为1,0正负项。

一、不分词划分数据集

用pandas读取csv数据文件，用sklearn中的train_test_split函数划分数据集

1.划分数据集

按8:1:1比例将数据集划分成训练集，验证集，测试集：

X_train, X_test, y_train, y_test = train_test_split(X,
                                                    y,
                                                    test_size=0.2,
                                                    shuffle=True,
                                                    stratify=y,
                                                    random_state=42)

X_valid, X_test, y_valid, y_test = train_test_split(X_test,
                                                    y_test,
                                                    test_size=0.5,
                                                    shuffle=True,
                                                    stratify=y_test,
                                                    random_state=42)

2.将各数据集写入txt文件

生成三个txt文件，test.txt，train.txt，dev.txt 用于匹配替换项目数据集格式

# 划分成txt文件
testdir = "./WeiboData/data/test.txt"
traindir = "./WeiboData/data/train.txt"
validdir = "./WeiboData/data/dev.txt"

print(X_test)
print(y_test)
with open(testdir, 'a+', encoding='utf-8-sig') as f:
    for i,j in zip(X_test,y_test):
        f.write(str(i)+'t'+str(j)+'n')

with open(traindir, 'a+', encoding='utf-8-sig') as f:
    for i,j in zip(X_train,y_train):
        f.write(str(i)+'t'+str(j)+'n')

with open(validdir, 'a+', encoding='utf-8-sig') as f:
    for i,j in zip(X_valid,y_valid):
        f.write(str(i)+'t'+str(j)+'n')

f.close()

完整代码：

import pandas as pd
import jieba
from sklearn.model_selection import train_test_split

data = pd.read_csv(r'D:StudyPycahrmProjectssentimentAnalysiswb_data1_denote1.csv', encoding='utf-8-sig')

X = data['review'].values
y = data.label.values

# 5:3:2
# 8:1:1
X_train, X_test, y_train, y_test = train_test_split(X,
                                                    y,
                                                    test_size=0.2,
                                                    shuffle=True,
                                                    stratify=y,
                                                    random_state=42)

X_valid, X_test, y_valid, y_test = train_test_split(X_test,
                                                    y_test,
                                                    test_size=0.5,
                                                    shuffle=True,
                                                    stratify=y_test,
                                                    random_state=42)

print("训练集样本数 = ", len(y_train))
print("训练集中正样本数 = ", len([w for w in y_train if w == 1]))
print("训练集中负样本数 = ", len([w for w in y_train if w == 0]))
print("验证集样本数 = ", len(y_valid))
print("验证集中正样本数 = ", len([w for w in y_valid if w == 1]))
print("验证集中负样本数 = ", len([w for w in y_valid if w == 0]))
print("测试集样本数 = ", len(y_test))
print("测试集中正样本数 = ", len([w for w in y_test if w == 1]))
print("测试集中负样本数 = ", len([w for w in y_test if w == 0]))

# 划分成txt文件
testdir = "./WeiboData/data/test.txt"
traindir = "./WeiboData/data/train.txt"
validdir = "./WeiboData/data/dev.txt"

print(X_test)
print(y_test)
with open(testdir, 'a+', encoding='utf-8-sig') as f:
    for i,j in zip(X_test,y_test):
        f.write(str(i)+'t'+str(j)+'n')

with open(traindir, 'a+', encoding='utf-8-sig') as f:
    for i,j in zip(X_train,y_train):
        f.write(str(i)+'t'+str(j)+'n')

with open(validdir, 'a+', encoding='utf-8-sig') as f:
    for i,j in zip(X_valid,y_valid):
        f.write(str(i)+'t'+str(j)+'n')

f.close()

二、分词划分数据集

1.分词

注意分词后需要按行划分成列表
代码如下（示例）：

# 用jieba对各数据集分词
def tokenizer(data):
    # 得到文本数据
    text = []
    for i in range(data.shape[0]):
        text.append(str(data[i]))

    comment = 'n'.join(text)

    # 清洗文本数据-用正则表达式删去数字、字母、标点符号、特殊符号等
    import re
    symbols = "[0-9!%,。.，、~?()（）？！“”:：;""；……&-_|．Ａ．Ｂ．Ｃ*^]"
    comments = re.sub(symbols, '', comment)

    comments_list = jieba.cut(comments)  # 精确模式
    # comments_list = jieba.cut_for_search(comments)#搜索引擎模式
    x_train = ' '.join([x for x in comments_list])  # 用空格连接分好的词

    return x_train

# 对各数据集分词

X_test = tokenizer(X_test)
X_train = tokenizer(X_train)
X_valid = tokenizer(X_valid)

# 按行将string划分成列表
X_valid = X_valid.split('n')
X_test = X_test.split('n')
X_train = X_train.split('n')

2.完整代码

import pandas as pd
import jieba
from sklearn.model_selection import train_test_split

# 用jieba对各数据集分词
def tokenizer(data):
    # 得到文本数据
    text = []
    for i in range(data.shape[0]):
        text.append(str(data[i]))

    comment = 'n'.join(text)

    # 清洗文本数据-用正则表达式删去数字、字母、标点符号、特殊符号等
    import re
    symbols = "[0-9!%,。.，、~?()（）？！“”:：;""；……&-_|．Ａ．Ｂ．Ｃ*^]"
    comments = re.sub(symbols, '', comment)

    comments_list = jieba.cut(comments)  # 精确模式
    # comments_list = jieba.cut_for_search(comments)#搜索引擎模式
    x_train = ' '.join([x for x in comments_list])  # 用空格连接分好的词

    return x_train


data = pd.read_csv(r'D:StudyPycahrmProjectssentimentAnalysiswb_data1_denote1.csv', encoding='utf-8-sig')


X = data['review'].values
y = data.label.values

# 5:3:2
X_train, X_test, y_train, y_test = train_test_split(X,
                                                    y,
                                                    test_size=0.5,
                                                    shuffle=True,
                                                    stratify=y,
                                                    random_state=42)

X_valid, X_test, y_valid, y_test = train_test_split(X_test,
                                                    y_test,
                                                    test_size=0.3,
                                                    shuffle=True,
                                                    stratify=y_test,
                                                    random_state=42)

print("训练集样本数 = ", len(y_train))
print("训练集中正样本数 = ", len([w for w in y_train if w == 1]))
print("训练集中负样本数 = ", len([w for w in y_train if w == 0]))
print("验证集样本数 = ", len(y_valid))
print("验证集中正样本数 = ", len([w for w in y_valid if w == 1]))
print("验证集中负样本数 = ", len([w for w in y_valid if w == 0]))
print("测试集样本数 = ", len(y_test))
print("测试集中正样本数 = ", len([w for w in y_test if w == 1]))
print("测试集中负样本数 = ", len([w for w in y_test if w == 0]))

# 划分成txt文件

testdir = "./WeiboData/data/test.txt"
traindir = "./WeiboData/data/train.txt"
validdir = "./WeiboData/data/dev.txt"

print(X_test)
print(y_test)

# 对各数据集分词

X_test = tokenizer(X_test)
X_train = tokenizer(X_train)
X_valid = tokenizer(X_valid)

X_valid = X_valid.split('n')
X_test = X_test.split('n')
X_train = X_train.split('n')


print(X_test)
print(type(X_test))
print(len(X_test))

with open(testdir, 'a+', encoding='utf-8-sig') as f:
    for i,j in zip(X_test,y_test):
        f.write(str(i)+'t'+str(j)+'n')


with open(traindir, 'a+', encoding='utf-8-sig') as f:
    for i,j in zip(X_train,y_train):
        f.write(str(i)+'t'+str(j)+'n')


with open(validdir, 'a+', encoding='utf-8-sig') as f:
    for i,j in zip(X_valid,y_valid):
        f.write(str(i)+'t'+str(j)+'n')

f.close()