新浪新闻分类 交叉验证 召回率 .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import sqlite3\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer as TFIDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cx = sqlite3.connect(r'D:\\Python\\获取网络信息用于文本分析\\data.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_sql_query('select * from junshi',cx)\n",
    "df1 = pd.read_sql_query('select * from NBA',cx)\n",
    "df0 = pd.read_sql_query('select * from gaokao',cx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = df0.dropna(axis=1, how='any')\n",
    "df1 = df1.dropna(axis=1, how='any')\n",
    "df2 = df2.dropna(axis=1, how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "junshi = df2.to_dict(orient='records') \n",
    "nba = df1.to_dict(orient='records')\n",
    "gaokao = df0.to_dict(orient='records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1122"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lable = []\n",
    "for i in range(len(junshi[3])-1):\n",
    "    lable.append(0)\n",
    "for i in range(len(junshi[3])-1,len(junshi[3])-1+len(gaokao[3])-1):\n",
    "    lable.append(1)\n",
    "for i in range(len(junshi[3])-1+len(gaokao[3])-1,len(junshi[3])-1+len(gaokao[3])-1+len(nba[3])-1):\n",
    "    lable.append(2)\n",
    "len(lable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = []\n",
    "for w in junshi[3]:\n",
    "        data1.append(junshi[3][w])\n",
    "for w in gaokao[3]:\n",
    "        data1.append(gaokao[3][w])\n",
    "for w in nba[3]:\n",
    "        data1.append(nba[3][w])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'acticle'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.pop(0)\n",
    "data1.pop(198)\n",
    "data1.pop(498)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "li = []\n",
    "for i in range(len(data1)):\n",
    "    reg = \"[^\\u4e00-\\u9fa5]\"\n",
    "    li.append(re.sub(reg,'',data1[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache C:\\Users\\14982\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 0.974 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    }
   ],
   "source": [
    "import jieba\n",
    "for  w  in range(len(li)):\n",
    "    data1[w]=jieba.lcut(li[w])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1122"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = []\n",
    "for i in  range(len(data1)):\n",
    "    X_train.append(' '.join(data1[i]))\n",
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain,Xtest,Ytrain,Ytest = train_test_split(X_train,lable,test_size=0.3,random_state=420)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer as TFIDF\n",
    "from sklearn.metrics import classification_report\n",
    "tfidf = TFIDF().fit(Xtrain)\n",
    "Xtrain_ = tfidf.transform(Xtrain)\n",
    "Ytrain_ = tfidf.transform(Xtest)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB,GaussianNB\n",
    "\n",
    "from sklearn.metrics import brier_score_loss as B\n",
    "clf = MultinomialNB()\n",
    "clf.fit(Xtrain_,Ytrain)\n",
    "y_pred = clf.predict(Ytrain_)\n",
    "proba = clf.predict_proba(Ytrain_)\n",
    "score = clf.score(Ytrain_,Ytest)\n",
    "predict = clf.predict(Ytrain_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.decomposition import TruncatedSVD as SVD\n",
    "tfidf = TFIDF().fit(X_train)\n",
    "X_tr = tfidf.transform(X_train)\n",
    "pca = SVD(n_components=2)           #实例化\n",
    "pca = pca.fit(X_tr)                    #拟合模型\n",
    "X_dr = pca.transform(X_tr)             #获取新矩阵\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9436757554014191"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_s = cross_val_score( GaussianNB(),X_dr,lable,cv=10)\n",
    "rfc_s.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9762611275964391\n"
     ]
    }
   ],
   "source": [
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率为： 0.9762611275964391\n"
     ]
    }
   ],
   "source": [
    "print(\"准确率为：\", clf.score(Ytrain_,Ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "         军事       1.00      0.87      0.93        53\n",
      "         高考       1.00      0.99      0.99        96\n",
      "        NBA       0.96      1.00      0.98       188\n",
      "\n",
      "avg / total       0.98      0.98      0.98       337\n",
      "\n"
     ]
    }
   ],
   "source": [
    "list1=[\"军事\",'高考','NBA']\n",
    "print(classification_report(Ytest, predict, target_names=list1))  #精确率（查的准）  召回率（查的全） 二者均值 标签出现的次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "fr = open('junshi.txt',\"r\")\n",
    "st=fr.read()\n",
    "fr.close()   #将文件关闭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "str1 = jieba.lcut(st)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'英国 近代 历史 中 占据 地位 无人 敢 小觑 遍布 世界各地 殖民地 区 更是 无人 正是 种种原因 才 日不落 称号 一战 二战时期 英国 国力 有所 滑坡 科技 实力 仍 全球 顶尖 当时 喷气式 发动机 正是 英国 带领 下 逐渐 得到 发展 普及 由此来看 英国 战斗机 应该 挑不出 毛病 说 英国 战机 外貌 设计 就要 打个 问号 全球 最丑 战斗机 两个 发动机 摞 一起   活像 一只 大肚 蝈蝈 \\n \\n 英国 战机 当今世界 军火 市场 上 占据 一席之地 英国 战机 作战 实力 不 含有 水分 看到 英国 战斗机 人 第一 反应 都 感到 会 不 协调 称做 丑 英国 战机 大腹便便 不说 更是 没有 丝毫 优雅 可言 英国 绅士 特征 或者说 气质 有着 本质 不同 好事者 全球 战机 做 排名榜 考核 因素 便是 最丑 榜单 中有 一半 都 英国 战机'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopwords = [line.strip() for line in open('D:\\Python\\Python\\stopwords\\哈工大停用词表.txt',encoding='UTF-8').readlines()]\n",
    "content = []\n",
    "line = []\n",
    "for word in str1:\n",
    "    if word  not in stopwords:\n",
    "             line.append(word) \n",
    "#content.append(line)\n",
    "line = ' '.join(line)\n",
    "line = [line]\n",
    "line[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xte=tfidf.transform(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "dimension mismatch",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-26-1fc58d8fc1b2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mproba\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXte\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXte\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    102\u001b[0m             \u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mappear\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mattribute\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    103\u001b[0m         \"\"\"\n\u001b[1;32m--> 104\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_log_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    105\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    106\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py\u001b[0m in \u001b[0;36mpredict_log_proba\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m     82\u001b[0m             \u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mappear\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mattribute\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;31m`\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m         \"\"\"\n\u001b[1;32m---> 84\u001b[1;33m         \u001b[0mjll\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_joint_log_likelihood\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     85\u001b[0m         \u001b[1;31m# normalize by P(x) = P(f_1, ..., f_n)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m         \u001b[0mlog_prob_x\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlogsumexp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjll\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py\u001b[0m in \u001b[0;36m_joint_log_likelihood\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    723\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    724\u001b[0m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'csr'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 725\u001b[1;33m         return (safe_sparse_dot(X, self.feature_log_prob_.T) +\n\u001b[0m\u001b[0;32m    726\u001b[0m                 self.class_log_prior_)\n\u001b[0;32m    727\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\extmath.py\u001b[0m in \u001b[0;36msafe_sparse_dot\u001b[1;34m(a, b, dense_output)\u001b[0m\n\u001b[0;32m    133\u001b[0m     \"\"\"\n\u001b[0;32m    134\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0missparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 135\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    136\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdense_output\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"toarray\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mret\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\scipy\\sparse\\base.py\u001b[0m in \u001b[0;36m__mul__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m    513\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    514\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 515\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'dimension mismatch'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    516\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    517\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mul_multivector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: dimension mismatch"
     ]
    }
   ],
   "source": [
    "\n",
    "proba =clf.predict_proba(Xte)\n",
    "y_pred = clf.predict(Xte)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "你输入的文章有0.699的概率属于军事\n",
      "你输入的文章有0.075的概率属于高考\n",
      "你输入的文章有0.225的概率属于NBA\n"
     ]
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    y='{:.3f}'.format(proba[0][i])\n",
    "    print('你输入的文章有'+y+'的概率属于'+list1[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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