第6章 逻辑回归.ipynb

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    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#线性回归\" data-toc-modified-id=\"线性回归-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>线性回归</a></span><ul class=\"toc-item\"><li><span><a href=\"#model：\" data-toc-modified-id=\"model：-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>model：</a></span></li><li><span><a href=\"#strategy：\" data-toc-modified-id=\"strategy：-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>strategy：</a></span></li><li><span><a href=\"#algorithm-：\" data-toc-modified-id=\"algorithm-：-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>algorithm ：</a></span></li></ul></li><li><span><a href=\"#逻辑斯提分布\" data-toc-modified-id=\"逻辑斯提分布-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>逻辑斯提分布</a></span><ul class=\"toc-item\"><li><span><a href=\"#逻辑分布代码实现\" data-toc-modified-id=\"逻辑分布代码实现-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>逻辑分布代码实现</a></span></li></ul></li><li><span><a href=\"#问题：\" data-toc-modified-id=\"问题：-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>问题：</a></span></li><li><span><a href=\"#model\" data-toc-modified-id=\"model-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>model</a></span></li><li><span><a href=\"#strategy-&amp;-algorithm：——>-MLE(极大似然估计)\" data-toc-modified-id=\"strategy-&amp;-algorithm：——>-MLE(极大似然估计)-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>strategy &amp; algorithm：——&gt; MLE(极大似然估计)</a></span></li><li><span><a href=\"#牛顿迭代法逻辑斯蒂回归代码实现\" data-toc-modified-id=\"牛顿迭代法逻辑斯蒂回归代码实现-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>牛顿迭代法逻辑斯蒂回归代码实现</a></span></li><li><span><a href=\"#鸢尾花数据集的逻辑回归\" data-toc-modified-id=\"鸢尾花数据集的逻辑回归-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>鸢尾花数据集的逻辑回归</a></span><ul class=\"toc-item\"><li><span><a href=\"#用自编函数\" data-toc-modified-id=\"用自编函数-7.1\"><span class=\"toc-item-num\">7.1&nbsp;&nbsp;</span>用自编函数</a></span></li><li><span><a href=\"#用sklearn封装好的函数\" data-toc-modified-id=\"用sklearn封装好的函数-7.2\"><span class=\"toc-item-num\">7.2&nbsp;&nbsp;</span>用sklearn封装好的函数</a></span></li></ul></li><li><span><a href=\"#逻辑回归中参数的含义\" data-toc-modified-id=\"逻辑回归中参数的含义-8\"><span class=\"toc-item-num\">8&nbsp;&nbsp;</span>逻辑回归中参数的含义</a></span></li><li><span><a href=\"#cohen-kappa系数-&amp;-ROC曲线曲线\" data-toc-modified-id=\"cohen-kappa系数-&amp;-ROC曲线曲线-9\"><span class=\"toc-item-num\">9&nbsp;&nbsp;</span>cohen kappa系数 &amp; ROC曲线曲线</a></span><ul class=\"toc-item\"><li><span><a href=\"#cohen-kappa系数：\" data-toc-modified-id=\"cohen-kappa系数：-9.1\"><span class=\"toc-item-num\">9.1&nbsp;&nbsp;</span>cohen kappa系数：</a></span></li><li><span><a href=\"#ROC曲线\" data-toc-modified-id=\"ROC曲线-9.2\"><span class=\"toc-item-num\">9.2&nbsp;&nbsp;</span>ROC曲线</a></span></li></ul></li><li><span><a href=\"#泰坦尼克号数据集(titanic.csv)逻辑回归分析\" data-toc-modified-id=\"泰坦尼克号数据集(titanic.csv)逻辑回归分析-10\"><span class=\"toc-item-num\">10&nbsp;&nbsp;</span>泰坦尼克号数据集(titanic.csv)逻辑回归分析</a></span><ul class=\"toc-item\"><li><span><a href=\"#数据描述性分析\" data-toc-modified-id=\"数据描述性分析-10.1\"><span class=\"toc-item-num\">10.1&nbsp;&nbsp;</span>数据描述性分析</a></span></li><li><span><a href=\"#逻辑回归参数估计\" data-toc-modified-id=\"逻辑回归参数估计-10.2\"><span class=\"toc-item-num\">10.2&nbsp;&nbsp;</span>逻辑回归参数估计</a></span></li><li><span><a href=\"#训练误差\" data-toc-modified-id=\"训练误差-10.3\"><span class=\"toc-item-num\">10.3&nbsp;&nbsp;</span>训练误差</a></span></li><li><span><a href=\"#测试误差\" data-toc-modified-id=\"测试误差-10.4\"><span class=\"toc-item-num\">10.4&nbsp;&nbsp;</span>测试误差</a></span></li><li><span><a href=\"#用sklearn进行逻辑回归\" data-toc-modified-id=\"用sklearn进行逻辑回归-10.5\"><span class=\"toc-item-num\">10.5&nbsp;&nbsp;</span>用sklearn进行逻辑回归</a></span></li><li><span><a href=\"#准确率（为了更好展示形式使用pandas库）\" data-toc-modified-id=\"准确率（为了更好展示形式使用pandas库）-10.6\"><span class=\"toc-item-num\">10.6&nbsp;&nbsp;</span>准确率（为了更好展示形式使用pandas库）</a></span></li><li><span><a href=\"#ROC和AUC\" data-toc-modified-id=\"ROC和AUC-10.7\"><span class=\"toc-item-num\">10.7&nbsp;&nbsp;</span>ROC和AUC</a></span></li></ul></li></ul></div>"
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    "# 线性回归\n",
    "* ## model：\n",
    "$$ Y = \\beta x+ \\epsilon$$\n",
    "\n",
    "$$\\begin{bmatrix}{y_1}\\\\{y_2}\\\\{\\vdots}\\\\{y_m}\\end{bmatrix}=\n",
    "\\begin{bmatrix}\n",
    "{x_{11}}&{x_{12}}&{\\cdots}&{x_{1n}}\\\\\n",
    "{x_{21}}&{x_{22}}&{\\cdots}&{x_{2n}}\\\\\n",
    "{\\vdots}&{\\vdots}&{\\ddots}&{\\vdots}\\\\\n",
    "{x_{m1}}&{x_{m2}}&{\\cdots}&{x_{mn}}\\\\\n",
    "\\end{bmatrix}\n",
    "\\begin{bmatrix}{x_1}\\\\{x_2}\\\\{\\vdots}\\\\{x_m}\\end{bmatrix}+\n",
    "\\begin{bmatrix}{\\epsilon_1}\\\\{\\epsilon_2}\\\\{\\vdots}\\\\{\\epsilon_m}\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "* ## strategy：\n",
    "平方损失 $E(f)=\\frac{1}{n}\\sum_{i=1}^N(y_i-\\hat y)^2 =\\frac{1}{n}\\sum_{i=1}^N(\\epsilon)^2 $\n",
    "\n",
    "* ## algorithm ：\n",
    "微分法——>最小二乘法得到**显式解**\n",
    "$$\\hat \\beta _{LS}=(X^TX)^{-1}X^TY$$"
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"
    }
   },
   "cell_type": "markdown",
   "id": "3624f512",
   "metadata": {},
   "source": [
    "# 逻辑斯提分布\n",
    "设 $X$ 是连续随机变量， $X$ 服从逻辑斯谛分布是指 $X$ 具有下列分布函数和密度函数:\n",
    "$$\n",
    "\\begin{gathered}\n",
    "F(x)=P(X \\leq x)=\\frac{1}{1+e^{-(x-\\mu) / \\gamma}} \\\\\n",
    "f(x)=F^{\\prime}(x)=\\frac{e^{-(x-\\mu) / \\gamma}}{\\gamma\\left(1+e^{-(x-\\mu) / \\gamma}\\right)^2}\n",
    "\\end{gathered}\n",
    "$$\n",
    "式中， $\\mu$ 为位置参数， $\\gamma>0$ 为形状参数。分布函数曲线以点 $\\left(\\mu, \\frac{1}{2}\\right)$ 为中心对称，曲线在中心附近增长速度较快，在两端增长速度较慢。形状参数 $\\gamma$ 的值越小，曲线在中心附近增长得越快。\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "416321d6",
   "metadata": {},
   "source": [
    "* ## 逻辑分布代码实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "caeb4263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Logistic CDF')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np#加载相关包\n",
    "from scipy.stats import logistic\n",
    "import matplotlib.pyplot as plt\n",
    "fig,ax = plt.subplots(1, 2, figsize=(8, 4))#指定图为两个子图一起展示的形式，并确定画布大小\n",
    "x = np.linspace(-5, 5, 100)#把区间(-5,5)平均分为100份\n",
    "ax[0].plot(x, logistic.pdf(x),linewidth=2)#画分布函数\n",
    "ax[0].vlines(0, 0, .255, linewidth=1)#画竖直线\n",
    "ax[0].hlines(0, -5, 5, linewidth=1)#画水平线\n",
    "ax[0].set_title('Logistic Density')#加图的标题\n",
    "ax[1].plot(x, logistic.cdf(x), linewidth=2)#画密度函数\n",
    "ax[1].vlines(0, 0, 1, linewidth=1)#画竖直线\n",
    "ax[1].hlines(0, -5, 5, linewidth=1)#画水平线\n",
    "ax[1].set_title('Logistic CDF')#加图的标题"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f667986",
   "metadata": {},
   "source": [
    "# 问题：\n",
    "* 已知 $Data=\\{{(x_i,y_i)},i=1,2,..,N\\}$，$x_i \\in R^n$， $ y_i\\in\\{0,1\\}$为分成的类别数，对于新的实例 $ x\\in R^n$，如何确定 $y$ ?"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "id": "224e1fe5",
   "metadata": {},
   "source": [
    "# model\n",
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99945b5d",
   "metadata": {},
   "source": [
    "* 根据  $Data=\\{{(x_i,y_i)},i=1,2,..,N\\}$ 学习出 $w$ ，使得\n",
    "$$ Y=y=  \\underset{y}{argmax}P(Y=1|x)=\\frac{exp(w·x)}{exp(w·x)+1}$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aca3fb34",
   "metadata": {},
   "source": [
    "# strategy & algorithm：——> MLE(极大似然估计)\n",
    "\n",
    "逻辑斯谛回归模型学习时, 对于给定的训练数据集 $T=\\left\\{\\left(x_1, y_1\\right),\\left(x_2, y_2\\right), \\cdots\\right.$, $\\left.\\left(x_N, y_N\\right)\\right\\}$, 其中, $x_i \\in \\mathbf{R}^n, y_i \\in\\{0,1\\}$, 可以应用极大似然估计法估计模型参数, 从而得到逻辑斯谛回归模型。\n",
    "\n",
    "设：\n",
    "$$\n",
    "P(Y=1 \\mid x)=\\pi(x), \\quad P(Y=0 \\mid x)=1-\\pi(x)\n",
    "$$\n",
    "似然函数为\n",
    "$$\n",
    "\\prod_{i=1}^N\\left[\\pi\\left(x_i\\right)\\right]^{y_i}\\left[1-\\pi\\left(x_i\\right)\\right]^{1-y_i}\n",
    "$$\n",
    "对数似然函数为\n",
    "$$\n",
    "\\begin{aligned}\n",
    "L(w) &=\\sum_{i=1}^N\\left[y_i \\log \\pi\\left(x_i\\right)+\\left(1-y_i\\right) \\log \\left(1-\\pi\\left(x_i\\right)\\right)\\right] \\\\\n",
    "&=\\sum_{i=1}^N\\left[y_i \\log \\frac{\\pi\\left(x_i\\right)}{1-\\pi\\left(x_i\\right)}+\\log \\left(1-\\pi\\left(x_i\\right)\\right)\\right] \\\\\n",
    "&=\\sum_{i=1}^N\\left[y_i\\left(w \\cdot x_i\\right)-\\log \\left(1+\\exp \\left(w \\cdot x_i\\right)\\right]\\right.\n",
    "\\end{aligned}\n",
    "$$\n",
    "对 $L(w)$ 求极大值, 得到 $w$ 的估计值。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62dfc0bf",
   "metadata": {},
   "source": [
    "* 求出**梯度** （对数似然函数在该点的**一阶**导数）：$$g=\\frac{\\partial lnL(w)}{\\partial w}= \\sum_{i=1}^{n}[y_i-\\frac{exp(w·x_i)}{1+exp(w·x_i)}]x_i $$\n",
    "\n",
    "* 求出**黑塞矩阵**（对数似然函数在该点的**二阶**导数）：$$H=\\frac{\\partial^2 lnL(w)}{\\partial w^2}= -[\\frac{exp(w·x_i)}{1+exp(w·x_i)}][\\frac{1}{1+exp(w·x_i)}]x_i^Tx_i $$\n",
    "\n",
    "* 使用**牛顿法**：得到迭代公式为 $$w^{(k+1)}=w^{(k)}-H^{-1}g$$\n",
    "迭代到收敛停止或人工设定停止迭代的次数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d51debf",
   "metadata": {},
   "source": [
    "# 牛顿迭代法逻辑斯蒂回归代码实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "31f0818a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 [0.38743095 0.74209881] 0.24655757814188128\n",
      "2 [0.39457986 0.73107965] 0.24385616592398154\n",
      "3 [0.40144185 0.72001873] 0.24125737133127184\n",
      "4 [0.40802464 0.70891688] 0.23875463570049013\n",
      "5 [0.41433572 0.69777521] 0.23634186294003107\n",
      "6 [0.42038233 0.68659511] 0.2340133892528616\n",
      "7 [0.42617157 0.67537821] 0.23176395415780043\n",
      "8 [0.43171037 0.66412636] 0.22958867293346297\n",
      "9 [0.43700552 0.6528416 ] 0.22748301055290449\n",
      "10 [0.44206372 0.6415262 ] 0.22544275713400658\n",
      "11 [0.44689157 0.63018254] 0.22346400489835133\n",
      "12 [0.45149561 0.61881317] 0.22154312660755734\n",
      "13 [0.45588227 0.60742076] 0.21967675542905446\n",
      "14 [0.46005795 0.5960081 ] 0.217861766171598\n",
      "15 [0.46402899 0.58457806] 0.216095257823309\n",
      "16 [0.46780167 0.57313357] 0.21437453732071352\n",
      "17 [0.4713822  0.56167764] 0.21269710447539336\n",
      "18 [0.47477677 0.5502133 ] 0.2110606379848293\n",
      "19 [0.47799148 0.53874364] 0.20946298245534545\n",
      "20 [0.4810324  0.52727174] 0.20790213636735388\n",
      "21 [0.48390552 0.51580068] 0.20637624091605855\n",
      "22 [0.48661678 0.50433353] 0.20488356966417104\n",
      "23 [0.48917203 0.49287337] 0.20342251894683436\n",
      "24 [0.49157706 0.4814232 ] 0.20199159897270985\n",
      "25 [0.49383758 0.46998601] 0.20058942556895304\n",
      "26 [0.49595922 0.45856474] 0.1992147125215051\n",
      "27 [0.49794751 0.44716225] 0.19786626446571368\n",
      "28 [0.49980789 0.43578137] 0.1965429702857173\n",
      "29 [0.50154569 0.42442482] 0.1952437969842735\n",
      "30 [0.50316617 0.41309528] 0.19396778398775305\n",
      "31 [0.50467444 0.40179533] 0.1927140378538621\n",
      "32 [0.50607553 0.39052745] 0.1914817273522928\n",
      "33 [0.50737434 0.37929407] 0.19027007889093292\n",
      "34 [0.50857566 0.36809749] 0.18907837226250698\n",
      "35 [0.50968416 0.35693995] 0.18790593668857422\n",
      "36 [0.51070437 0.34582355] 0.1867521471396847\n",
      "37 [0.51164071 0.33475034] 0.18561642091221234\n",
      "38 [0.51249747 0.32372224] 0.1844982144439402\n",
      "39 [0.51327882 0.3127411 ] 0.18339702035189553\n",
      "40 [0.51398879 0.30180863] 0.18231236467722137\n",
      "41 [0.51463129 0.29092649] 0.18124380432304385\n",
      "42 [0.51521008 0.28009622] 0.18019092467235664\n",
      "43 [0.51572883 0.26931925] 0.17915333737391126\n",
      "44 [0.51619105 0.25859695] 0.17813067828497925\n",
      "45 [0.51660013 0.24793057] 0.17712260556064943\n",
      "46 [0.51695934 0.23732128] 0.17612879788005142\n",
      "47 [0.51727183 0.22677017] 0.17514895280055548\n",
      "48 [0.51754062 0.21627823] 0.17418278523160796\n",
      "49 [0.5177686  0.20584637] 0.1732300260204097\n",
      "50 [0.51795856 0.19547543] 0.17229042064215555\n",
      "51 [0.51811316 0.18516615] 0.17136372798801763\n",
      "52 [0.51823496 0.17491922] 0.17044971924448404\n",
      "53 [0.5183264  0.16473524] 0.1695481768580636\n",
      "54 [0.51838981 0.15461473] 0.168658893579731\n",
      "55 [0.51842743 0.14455818] 0.1677816715838324\n",
      "56 [0.51844136 0.13456597] 0.16691632165648504\n",
      "57 [0.51843365 0.12463845] 0.16606266244880413\n",
      "58 [0.51840621 0.1147759 ] 0.1652205197905666\n",
      "59 [0.51836089 0.10497855] 0.16438972606018112\n",
      "60 [0.51829942 0.09524656] 0.1635701196070797\n",
      "61 [0.51822346 0.08558006] 0.162761544222875\n",
      "62 [0.51813458 0.07597911] 0.16196384865784622\n",
      "63 [0.51803428 0.06644374] 0.16117688617952006\n",
      "64 [0.51792395 0.05697394] 0.16040051417030818\n",
      "65 [0.51780494 0.04756965] 0.1596345937613461\n",
      "66 [0.51767851 0.03823077] 0.158878989499851\n",
      "67 [0.51754584 0.02895716] 0.15813356904748307\n",
      "68 [0.51740807 0.01974867] 0.1573982029073485\n",
      "69 [0.51726624 0.01060508] 0.15667276417743217\n",
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    {
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      "329 [ 0.61096086 -1.18440668] 0.10458554539783521\n",
      "330 [ 0.61141689 -1.18674974] 0.1045547376306982\n",
      "331 [ 0.61187201 -1.18908407] 0.10452421940594611\n",
      "332 [ 0.61232621 -1.19140972] 0.10449398750468947\n",
      "333 [ 0.6127795  -1.19372674] 0.10446403874974833\n",
      "334 [ 0.61323187 -1.19603517] 0.10443437000503915\n",
      "335 [ 0.61368332 -1.19833506] 0.10440497817497117\n",
      "336 [ 0.61413385 -1.20062645] 0.10437586020385323\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "337 [ 0.61458346 -1.20290939] 0.10434701307530943\n",
      "338 [ 0.61503214 -1.20518392] 0.10431843381170491\n",
      "339 [ 0.61547989 -1.20745009] 0.10429011947358062\n",
      "340 [ 0.61592672 -1.20970795] 0.10426206715909701\n",
      "341 [ 0.61637262 -1.21195753] 0.10423427400348714\n",
      "342 [ 0.61681759 -1.21419887] 0.10420673717851832\n",
      "343 [ 0.61726163 -1.21643204] 0.10417945389196236\n",
      "344 [ 0.61770474 -1.21865705] 0.10415242138707446\n",
      "345 [ 0.61814691 -1.22087397] 0.10412563694208042\n",
      "346 [ 0.61858815 -1.22308283] 0.10409909786967204\n",
      "347 [ 0.61902846 -1.22528367] 0.10407280151651058\n",
      "348 [ 0.61946782 -1.22747653] 0.10404674526273805\n",
      "349 [ 0.61990626 -1.22966146] 0.1040209265214968\n",
      "350 [ 0.62034375 -1.23183849] 0.10399534273845586\n",
      "351 [ 0.62078031 -1.23400767] 0.10396999139134568\n",
      "352 [ 0.62121593 -1.23616904] 0.10394486998949974\n",
      "353 [ 0.62165061 -1.23832264] 0.10391997607340382\n",
      "354 [ 0.62208435 -1.2404685 ] 0.10389530721425178\n",
      "355 [ 0.62251715 -1.24260667] 0.10387086101350923\n",
      "356 [ 0.62294902 -1.24473719] 0.10384663510248333\n",
      "357 [ 0.62337994 -1.24686009] 0.10382262714189987\n",
      "358 [ 0.62380992 -1.24897542] 0.1037988348214867\n",
      "359 [ 0.62423895 -1.25108321] 0.10377525585956397\n",
      "360 [ 0.62466705 -1.2531835 ] 0.10375188800264055\n",
      "361 [ 0.62509421 -1.25527633] 0.10372872902501705\n",
      "362 [ 0.62552042 -1.25736174] 0.10370577672839486\n",
      "363 [ 0.6259457  -1.25943975] 0.10368302894149138\n",
      "364 [ 0.62637003 -1.26151042] 0.10366048351966127\n",
      "365 [ 0.62679342 -1.26357378] 0.10363813834452358\n",
      "366 [ 0.62721587 -1.26562986] 0.10361599132359482\n",
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      "369 [ 0.62847756 -1.27175482] 0.10355071864213794\n",
      "370 [ 0.62889625 -1.27378216] 0.10352934381862715\n",
      "371 [ 0.62931399 -1.2758024 ] 0.10350815706291598\n",
      "372 [ 0.6297308  -1.27781558] 0.10348715643051039\n",
      "373 [ 0.63014666 -1.27982174] 0.103466340000399\n",
      "374 [ 0.63056159 -1.2818209 ] 0.10344570587472929\n",
      "375 [ 0.63097557 -1.28381311] 0.10342525217848841\n",
      "376 [ 0.63138862 -1.28579839] 0.1034049770591893\n",
      "377 [ 0.63180073 -1.28777678] 0.10338487868656122\n",
      "378 [ 0.63221191 -1.28974832] 0.10336495525224543\n",
      "379 [ 0.63262214 -1.29171304] 0.10334520496949517\n",
      "380 [ 0.63303144 -1.29367096] 0.10332562607288054\n",
      "381 [ 0.63343981 -1.29562213] 0.1033062168179976\n",
      "382 [ 0.63384724 -1.29756658] 0.10328697548118207\n",
      "383 [ 0.63425373 -1.29950434] 0.10326790035922731\n",
      "384 [ 0.63465929 -1.30143543] 0.10324898976910662\n",
      "385 [ 0.63506392 -1.3033599 ] 0.10323024204769979\n",
      "386 [ 0.63546762 -1.30527778] 0.10321165555152378\n",
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      "388 [ 0.63627222 -1.30909388] 0.10317495975752955\n",
      "389 [ 0.63667313 -1.31099216] 0.1031568472685632\n",
      "390 [ 0.63707311 -1.31288398] 0.10313888962202036\n",
      "391 [ 0.63747216 -1.31476935] 0.10312108526870352\n",
      "392 [ 0.63787029 -1.31664832] 0.10310343267751926\n",
      "393 [ 0.63826749 -1.31852091] 0.10308593033523619\n",
      "394 [ 0.63866376 -1.32038716] 0.1030685767462464\n",
      "395 [ 0.63905911 -1.32224709] 0.10305137043233031\n",
      "396 [ 0.63945354 -1.32410073] 0.1030343099324253\n",
      "397 [ 0.63984705 -1.32594811] 0.10301739380239754\n",
      "398 [ 0.64023963 -1.32778927] 0.10300062061481738\n",
      "399 [ 0.6406313  -1.32962423] 0.102983988958738\n",
      "400 [ 0.64102205 -1.33145302] 0.10296749743947747\n",
      "401 [ 0.64141188 -1.33327567] 0.10295114467840373\n",
      "402 [ 0.64180079 -1.3350922 ] 0.10293492931272315\n",
      "403 [ 0.6421888  -1.33690266] 0.10291884999527197\n",
      "404 [ 0.64257588 -1.33870706] 0.1029029053943108\n",
      "405 [ 0.64296206 -1.34050543] 0.10288709419332226\n",
      "406 [ 0.64334732 -1.3422978 ] 0.10287141509081171\n",
      "407 [ 0.64373168 -1.34408421] 0.10285586680011047\n",
      "408 [ 0.64411512 -1.34586467] 0.10284044804918242\n",
      "409 [ 0.64449766 -1.34763921] 0.10282515758043308\n",
      "410 [ 0.6448793  -1.34940787] 0.1028099941505218\n",
      "411 [ 0.64526002 -1.35117066] 0.10279495653017637\n",
      "412 [ 0.64563985 -1.35292762] 0.1027800435040105\n",
      "413 [ 0.64601877 -1.35467877] 0.10276525387034394\n",
      "414 [ 0.6463968  -1.35642414] 0.10275058644102529\n",
      "415 [ 0.64677392 -1.35816375] 0.10273604004125722\n",
      "416 [ 0.64715015 -1.35989764] 0.1027216135094243\n",
      "417 [ 0.64752548 -1.36162582] 0.10270730569692338\n",
      "418 [ 0.64789991 -1.36334833] 0.10269311546799638\n",
      "419 [ 0.64827345 -1.36506519] 0.10267904169956545\n",
      "420 [ 0.6486461  -1.36677642] 0.10266508328107048\n",
      "421 [ 0.64901786 -1.36848205] 0.10265123911430911\n",
      "422 [ 0.64938873 -1.3701821 ] 0.10263750811327889\n",
      "423 [ 0.64975872 -1.37187661] 0.10262388920402182\n",
      "424 [ 0.65012781 -1.37356559] 0.10261038132447087\n",
      "425 [ 0.65049603 -1.37524907] 0.10259698342429906\n",
      "426 [ 0.65086336 -1.37692708] 0.10258369446477043\n",
      "427 [ 0.65122981 -1.37859964] 0.10257051341859322\n",
      "428 [ 0.65159537 -1.38026677] 0.10255743926977506\n",
      "429 [ 0.65196006 -1.3819285 ] 0.10254447101348052\n",
      "430 [ 0.65232388 -1.38358485] 0.10253160765589017\n",
      "431 [ 0.65268682 -1.38523584] 0.10251884821406207\n",
      "432 [ 0.65304888 -1.38688151] 0.10250619171579509\n",
      "433 [ 0.65341008 -1.38852186] 0.10249363719949413\n",
      "434 [ 0.6537704  -1.39015694] 0.10248118371403722\n",
      "435 [ 0.65412986 -1.39178675] 0.10246883031864458\n",
      "436 [ 0.65448844 -1.39341133] 0.10245657608274943\n",
      "437 [ 0.65484617 -1.39503069] 0.1024444200858707\n",
      "438 [ 0.65520303 -1.39664487] 0.10243236141748747\n",
      "439 [ 0.65555902 -1.39825387] 0.10242039917691517\n",
      "440 [ 0.65591416 -1.39985773] 0.10240853247318363\n",
      "441 [ 0.65626844 -1.40145647] 0.10239676042491648\n",
      "442 [ 0.65662186 -1.4030501 ] 0.10238508216021276\n",
      "443 [ 0.65697442 -1.40463866] 0.10237349681652982\n",
      "444 [ 0.65732613 -1.40622216] 0.10236200354056785\n",
      "445 [ 0.65767699 -1.40780062] 0.10235060148815607\n",
      "446 [ 0.658027   -1.40937408] 0.10233928982414073\n",
      "447 [ 0.65837617 -1.41094254] 0.10232806772227426\n",
      "448 [ 0.65872448 -1.41250603] 0.1023169343651062\n",
      "449 [ 0.65907195 -1.41406457] 0.10230588894387553\n",
      "450 [ 0.65941858 -1.41561819] 0.10229493065840473\n",
      "451 [ 0.65976436 -1.4171669 ] 0.1022840587169948\n",
      "452 [ 0.66010931 -1.41871073] 0.10227327233632233\n",
      "453 [ 0.66045341 -1.42024969] 0.10226257074133745\n",
      "454 [ 0.66079668 -1.42178381] 0.10225195316516347\n",
      "455 [ 0.66113912 -1.42331311] 0.10224141884899794\n",
      "456 [ 0.66148072 -1.42483761] 0.10223096704201479\n",
      "457 [ 0.66182149 -1.42635732] 0.1022205970012681\n",
      "458 [ 0.66216143 -1.42787228] 0.1022103079915969\n",
      "459 [ 0.66250055 -1.42938249] 0.1022000992855316\n",
      "460 [ 0.66283884 -1.43088799] 0.10218997016320136\n",
      "461 [ 0.6631763  -1.43238878] 0.10217991991224284\n",
      "462 [ 0.66351295 -1.43388489] 0.10216994782771037\n",
      "463 [ 0.66384877 -1.43537634] 0.10216005321198698\n",
      "464 [ 0.66418377 -1.43686315] 0.10215023537469697\n",
      "465 [ 0.66451796 -1.43834534] 0.10214049363261943\n",
      "466 [ 0.66485133 -1.43982293] 0.10213082730960299\n",
      "467 [ 0.66518389 -1.44129593] 0.10212123573648174\n",
      "468 [ 0.66551564 -1.44276437] 0.10211171825099237\n",
      "469 [ 0.66584657 -1.44422826] 0.10210227419769219\n",
      "470 [ 0.6661767  -1.44568763] 0.10209290292787841\n",
      "471 [ 0.66650603 -1.44714249] 0.1020836037995085\n",
      "472 [ 0.66683454 -1.44859286] 0.10207437617712153\n",
      "473 [ 0.66716226 -1.45003876] 0.10206521943176065\n",
      "474 [ 0.66748918 -1.45148021] 0.10205613294089654\n",
      "475 [ 0.6678153  -1.45291723] 0.1020471160883518\n",
      "476 [ 0.66814062 -1.45434983] 0.10203816826422671\n",
      "477 [ 0.66846514 -1.45577804] 0.10202928886482536\n",
      "478 [ 0.66878887 -1.45720187] 0.1020204772925833\n",
      "479 [ 0.66911181 -1.45862134] 0.10201173295599589\n",
      "480 [ 0.66943396 -1.46003647] 0.10200305526954764\n",
      "481 [ 0.66975533 -1.46144727] 0.10199444365364262\n",
      "482 [ 0.6700759  -1.46285376] 0.10198589753453534\n",
      "483 [ 0.6703957  -1.46425597] 0.1019774163442632\n",
      "484 [ 0.67071471 -1.46565391] 0.10196899952057926\n",
      "485 [ 0.67103294 -1.46704759] 0.10196064650688617\n",
      "486 [ 0.67135039 -1.46843703] 0.10195235675217092\n",
      "487 [ 0.67166706 -1.46982225] 0.10194412971094031\n",
      "488 [ 0.67198296 -1.47120328] 0.10193596484315766\n",
      "489 [ 0.67229809 -1.47258011] 0.10192786161417967\n",
      "490 [ 0.67261244 -1.47395278] 0.10191981949469472\n",
      "491 [ 0.67292602 -1.47532129] 0.10191183796066185\n",
      "492 [ 0.67323884 -1.47668567] 0.10190391649325002\n",
      "493 [ 0.67355089 -1.47804594] 0.101896054578779\n",
      "494 [ 0.67386218 -1.4794021 ] 0.10188825170866027\n",
      "495 [ 0.6741727  -1.48075417] 0.10188050737933915\n",
      "496 [ 0.67448247 -1.48210218] 0.10187282109223748\n",
      "497 [ 0.67479147 -1.48344613] 0.10186519235369702\n",
      "498 [ 0.67509972 -1.48478605] 0.10185762067492371\n",
      "499 [ 0.67540721 -1.48612194] 0.10185010557193248\n",
      "500 [ 0.67571395 -1.48745384] 0.10184264656549297\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "def logistic(x):\n",
    "    return 1/(1+np.exp(-x))\n",
    "\n",
    "\n",
    "###########################################\n",
    "w = np.zeros(2)\n",
    "w[0] = 1\n",
    "w[1] = -1\n",
    "\n",
    "x_train = pd.DataFrame({'x1': np.random.uniform(-5, 5, 50), 'x2': np.ones(50)})\n",
    "y_train = np.array([np.random.binomial(1, logistic(\n",
    "    np.dot(w, x_train.iloc[i, :])), 1) for i in range(x_train.shape[0])])\n",
    "\n",
    "############################################\n",
    "eps = 1e-4\n",
    "eta = 5*1e-2\n",
    "\n",
    "k = 0\n",
    "errval = 0\n",
    "w_0 = np.random.uniform(0, 1, 2)\n",
    "max_it = 500\n",
    "w_n = w_0\n",
    "\n",
    "for k in range(max_it):\n",
    "    fx = np.array(sum([(y_train[i]-logistic(np.dot(w_n, x_train.iloc[i, :])))\n",
    "                  * x_train.iloc[i, :] for i in range(x_train.shape[0])]))\n",
    "    dfx = -sum([logistic(np.dot(w_n, x_train.iloc[i, :]))*(1-logistic(np.dot(w_n, x_train.iloc[i, :])))\n",
    "               * np.dot(x_train.iloc[i, :], x_train.iloc[i, :]) for i in range(x_train.shape[0])])\n",
    "    w_n = w_n-eta*fx/dfx\n",
    "    errval = np.sum([(y_train[i]-logistic(np.dot(w_n, x_train.iloc[i, :])))\n",
    "                    ** 2 for i in range(x_train.shape[0])])/50\n",
    "\n",
    "    #print(\"%3d [%22.15f %22.15f] %25.15f\" % (k+1,np.array(w_n),errval))\n",
    "    print(k+1, np.array(w_n), errval)\n",
    "    if errval <= eps:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aec20859",
   "metadata": {},
   "source": [
    "# 鸢尾花数据集的逻辑回归"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6fcee67",
   "metadata": {},
   "source": [
    "* ## 用自编函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e2a13976",
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "#加载相关包\n",
    "from math import exp\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 定义加载数据函数\n",
    "def create_data():\n",
    "    iris = load_iris()#加载鸢尾花数据集\n",
    "    df = pd.DataFrame(iris.data, columns=iris.feature_names)#创建dataframe，列为鸢尾花数据集的特征\n",
    "    df['label'] = iris.target#定义因变量y\n",
    "    df.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'label']#定义列名\n",
    "    data = np.array(df.iloc[:100, [0,1,-1]])\n",
    "    # print(data)\n",
    "    return data[:,:2], data[:,-1]#返回前两列的x，以及y的列\n",
    "\n",
    "X, y = create_data()#调用加载数据函数\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)#按照3:7将数据拆分成测试集和训练集\n",
    "\n",
    "class LogisticReressionClassifier:#定义逻辑回归类\n",
    "    def __init__(self, max_iter=200, learning_rate=0.01):#定义最大迭代次数为100，学习率为0.01\n",
    "        self.max_iter = max_iter\n",
    "        self.learning_rate = learning_rate\n",
    "\n",
    "    def sigmoid(self, x):#定义sigmoid函数\n",
    "        return 1 / (1 + exp(-x))\n",
    "\n",
    "    def data_matrix(self, X):#定义数据矩阵\n",
    "        data_mat = []\n",
    "        for d in X:\n",
    "            data_mat.append([1.0, *d])\n",
    "        return data_mat\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        # label = np.mat(y)\n",
    "        data_mat = self.data_matrix(X)  # m*n\n",
    "        self.weights = np.zeros((len(data_mat[0]), 1), dtype=np.float32)\n",
    "\n",
    "        for iter_ in range(self.max_iter):\n",
    "            for i in range(len(X)):\n",
    "                result = self.sigmoid(np.dot(data_mat[i], self.weights))\n",
    "                error = y[i] - result\n",
    "                self.weights += self.learning_rate * error * np.transpose(\n",
    "                    [data_mat[i]])\n",
    "        print('LogisticRegression Model(learning_rate={},max_iter={})'.format(\n",
    "            self.learning_rate, self.max_iter))\n",
    "\n",
    "    # def f(self, x):\n",
    "    #     return -(self.weights[0] + self.weights[1] * x) / self.weights[2]\n",
    "\n",
    "    def score(self, X_test, y_test):#定义计算score的函数来评判模型好坏\n",
    "        right = 0\n",
    "        X_test = self.data_matrix(X_test)\n",
    "        for x, y in zip(X_test, y_test):\n",
    "            result = np.dot(x, self.weights)\n",
    "            if (result > 0 and y == 1) or (result < 0 and y == 0):\n",
    "                right += 1\n",
    "        return right / len(X_test)#正确判断个数/测试集所有数据个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3664f6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression Model(learning_rate=0.01,max_iter=200)\n"
     ]
    }
   ],
   "source": [
    "lr_clf = LogisticReressionClassifier()#加载逻辑回归模型\n",
    "lr_clf.fit(X_train, y_train)#训练集数据带入模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ec605b2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_clf.score(X_test, y_test)#在测试集上计算模型的好坏"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35255f30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x15492d072e0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_ponits = np.arange(4, 8)#在4到8之间绘制散点\n",
    "y_ = -(lr_clf.weights[1]*x_ponits + lr_clf.weights[0])/lr_clf.weights[2]#可视化分割直线即逻辑回归模型\n",
    "plt.plot(x_ponits, y_)#画图\n",
    "\n",
    "#lr_clf.show_graph()\n",
    "plt.scatter(X[:50,0],X[:50,1], label='0')#绘制类别为0的点\n",
    "plt.scatter(X[50:,0],X[50:,1], label='1')#绘制类别为1的点\n",
    "plt.legend()#加上图例"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c86b91f",
   "metadata": {},
   "source": [
    "* ## 用sklearn封装好的函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1d1f13fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=200)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression#加载相关包\n",
    "clf = LogisticRegression(max_iter=200)#加载sklearn的逻辑回归模型，设置最大迭代次数为200\n",
    "clf.fit(X_train, y_train)#训练集数据带入模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8ef62d80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)#在测试集上计算模型的好坏"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6d6f91c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2.63015505 -2.72358949]] [-5.62756707]\n"
     ]
    }
   ],
   "source": [
    "print(clf.coef_, clf.intercept_)#显示逻辑回归的自变量前系数和截距"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "35e6f424",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_17384\\1685064711.py:5: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"bo\" (-> color='b'). The keyword argument will take precedence.\n",
      "  plt.plot(X[:50, 0], X[:50, 1], 'bo', color='blue', label='0')#绘制类别为0的点\n",
      "C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_17384\\1685064711.py:6: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"bo\" (-> color='b'). The keyword argument will take precedence.\n",
      "  plt.plot(X[50:, 0], X[50:, 1], 'bo', color='orange', label='1')#绘制类别为1的点\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x15492efeb50>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_ponits = np.arange(4, 8)#在4到8之间绘制散点\n",
    "y_ = -(clf.coef_[0][0]*x_ponits + clf.intercept_)/clf.coef_[0][1]#可视化分割直线即逻辑回归模型\n",
    "plt.plot(x_ponits, y_)#画图\n",
    "\n",
    "plt.plot(X[:50, 0], X[:50, 1], 'bo', color='blue', label='0')#绘制类别为0的点\n",
    "plt.plot(X[50:, 0], X[50:, 1], 'bo', color='orange', label='1')#绘制类别为1的点\n",
    "plt.xlabel('sepal length')#设置坐标名称\n",
    "plt.ylabel('sepal width')\n",
    "plt.legend()#加上图例"
   ]
  },
  {
   "attachments": {
    "image-3.png": {
     "image/png": 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"
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"
    }
   },
   "cell_type": "markdown",
   "id": "13c3116f",
   "metadata": {},
   "source": [
    "# 逻辑回归中参数的含义\n",
    "![image.png](attachment:image.png)\n",
    "![image-3.png](attachment:image-3.png)"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    },
    "image-5.png": {
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"
    },
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4OhJfTwrDY4HyRlTnMWhVIXb4Fgc6PRSbhxmDQzCpWyPY30ynx4RsqQZI7NytCgvG3fLJNKh1JD4bbhz93dBhfPuliqZAVZgTxNa5vJz8Sk2QqhYB6OkpvocdQlqH4b9DG6KxYZVM7nx+7maviG+26dARE9u4CldvZUhOSEOVSjghJHjgmdbiSMqliI6+Yqa2q7LQZn54blILzO0oF9ggVUcPhMk1VNpsdQ1j8RTiQKaxTssOwUG+bD60CRXDO7w1KhAjAw2PcinRarH373hsuk13GpNytCfiMS+qELnysyo9Grpj5F2Gffp0V0908naAQ2kJcmp47i3VPQxaNdh+NhszVl+Ci6qe1JzYs6n4SOgbIHfu/tslEO/d6SYNGHo6Kg1ReWVo4O+LufeFYUqwRuowvja24o6zQf5iE1wZUlNMar4a+GFWdzHMCO+xLwHbs0rhGeiPtyeYDqUgdz4/W96D4AbdZNOhfyP0EseC0hdj76Eqd1s6uOKRewPQydUO2qxsfHPi2lfr5X25sgzLtijQyU5uLtQi6kTNDclHLxQiTW5KLK5nD14gWys1LgqhuXrzsB7pWYWIOnUZs3+8hC9Mn5VKFqwMcVFxeHpFAlZFq5GQX7FvM3OKcDI6HQtXxWApW4FJZlcmkOctithcZinEOxLHd/HDlXwtfj6SjtRcY72MH+ZN9UFoZgYmr02Xy4zs0aFnCKa1rA+PeqVIPpuIN/cWQAsVBg0KwcQmwpWscNWTEpuKuX/lGsbjgjdmPdwIkfWKsXFZDFYJV7cqb1+8OtQHkcJlb3Z8Ml7aKj7iRniPYaGYHOAgXF5psHNrDL5PqbwbOw+KxAtNgKitlZsWxTGzRngL779YeP+rlNWk8rZ2GDmyBSb42UGdnIzpm00u4Ro0xMyhjdDWzQ4lxYX4ZWMcNphe4XVtiuXtnZETn4hnthpvsXPEI+MiMMCzGJuXxGBFlat7sUbNko4LujHcf2QOj4vaJf7+SVms0boOJ5LUeHNdDOIyiqWR5V8fGICmvm4YMdgDAcL6kkpZ1Q6hkQGY+0AkXm4thCyU4IJw9WMIWSIttm69hDf3qZEphLHG4QH40DjIZws3BIlNSnlF2KYX3qdNCP47ylcKWXlpV/CuFLLk9/j9Ihac1UCncsKAoWG4L9gXrwwNwuQe/pjcJwT3iyEMpSi6ldHlG3kZ3s849WmC7uJtj/pSORg6QVVWAj10OPmPMUWJATMMn431N4SsggIsrxqyROk66en2ngH+eHdUGOaK07gw9PUUCgt1OMcmFCIisgEMWjdgw/EMzDinQatQT7w1KgQTg1VQiX2PYowDZzlgyPBIzLvTE+H1hbyQm48V6y/grSjTp7sbpJxJwIz1V3ChGHBp4IYhbRwR5iO27QPZOfnQdQzFGz3c4C0EtZToy3hlYwZS5D9rUIYze2Mw72Qhzp1MwspEO3gEegjv0xBDmhse7VGcmYd1tzKml5cH7hTfzzg1d4WfQxmyk7PkTu3FWLPxAp5Zm4iVxucoNmmMx1rWR4N6ZchNvYLZq+Ox1Vxfhfg0bEnVQ+/oiBDf+ggXJ0/x36/HuTNpOCpvRkREZM3YdHij3Dwwuo0LmjVQIaJRfSSmqvHx36nIL5aHdXDwwHMjvFFwOgXLhBR1rc7f4vYPdAZWHzTUVqlcPdDKLQ/H0lSYODQAqlOJ+CGxhiEjKrFDw4bO8LS3R2iIM4pic3E0S1ft7+/TNwwDPTXYtv4ydstlkS390a2BBocOZOOCXCZxEEKQlxB+5EWJRouYmoawkNihe+8gRMZfvs6f+/qxicG6cf+ROTwuahebDpXHoHULxLsRJ3TxQ7/mnlLfLbEDPSmHJ2Trxv1H5vC4qF0MWspj0+EtKCktw0+H0vD+nwnoFuaBV4c2kcZSISIiIhIxaN0G0elFeO/3eByJy8PMISG4r1sj1OOjNIiIiOo8Bq3baJs49taaaLg52Utjb/W40bG3iIiIyKYwaN1meUV6LN59GUv3pWJ4ex/8Z0AQGnlwTG8iIqK6iEFLIceT1HhjbQwSMovxv/HNMEIIXURERFS3MGgpbP2xDLyy5hKa+jrj7XvD0DbQVV5DREREto5B61+QkqvFwm1J2HwyC4/2DhCmxnBzFp/pR0RERLaMQetftC86FzPWXEKxrlRqTuzfwkteQ0RERLaIQetfpi8pw48H0/C/PxOkuxJnimNv+XDsLSIiIlvEoFVLLqUX4d3f4xEVn49Xh4VgYlc/cOgtIiIi28KgVcu2nsnCjNXR8HB2kJoTu4dx7C0iIiJbwaBlAXKL9Phm92Us25+KkR188Ex/jr1FRERkCxi0LMjxRDX+b20MkrKL8d/x4RjezlteQ0RERNaIQcsCrfsnA6/+EoNmfi54a1QY2nDsLSIiIqvEoGWhLudo8PG2RPxxOgtT+wTgkTsaS89QJCIiIuvBoGXh9l3KxYzVl6DVl+K/45vhLo69RUREZDUYtKyArqQMKw6m4cOtiegV7oGZQ0IQ5uMsryUiIiJLxaBlRS6mFeKdTfH4J0GN1+8JxQRx7C15HREREVkeBi0rtEUae+sSPOs7SM2J3Tj2FhERkUWy/aDl4Iw7+4Th04lB6CwXWQY3PDm+Ob4aH4i2ckmNujbF8qmtMK+rvCzIKdTj678vY/mBVNzb0TD2lp87x94iIiKyJHZlAnneokxefEaeu0UOHnhRCDOdXIHs+GS8tDUPWnnVVUUEYG4rJ3nBVAlO7k3Amgx58aa548X7g9EJ+fjox0QclUvNEoNWe2fEHT+DNw7LZVWM7ugrBa5tZ7Lww4E0uVQWGIj/9XcT/sYbUYJjOy7hi2R50QKIYfO2HRf0r+P+I3N4XNQu8fdPyrL9oCVqEoxFg9zhWarBtrXRWJItl1+NHG6q0yNq6wUkd2qFETcwnmhOfCKe2ZovL4mqB62Jo2/sPSsUY+PiRGhHNMUYv3rQFGrw0a40nL5cYFhd/u8vRYHu2rvbzr4eXBxKpH/nh/FyoQXgCdm6cf+ROTwuaheDlvKsPGh54olRXgiQl2pmB48GKtTL1yCnVC6qSU42Zu/KMVOL5IhHxkVggKcWe9ddwqnWYRjoKf2J61KQlIr3jxbJS6LqQSuypT+6NTCsrcTbHUMaOyIjJQtHMuWySjQ4dCAb+cLP/LbwMzuXlUCnLcOumHysPpKOAv8AQ9DKzMDktenyn6lZ50GReKEJGLTotuL+I3N4XNQuBi3lWXnQ8sO8qT4IlZduC2MYqRq03Brj/fuEUJefizdXJSPWsPUtkIOWgwaJhaU4tjUWq3LlVVVdR9OhgR269wvH481UcC7VIz0pD85+Hvg5Ro9xrZwZtKhWcf+ROTwuaheDlvJsI2gVXkc/J4HK1RlBLnYozCtCqkYuNDI2r9UQtFTC8jfCck50HJ77q1D+QzfBwQtP3uOJ0jMZcO0qBq0SqB3sUb8oH5+v1mDEwzceHCs3Swpha1AEnmriAHtdMfbuSYV3h2CEe9nDMTcHk1dflrerGYMWKYH7j8zhcVG7GLSUZ2N3HdrhrmHN8fV9oZgSXP1xNW17hUjPDnymnVxw3ewwpLET7KFHfMwthCxRoBta+9ZHsLHZUV+IX04WQ+fqjvvv1GPnqSz8UXVK0Umbik2H1dYJ0/ZLxdJ6gzIc3JqEA/llKCkpRXJGIeYdKRR+coFHA4zv4idtRURERMqz8qClNgSTs7mIk5brw8+hDCo3FwweHImvxwdjqPd1DumZmYvt4nudU8sFJhy80amh8D6FRdgV74KBPfwx+Wamli5AA3u4CLEny6QFL+twIlYcu4IfThYhJjoX+6pOeYaOZcV5ZtYJ03G1A0JcTf+dhfjij3jMWx2HDXJzpFSBl5ONhq6Gsbe6hnLsLSIiIqXZ5F2Hbo18MO1Ob7QXQo19aQku/hONuf/opdquxwLsr6Ovk8C06VATiu+6CfHocgqm/e548/3CMjPwXLo7FrYEtq9Mh9dIk87wxqZLedMbVf2uRhNVmkU7hrhjQhc/JGUX4+cj6biSb6gxM2LTISmB+4/M4XFRu9h0qDybCVqqFo3woBBaVp4rhKFOyg6hbYLxQhvgxzUJOKg3Dp9QgtN7zuO9c9JGNTMJWm9lBOODu9zhpSvAtz8l4ayXCi7yZqYiuwRjciBw6kAifq4ylJVEo0WT3s3wmE8xvl2WhY6mdx26eWB0Gxe4ypveqIK0TKyNrRyYyhmDlvD3R+eVyIWAl4sDvFwdkVWgkwZANXJycUaQK4d3oNuL+4/M4XFRuxi0lGcjfbQ88GwXb/TrHYpFDzTF0+1d4IYyxJ1KwHMrDSGrQhk0pqMsXAdtbCIWxWhR4uSKsXe5IPVKEWLMTFfkv0ebb359TJ4KLdzthcCll5s6TajzsPZAJi5U7aR/LZpCbD6QWj1kNXJDh6rjrTqpEO5bv3xqKIQsscHRW3g1LQ+q1AxJREREN8tGglYePlmbhD/T9Sh1ckbPrmLgCsPUcNNH0gjBQlosRZE8jueNOL8nE+eEEOQZ6IORDnIhVIhoVL3TfQU7uDmZhpYifLHqPF7enFrD8BDO6N2yIYa0uYGpZQOzzZj3dAvGy/c1wzMR5T+sYegK4crxo3gxEYoDr54xWRZKdDppZPnPEyslUyIiIrpJNnPXobYgDz9suIDp69MQlVsCu/oq+Dqa1vI4w1Oq4SlF1s08PkefjS2pQgBxcEbX9oYiVYdAvDYiEp8NMtexvAGev78FvpwQjN5yiUEJUk2a78wSkuDy9bGYddUpFadqqplz8EFX8SYARzuU5Qs/s9h/SwhUxjG0PIRyc5LPRUNfCjzgA3yzO92img2JiIiskc0ELSNtZiY+XH0BszfG44NzJt3PfFzgK9ZoFWhxs70BjmbphJhkB6+G4lMD7TA6zBkqIbglxOUZNqgkD8fFYeidXDGil6Ncdp3s7RER3gC9rjq5wKOGyjRVRw+EOQiRLicfK1PlwnJ2CHAW/6AeaVWeY1hWWio9pPqT7Um4M8ITMwaHoIm3uccQERER0fWw7qAldlif2srM1BLzRjTFd6Zl9zaANIKUqwdmmpZXmyLxYhPp3avL0KP8vj63RujkZQeoC7DuolxWSRn+2puHy6V2CAz1R2e59LqonNHdXDNhpckDIaYto+UcMamJOOZXGWIvpaH6E3s8EeImvGhLcLmGFsLzqYV4+7c4nEhSY9aIUI69RUREdJOsO2jpS1GgKbn2pC2DobGuDMXm1leaSlFUQwDxDlJJwy+IN2o27+qOwHplSE5Iw3nD6upyU7EnowxwccPorjdQqyWOdC829V11SkSUubFT/YUA6CkEQE0Bth0zc0NpEzcECgGtJL8Ie+Wimvx5OgszVkdLnebfHxeOLqFiTR4RERFdL5scR6sSBw88OSYAd3gImbJAjR92JOPPtGv0kRLJwzskZ6phV2qPIiGT+ng7oUG9Epzck4n6PfzQTCj9eXksNsjBzOz4U/5BWDjcA97qPLy9MskklFV/qHTF8w+LcfBCIbKlspo4oFWkB0L0lR8/1Htoc0wPtEem2UcF2WHkyBaY4Ackn7mEmfsMfdgMP7dDtfHF7h3aDG2Ks/Dr7iw4BxrG3krIKsbPh9ORoa5hKAkFiTWOt+24oH8d9x+Zw+Oidom/f1KWzfXRMuUW7I+37wuUQlZJaRlKXN0wZUQkvrs/DFNbiENAXJsuuwQO0rAHQshCCeLPJmGTv6fUByozPq08ZNUoNQ1ROUKWdXPDxHbmO6FXc7NNh27+GNHYHijVIOpw9equxl3DMNJPrO0qxB+HrhWU7OBaX4UW4V7o7iyEx/h8vPpLNFJztdLI8sPaesvbERERUU1sMmi5NfLC1JGRWDS4IcKEkFCYlY0Fy87hhW0ZOJlTinou9aUxtz57MAJv9PFEqMkICNUUJONFqalOmL47j/876IwxTVSw1xdjn5kwU50OS48XQC38qsMj/dFYLr2qm2w67NzTA4HCHtVm5OKnSk8SskNo9zDMbesMZ5TiwvEE/HWtgCjE0EZiP/jSUuSbvNevUVcwa30MWjZ2wewRYcLrzQ6xSkREZPtsJmipXF3Rv0cw3rm/JT4b0Rj9/Bxgp9cj+nQiXvw1BWf0ZciKS8eCNefxzMZ07M/Uo8TBES2aB2Deg83x/lA/dL2egTpd9bicrUdm8hWsqhRm5MFIzbmYgf2phdh2JMNM5/Sb5QAn04Do4IMRgWJBCc6fzYDWUCqUO2Po0GaY07Y+XOqVIu5kHOafqNxarJUXnZ0rqshU3u7wF4NWka5aH7TELA0+2JKIHeey8VS/QDzUyx/1VTZdOUpERHRTrLqPVmjbIEyOcEZjd0c0MBkbSq/R4lJcBpbvz0HcVWpuVK4emDjAH3f5OEDKCaVlyL6SgQ82XkGc6bMOa3guoqp7U3zX1glabSn09erBxUH4GUo12PpzNJaaeTZ1ZVfpo1WpzKBtv2Z4JtgkyAl/n6vwby7JzMDja9OFYCU+cigIT0eWYvGvyVI48m4RhFe7uaOxSvy59DgXFYd5x8ojWIWIEHzZ181sU6o6ORnTN8tPpjbD2bGedFdi9zAPrD6Sjl0XcuQ1tx/7clg37j8yh8dF7WIfLeVZdTXEZXU9+DdUSSFLHNX8cnIeVm27iGk/XMK83VcPWaLyQU5XJeHPVB0KhbDiWKLHZXn9tWiTNEjVlEInRNWyklIUFGkQdSDxOkLWjTuZW+UfU1qK3Hw1ftkhhiyR+MihRMyQQ5YoM6UEZQ5AYW4+Vqy/aD5kiS4mYv6BPJy7UoRok+lcdBrmb605ZImKdaX4YX8qFu1IQt/mnnh5cAhCGnLsLSIiIpGV33Voh1aRnihLzsHZglv/Z6hcXRBgV4g4MSg5qdDUwx6FeUVCmDKsv73s0FAIJJ7QIylLV9HUd5u5uToKgVK5969K7Kgv3p34+8lMrDl6RS69PXjla924/8gcHhe1izVayrPyjjVlOHMh+7aELJG2QA5ZIo1WehC0MiFLVIasrCLEKBiyROp/MWSJ/jhlGHvLx12FBeLYW0049hYREdVd7MFMt12mEO6+3Jksjbcl9t96sl8gvN1u8DFERERENoBBixRzND4fM3+JRnq+YeytoW049hYREdUtDFqkuF+OXsGc9bFoFeAqPTuxRWMXeQ0REZFtY9Cif4X46J4PtiRg5/kcPHNXEB7s6S8NDUFERGTL+E1H/6q/L+RgxupL0rzYnHhnpPiYbiIiItvEoEX/uiJdKZbtT8XnfyWhX3NPvHR3CII59hYREdkgBi2qNWdTCvHWxjicSSnAW6PCMLazr7yGiIjINjBoUa3bfDJTak70c1dh/thwdObYW0REZCMYtMgiZKh1+GJnMtYcTceErn6Y3i8QDV059hYREVk3Bi2yKEfi8jFzTTQy8nX43/hw6ZE+RERE1opBiyySWLM1d2Mc2gS64c3hoWjhz7G3iIjI+ljsQ6WJiIiIrB1rtIiIiIgUwqBFREREpBAGLSIiIiKFWGwfrZOxOfIckUHbME8eF1aM+4/M4XFRu8TfPymLNVpERERECmHQIiIiIlIIgxYRERGRQhi0iIiIiBTCoEVERESkEAYtIiIiIoUwaBEREREphONoWbLU3VixbAeuIBR3TXsI7ev4cCdWM96OJgexZ/fin71HkFAgLPu2Rp8evdGmpT+cDFtU0KuRevEEzpzYjxPxaqGgESL79UbX9m3gW21jkQZXzh7F4QNbceGKsHi197YwdXK8JM15bFu8EqcaDcfUcZ3hJheboy+Ixtm9e3HgWCwKhC1DOvRE+85dEe7tKG9hm6z5uLCFfcZxtJTHoGXBopfcgdFvnZHmO74XhaUTw6T5usryT8j5uLDmRbzwxhokauUiE653zsf3Hz+BFvJ5TX38E0yfPBsnxDBWlesAzFz5DR5o7SUXCCf1zL/wydhJWJKgkUsquN65CCsXP4AmDnKBBaprQSv39DLMm/Yc/kwRFjovxKbVDyLYsKoKDaLXP4NnZ5o5bq7652yDdR4XtrPPGLSUx6ZDi3UeB1YJIat5K0QIS/8s2YJowwqyWBk4tWoN0tzuwMg3v8GXS3/B/GntoJLXFvz9Kp749jD08nL2ud+FkOWG8MEvYM6Xv+DLL+djWIhcL1WwHQumfY+zhiUhZZ3G8slj5JAViF4vi+//DZ7tEyitLvj7GTz+2dHy96ZapL+C00vGYciI5/CXYxj85eKaJK4Zj4kvCMeN/6P4aGssDsdk40RMKvb8vRtfPt8ZFVGbLAX3Gd0IBi1LdW4LVp0HOj78NZ4dLSyfX4YDFw2ryFL5oMvs/dhy4DfMe2QcevXpj2GvrsOC4fJqQfY3v+G4PO/VfT7WHo7B2i9mYczd/dHr7icwf+Wn6C2vR8oWnE0wzOoPr8Ai4XgQhb7yIxY9Jb7/OEz99kc819RQnvrFUhwQWx+pVl1e9xgmvbUHwdPWYP2fCzFQLjdLvQ3fvrEb2oaP4dN1H2BAuKfcBOwEj6A26HVH66s2N1It4D6jG8SgZaFO/fkJ4tAdo3q2RucRU6DCGazaeVpeS5bJHSFtWqBhpeY7L0Te0V2eF2h1KJFn3UI7VO/L4e+PUHlWDG4uLoa5lIQoGFsoerdvh/K/wqEduo/wMcxrN+Mkqz1rnVODnnh21Un8+OoABF6j41zmziX4Vdixvd98CT3ZgmMVuM/oRjFoWSL9Yfz1QwbQ+X50DRG+kLuMxHAVEPfVbzglb0LWo1Rv0qDXsAHk7GSGDlm71mOTvKS6eww6yRnqahr4h8tzGUjKzJfnqbZ4D3oNU7v6VoThGulw4ZC4tzujX7vGhiKycNxndOMYtCyQ/sSfWJMFdBzXx9Ch0q07Bt4rXBpnLcZuY7sTWYkUnNp+VJ4HvCb1Rwt5XqQ+vgIfvz9Xmt6Y1Ap3P/ItsoVy145v4ut3RsDPsBlcXCsS157jJ0z6YumgLa5YSroiBHSyEklIkTrhdUNosBqpZ7fj188Mx8I3qzbhZJyafe4sDvcZ3TgGLYujQ9Tvi4QvW7HZ0HiXoTs6DJkAFTKwcltFZ2qyfLl7PsD7O+UFYZ8+N75rpZqO7HM/4LsvP5amDQczDM2Dqna4c3gkPO0rmhW9u4xBf7lXfdz792D8ky8LJ/eX8cKIVpg4tyLIkTVJQdxJ8fUHzOjRDHffMw5zPjAcC5++NhkP9G+G6UtOg93uLAn3Gd04Bi1Loz+Ancs15c2GRsbmw+yf/sRpJi2roI9dhpemGmqoxI6y3d5bhJEm+1TU+M750t2J4vTxey+gr3jXofYENr89Bffe8zoOGe969x+BVz9/FMFS2FIj+s9vhZP7t9h+uhjBEYY7D0WO9vbyHFm+EujljnfBExdhhcnda9t/eQvdVBocemsa1vMmGAvCfUY3jkHLwqj3rMbPwge504hOqH8lHRnGqagV7hghbJC1CH8e1hk2JsuVsxsfTn4Oh+STsqrn+3hzbLNq/XYcGneQ7k4Up/4TZ+HTbXswp6fcgzrlC8woHw7CEf79P8Cm47H4Y9MmQzhbuRtbTsbgk0eDpC1EkYFVkhxZgSl4dsY4tDW5e82343/wykviwC5nsCUqViolS8J9RtePQcui5OPYHz9LzUdRc/qgf/fmJlNrvPSLuI0GP6/fxappi5aCP14dj+XiQJUCVc+F+GXpg9c3mKhDM4x8crK8AGQfOCu8mwknTwS07GUIZ93awN9V+NsuHpNX9kGkLY9saXOC0Kyz+JqFIjMf6Mg2Vx0YgmoF9xndOAYtS6Leh80/a4DmT2GB3JxUeXobA1SAdt0GHGPSslA6xK96Am9skUdvb/wkPv/sOkOWOSp7XLUxUH0AO9fJf1fze9C2onKLLJ4PvKUKyCjEJkkFlWRmyoOokQXhPqMbx6BlQTJ3/oCNwmvExAcxVG5Oqjw9hLHi3YfaH7D5EG/jt0S5+2fj8dd2y53a+2DO8rnoZnasnXQcXbMaJ9LkkGSkv4QNXyyXF4RjYVAXBEhzOmg0VZqM9Vew/+MnsTxLXHBC/6dHwjjQA1kDd7S8U6wBuYiNu09XvslFOA62LxWHEWiFuzvV7UdvWRbuM7pxDFoWIx2HfzN8SMfc0dxQVI07eox6VHqky8ZN+9h8aGnU2/DRI18gVV50axuMhNXvlg/fYJzWHRdDcgHiV03D5J5N0HfEFLwjrps1BRN69Mac/cbasGfx+jjjsXAAC1sGo2+/sXhDep+n8GiPtnjiu2Rpreudn+Cl4RzXxyJocir6Vl7JQ5FYps9DvrEstyJc+w14FpMbCl/b703Ea0v2IFZcH70HK164F/OOAv6PzsdwsdsPWQzuM7pRfKi0pUhdjad6TcOe5vOxdvMTNddM6Hfj/TYjsVx7D94/tBxDrmNAS1th8Q+fTViGh/o9h3/kxZoYHhAehNQ97+H15z/HkawqtVpwQ/i4d/De6w+WP4AaOI7Fd/TDJ5U6bAlUPujy7DL8b1rPKiPSW5668lDpy2tGYsgru+UlM6o8dDj39FeYed+r2Ffp4eLCMfDwYnzx+mD4W/h+vVXWeFzY0j7jQ6WVx6BlKXIu4OjJJKBxR3RudrVHkuqQemo3YrJdEdSxO0Lq0EO1LP+EnI2YQ/8gtWpuqsKzWR+0amwcI0uHgqw4xJxOhFjP5eTTDE1DGsPLtcqjeSQa5CVdxMXYdGHOsG1E0xB4yDcpWrq6ErT0Kcdw6JLUnmueV3N0axNY+Q5UvRqpF08gJqNY2LF+CG0egYAGVrJjb5HVHhc2ss8YtJTHoEVWo658Udsq7j8yh8dF7WLQUh77aBEREREphEGLiIiISCEMWkREREQKYdAiIiIiUgiDFhEREZFCGLSIiIiIFMKgRURERKQQBi0iIiIihTBoERERESmEQYuIiIhIIQxaRERERAph0CIiIiJSCIMWERERkUIYtIiIiIgUwqBFREREpBAGLSIiIiKFMGgRERERKYRBi4iIiEghDFpERERECmHQIiIiIlIIgxYRERGRQhi0iIiIiBTCoEVERESkEAYtIiIiIoUwaBEREREphEGLiIiISCEMWkREREQKYdAiIiIiUgiDFhEREZFCGLSIiIiIFMKgRURERKQQBi0iIiIihdiVCeR5IiIiIrqNLDZoXckplueIDHw9nXlcWDHuPzKHx0XtEn//pCw2HRIREREphEGLiIiISCEMWkREREQKYdAiIiIiUgiDFhEREZFCGLSIiIiIFMKgRURERKQQBi0iIiIihTBoERERESmEQcvSHJqFpl714Vdp8kP7gRPx8W/nkC1vRlZGdwXnf3sfDw8Ml/evJ1r2Ffbp9jho5E3Myj6O5Y9GSNvP3SeXkcVKXDHE5HNb0zQEPyXIf0ATh73LZgnHRWu09JfXB4djyJT3sSOeo6VbKt2VKKyZOQLdIjwr9tnT3+PYFZ28BVEFBi1LoymCWnjx7vMY/vP8S4ZpUgdoT23Au1M6ov+CI+BH2drE4KexEegz5V0c1HXBeGGfPjI4EPknhH06rgMmLbtoZp/qkPHPZ5jYrgdeXJskLGugLzGsIWunRYm0L+Px0/iWGP3cf/H70RhkGhO3+jKifpuN+7p3xsdH+Wm3NLpLyzC50x146uvdyG8+XjpHT2yvxakfn8Ldd72OPbwapioYtCxU+LgX8ebseYbp8y04sedz9HYCkue/j03p8kZkJbKQlDscH+64iBO7VmGBsE8XrDyGXQv7QiUEqD2vfC0EMHlTSTaOfDgQ3fq/jL2+0zDn+S5yOVm64Af+QHp2UfXpzPcYIG+DVmPRNUycKRH+a4q731yFv06lI1nYLvnCXszpI3zQRZoYvPvNVuQblsgiXMb6157AX2ofPLJG+Dz/9o10jv70txjs+rCncIJehMc/O8iLYaqEQctKODYbi+n3inP7cTFRKiKr0QUzdi3H5I6+cJRLhD2K8Ekz8IA4qzmKOLHSqtwJrH/7LJq/uQ3HDi7EsLCKP0XW6fyGD7Bdnu/2xFA0l+aaYvKG01j+4ki0DnSXjg1H3054/IWHpbWSuDRkyrNkAdL3YeM24TXiJTw0oMrnedxMPCBk5Myl23BSLiUSMWhZjWIUim2KiEDjhlIBWTtHB6jk2cra4fEjp7HhxTvgw4xl/XQH8ev/TskLg/HwoKbyvHnF2oo6LFWnpgiU58kCFOQbgu/AjmglFZhw74i+A4XXjO04E2soIhIxaFkJ3aX1WLxJmOk+Bb2lZgeyeulXYOgTHQY/H2lG5oWQcNOrZbJm+buW4LMMw7xqypMY3NgwX10xUo98jxdf+tGw6NQJsx/oxePAEp2KR/WGBT/4hIivUYhOkQqIJAxaFip6zYd4e+4bhumpu9Gu97M402UuNvwwGaHyNmTd4v74HJuFV9WUCejpbigjW3MZf361BFpp3gdPT+mPyrs6Hj+NNN6N6IV2g57C+mTAo9tMrDy4HY+3ZsyyKEFN0VHsQrd7CXZcqtITS5OC9DRphjeuUCUMWhYqc/e3+PTjDwzTT7ulO5LUR/6HuV/8jWT2tLR+2b/jv6/sF2Z6Yv4Lg6t8+ZLNOL8Wn4p9ekQRL2FU1+sLTnmHFmDqfW9gCz/slsWxFx5+X7yJZT9m9G6Ke58yXAzPnNIDLZs0xfRf5e2ITDBoWahuC0+b3LWUjQuH1uPpdloc/Wg4hn/IIR6sWza2vHY/Vmuc0HvhV5jIpmAbpcOR1fNxVl5q+djg6v160ASTNhg/53mIObULH44Oktaoz32GyYNnYz9vO7Qgjgh/cB32rZmLYc2BIz8ZLoZXH/XFwDnr8fFD4jaR7EdLlTBoWQVneEbcjdkrv5RuEU/+6Fv8zZOvlSrG+e8n4dFVGgQ+uR7fPhjBPji2KuUXfPCB3DkLg/GfkS3l+Zo4wi2wGyZ/9SvmGBNZ8kf4civHc7EszggZ8AqW7EpEknwxHHNmIz6d3gM+0hizzRHgK21IJGHQsiaNu+Ge7sKr5jxSjOdvsiI6RC+7FwNe3AX0+Rxr5vaFl7yGbI/pkA5X7wRfhWNbtOsrzwsy1dLtxmTpdP9g9zrhNaIXIv0MRUQiBi2rkoWUaPHVB26uUgFZDR2S/3wW454TQ9Z72Lz0EYSzKst2VRrSwVwn+Ku5jNSL8qygSaNKt6SShUrZ+Bm+19TUREx1GYOW1RBrQ97AQrEma+AYdOcVk1XJ3v06ht+3BMmBz2Dl0ufRllVZNs10SAf4TMdgc53gY7/HmE5j8fFvJ5CsNvS61KmTcPTTR/G8sQO9zzOY1MtDXiBLoNFUfQal+Lisj/H0Uxuh9XkCb024VhMx1TV2ZQJ53qJcyamjD1TdPQN+IxdJzzq8v7OnXAikH16KtXszoHUbhIV//YJJzepedYivp7N1Hhfp6/B4u0lYr2mArvdPRQ9zIbnZvXj+gS5yrcdl7P7ic+yUu+bkHl2KZbsz0HLkSxgojXXpjp4PzsRAK+tEb7X774Zdxprx4XhKDkst50dh1xNmvnwTlmFM+yewR16sxqk9ZmzcjBldbTuVW9dxEYPlQ1pjVkIX3DmsL8Ld1Yje8Qu2nRDOzU59MWfLT3iqnXXtL/H3T8pi0LI0h2ah6eD/Sg+WNqXyaY+BU57H80+ORQffutnmZLVf1Nf6QhX1+QpHNzyIYGlhD+b6D8JnxocMV+OEpzflYHYvedFK1JmgdXk1Huz0IP4Q95/bWHx3aDmG19A/SxP/N1Yv/QzLV/6FqBTxDhcneIe1RffJM/DalKFoXgc+69Z1XOgQ/fureG3OOhy5eFk6T7s1FkLXVOvdXwxaymPQIqtRZ76obRT3H5nD46J2MWgpj320iIiIiBTCoEVERESkEAYtIiIiIoUwaBEREREphEGLiIiISCEMWkREREQKYdAiIiIiUgiDFhEREZFCGLSIiIiIFMKgRURERKQQBi0iIiIihTBoERERESmEQYuIiIhIIQxaRERERAph0CIiIiJSCIMWERERkUIYtIiIiIgUwqBFREREpBAGLSIiIiKFMGgRERERKYRBi4iIiEghDFpERERECrErE8jzRERERHQbWWzQOhmbI88RGbQN8+RxYcW4/8gcHhe1S/z9k7LYdEhERESkEAYtIiIiIoUwaBEREREphEGLiIiISCEMWkREREQKYdAiIiIiUgiDFhEREZFCGLSIiIiIFMKgRURERKQQjgxv4TS5CYg7fwmZGmHBPRiRYSHwaeBkWFnH2M4I0tmIOfQPUl2ao1ubQDjIpebpUJAVh5jTicgXltwD2qBJkB88rPAQ4AjglWkSj+BoXJ6wU8PRuUMT1M1PtQUdF3o1suPP4exlYZ/AGd5hrRAa5FnzftHk4HLMGcRlFAsL19q+6ue4HZo28YHr1T/8/wqODK88Bi0LpU/5C5+/OA3LDmZAK5cZuXachvc/WoA+IXJBHWELX9T6zH+w6qWRWPC3Gui8EJtWP4hgeV1V0jHwzINY/I+wrSnXF7Ds5Cx0kBetBYOWiZw/8X897sNG6cPdHXN2/oExdezzbFTrx4UmHns/eRqvf7sX2VVOtqqQcZj7zSLcE2Ean7JxbsnTeGL+5urbNxyKF5Z+hgdae8klgpzDWPzYGHxS5XNsdttawKClPDYdWiB97DJMv2sMFh8sRosnv8GKreex46Awbd2K+W9OQviFr7E/Wd6YrIQOWYc/xMP9+mPBAR1UcmlNyo+Bfxpg8Kw1WP23eAwcx+qV32Dm6OZ1tvbDNuhwaulLQsjygVdDuYhqT9RXePKLvShwa4cBk5/Ho5MHI1j+gGoT1uC1ETOwxyQjJa55CJPfMoQsVchgTJz+PCb2D5M+09qszVgwdnbF9vrTWDLpbjlkuaHdWOH9x3aBq7Bk2PY/+CNV2pJsGGu0LI3+KL7oOxBfpPhg4vf7MLOvb/WmJb0aBSVucK1j37bWXCOS+cejGPzUWjh2fAEfLuqD3XeMwfIaa7RS8Mf0jnhliw8mr9iNV3rW7hXv7cIaLVnCMjzU7zmkPPctpu55DPOOskarVo+Li+ux4nIHjOtb0Xybu+dljHzwW2TLy/0/OY+Ph/sJc8L5uYtwfs4SZhu+hB8OvIH20glah3Nf3oEJ718UF4TPbbbwuQXS14/HwBe2CSVOGLH4LN7pb/gsp/82GcOe3SS1VkTMOoBfHm4uldcG1mgpjzVaFka9Zym+TRGulO5egMfNhSyRQ90LWdZOoy5Gl1k7sGnVLPRsfI2OGRc34JstGqhGf4AnbCRkkVE2di18Bf+oJuL/Hup4jf559K+IGIUHTEKWqEGPe3C3PC/KLiiQ5wqRL4YsUVgIGpbvQEe4VqueTEfUZjFkiSZj+J0Vn2W/flMwWJ6/uH4f4uV5sk0MWhYlH0c3/iBd5QyeMAji9RPZhoBxP+LLhzuanJhrdvn4JojXxYPv6YUGhiKyEfrjX+OdtRp0nDsTfVmRYLkcHIToVMHR3l6eawi/pvLs0R+x+6J4l5JAfwmHNxwzzKtGo1OEOHMeJ7ZIJUDndggy/ey7BSHC+D7HLyJNniXbxKBlUS7gzC7xtQ+6RrpLJVT3JJ3bLfy/M9qHCceAJgexx3Zg325hOnZRuLLWGTYiKxSLDfPmI7Xxq5gxNkwuI4t07hj2yLNAK9zV3tiu2xrDnr9H7mN5EPNH9MSrS9Zg8dS7MGe/GLoCMfh/r6OfjzCbEIvT0nYClT2MUc3ABR7lFVxRSEqQZ8kmMWhZFGO1dBsEBEkFVOekIytJfI3HlncGoWfLMIwaMxbTHxKmMd3Qt21TTF91HvJ1NFkR9c6P8O5RJ4x4exrasM3QgmVj1zfvIE5eUt39EgZJNVQGfsO/x2+LHzV0mNfG4ve3Hscn4l3Ewnl7+uq/8N7wZtWbhFuGIECepbqHQYvIohSgMFN8zcCxS96Y9J7JHYdfPIsWKjX2vTYW3x1nzZZV0Z/GmgU/QNv5fUyXO0STJdIhftVDeGmtfCmj6oPXZ46o0o2jFCX2DeBl2rYoOYUvH34Sq04bu9ATGTBoWaQsFFUecoXqnO54fdlKPDtxAJoH+cHHNwTNB8/FogUDhXXJ+HbzYcNmZBXS/3gPi84H4sk3JtU4bhrVvtz9s/H4a7vlsQud0G3eQowJM01UYhAbj1GPfIQTBYD/PQuxfOtWzBlpGN4BBduxYOxD+DW2yoXQ2QRclmerU6Eev4ltGnevRWmM0Pbi63aciZYKqM4JQuOW4qse+hKpoBK/dsPQRnjVmltJlkm9G0vmbAJGf4D721erBiFLkbACLz3yBQzDWgkh6+1d+Gxclb50F7/Di3IQU939NZZ89CDahXfBmI8P4pe3uxu20e7Gu1/tgtrFDeV1l9oS1PyJZVcRW8egZVGaoZXUizIDK7cdFr5qqe5xhLu3eAwcxcVkM82DGjVY2WllzmzDz1nCd+3a+9CnqRfalU+dMOeouMFBzOknLo/EOql/Hv3r9Jfw62sv4ZChKgv+j67GBw9UHxjYeEewaPCEIQgwGd6hSZ/70VFe0kZfRrZPEzQ3jvhwMhrp8qxEH4ez0r4X9GwtXF6RLWPQsjBthr0Msd9l9jdvY0PV6meqE0LbD5WaIbYcPFYlbOtwYfdKqZNu77bNDEVk+UIG4Pnpz+PRatPD6CZ9Efug2yRxeSKacciHWpCNQ/Pvle8aBFQ9F+KbV/uYHVrFydlDnhOzU+VbBfX5WeUDnCKkEYTojM73ylFN+yP2RVWcz3P/XoW18nzHkb3YUd7G2c8RyPMWJT1HfFBnHeTdHq0d12Dj7uPY/stf0LXuiZaNveEkXjmJDzG9uA2rXrsPX2uHYnirujXKUiMvZxs5LhKwd+FPOBEwFA9MaF/thK4K8kPJmmXYvWMHrrQYjK6hDaEqVSNh60xMf3U71I2fxdxZIxBguMfcatjO/rtBbk3Q7o5+6FFtag791q+wM6UtHl+0GI8Mawc/K9unt0NtHxfpvz2O+989KjfttcOUV0aiwZVYJCaYTGk6eAR6w8ujBCe/3Sh8goGcPX/iuNYNLqWZiD3wJWY/+yHOS1nKB5PfnIe7guujkZ8Tdqz4C1koRNTuo3D09UXx+SX4v+e/Q5r4Fwqf5ffeGl2rn2Xx90/K4iN4LJL40NLH8chb22Ecj7gS1xZ48tsteLJb3Rpry6ofwfPbZNz17CZ5yYyei/DHigfKr2xzoxbg8fvn45zclFGu8Wi8v/wrDKnUQdc68BE8VcXi1/Fi8yEfwVObx8WheV6Y+p28UKMnsTjmXXSDDqk7XsVjT32HxKqfTZEqDMMW/IS5o4zNjjrE//YEHn92rdz3y4TrALy14SfcW8ufZT6CR3kMWhZMXxCNs3v34sCxWClwuTbpgk7tOqBFRCBc6+A4PNb8Ra0+vgKL/7wkL5nRdDimjusMN3lRJO3/7Vux/Zw4bnQjRPbrhx6dWlzX6PKWiEGrqnycWvMhtsU0w8CnHkAb051fh9T2cZEbtRTfbzOOmlUD114Y+/SgijtGNak4f3wPjuw8jStSgfj57I1OrZvD37V6cNKkncKpg3uwu/yz3Btd27eBrwU8So1BS3kMWmQ1+EVt3bj/yBweF7WLQUt57AxPREREpBAGLSIiIiKFMGgRERERKYRBi4iIiEghDFpERERECmHQIiIiIlIIgxYRERGRQhi0iIiIiBRisQOWEhEREVk71mgRERERKYRBi4iIiEghDFpERERECmHQIiIiIlIIgxYRERGRQhi0iIiIiBTCoEVERESkEAYtIiIiIoUwaBEREREphEGLiIiISCEMWkREREQKYdAiIiIiUgiDFhEREZFCGLSIiIiIFMKgRURERKQQBi0iIiIihTBoERERESmEQYuIiIhIIQxaRERERAph0CIiIiJSCIMWERERkUIYtIiIiIgUwqBFREREpBAGLSIiIiKFMGgRERERKYRBi4iIiEghDFpERERECmHQIiIiIlIIgxYRERGRQhi0iIiIiBTCoEVERESkEAYtIiIiIoUwaBEREREphEGLiIiISCEMWkREREQKYdAiIiIiUgiDFhEREZFCGLSIiIiIFMKgRURERKQQBi0iIiIihTBoERERESmEQYuIiIhIIQxaRERERAph0CIiIiJSCIMWERERkUIYtIiIiIgUwqBFREREpBAGLSIiIiKFMGgRERERKQL4f/OUHqohCykaAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "id": "ba7b527e",
   "metadata": {},
   "source": [
    "# cohen kappa系数 & ROC曲线曲线\n",
    "* ## cohen kappa系数：\n",
    "$$\n",
    "k=\\frac{p_o-p_e}{1-p_e} .\n",
    "$$\n",
    "其中， $p_o$ 是每一类正确分类的样本数量之和除以总样本数，也就是总体分类精度。\n",
    "假设每一类的真实样本个数分别为 $a 1, a 2, \\ldots, \\mathrm{aC}$ ，而预测出来的每一类的样本个数分别为 $\\mathrm{b} 1, \\mathrm{~b} 2, \\ldots, \\mathrm{bC}$\n",
    "总样本个数为 $n$ ，则有: $\\mathrm{pe}=\\frac{\\mathrm{a} 1 \\times \\mathrm{b} 1+\\mathrm{a} 2 \\times \\mathrm{b} 2+\\ldots+\\mathrm{aC} \\times \\mathrm{bC}}{\\mathrm{n} \\times \\mathrm{n}}$\n",
    "\n",
    "kappa计算结果为-1到1，但通常kappa是落在 0 到1 间，可分为五组来表示不同级别的一致性：\\\n",
    "    0.0到0.20极低的一致性(slight)\\\n",
    "    0.21到0.40一般的一致性(fair)\\\n",
    "    0.41到0.60 中等的一致性(moderate)\\\n",
    "    0.61到0.80 高度的一致性(substantial)\\\n",
    "    0.81到1几乎完全一致(almost perfect)\n",
    "\n",
    "分类问题中，最常见的评价指标是acc，它能够直接反映分正确的比例，同时计算非常简单。但是实际的分类问题种，各个类别的样本数量往往不太平衡。在这种不平衡数据集上如不加以调整，模型很容易偏向大类别而放弃小类别，此时整体acc挺高，但是部分类别完全不能被召回。这时需要一种能够惩罚模型的“偏向性”的指标来代替acc。而根据kappa的计算公式，越不平衡的混淆矩阵，acc越高，kappa值就越低，正好能够给“偏向性”强的模型打低分。\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "上图就是个混淆矩阵，\n",
    "其中， $p_o=\\frac{239+73+280}{664}=0.8916$.\n",
    "$$\n",
    "p_e=\\frac{261 \\times 276+103 \\times 93+300 \\times 295}{664 \\times 664}=0.3883 .\n",
    "$$\n",
    "因此:\n",
    "$$\n",
    "k=\\frac{0.8916-0.3883}{1-0.3883} .\n",
    "$$\n",
    "* ## ROC曲线\n",
    "ROC的全称是Receiver Operating Characteristic Curve，中文名字叫“受试者工作特征曲线”。\n",
    "![image-2.png](attachment:image-2.png)\n",
    "该曲线的横坐标为假阳性率（False Positive Rate, FPR），N是真实负样本的个数，FP是N个负样本中被分类器预测为正样本的个数。\n",
    "$$FPR=\\frac{FP}{N} $$\n",
    "纵坐标为真阳性率（True Positive Rate, TPR），P是真实正样本的个数，TP是P个正样本中被分类器预测为正样本的个数。\n",
    "$$TPR=\\frac{TP}{P} $$\n",
    "![image-5.png](attachment:image-5.png)\n",
    "绘制完成曲线后，就会对模型有一个定性的分析，如果要对模型进行量化的分析，此时需要引入一个新的概念，就是AUC（Area under roc Curve）面积，就是指ROC曲线下的面积大小，而计算AUC值只需要沿着ROC横轴做积分就可以了。真实场景中ROC曲线一般都会在这条直线的上方，所以AUC的取值一般在0.5到1之间。AUC的值越大，说明该模型的性能越好。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4928529",
   "metadata": {},
   "source": [
    "# 泰坦尼克号数据集(titanic.csv)逻辑回归分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b00323b",
   "metadata": {},
   "source": [
    "* ## 数据描述性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fc7ff3c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd#加载相关包\n",
    "import numpy as np\n",
    "titanic = pd.read_csv('titanic.csv')#读入数据\n",
    "titanic.shape#返回数据的形状（矩阵维度）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f4f2f18c",
   "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>Class</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1st</td>\n",
       "      <td>Male</td>\n",
       "      <td>Child</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2nd</td>\n",
       "      <td>Male</td>\n",
       "      <td>Child</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3rd</td>\n",
       "      <td>Male</td>\n",
       "      <td>Child</td>\n",
       "      <td>No</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Crew</td>\n",
       "      <td>Male</td>\n",
       "      <td>Child</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1st</td>\n",
       "      <td>Female</td>\n",
       "      <td>Child</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Class     Sex    Age Survived  Freq\n",
       "0   1st    Male  Child       No     0\n",
       "1   2nd    Male  Child       No     0\n",
       "2   3rd    Male  Child       No    35\n",
       "3  Crew    Male  Child       No     0\n",
       "4   1st  Female  Child       No     0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()#返回前五个数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b709d227",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2201,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq = titanic.Freq.to_numpy()\n",
    "index = np.repeat(np.arange(32), freq)#对Freq列的内容进行求和操作\n",
    "index.shape#显示结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bef43465",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3rd</td>\n",
       "      <td>Male</td>\n",
       "      <td>Child</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3rd</td>\n",
       "      <td>Male</td>\n",
       "      <td>Child</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3rd</td>\n",
       "      <td>Male</td>\n",
       "      <td>Child</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3rd</td>\n",
       "      <td>Male</td>\n",
       "      <td>Child</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3rd</td>\n",
       "      <td>Male</td>\n",
       "      <td>Child</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Class   Sex    Age Survived\n",
       "2   3rd  Male  Child       No\n",
       "2   3rd  Male  Child       No\n",
       "2   3rd  Male  Child       No\n",
       "2   3rd  Male  Child       No\n",
       "2   3rd  Male  Child       No"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = titanic.iloc[index, :]#将所有列的内容赋给titanic变量\n",
    "titanic = titanic.drop('Freq', axis=1)#去除‘Freq’列内容\n",
    "titanic.head()#查看此时数据前五行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f07cf925",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2201 entries, 2 to 31\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   Class     2201 non-null   object\n",
      " 1   Sex       2201 non-null   object\n",
      " 2   Age       2201 non-null   object\n",
      " 3   Survived  2201 non-null   object\n",
      "dtypes: object(4)\n",
      "memory usage: 86.0+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic.info()#给出样本数据的相关信息概览 ：行数，列数，列索引，列非空值个数，列类型，内存占用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "59b8a130",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2201</td>\n",
       "      <td>2201</td>\n",
       "      <td>2201</td>\n",
       "      <td>2201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Crew</td>\n",
       "      <td>Male</td>\n",
       "      <td>Adult</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>885</td>\n",
       "      <td>1731</td>\n",
       "      <td>2092</td>\n",
       "      <td>1490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Class   Sex    Age Survived\n",
       "count   2201  2201   2201     2201\n",
       "unique     4     2      2        2\n",
       "top     Crew  Male  Adult       No\n",
       "freq     885  1731   2092     1490"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe()#直接给出样本数据的一些基本的统计量，包括均值，标准差，最大值，最小值，分位数等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3081cb2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Survived</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>126</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>1364</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Survived    No  Yes\n",
       "Sex                \n",
       "Female     126  344\n",
       "Male      1364  367"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(titanic.Sex, titanic.Survived)#画出生存与否与性别的列联表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "432603e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Survived</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>0.268085</td>\n",
       "      <td>0.731915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.787984</td>\n",
       "      <td>0.212016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Survived        No       Yes\n",
       "Sex                         \n",
       "Female    0.268085  0.731915\n",
       "Male      0.787984  0.212016"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(titanic.Sex, titanic.Survived, normalize='index')#画出生存与否与性别的列联表，对数据进行归一化操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2e4a1c76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Survived</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adult</th>\n",
       "      <td>0.687380</td>\n",
       "      <td>0.312620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Child</th>\n",
       "      <td>0.477064</td>\n",
       "      <td>0.522936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Survived        No       Yes\n",
       "Age                         \n",
       "Adult     0.687380  0.312620\n",
       "Child     0.477064  0.522936"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(titanic.Age, titanic.Survived, normalize='index')#画出生存与否与年龄的列联表，对数据进行归一化操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "509a1fa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Survived</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1st</th>\n",
       "      <td>0.375385</td>\n",
       "      <td>0.624615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2nd</th>\n",
       "      <td>0.585965</td>\n",
       "      <td>0.414035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3rd</th>\n",
       "      <td>0.747875</td>\n",
       "      <td>0.252125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crew</th>\n",
       "      <td>0.760452</td>\n",
       "      <td>0.239548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Survived        No       Yes\n",
       "Class                       \n",
       "1st       0.375385  0.624615\n",
       "2nd       0.585965  0.414035\n",
       "3rd       0.747875  0.252125\n",
       "Crew      0.760452  0.239548"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(titanic.Class, titanic.Survived, normalize='index')#画出生存与否与船舱种类的列联表，对数据进行归一化操作"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2fb24bd",
   "metadata": {},
   "source": [
    "* ## 逻辑回归参数估计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b81d4baa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Intercept</th>\n",
       "      <th>Class[T.2nd]</th>\n",
       "      <th>Class[T.3rd]</th>\n",
       "      <th>Class[T.Crew]</th>\n",
       "      <th>Sex[T.Male]</th>\n",
       "      <th>Age[T.Child]</th>\n",
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       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Intercept  Class[T.2nd]  Class[T.3rd]  Class[T.Crew]  Sex[T.Male]  \\\n",
       "11        1.0           0.0           0.0            1.0          1.0   \n",
       "27        1.0           0.0           0.0            1.0          1.0   \n",
       "22        1.0           0.0           1.0            0.0          0.0   \n",
       "11        1.0           0.0           0.0            1.0          1.0   \n",
       "27        1.0           0.0           0.0            1.0          1.0   \n",
       "\n",
       "    Age[T.Child]  \n",
       "11           0.0  \n",
       "27           0.0  \n",
       "22           1.0  \n",
       "11           0.0  \n",
       "27           0.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import statsmodels.api as sm\n",
    "from patsy import dmatrices\n",
    "\n",
    "train, test = train_test_split(#将数据按照3:7拆分成测试集和训练集，并且设置随机数种子\n",
    "    titanic, test_size=0.3, stratify=titanic.Survived, random_state=0)#stratify是保持测试集与整个数据集里result的数据分类比例一致。\n",
    "\n",
    "# 将分类变量转化为哑变量\n",
    "\n",
    "y_train, X_train = dmatrices(#线性模型产生设计矩阵，模型为下面的线性模型，数据为train，返回形式是数据框\n",
    "    'Survived ~ Class + Sex + Age', data=train, return_type='dataframe')\n",
    "\n",
    "pd.options.display.max_columns = 10\n",
    "X_train.head()#返回数据特征的前五行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "67e79a16",
   "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>Survived[No]</th>\n",
       "      <th>Survived[Yes]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Survived[No]  Survived[Yes]\n",
       "11           1.0            0.0\n",
       "27           0.0            1.0\n",
       "22           0.0            1.0\n",
       "11           1.0            0.0\n",
       "27           0.0            1.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()#返回数据label的前五行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7ab539ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train.iloc[:,1]#y的训练集选择所有行\n",
    "y_test, X_test = dmatrices('Survived ~ Class + Sex + Age', data=test, return_type='dataframe')#线性模型产生设计矩阵，模型为下面的线性模型，数据为train，返回形式是数据框\n",
    "y_test = y_test.iloc[:,1]#y的测试集选择所有行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b99cae52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.501935\n",
      "         Iterations 6\n"
     ]
    }
   ],
   "source": [
    "model = sm.Logit(y_train, X_train)#带入statsmodels.api包的logit函数\n",
    "results = model.fit()#拟合模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "96d4e387",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept        2.023452\n",
       "Class[T.2nd]    -1.045877\n",
       "Class[T.3rd]    -1.869105\n",
       "Class[T.Crew]   -0.882650\n",
       "Sex[T.Male]     -2.355785\n",
       "Age[T.Child]     1.126943\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.params#返回模型参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "83c290e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept        7.564391\n",
       "Class[T.2nd]     0.351384\n",
       "Class[T.3rd]     0.154262\n",
       "Class[T.Crew]    0.413685\n",
       "Sex[T.Male]      0.094819\n",
       "Age[T.Child]     3.086208\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(results.params)   # 返回优比（比值比）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c2b8ce99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>Survived[Yes]</td>  <th>  No. Observations:  </th>  <td>  1540</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>  1534</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     5</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 23 Nov 2022</td> <th>  Pseudo R-squ.:     </th>  <td>0.2019</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>20:46:13</td>     <th>  Log-Likelihood:    </th> <td> -772.98</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -968.52</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>2.482e-82</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>     <td>    2.0235</td> <td>    0.200</td> <td>   10.141</td> <td> 0.000</td> <td>    1.632</td> <td>    2.415</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Class[T.2nd]</th>  <td>   -1.0459</td> <td>    0.232</td> <td>   -4.509</td> <td> 0.000</td> <td>   -1.501</td> <td>   -0.591</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Class[T.3rd]</th>  <td>   -1.8691</td> <td>    0.206</td> <td>   -9.059</td> <td> 0.000</td> <td>   -2.273</td> <td>   -1.465</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Class[T.Crew]</th> <td>   -0.8826</td> <td>    0.192</td> <td>   -4.598</td> <td> 0.000</td> <td>   -1.259</td> <td>   -0.506</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sex[T.Male]</th>   <td>   -2.3558</td> <td>    0.167</td> <td>  -14.079</td> <td> 0.000</td> <td>   -2.684</td> <td>   -2.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Age[T.Child]</th>  <td>    1.1269</td> <td>    0.287</td> <td>    3.927</td> <td> 0.000</td> <td>    0.565</td> <td>    1.689</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:          Survived[Yes]   No. Observations:                 1540\n",
       "Model:                          Logit   Df Residuals:                     1534\n",
       "Method:                           MLE   Df Model:                            5\n",
       "Date:                Wed, 23 Nov 2022   Pseudo R-squ.:                  0.2019\n",
       "Time:                        20:46:13   Log-Likelihood:                -772.98\n",
       "converged:                       True   LL-Null:                       -968.52\n",
       "Covariance Type:            nonrobust   LLR p-value:                 2.482e-82\n",
       "=================================================================================\n",
       "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "Intercept         2.0235      0.200     10.141      0.000       1.632       2.415\n",
       "Class[T.2nd]     -1.0459      0.232     -4.509      0.000      -1.501      -0.591\n",
       "Class[T.3rd]     -1.8691      0.206     -9.059      0.000      -2.273      -1.465\n",
       "Class[T.Crew]    -0.8826      0.192     -4.598      0.000      -1.259      -0.506\n",
       "Sex[T.Male]      -2.3558      0.167    -14.079      0.000      -2.684      -2.028\n",
       "Age[T.Child]      1.1269      0.287      3.927      0.000       0.565       1.689\n",
       "=================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary()#结果汇总"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "db684255",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Marginal Effects</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th> <td>Survived[Yes]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>            <td>dydx</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>At:</th>               <td>overall</td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <th></th>           <th>dy/dx</th>    <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Class[T.2nd]</th>  <td>   -0.1708</td> <td>    0.037</td> <td>   -4.595</td> <td> 0.000</td> <td>   -0.244</td> <td>   -0.098</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Class[T.3rd]</th>  <td>   -0.3053</td> <td>    0.031</td> <td>   -9.788</td> <td> 0.000</td> <td>   -0.366</td> <td>   -0.244</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Class[T.Crew]</th> <td>   -0.1442</td> <td>    0.031</td> <td>   -4.711</td> <td> 0.000</td> <td>   -0.204</td> <td>   -0.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sex[T.Male]</th>   <td>   -0.3848</td> <td>    0.021</td> <td>  -18.409</td> <td> 0.000</td> <td>   -0.426</td> <td>   -0.344</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Age[T.Child]</th>  <td>    0.1841</td> <td>    0.046</td> <td>    3.980</td> <td> 0.000</td> <td>    0.093</td> <td>    0.275</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "        Logit Marginal Effects       \n",
       "=====================================\n",
       "Dep. Variable:          Survived[Yes]\n",
       "Method:                          dydx\n",
       "At:                           overall\n",
       "=================================================================================\n",
       "                   dy/dx    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "Class[T.2nd]     -0.1708      0.037     -4.595      0.000      -0.244      -0.098\n",
       "Class[T.3rd]     -0.3053      0.031     -9.788      0.000      -0.366      -0.244\n",
       "Class[T.Crew]    -0.1442      0.031     -4.711      0.000      -0.204      -0.084\n",
       "Sex[T.Male]      -0.3848      0.021    -18.409      0.000      -0.426      -0.344\n",
       "Age[T.Child]      0.1841      0.046      3.980      0.000       0.093       0.275\n",
       "=================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "margeff = results.get_margeff()#多元离散因变量回归模型的边际效应\n",
    "margeff.summary()#结果汇总"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4de3bfef",
   "metadata": {},
   "source": [
    "* ## 训练误差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "21802c46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[949.,  94.],\n",
       "       [252., 245.]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = results.pred_table()  # 返回训练集的混淆矩阵（预测结果与实际结果）\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f0293292",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7753246753246753"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Accuracy = (table[0, 0] + table[1, 1]) / np.sum(table)#计算精确率\n",
    "Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c49da0d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2246753246753247"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Error_rate = 1 - Accuracy#计算错误率\n",
    "Error_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "89889878",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49295774647887325"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sensitivity  = table[1, 1] / (table[1, 0] + table[1, 1])#计算敏感度\n",
    "Sensitivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "67a1732d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.909875359539789"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Specificity = table[0, 0] / (table[0, 0] + table[0, 1])#计算特异性\n",
    "Specificity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "91d04004",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7227138643067846"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Recall = table[1, 1] / (table[0, 1] + table[1, 1])#计算召回率\n",
    "Recall"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22c48916",
   "metadata": {},
   "source": [
    "* ## 测试误差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "949073e5",
   "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>Predicted</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived[Yes]</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>415</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>110</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted      False  True\n",
       "Survived[Yes]             \n",
       "0.0              415    32\n",
       "1.0              110   104"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob = results.predict(X_test)#对测试集进行预测概率\n",
    "pred = (prob >= 0.5)#概率大于0.5的\n",
    "table = pd.crosstab(y_test, pred, colnames=['Predicted'])#返回预测结果和实际结果的混淆矩阵\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3fe38ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7851739788199698"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = np.array(table)   # 将pandas的dataframe转化为numpy的\n",
    "Accuracy = (table[0, 0] + table[1, 1]) / np.sum(table)#计算准确率\n",
    "Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d892c9ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21482602118003025"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Error_rate = 1 - Accuracy#计算错误率\n",
    "Error_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f13a045d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48598130841121495"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sensitivity  = table[1, 1] / (table[1, 0] + table[1, 1])#计算敏感度\n",
    "Sensitivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8c562988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9284116331096197"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Specificity = table[0, 0] / (table[0, 0] + table[0, 1])#计算特异性\n",
    "Specificity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "df0b7e94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7647058823529411"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Recall = table[1, 1] / (table[0, 1] + table[1, 1])#计算召回率\n",
    "Recall"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "934975d4",
   "metadata": {},
   "source": [
    "* ## 用sklearn进行逻辑回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5f42835c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=10000000000.0)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#加载相关包\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "from sklearn.metrics import plot_roc_curve\n",
    "\n",
    "model =  LogisticRegression(C=1e10)#设置逻辑回归模型，c取10^10\n",
    "model.fit(X_train, y_train)#将数据带入模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "931d0393",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.01172396, -1.04587333, -1.86910362, -0.88265229, -2.35577901,\n",
       "         1.1269402 ]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_#返回模型的各项参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a8f06c8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept        2.023452\n",
       "Class[T.2nd]    -1.045877\n",
       "Class[T.3rd]    -1.869105\n",
       "Class[T.Crew]   -0.882650\n",
       "Sex[T.Male]     -2.355785\n",
       "Age[T.Child]     1.126943\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.params#返回最终参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "de4914cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.02345954, -1.04589058, -1.86912051, -0.88265515, -2.35578706,\n",
       "         1.12695371]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(C=1e10, fit_intercept=False)#和刚才模型一样，但是不含有截距项\n",
    "model.fit(X_train, y_train)\n",
    "model.coef_#返回模型的各项参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b757bdea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7851739788199698"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)#对去掉截距项的模型在测试集上进行评分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8189b196",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.24217271, 0.75782729],\n",
       "       [0.90037947, 0.09962053],\n",
       "       [0.90037947, 0.09962053],\n",
       "       [0.77117939, 0.22882061],\n",
       "       [0.27337442, 0.72662558]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob = model.predict_proba(X_test)#带入测试集，计算出其属于每个类别的概率\n",
    "prob[:5]#返回前5行的预测的概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "600d414b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 0., 0., 0., 1.])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = model.predict(X_test)#根据概率大小对指明所属类别\n",
    "pred[:5]#返回前5行的预测种类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "515d9868",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[415,  32],\n",
       "       [110, 104]], dtype=int64)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, pred)#返回预测结果和实际结果的混淆矩阵"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0280277c",
   "metadata": {},
   "source": [
    "* ## 准确率（为了更好展示形式使用pandas库）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "31d22ea8",
   "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>Predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>415</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>110</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Predicted  0.0  1.0\n",
       "Actual             \n",
       "0.0        415   32\n",
       "1.0        110  104"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.crosstab(y_test, pred, rownames=['Actual'], colnames=['Predicted'])#可视化混淆矩阵\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d9c880dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "Not Survived       0.79      0.93      0.85       447\n",
      "    Survived       0.76      0.49      0.59       214\n",
      "\n",
      "    accuracy                           0.79       661\n",
      "   macro avg       0.78      0.71      0.72       661\n",
      "weighted avg       0.78      0.79      0.77       661\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred, target_names=['Not Survived', 'Survived']))#对模型结果做精确度、召回率、F1-score、支持度和准确率、宏平均、加权平均做混淆矩阵"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e40788b1",
   "metadata": {},
   "source": [
    "* ## ROC和AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0776ba73",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; Function :func:`plot_roc_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: :meth:`sklearn.metric.RocCurveDisplay.from_predictions` or :meth:`sklearn.metric.RocCurveDisplay.from_estimator`.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.4578900555613312"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(model, X_test, y_test)#画出模型在测试集上的roc曲线\n",
    "x = np.linspace(0, 1, 100)#将0到1均分为100份\n",
    "plt.plot(x, x, 'k--', linewidth=1)#画出对角线虚线\n",
    "plt.title('ROC Curve (Test Set)')#给图添加标题\n",
    "\n",
    "cohen_kappa_score(y_test, pred)#返回卡帕系数分数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6029c5d1",
   "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",
   "nbconvert_exporter": "python",
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