DataClassification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "start_time": "2023-05-19T08:58:27.674077Z",
     "end_time": "2023-05-19T08:58:27.695075Z"
    }
   },
   "outputs": [],
   "source": [
    "import jieba  # 分词包\n",
    "import matplotlib.pyplot as plt  # 数据可视化\n",
    "import pandas as pd  # 数据分析\n",
    "import seaborn as sns  # 数据可视化\n",
    "from sklearn.feature_extraction.text import CountVectorizer  # 特征提取\n",
    "from sklearn.model_selection import train_test_split  # 数据集划分\n",
    "from sklearn.metrics import confusion_matrix  # 混淆矩阵\n",
    "# 定义朴素贝叶斯模型，然后对训练集进行模型训练，直接使用 sklearn 中的 MultinomialNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "import warnings  # 忽略警告\n",
    "import random  # 随机数\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决保存图像是负号'-'显示为方块的问题\n",
    "warnings.filterwarnings(\"ignore\")  # 忽略警告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "# 加载停用词\n",
    "stopwords = pd.read_csv('data\\\\stopwords.txt', index_col=False, quoting=3, sep=\"\\t\", names=['stopword'], encoding='utf-8')\n",
    "stopwords = stopwords['stopword'].values"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-19T07:53:39.668342Z",
     "end_time": "2023-05-19T07:53:39.697363Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "   Unnamed: 0                                            content  labels\n0           0  你真的红了很开心内场票也可以卖到虽然以前就能听你的越来越听不起你的演唱会不过没关系我会努力变...       1\n1           1  我是一位土生土长的成都姑娘所有成都的回忆都汇成这首歌喜欢赵雷已经三年了年第一次在沈阳看赵雷演...       7\n2           2                         为什么你们成都有成都我们石家庄就只能杀死那个石家庄人       2\n3           3  当初一首郝云的去大理然我义无反顾的背起行囊去了云南现在听到赵雷的成都好吧我正在定机票那么多年...       7\n4           4  我是安徽人高考填志愿我一本我女朋友二本为了能和她在一起我降分到二本和她一起来到了成都不管选择...       6",
      "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>Unnamed: 0</th>\n      <th>content</th>\n      <th>labels</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>你真的红了很开心内场票也可以卖到虽然以前就能听你的越来越听不起你的演唱会不过没关系我会努力变...</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>我是一位土生土长的成都姑娘所有成都的回忆都汇成这首歌喜欢赵雷已经三年了年第一次在沈阳看赵雷演...</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>为什么你们成都有成都我们石家庄就只能杀死那个石家庄人</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>当初一首郝云的去大理然我义无反顾的背起行囊去了云南现在听到赵雷的成都好吧我正在定机票那么多年...</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>我是安徽人高考填志愿我一本我女朋友二本为了能和她在一起我降分到二本和她一起来到了成都不管选择...</td>\n      <td>6</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加载语料\n",
    "music_df = pd.read_csv('data\\\\labels.csv', encoding='utf-8')\n",
    "music_df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-19T07:53:39.699363Z",
     "end_time": "2023-05-19T07:53:39.760591Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "#定义分词和打标签函数preprocess_text\n",
    "#参数content_lines即为上面转换的list\n",
    "#参数sentences是定义的空list，用来储存打标签之后的数据\n",
    "def preprocess_text(content_lines, sentences):\n",
    "    for line in content_lines:\n",
    "        segs = jieba.lcut(line)\n",
    "        segs = [v for v in segs if not str(v).isdigit()]  #去数字\n",
    "        segs = list(filter(lambda x: x.strip(), segs))  #去左右空格\n",
    "        segs = list(filter(lambda x: len(x) > 1, segs))  #长度为1的字符\n",
    "        segs = list(filter(lambda x: x not in stopwords, segs))  #去掉停用词\n",
    "        sentences.append((\" \".join(segs)))  # 打标签"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-19T07:53:39.762593Z",
     "end_time": "2023-05-19T07:53:39.795639Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Dumping model to file cache C:\\Users\\24835\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 0.561 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    }
   ],
   "source": [
    "# 转换\n",
    "music = music_df['content'].tolist()\n",
    "#调用函数，生成训练集\n",
    "sentences = []\n",
    "preprocess_text(music, sentences)\n",
    "random.shuffle(sentences)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-19T07:53:39.779643Z",
     "end_time": "2023-05-19T07:53:48.037690Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "# 删除语料的nan行\n",
    "music_df.dropna(inplace=True)\n",
    "content = music_df['content'].tolist()\n",
    "labels = music_df['labels'].tolist()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-19T07:53:48.039672Z",
     "end_time": "2023-05-19T07:53:48.051673Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "#调用函数、生成训练数据\n",
    "#参数df2是类型标签\n",
    "sentences = []\n",
    "preprocess_text(content, sentences)\n",
    "sentences = list(zip(sentences, labels))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-19T07:53:48.053675Z",
     "end_time": "2023-05-19T07:53:55.658678Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开心 内场 越来越 听不起 演唱会 没关系 我会 努力 变得 民谣 值得 难过 日子 普通人 想要 买房 结婚 姑娘 赵雷 1\n",
      "一位 土生土长 成都 姑娘 成都 回忆 汇成 喜欢 赵雷 三年 第一次 沈阳 赵雷 演出 大二 满满的 对家 回忆 男朋友 日子 迷笛 草莓 赵雷 现场 昆明 研究生 成都 工作 成都 领证 7\n",
      "成都 成都 石家庄 只能 杀死 石家庄 2\n",
      "当初 一首 郝云 大理 义无反顾 背起 行囊 云南 听到 赵雷 成都 机票 多年 听的歌 腾格尔 天堂 7\n",
      "安徽 高考 填志愿 一本 女朋友 二本 我降 分到 二本 来到 成都 选择 至少 后悔 喜欢 城市 喜欢 成都 那种 情怀 听到 总有 一小块 地方 触动 五年 6\n",
      "年前 成都 大学 七年 大学毕业 含泪 分手 五年 两年 五年 互相联系 情况 结束 失败 婚姻 一年 毫不犹豫 抓紧 再也 放弃 两个 结婚 成都 留下 爱情 2\n",
      "靠靠 终于 开曲 两秒钟 尖叫 1\n",
      "第一次 成都 音质 不清 版本 女生 四川 口音 吃火锅 成都 微博上 钢琴 纯净 版短 难忘 第三版 成都 时光 西安 演唱会 打开 手电筒 副歌 旋律 重复 星空 闪耀 公车 很挤 音乐 拯救 一刻 最终 成都 8\n",
      "喜欢 街头 酒吧 草地 青岛 大海 对面 这首 熟悉 歌谣 个中滋味 体会 踏上 一段 美妙 搭车 民谣 旅行 期待 成都 相遇 成都 街头 哼唱 把手 揣进 裤兜 坐在 小酒馆 门口 一个个 静静的 消失 灯光 尽头 选择 人生 6\n",
      "一首歌 怀念 一座 天府广场 武侯祠 锦里 杜甫 草堂 青羊宫 春熙路 宽窄 巷子 昭觉寺 文殊院 回忆 1\n",
      "四川 三年 听过 三年 离开 四川 听到 位置 触动 喜欢 歌词 喜欢 旋律 循环 播放 好久好久 现场版 成都 成都 成都 0\n",
      "喜欢 成都 阴晴不定 天气 小雨 绵绵 喜欢 成都 喜欢 随处可见 火锅店 小吃 喜欢 每月 小酒馆 现场 大三 毕业 也许 离开 成都 成都 一场 雷子 演唱会 感觉 完美 成都 成都 成都 0\n",
      "李志爱 南京 赵雷爱 成都 路上 故乡 和解 歌唱 好好 活着 有用 2\n",
      "反复 不厌 现场 前些天 电台 算是 赵雷 崇敬 朋友 几天 成都 推荐 感动 成都 街道 转转 深秋 刚来 夜里 感觉 听到 版本 4\n",
      "一位 江苏 男孩 一位 成都 女孩 喜欢 女孩 喜欢 异地 喜欢 男孩 异地 恋天 原谅 男孩 成都 男孩 0\n",
      "奶常扇 赵子龙 1\n",
      "对不起 学习 学好 工作 做好 白活 好多年 1\n",
      "回到 第一次 那天 1\n",
      "身在 异乡 成都 眼泪 眼角 滴下 成都 深沉 2\n",
      "核桃树 金银花 打卡 1\n"
     ]
    }
   ],
   "source": [
    "for sentence in sentences[:20]:\n",
    "    print(sentence[0], sentence[1])  #下标0是词列表，1是标签"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-19T07:53:55.659679Z",
     "end_time": "2023-05-19T07:53:55.675680Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "vec = CountVectorizer(\n",
    "    analyzer='word',  # tokenise by character ngrams\n",
    "    max_features=23,  # keep the most common 1000 ngrams\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-19T07:53:55.677684Z",
     "end_time": "2023-05-19T07:53:55.734679Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "15000"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, y = zip(*sentences)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42)\n",
    "len(x_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-19T07:53:55.691682Z",
     "end_time": "2023-05-19T07:53:55.741678Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "CountVectorizer(max_features=23)",
      "text/html": "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>CountVectorizer(max_features=23)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CountVectorizer</label><div class=\"sk-toggleable__content\"><pre>CountVectorizer(max_features=23)</pre></div></div></div></div></div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#把训练数据转换为词袋模型：\n",
    "vec.fit(x_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-19T07:53:55.706679Z",
     "end_time": "2023-05-19T07:53:55.814680Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "MultinomialNB()",
      "text/html": "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = MultinomialNB()\n",
    "classifier.fit(vec.transform(x_train), y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-19T07:53:55.818681Z",
     "end_time": "2023-05-19T07:53:55.912679Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9386122775444911\n"
     ]
    }
   ],
   "source": [
    "#评估、计算 AUC 值\n",
    "print(classifier.score(vec.transform(x_test), y_test))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-19T07:53:55.912679Z",
     "end_time": "2023-05-19T07:53:55.956680Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "array([1, 1, 1, ..., 1, 1, 4])"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 进行测试集的预测：\n",
    "pre = classifier.predict(vec.transform(x_test))\n",
    "pre"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-19T07:53:55.959679Z",
     "end_time": "2023-05-19T07:53:56.023679Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[  47,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0],\n       [   0, 3119,    0,    0,    0,    1,    0,    0,    0,    2,    0],\n       [   0,    0,  593,    0,    0,    0,    0,    0,    0,    0,    0],\n       [   0,    0,    0,  137,    0,    1,    0,    0,    0,    0,    0],\n       [   0,    0,    0,    0,  238,    0,    0,    1,    0,    0,    0],\n       [   0,    7,    0,    7,    0,   96,    5,    0,    0,    0,    0],\n       [   4,    0,    0,    0,    0,    1,   38,    0,    0,    0,    1],\n       [   0,    9,    0,    0,   79,    0,    0,   19,    0,    0,    0],\n       [   0,    0,    3,    0,   79,    0,    0,    0,    0,    0,    0],\n       [   0,   94,    0,    0,    0,    0,    0,    0,    0,  399,    0],\n       [   7,    0,    0,    0,    0,    0,    6,    0,    0,    0,    8]],\n      dtype=int64)"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 混淆矩阵\n",
    "# 真实标签\n",
    "y_true = y_test\n",
    "# 预测标签\n",
    "y_pred = classifier.predict(vec.transform(x_test)).tolist()\n",
    "c = confusion_matrix(y_true, y_pred)\n",
    "c"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-19T07:53:55.991683Z",
     "end_time": "2023-05-19T07:53:56.023679Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x600 with 2 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制热力图\n",
    "# fmt参数用于指定数值的格式\n",
    "# cmap参数用于指定颜色映射\n",
    "# annot参数用于指定是否在热力图中显示数值\n",
    "# 绘制热力图\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(c, annot=True, cmap='summer', fmt='d', vmax=300)\n",
    "plt.xlabel('预测标签')\n",
    "plt.ylabel('真实标签')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-05-19T08:19:50.354200Z",
     "end_time": "2023-05-19T08:19:50.800201Z"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}