{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 文本的预处理\n",
"关于这个模块，你并不需要完成任何任务，所有的模块已经写好，你只需要读一读就可以了。输入为question_answer.txt，最后处理之后的结果存放在question_answer_parse.pkl文件中。"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import emoji\n",
"import re\n",
"import jieba\n",
"import os\n",
"from collections import Counter"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# 导入原始数据\n",
"QApares_df = pd.read_csv('data/question_answer.txt',sep='\\t',header=None)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>买 二份 有没有 少点 呀</td>\n",
" <td>亲亲 真的 不好意思 我们 已经 是 优惠价 了 呢 小本生意 请亲 谅解</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>那 就 等 你们 处理 喽</td>\n",
" <td>好 的 亲退 了</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>那 我 不 喜欢</td>\n",
" <td>颜色 的话 一般 茶刀 茶针 和 二合一 的话 都 是 红木 檀 和 黑木 檀 哦</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>不是 免 运费</td>\n",
" <td>本店 茶具 订单 满 99 包邮除 宁夏 青海 内蒙古 海南 新疆 西藏 满 39 包邮</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>好吃 吗</td>\n",
" <td>好吃 的</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1\n",
"0 买 二份 有没有 少点 呀 亲亲 真的 不好意思 我们 已经 是 优惠价 了 呢 小本生意 请亲 谅解\n",
"1 那 就 等 你们 处理 喽 好 的 亲退 了\n",
"2 那 我 不 喜欢 颜色 的话 一般 茶刀 茶针 和 二合一 的话 都 是 红木 檀 和 黑木 檀 哦\n",
"3 不是 免 运费 本店 茶具 订单 满 99 包邮除 宁夏 青海 内蒙古 海南 新疆 西藏 满 39 包邮\n",
"4 好吃 吗 好吃 的"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#观察原始数据格式，查看头几个样本\n",
"QApares_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>question_before_preprocessing</th>\n",
" <th>answer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>买 二份 有没有 少点 呀</td>\n",
" <td>亲亲 真的 不好意思 我们 已经 是 优惠价 了 呢 小本生意 请亲 谅解</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>那 就 等 你们 处理 喽</td>\n",
" <td>好 的 亲退 了</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>那 我 不 喜欢</td>\n",
" <td>颜色 的话 一般 茶刀 茶针 和 二合一 的话 都 是 红木 檀 和 黑木 檀 哦</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>不是 免 运费</td>\n",
" <td>本店 茶具 订单 满 99 包邮除 宁夏 青海 内蒙古 海南 新疆 西藏 满 39 包邮</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>好吃 吗</td>\n",
" <td>好吃 的</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" question_before_preprocessing answer\n",
"0 买 二份 有没有 少点 呀 亲亲 真的 不好意思 我们 已经 是 优惠价 了 呢 小本生意 请亲 谅解\n",
"1 那 就 等 你们 处理 喽 好 的 亲退 了\n",
"2 那 我 不 喜欢 颜色 的话 一般 茶刀 茶针 和 二合一 的话 都 是 红木 檀 和 黑木 檀 哦\n",
"3 不是 免 运费 本店 茶具 订单 满 99 包邮除 宁夏 青海 内蒙古 海南 新疆 西藏 满 39 包邮\n",
"4 好吃 吗 好吃 的"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 将 QApares_df列重命名\n",
"QApares_df.columns = ['question_before_preprocessing','answer']\n",
"QApares_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def rm_space(sentence):\n",
" '''将已分好词的句子去掉词之间的空格变成原句子，如将‘那 就 等 你们 处理 喽’改为‘那就等你们处理喽’'''\n",
" return ''.join(sentence.split())\n",
"QApares_df['question'] = QApares_df.question_before_preprocessing.apply(rm_space)\n",
"QApares_df['answer'] = QApares_df.answer.apply(rm_space)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>question_before_preprocessing</th>\n",
" <th>answer</th>\n",
" <th>question</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>买 二份 有没有 少点 呀</td>\n",
" <td>亲亲真的不好意思我们已经是优惠价了呢小本生意请亲谅解</td>\n",
" <td>买二份有没有少点呀</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>那 就 等 你们 处理 喽</td>\n",
" <td>好的亲退了</td>\n",
" <td>那就等你们处理喽</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>那 我 不 喜欢</td>\n",
" <td>颜色的话一般茶刀茶针和二合一的话都是红木檀和黑木檀哦</td>\n",
" <td>那我不喜欢</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>不是 免 运费</td>\n",
" <td>本店茶具订单满99包邮除宁夏青海内蒙古海南新疆西藏满39包邮</td>\n",
" <td>不是免运费</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>好吃 吗</td>\n",
" <td>好吃的</td>\n",
" <td>好吃吗</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" question_before_preprocessing answer question\n",
"0 买 二份 有没有 少点 呀 亲亲真的不好意思我们已经是优惠价了呢小本生意请亲谅解 买二份有没有少点呀\n",
"1 那 就 等 你们 处理 喽 好的亲退了 那就等你们处理喽\n",
"2 那 我 不 喜欢 颜色的话一般茶刀茶针和二合一的话都是红木檀和黑木檀哦 那我不喜欢\n",
"3 不是 免 运费 本店茶具订单满99包邮除宁夏青海内蒙古海南新疆西藏满39包邮 不是免运费\n",
"4 好吃 吗 好吃的 好吃吗"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"QApares_df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"接下来对数据进行预处理"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# 清洗数据\n",
"def clean(content):\n",
" #将数据中的emoji表情转化为文字\n",
" content = emoji.demojize(content)\n",
" #过滤其中的html标签\n",
" content = re.sub('<.*>','',content)\n",
" return content"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# 对每个问题应用上一clean函数进行清洗\n",
"QApares_df['question_after_preprocessing'] = QApares_df['question_before_preprocessing'].apply(clean)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# 按照一般的流程，之后应该是对问题进行分词，但由于我们的数据已经是分好词的数据，所以这里不再进行中文分词，而是直接将分好的词以列表的形式存储。\n",
"QApares_df['question_after_preprocessing'] = QApares_df['question_after_preprocessing'].apply(lambda x:x.split())"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# 对question_after_preprocessing列去除单词中的空格回车等符号\n",
"def strip(wordList):\n",
" return [word.strip() for word in wordList if word.strip()!='']\n",
"QApares_df['question_after_preprocessing'] = QApares_df['question_after_preprocessing'].apply(strip)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# 读取data/stopWord.json中保存的的停用词表，并保存在列表中\n",
"# 中文停用词表的下载地址:https://github.com/goto456/stopwords/\n",
"with open(\"data/stopWord.json\",\"r\") as f:\n",
" stopWords = f.read().split(\"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# 去除停用词\n",
"def rm_stop_word(wordList):\n",
" return [word for word in wordList if word not in stopWords]\n",
"QApares_df['question_after_preprocessing'] = QApares_df['question_after_preprocessing'].apply(rm_stop_word)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# 去除低频词\n",
"allWords = [word for question in QApares_df['question_after_preprocessing'] for word in question] #所有词组成的列表\n",
"freWord = Counter(allWords) #统计词频，一个字典，键为词，值为词出现的次数\n",
"highFreWords = [word for word in freWord.keys() if freWord[word]>5] # 词频超过5的词列表, 剩下的词去掉\n",
"def rm_low_fre_word(content):\n",
" return [word for word in content if word in highFreWords]\n",
"\n",
"# 去除低频词\n",
"QApares_df['question_after_preprocessing'] = QApares_df['question_after_preprocessing'].apply(rm_low_fre_word)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>question</th>\n",
" <th>answer</th>\n",
" <th>question_after_preprocessing</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>买二份有没有少点呀</td>\n",
" <td>亲亲真的不好意思我们已经是优惠价了呢小本生意请亲谅解</td>\n",
" <td>[买, 二份, 有没有, 少点]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>那就等你们处理喽</td>\n",
" <td>好的亲退了</td>\n",
" <td>[处理]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>那我不喜欢</td>\n",
" <td>颜色的话一般茶刀茶针和二合一的话都是红木檀和黑木檀哦</td>\n",
" <td>[喜欢]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>不是免运费</td>\n",
" <td>本店茶具订单满99包邮除宁夏青海内蒙古海南新疆西藏满39包邮</td>\n",
" <td>[免, 运费]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>好吃吗</td>\n",
" <td>好吃的</td>\n",
" <td>[好吃]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" question answer question_after_preprocessing\n",
"0 买二份有没有少点呀 亲亲真的不好意思我们已经是优惠价了呢小本生意请亲谅解 [买, 二份, 有没有, 少点]\n",
"1 那就等你们处理喽 好的亲退了 [处理]\n",
"2 那我不喜欢 颜色的话一般茶刀茶针和二合一的话都是红木檀和黑木檀哦 [喜欢]\n",
"3 不是免运费 本店茶具订单满99包邮除宁夏青海内蒙古海南新疆西藏满39包邮 [免, 运费]\n",
"4 好吃吗 好吃的 [好吃]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 只保留question,answer和question_after_preprocessing列\n",
"QApares_df = QApares_df[['question','answer','question_after_preprocessing']]\n",
"#查看预处理之后的数据\n",
"QApares_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"#由于保存为csv格式会将数据默认保存为str格式，但我们的数据中question_after_preprocessing列中每一项为列表形式，保存为csv格式会增加我们后面处理数据的难度，所以这里我们将保存为pickle形式\n",
"import pickle\n",
"with open('data/question_answer_pares.pkl','wb') as f:\n",
" pickle.dump(QApares_df,f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"pygments_lexer": "ipython3",
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}