五_Logsitc_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## L1与L2的区别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-26T13:24:57.529949Z",
     "start_time": "2019-12-26T13:24:52.528584Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression as LR\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "data = load_breast_cancer()\n",
    "X = data.data\n",
    "y = data.target\n",
    "data.data.shape\n",
    "lrl1 = LR(penalty=\"l1\",solver=\"liblinear\",C=0.5,max_iter=1000)\n",
    "lrl2 = LR(penalty=\"l2\",solver=\"liblinear\",C=0.5,max_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T01:01:13.668449Z",
     "start_time": "2019-12-23T01:01:12.617781Z"
    }
   },
   "outputs": [],
   "source": [
    "lrl1 = lrl1.fit(X, y)\n",
    "lrl2 = lrl2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T01:01:46.449694Z",
     "start_time": "2019-12-23T01:01:46.433824Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.01962915,  0.0322467 , -0.1398271 , -0.01623377,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.50570345,  0.        , -0.07126632,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        , -0.24621131, -0.1288122 , -0.0143969 ,  0.        ,\n",
       "         0.        , -2.02143139,  0.        ,  0.        ,  0.        ]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看各个特征的系数\\theta\n",
    "lrl1.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T01:01:54.521081Z",
     "start_time": "2019-12-23T01:01:54.506203Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.61543234e+00,  1.02284415e-01,  4.78483684e-02,\n",
       "        -4.43927107e-03, -9.42247882e-02, -3.01420673e-01,\n",
       "        -4.56065677e-01, -2.22346063e-01, -1.35660484e-01,\n",
       "        -1.93917198e-02,  1.61646580e-02,  8.84531037e-01,\n",
       "         1.20301273e-01, -9.47422278e-02, -9.81687769e-03,\n",
       "        -2.37399092e-02, -5.71846204e-02, -2.70190106e-02,\n",
       "        -2.77563737e-02,  1.98122260e-04,  1.26394730e+00,\n",
       "        -3.01762592e-01, -1.72784162e-01, -2.21786411e-02,\n",
       "        -1.73339657e-01, -8.79070550e-01, -1.16325561e+00,\n",
       "        -4.27661014e-01, -4.20612369e-01, -8.69820058e-02]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrl2.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T01:03:42.227102Z",
     "start_time": "2019-12-23T01:03:42.162638Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n可以看出，L1正则化可以把参数的系数变为0，而L2正则化只会减小其系数，不会等于0。\\n默认使用L2正则化，如果L2正则化的结果不行，再用L1正则化试一试。\\n'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "可以看出，L1正则化可以把参数的系数变为0，而L2正则化只会减小其系数，不会等于0。\n",
    "默认使用L2正则化，如果L2正则化的结果不行，再用L1正则化试一试。\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T01:24:54.304712Z",
     "start_time": "2019-12-23T01:24:39.263893Z"
    }
   },
   "outputs": [],
   "source": [
    "scores_l1_train = []\n",
    "scores_l2_train = []\n",
    "scores_l1_test = []\n",
    "scores_l2_test = []\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=420)\n",
    "\n",
    "for i in np.linspace(0.01, 1, 21):\n",
    "    lrl1 = LR(penalty='l1', C=i, solver='liblinear', max_iter=1000).fit(X, y)\n",
    "    lrl2 = LR(penalty='l2', C=i, solver='liblinear', max_iter=1000).fit(X, y)    \n",
    "    score_l1_train = accuracy_score(lrl1.predict(X_train), y_train)\n",
    "    score_l2_train = accuracy_score(lrl2.predict(X_train), y_train)    \n",
    "    score_l1_test = accuracy_score(lrl1.predict(X_test), y_test)    \n",
    "    score_l2_test = accuracy_score(lrl2.predict(X_test), y_test)    \n",
    "    \n",
    "    scores_l1_train.append(score_l1_train)\n",
    "    scores_l2_train.append(score_l2_train)    \n",
    "    scores_l1_test.append(score_l1_test)    \n",
    "    scores_l2_test.append(score_l2_test)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T01:24:54.887017Z",
     "start_time": "2019-12-23T01:24:54.368152Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph = [scores_l1_train, scores_l1_test, scores_l2_train, scores_l2_test]\n",
    "label = ['scores_l1_train', 'scores_l1_test', 'scores_l2_train', 'scores_l2_test']\n",
    "colors = ['green', 'lightgreen', 'blue', 'lightblue']\n",
    "\n",
    "fig = plt.figure(figsize=(6,6))\n",
    "for i in range(4):\n",
    "    plt.plot(np.linspace(0.01, 1, 21), graph[i], c=colors[i], label=label[i])\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T01:27:36.518415Z",
     "start_time": "2019-12-23T01:27:36.509979Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'可以看到，在训练集上越好，不代表在测试集上的分数越高，。在逻辑回归中，\\n会先上升，而后到达测试集最好的分数，然后就开始平稳或者波动。  '"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"可以看到，在训练集上越好，不代表在测试集上的分数越高，。在逻辑回归中，\n",
    "会先上升，而后到达测试集最好的分数，然后就开始平稳或者波动。  \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用嵌入法过滤特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-25T23:44:31.716244Z",
     "start_time": "2019-12-25T23:44:31.485881Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.feature_selection import SelectFromModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T01:36:26.183370Z",
     "start_time": "2019-12-23T01:36:26.167002Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T01:36:30.787819Z",
     "start_time": "2019-12-23T01:36:30.771898Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569,)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T01:38:15.913204Z",
     "start_time": "2019-12-23T01:38:15.636773Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9508998790078644"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 这里使用L1正则化过滤\n",
    "lr = LR(solver='liblinear', C=0.8, max_iter=1000)\n",
    "cross_val_score(lr, X, y, cv=10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T01:39:11.813561Z",
     "start_time": "2019-12-23T01:39:11.786606Z"
    }
   },
   "outputs": [],
   "source": [
    "X_embedded = SelectFromModel(lr, norm_order=1).fit_transform(X, y) # 使用L1正则化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T01:39:42.857250Z",
     "start_time": "2019-12-23T01:39:42.847823Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 9)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_embedded.shape  # 可以看到嵌入法为我们选择了9个特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T01:40:02.612344Z",
     "start_time": "2019-12-23T01:40:02.560616Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9368323826808401"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(lr, X_embedded, y, cv=10).mean()  \n",
    "\"\"\"虽然模型分数降低了，但是对于30维的数据,降到9维，可以简化我们现实的预测\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 调整threshold, 试一试模型效果与维度能否达到一个更好的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T01:52:20.773621Z",
     "start_time": "2019-12-23T01:52:20.737419Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9407192479360273"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(lr.fit(X, y).coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T01:55:21.098018Z",
     "start_time": "2019-12-23T01:55:19.960700Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 30\n",
      "0.1021431183124225 17\n",
      "0.204286236624845 12\n",
      "0.3064293549372675 10\n",
      "0.40857247324969 8\n",
      "0.5107155915621124 8\n",
      "0.612858709874535 5\n",
      "0.7150018281869575 5\n",
      "0.81714494649938 5\n",
      "0.9192880648118025 5\n",
      "1.0214311831242249 5\n",
      "1.1235743014366475 4\n",
      "1.22571741974907 3\n",
      "1.3278605380614925 2\n",
      "1.430003656373915 2\n",
      "1.5321467746863375 1\n",
      "1.63428989299876 1\n",
      "1.7364330113111823 1\n",
      "1.838576129623605 1\n",
      "1.9407192479360273 1\n"
     ]
    }
   ],
   "source": [
    "scores_th = []\n",
    "lr = LR(solver='liblinear', C=0.8, max_iter=1000)\n",
    "ths = np.linspace(0, np.max(abs(lr.fit(X, y).coef_)), 20)\n",
    "i = 0\n",
    "shapes = []\n",
    "for th in ths:\n",
    "    X_embedded = SelectFromModel(lr, threshold=th).fit_transform(X, y)\n",
    "    score = cross_val_score(lr, X_embedded, y, cv=10).mean()\n",
    "    scores_th.append(score)\n",
    "    print(ths[i], X_embedded.shape[1])\n",
    "    shapes.append(X_embedded.shape[1])\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T01:55:54.081212Z",
     "start_time": "2019-12-23T01:55:53.698984Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f22037ac18>]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(scores_th)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 试一试C的学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T02:17:25.514998Z",
     "start_time": "2019-12-23T02:17:21.281317Z"
    }
   },
   "outputs": [],
   "source": [
    "scores_lr = []\n",
    "scores_embedded = []\n",
    "embedded_shape = []\n",
    "for i in np.arange(2.05, 12.05, 0.5):\n",
    "    lr = LR(solver='liblinear', C=i, max_iter=1000)\n",
    "    scores_lr.append(cross_val_score(lr, X, y, cv=10).mean())\n",
    "    X_embedded = SelectFromModel(lr,norm_order=1).fit_transform(X, y)\n",
    "    embedded_shape.append(X_embedded.shape[1])\n",
    "    scores_embedded.append(cross_val_score(lr, X_embedded, y, cv=10).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T02:17:34.361941Z",
     "start_time": "2019-12-23T02:17:33.964620Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(2.05, 12.05, 0.5), scores_lr, c='g', label='full')\n",
    "plt.plot(np.arange(2.05, 12.05, 0.5), scores_embedded, c='b', label='embedded')\n",
    "plt.xticks()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T02:18:11.091771Z",
     "start_time": "2019-12-23T02:18:11.072959Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2.05, 9, 0.9473597787572379),\n",
       " (2.55, 9, 0.9456972171808833),\n",
       " (3.05, 10, 0.9492059891107077),\n",
       " (3.55, 9, 0.9527147610405324),\n",
       " (4.05, 10, 0.9545004753262466),\n",
       " (4.55, 10, 0.9545004753262466),\n",
       " (5.05, 9, 0.9545004753262466),\n",
       " (5.55, 9, 0.928182525278714),\n",
       " (6.05, 9, 0.928182525278714),\n",
       " (6.55, 9, 0.928182525278714),\n",
       " (7.05, 9, 0.9545620516809263),\n",
       " (7.55, 10, 0.9545620516809263),\n",
       " (8.05, 10, 0.9334467634603749),\n",
       " (8.55, 9, 0.9545620516809263),\n",
       " (9.05, 10, 0.9352011494252872),\n",
       " (9.55, 9, 0.9563164376458386),\n",
       " (10.05, 10, 0.9352011494252872),\n",
       " (10.55, 8, 0.9334770114942528),\n",
       " (11.05, 10, 0.9369555353901996),\n",
       " (11.55, 10, 0.9351698211044853)]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[*zip(np.arange(2.05, 12.05, 0.5), embedded_shape, scores_embedded)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到在9.5左右会达到一个峰值，此时的特征有10个，所以我们可以再试一试细化学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T02:19:33.516335Z",
     "start_time": "2019-12-23T02:19:29.006204Z"
    }
   },
   "outputs": [],
   "source": [
    "scores_lr = []\n",
    "scores_embedded = []\n",
    "embedded_shape = []\n",
    "for i in np.arange(9.01, 10.01, 0.05):\n",
    "    lr = LR(solver='liblinear', C=i, max_iter=1000)\n",
    "    scores_lr.append(cross_val_score(lr, X, y, cv=10).mean())\n",
    "    X_embedded = SelectFromModel(lr,norm_order=1).fit_transform(X, y)\n",
    "    embedded_shape.append(X_embedded.shape[1])\n",
    "    scores_embedded.append(cross_val_score(lr, X_embedded, y, cv=10).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T02:36:17.897732Z",
     "start_time": "2019-12-23T02:36:17.512839Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(9.01, 10.01, 0.05), scores_lr, c='g', label='full')\n",
    "plt.plot(np.arange(9.01, 10.01, 0.05), scores_embedded, c='b', label='embedded')\n",
    "plt.xticks()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T02:38:36.042914Z",
     "start_time": "2019-12-23T02:38:36.013077Z"
    }
   },
   "outputs": [],
   "source": [
    "lr = LR(solver='liblinear', C=9.15, max_iter=1000)\n",
    "X_embedded = SelectFromModel(lr, norm_order=1).fit_transform(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T02:38:38.293347Z",
     "start_time": "2019-12-23T02:38:38.226428Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9545620516809263"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(lr, X_embedded, y, cv=10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T02:39:00.777604Z",
     "start_time": "2019-12-23T02:39:00.761084Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.40114534, -3.08996811, -2.94464718, -0.91303347, -1.93492208,\n",
       "        -1.142004  , -2.94266703, -3.96002176, -0.41695769]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_embedded, y).coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## max_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-23T02:46:54.852105Z",
     "start_time": "2019-12-23T02:46:53.834121Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3000x750 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([25], dtype=int32)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l2 = []\n",
    "l2test = []\n",
    "Xtrain, Xtest, Ytrain, Ytest = train_test_split(X,y,test_size=0.3,random_state=420)\n",
    "for i in np.arange(1,201,10):\n",
    "    lrl2 = LR(penalty=\"l2\",solver=\"liblinear\",C=0.9,max_iter=i)\n",
    "    lrl2 = lrl2.fit(Xtrain,Ytrain)\n",
    "    l2.append(accuracy_score(lrl2.predict(Xtrain),Ytrain))\n",
    "    l2test.append(accuracy_score(lrl2.predict(Xtest),Ytest))\n",
    "graph = [l2,l2test]\n",
    "color = [\"black\",\"gray\"]\n",
    "label = [\"L2\",\"L2test\"]\n",
    "plt.figure(figsize=(20,5))\n",
    "for i in range(len(graph)):\n",
    "    plt.plot(np.arange(1,201,10),graph[i],color[i],label=label[i])\n",
    "    plt.legend(loc=4)\n",
    "plt.xticks(np.arange(1,201,10))\n",
    "plt.show()\n",
    "#我们可以使用属性.n_iter_来调用本次求解中真正实现的迭代次数\n",
    "lr = LR(penalty=\"l2\",solver=\"liblinear\",C=0.9,max_iter=300).fit(Xtrain,Ytrain)\n",
    "lr.n_iter_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-26T14:48:58.568522Z",
     "start_time": "2019-12-26T14:48:58.450699Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training score : 0.980 (multinomial)\n",
      "training score : 0.953 (ovr)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.dpi'] = 150\n",
    "data = load_iris()\n",
    "X = data.data\n",
    "y = data.target\n",
    "\n",
    "for i in ['multinomial', 'ovr']:\n",
    "    lr = LR(solver='sag', max_iter=2000, random_state=42, multi_class=i).fit(X, y)\n",
    "    print(\"training score : %.3f (%s)\" % (lr.score(X, y),\n",
    "i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 逻辑回归与评分卡"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-26T23:36:23.538190Z",
     "start_time": "2019-12-26T23:36:23.124679Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('data/rankingcard.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-26T23:36:23.704619Z",
     "start_time": "2019-12-26T23:36:23.677785Z"
    }
   },
   "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>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.766127</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.957151</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.658180</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.233810</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0.907239</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "1                 1                              0.766127   45   \n",
       "2                 0                              0.957151   40   \n",
       "3                 0                              0.658180   38   \n",
       "4                 0                              0.233810   30   \n",
       "5                 0                              0.907239   49   \n",
       "\n",
       "   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "1                                     2   0.802982         9120.0   \n",
       "2                                     0   0.121876         2600.0   \n",
       "3                                     1   0.085113         3042.0   \n",
       "4                                     0   0.036050         3300.0   \n",
       "5                                     1   0.024926        63588.0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "1                               13                        0   \n",
       "2                                4                        0   \n",
       "3                                2                        1   \n",
       "4                                5                        0   \n",
       "5                                7                        0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "1                             6                                     0   \n",
       "2                             0                                     0   \n",
       "3                             0                                     0   \n",
       "4                             0                                     0   \n",
       "5                             1                                     0   \n",
       "\n",
       "   NumberOfDependents  \n",
       "1                 2.0  \n",
       "2                 1.0  \n",
       "3                 0.0  \n",
       "4                 0.0  \n",
       "5                 0.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/5_1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看数据信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-26T23:36:25.466743Z",
     "start_time": "2019-12-26T23:36:25.384683Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 150000 entries, 1 to 150000\n",
      "Data columns (total 11 columns):\n",
      "SeriousDlqin2yrs                        150000 non-null int64\n",
      "RevolvingUtilizationOfUnsecuredLines    150000 non-null float64\n",
      "age                                     150000 non-null int64\n",
      "NumberOfTime30-59DaysPastDueNotWorse    150000 non-null int64\n",
      "DebtRatio                               150000 non-null float64\n",
      "MonthlyIncome                           120269 non-null float64\n",
      "NumberOfOpenCreditLinesAndLoans         150000 non-null int64\n",
      "NumberOfTimes90DaysLate                 150000 non-null int64\n",
      "NumberRealEstateLoansOrLines            150000 non-null int64\n",
      "NumberOfTime60-89DaysPastDueNotWorse    150000 non-null int64\n",
      "NumberOfDependents                      146076 non-null float64\n",
      "dtypes: float64(4), int64(7)\n",
      "memory usage: 13.7 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-26T23:36:27.237665Z",
     "start_time": "2019-12-26T23:36:27.058686Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SeriousDlqin2yrs [0 1]\n",
      "RevolvingUtilizationOfUnsecuredLines [0.0000e+00 8.3700e-06 9.9300e-06 ... 2.2198e+04 2.9110e+04 5.0708e+04]\n",
      "age [  0  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37\n",
      "  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55\n",
      "  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73\n",
      "  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91\n",
      "  92  93  94  95  96  97  98  99 101 102 103 105 107 109]\n",
      "NumberOfTime30-59DaysPastDueNotWorse [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 96 98]\n",
      "DebtRatio [0.00000e+00 2.60000e-05 3.69000e-05 ... 3.07001e+05 3.26442e+05\n",
      " 3.29664e+05]\n",
      "MonthlyIncome [ 0.  1.  2. ... nan nan nan]\n",
      "NumberOfOpenCreditLinesAndLoans [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
      " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n",
      " 48 49 50 51 52 53 54 56 57 58]\n",
      "NumberOfTimes90DaysLate [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 17 96 98]\n",
      "NumberRealEstateLoansOrLines [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 23 25\n",
      " 26 29 32 54]\n",
      "NumberOfTime60-89DaysPastDueNotWorse [ 0  1  2  3  4  5  6  7  8  9 11 96 98]\n",
      "NumberOfDependents [ 0.  1.  2. ... nan nan nan]\n"
     ]
    }
   ],
   "source": [
    "for column in data.columns.values:\n",
    "    print(column, np.unique(data[column]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 处理异常数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-26T23:36:27.786942Z",
     "start_time": "2019-12-26T23:36:27.769703Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(data['SeriousDlqin2yrs'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-26T23:36:29.070559Z",
     "start_time": "2019-12-26T23:36:29.051627Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65696    0\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"先处理年龄为0的问题\"\"\"\n",
    "data.loc[data['age'] == 0, 'age']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，只有一个人的年龄为0，一般而言，银行不会给未成年人放贷的，这里出现年龄为0的情况，要么是父母给孩子办理的，要么就是银行出错了。  \n",
    "对于有15000的数据集来说，我们完全可以删除这一个数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-26T23:36:30.688644Z",
     "start_time": "2019-12-26T23:36:30.602716Z"
    }
   },
   "outputs": [],
   "source": [
    "data.drop(index=65696, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T04:19:18.448548Z",
     "start_time": "2019-12-27T04:19:18.434312Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "269"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"处理违约异常的\"\"\"\n",
    "(data['NumberOfTime60-89DaysPastDueNotWorse'] > 90).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T04:20:59.346389Z",
     "start_time": "2019-12-27T04:20:59.317109Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5464684014869888"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['SeriousDlqin2yrs'][data['NumberOfTime60-89DaysPastDueNotWorse'] > 90].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 处理缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-26T23:36:32.153322Z",
     "start_time": "2019-12-26T23:36:32.124416Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "处理缺失值。\n",
    "在银行借贷系统中，我们要捕抓的是少数违约的人群，那些月收入没有填写的很有可能是那些不良信用贷款比较高的人群，所以最好不要删除，  \n",
    "我们这里先查看月收入与违约的比例\n",
    "\"\"\"\n",
    "no_monthly_target = data.loc[data['MonthlyIncome'].isnull(), 'SeriousDlqin2yrs']\n",
    "monthly_target = data.loc[data['MonthlyIncome'].notnull(), 'SeriousDlqin2yrs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-26T23:36:32.554436Z",
     "start_time": "2019-12-26T23:36:32.530716Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05613669234132723"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_monthly_target.sum() / no_monthly_target.count()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-26T23:36:33.005279Z",
     "start_time": "2019-12-26T23:36:32.989665Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06948648019423288"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_target.sum() / monthly_target.count()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "算出没有填写月收入的贷款违约比例， 可以看到贷款违约比例为5%左右，貌似比填写了月收入的还低了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-26T23:36:34.054675Z",
     "start_time": "2019-12-26T23:36:33.977872Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 149999 entries, 1 to 150000\n",
      "Data columns (total 11 columns):\n",
      "SeriousDlqin2yrs                        149999 non-null int64\n",
      "RevolvingUtilizationOfUnsecuredLines    149999 non-null float64\n",
      "age                                     149999 non-null int64\n",
      "NumberOfTime30-59DaysPastDueNotWorse    149999 non-null int64\n",
      "DebtRatio                               149999 non-null float64\n",
      "MonthlyIncome                           120268 non-null float64\n",
      "NumberOfOpenCreditLinesAndLoans         149999 non-null int64\n",
      "NumberOfTimes90DaysLate                 149999 non-null int64\n",
      "NumberRealEstateLoansOrLines            149999 non-null int64\n",
      "NumberOfTime60-89DaysPastDueNotWorse    149999 non-null int64\n",
      "NumberOfDependents                      146075 non-null float64\n",
      "dtypes: float64(4), int64(7)\n",
      "memory usage: 13.7 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T00:15:51.928755Z",
     "start_time": "2019-12-27T00:15:51.900778Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def fill_nan(df):\n",
    "    \"\"\"\n",
    "    利用随机森林填补缺失值。(把target也当成是一个特征，而把缺失值的特征当成target)\n",
    "    如果数据不止一个特征有缺失值的话，那么按缺失值最少的特征先进行填补，\n",
    "    缺失值的多的特征，先用均值代。\n",
    "    填补完缺失值少的特征后，再继续用随机森林json补缺失值多的特征。\n",
    "    \n",
    "    Parameters:\n",
    "    ----------\n",
    "    df: Pd.DataFrame\n",
    "    \n",
    "    Returns:\n",
    "    --------\n",
    "    df:Pd.DataFrame\n",
    "    \"\"\"\n",
    "    df = df.copy()\n",
    "    \n",
    "    from sklearn.impute import SimpleImputer as SI\n",
    "    from sklearn.ensemble import RandomForestRegressor as RFR\n",
    "    # 实例化用均值填补缺失值的类\n",
    "    SI = SI(missing_values=np.nan) \n",
    "    \n",
    "    # 计算每一列缺失值个数\n",
    "    missing_count_list = df.isnull().sum(axis=0)\n",
    "    # 按照缺失值个从小到大排序其索引\n",
    "    sorted_missing_count = np.argsort(missing_count_list)\n",
    "    # 按照缺失值的个数，由少到多进行填补\n",
    "    for i in sorted_missing_count:\n",
    "        # 取出标签，此时还没有分训练集和测试集\n",
    "        y = df.iloc[:, i]\n",
    "        # 分训练集和测试集\n",
    "        null_index = y.isnull()\n",
    "        if null_index.sum() == 0:\n",
    "            print('没有数据缺失')\n",
    "            continue\n",
    "            \n",
    "        # 取出特征\n",
    "        X = df.loc[:, df.columns != df.columns[i]]\n",
    "        X = SI.fit_transform(X)  # 用中位数暂时填补缺失值\n",
    "\n",
    "        # 划分训练集和测试集\n",
    "        X_train = X[y.notnull(), :]\n",
    "        y_train = y[~null_index]\n",
    "        X_test = X[null_index , :]\n",
    "        \n",
    "        \n",
    "        # 用随机森林预测缺失值\n",
    "        rfr = RFR(n_estimators=20, random_state=1).fit(X_train, y_train)\n",
    "        y_predict = rfr.predict(X_test)\n",
    "        \n",
    "        # 填补缺失值\n",
    "        df.loc[null_index, df.columns[i]] = y_predict\n",
    "#         print(y_predict.shape)\n",
    "#         print(df.info())\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "鉴于用随机森林填补缺失值效果不是很好，这里采用均值填补月收入，中位数填补NumberOfDependent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T04:10:58.208536Z",
     "start_time": "2019-12-27T04:10:58.189201Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer as SI\n",
    "\n",
    "impute_mean = SI(missing_values=np.nan, strategy='mean')\n",
    "impute_median = SI(missing_values=np.nan, strategy='median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T04:13:09.453485Z",
     "start_time": "2019-12-27T04:13:09.391994Z"
    }
   },
   "outputs": [],
   "source": [
    "monthly_income = impute_mean.fit_transform(data['MonthlyIncome'].values.reshape(-1,1))\n",
    "number_of_dependents = impute_median.fit_transform(data['NumberOfDependents'].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T04:13:30.554859Z",
     "start_time": "2019-12-27T04:13:30.544560Z"
    }
   },
   "outputs": [],
   "source": [
    "data['MonthlyIncome'] = monthly_income\n",
    "data['NumberOfDependents'] = number_of_dependents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T04:13:39.888610Z",
     "start_time": "2019-12-27T04:13:39.775616Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 149999 entries, 1 to 150000\n",
      "Data columns (total 11 columns):\n",
      "SeriousDlqin2yrs                        149999 non-null int64\n",
      "RevolvingUtilizationOfUnsecuredLines    149999 non-null float64\n",
      "age                                     149999 non-null int64\n",
      "NumberOfTime30-59DaysPastDueNotWorse    149999 non-null int64\n",
      "DebtRatio                               149999 non-null float64\n",
      "MonthlyIncome                           149999 non-null float64\n",
      "NumberOfOpenCreditLinesAndLoans         149999 non-null int64\n",
      "NumberOfTimes90DaysLate                 149999 non-null int64\n",
      "NumberRealEstateLoansOrLines            149999 non-null int64\n",
      "NumberOfTime60-89DaysPastDueNotWorse    149999 non-null int64\n",
      "NumberOfDependents                      149999 non-null float64\n",
      "dtypes: float64(4), int64(7)\n",
      "memory usage: 13.7 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T04:13:46.371579Z",
     "start_time": "2019-12-27T04:13:46.136550Z"
    }
   },
   "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>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>149999.000000</td>\n",
       "      <td>149999.000000</td>\n",
       "      <td>149999.000000</td>\n",
       "      <td>149999.000000</td>\n",
       "      <td>149999.000000</td>\n",
       "      <td>1.499990e+05</td>\n",
       "      <td>149999.000000</td>\n",
       "      <td>149999.000000</td>\n",
       "      <td>149999.000000</td>\n",
       "      <td>149999.000000</td>\n",
       "      <td>149999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.066840</td>\n",
       "      <td>6.048472</td>\n",
       "      <td>52.295555</td>\n",
       "      <td>0.421029</td>\n",
       "      <td>353.007426</td>\n",
       "      <td>6.670227e+03</td>\n",
       "      <td>8.452776</td>\n",
       "      <td>0.265975</td>\n",
       "      <td>1.018233</td>\n",
       "      <td>0.240388</td>\n",
       "      <td>0.737405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.249746</td>\n",
       "      <td>249.756203</td>\n",
       "      <td>14.771298</td>\n",
       "      <td>4.192795</td>\n",
       "      <td>2037.825113</td>\n",
       "      <td>1.288049e+04</td>\n",
       "      <td>5.145964</td>\n",
       "      <td>4.169318</td>\n",
       "      <td>1.129772</td>\n",
       "      <td>4.155193</td>\n",
       "      <td>1.107020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.175074</td>\n",
       "      <td>3.903000e+03</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.154176</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.366503</td>\n",
       "      <td>6.600000e+03</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.559044</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.868257</td>\n",
       "      <td>7.400000e+03</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>50708.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>329664.000000</td>\n",
       "      <td>3.008750e+06</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines            age  \\\n",
       "count     149999.000000                         149999.000000  149999.000000   \n",
       "mean           0.066840                              6.048472      52.295555   \n",
       "std            0.249746                            249.756203      14.771298   \n",
       "min            0.000000                              0.000000      21.000000   \n",
       "25%            0.000000                              0.029867      41.000000   \n",
       "50%            0.000000                              0.154176      52.000000   \n",
       "75%            0.000000                              0.559044      63.000000   \n",
       "max            1.000000                          50708.000000     109.000000   \n",
       "\n",
       "       NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  MonthlyIncome  \\\n",
       "count                         149999.000000  149999.000000   1.499990e+05   \n",
       "mean                               0.421029     353.007426   6.670227e+03   \n",
       "std                                4.192795    2037.825113   1.288049e+04   \n",
       "min                                0.000000       0.000000   0.000000e+00   \n",
       "25%                                0.000000       0.175074   3.903000e+03   \n",
       "50%                                0.000000       0.366503   6.600000e+03   \n",
       "75%                                0.000000       0.868257   7.400000e+03   \n",
       "max                               98.000000  329664.000000   3.008750e+06   \n",
       "\n",
       "       NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "count                    149999.000000            149999.000000   \n",
       "mean                          8.452776                 0.265975   \n",
       "std                           5.145964                 4.169318   \n",
       "min                           0.000000                 0.000000   \n",
       "25%                           5.000000                 0.000000   \n",
       "50%                           8.000000                 0.000000   \n",
       "75%                          11.000000                 0.000000   \n",
       "max                          58.000000                98.000000   \n",
       "\n",
       "       NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "count                 149999.000000                         149999.000000   \n",
       "mean                       1.018233                              0.240388   \n",
       "std                        1.129772                              4.155193   \n",
       "min                        0.000000                              0.000000   \n",
       "25%                        0.000000                              0.000000   \n",
       "50%                        1.000000                              0.000000   \n",
       "75%                        2.000000                              0.000000   \n",
       "max                       54.000000                             98.000000   \n",
       "\n",
       "       NumberOfDependents  \n",
       "count       149999.000000  \n",
       "mean             0.737405  \n",
       "std              1.107020  \n",
       "min              0.000000  \n",
       "25%              0.000000  \n",
       "50%              0.000000  \n",
       "75%              1.000000  \n",
       "max             20.000000  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用pca降维，看看数据的大致分布状态"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T04:25:00.491761Z",
     "start_time": "2019-12-27T04:24:59.986798Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "PCA_2 = PCA(n_components=2).fit(data.iloc[:,1:])  # 把标签外的数据进行降维\n",
    "two_dim_data = PCA_2.transform(data.iloc[:, 1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T04:25:26.371002Z",
     "start_time": "2019-12-27T04:25:26.353477Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9996315993461974"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PCA_2.explained_variance_ratio_.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T04:26:26.536799Z",
     "start_time": "2019-12-27T04:26:26.090408Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x242037de470>]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(two_dim_data[0], two_dim_data[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 试一试用逻辑回归跑一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T04:30:02.383553Z",
     "start_time": "2019-12-27T04:29:53.022474Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9331595557920871"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression as LR\n",
    "from sklearn.model_selection import cross_val_score\n",
    "lr = LR()\n",
    "cross_val_score(lr, two_dim_data, data.iloc[:, 0], cv=10).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T04:28:37.349784Z",
     "start_time": "2019-12-27T04:28:37.327062Z"
    }
   },
   "source": [
    "### 试一试对C画学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T04:51:25.476856Z",
     "start_time": "2019-12-27T04:50:47.350296Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "D:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "scores_lr = []\n",
    "for c in np.arange(0.01, 10.01):\n",
    "    score = cross_val_score(LR(C=c, max_iter=500), two_dim_data, data.iloc[:, 0], cv=5).mean()\n",
    "    scores_lr.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T04:51:27.842850Z",
     "start_time": "2019-12-27T04:51:27.477542Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x242033b14a8>]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(0.01, 10.01), scores_lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 逻辑回归制作评分卡"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导库，并加载数据\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:52:06.619870Z",
     "start_time": "2019-12-29T03:52:06.598267Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression as LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:52:07.037542Z",
     "start_time": "2019-12-29T03:52:07.024512Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.rcParams['figure.dpi'] = 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:52:07.789658Z",
     "start_time": "2019-12-29T03:52:07.372763Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/rankingcard.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 探索数据与数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:52:08.770498Z",
     "start_time": "2019-12-29T03:52:08.749154Z"
    }
   },
   "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>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.766127</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.957151</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.658180</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.233810</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0.907239</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "1                 1                              0.766127   45   \n",
       "2                 0                              0.957151   40   \n",
       "3                 0                              0.658180   38   \n",
       "4                 0                              0.233810   30   \n",
       "5                 0                              0.907239   49   \n",
       "\n",
       "   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "1                                     2   0.802982         9120.0   \n",
       "2                                     0   0.121876         2600.0   \n",
       "3                                     1   0.085113         3042.0   \n",
       "4                                     0   0.036050         3300.0   \n",
       "5                                     1   0.024926        63588.0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "1                               13                        0   \n",
       "2                                4                        0   \n",
       "3                                2                        1   \n",
       "4                                5                        0   \n",
       "5                                7                        0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "1                             6                                     0   \n",
       "2                             0                                     0   \n",
       "3                             0                                     0   \n",
       "4                             0                                     0   \n",
       "5                             1                                     0   \n",
       "\n",
       "   NumberOfDependents  \n",
       "1                 2.0  \n",
       "2                 1.0  \n",
       "3                 0.0  \n",
       "4                 0.0  \n",
       "5                 0.0  "
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/5_2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:52:10.054878Z",
     "start_time": "2019-12-29T03:52:09.976019Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 150000 entries, 1 to 150000\n",
      "Data columns (total 11 columns):\n",
      "SeriousDlqin2yrs                        150000 non-null int64\n",
      "RevolvingUtilizationOfUnsecuredLines    150000 non-null float64\n",
      "age                                     150000 non-null int64\n",
      "NumberOfTime30-59DaysPastDueNotWorse    150000 non-null int64\n",
      "DebtRatio                               150000 non-null float64\n",
      "MonthlyIncome                           120269 non-null float64\n",
      "NumberOfOpenCreditLinesAndLoans         150000 non-null int64\n",
      "NumberOfTimes90DaysLate                 150000 non-null int64\n",
      "NumberRealEstateLoansOrLines            150000 non-null int64\n",
      "NumberOfTime60-89DaysPastDueNotWorse    150000 non-null int64\n",
      "NumberOfDependents                      146076 non-null float64\n",
      "dtypes: float64(4), int64(7)\n",
      "memory usage: 13.7 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:52:10.502565Z",
     "start_time": "2019-12-29T03:52:10.480267Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150000, 11)"
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:52:11.154434Z",
     "start_time": "2019-12-29T03:52:10.893230Z"
    }
   },
   "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>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.202690e+05</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>146076.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.066840</td>\n",
       "      <td>6.048438</td>\n",
       "      <td>52.295207</td>\n",
       "      <td>0.421033</td>\n",
       "      <td>353.005076</td>\n",
       "      <td>6.670221e+03</td>\n",
       "      <td>8.452760</td>\n",
       "      <td>0.265973</td>\n",
       "      <td>1.018240</td>\n",
       "      <td>0.240387</td>\n",
       "      <td>0.757222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.249746</td>\n",
       "      <td>249.755371</td>\n",
       "      <td>14.771866</td>\n",
       "      <td>4.192781</td>\n",
       "      <td>2037.818523</td>\n",
       "      <td>1.438467e+04</td>\n",
       "      <td>5.145951</td>\n",
       "      <td>4.169304</td>\n",
       "      <td>1.129771</td>\n",
       "      <td>4.155179</td>\n",
       "      <td>1.115086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.175074</td>\n",
       "      <td>3.400000e+03</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.154181</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>5.400000e+03</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.559046</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.868254</td>\n",
       "      <td>8.249000e+03</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>50708.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>329664.000000</td>\n",
       "      <td>3.008750e+06</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines            age  \\\n",
       "count     150000.000000                         150000.000000  150000.000000   \n",
       "mean           0.066840                              6.048438      52.295207   \n",
       "std            0.249746                            249.755371      14.771866   \n",
       "min            0.000000                              0.000000       0.000000   \n",
       "25%            0.000000                              0.029867      41.000000   \n",
       "50%            0.000000                              0.154181      52.000000   \n",
       "75%            0.000000                              0.559046      63.000000   \n",
       "max            1.000000                          50708.000000     109.000000   \n",
       "\n",
       "       NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  MonthlyIncome  \\\n",
       "count                         150000.000000  150000.000000   1.202690e+05   \n",
       "mean                               0.421033     353.005076   6.670221e+03   \n",
       "std                                4.192781    2037.818523   1.438467e+04   \n",
       "min                                0.000000       0.000000   0.000000e+00   \n",
       "25%                                0.000000       0.175074   3.400000e+03   \n",
       "50%                                0.000000       0.366508   5.400000e+03   \n",
       "75%                                0.000000       0.868254   8.249000e+03   \n",
       "max                               98.000000  329664.000000   3.008750e+06   \n",
       "\n",
       "       NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "count                    150000.000000            150000.000000   \n",
       "mean                          8.452760                 0.265973   \n",
       "std                           5.145951                 4.169304   \n",
       "min                           0.000000                 0.000000   \n",
       "25%                           5.000000                 0.000000   \n",
       "50%                           8.000000                 0.000000   \n",
       "75%                          11.000000                 0.000000   \n",
       "max                          58.000000                98.000000   \n",
       "\n",
       "       NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "count                 150000.000000                         150000.000000   \n",
       "mean                       1.018240                              0.240387   \n",
       "std                        1.129771                              4.155179   \n",
       "min                        0.000000                              0.000000   \n",
       "25%                        0.000000                              0.000000   \n",
       "50%                        1.000000                              0.000000   \n",
       "75%                        2.000000                              0.000000   \n",
       "max                       54.000000                             98.000000   \n",
       "\n",
       "       NumberOfDependents  \n",
       "count       146076.000000  \n",
       "mean             0.757222  \n",
       "std              1.115086  \n",
       "min              0.000000  \n",
       "25%              0.000000  \n",
       "50%              0.000000  \n",
       "75%              1.000000  \n",
       "max             20.000000  "
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 去除重复值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:52:12.306073Z",
     "start_time": "2019-12-29T03:52:11.998472Z"
    }
   },
   "outputs": [],
   "source": [
    "data.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:52:12.788363Z",
     "start_time": "2019-12-29T03:52:12.710746Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 149391 entries, 1 to 150000\n",
      "Data columns (total 11 columns):\n",
      "SeriousDlqin2yrs                        149391 non-null int64\n",
      "RevolvingUtilizationOfUnsecuredLines    149391 non-null float64\n",
      "age                                     149391 non-null int64\n",
      "NumberOfTime30-59DaysPastDueNotWorse    149391 non-null int64\n",
      "DebtRatio                               149391 non-null float64\n",
      "MonthlyIncome                           120170 non-null float64\n",
      "NumberOfOpenCreditLinesAndLoans         149391 non-null int64\n",
      "NumberOfTimes90DaysLate                 149391 non-null int64\n",
      "NumberRealEstateLoansOrLines            149391 non-null int64\n",
      "NumberOfTime60-89DaysPastDueNotWorse    149391 non-null int64\n",
      "NumberOfDependents                      145563 non-null float64\n",
      "dtypes: float64(4), int64(7)\n",
      "memory usage: 13.7 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:52:13.670644Z",
     "start_time": "2019-12-29T03:52:13.660687Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"恢复索引\"\"\"\n",
    "data.index = range(data.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T05:28:28.117798Z",
     "start_time": "2019-12-27T05:28:28.097200Z"
    }
   },
   "source": [
    "### 填补缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:52:14.922351Z",
     "start_time": "2019-12-29T03:52:14.863574Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SeriousDlqin2yrs                        0.000000\n",
       "RevolvingUtilizationOfUnsecuredLines    0.000000\n",
       "age                                     0.000000\n",
       "NumberOfTime30-59DaysPastDueNotWorse    0.000000\n",
       "DebtRatio                               0.000000\n",
       "MonthlyIncome                           0.195601\n",
       "NumberOfOpenCreditLinesAndLoans         0.000000\n",
       "NumberOfTimes90DaysLate                 0.000000\n",
       "NumberRealEstateLoansOrLines            0.000000\n",
       "NumberOfTime60-89DaysPastDueNotWorse    0.000000\n",
       "NumberOfDependents                      0.025624\n",
       "dtype: float64"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"查看缺失值的比例\"\"\"\n",
    "data.isnull().sum() / data.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，“月收入”，“家属人数”都有缺失值。  \n",
    "一般而言，月收入是衡量一个人信用评分的一个很好的指标，所以我们不能简单粗暴地把月收入为nan值的样本删除，而且缺失值占有差不多20%。  \n",
    "对于家属人数缺失的，我们可以用均值填补。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:52:15.842207Z",
     "start_time": "2019-12-29T03:52:15.829913Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"先填补家属人数缺失的样本\"\"\"\n",
    "data['NumberOfDependents'].fillna(int(data['NumberOfDependents'].mean()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:52:16.392594Z",
     "start_time": "2019-12-29T03:52:16.277947Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 149391 entries, 0 to 149390\n",
      "Data columns (total 11 columns):\n",
      "SeriousDlqin2yrs                        149391 non-null int64\n",
      "RevolvingUtilizationOfUnsecuredLines    149391 non-null float64\n",
      "age                                     149391 non-null int64\n",
      "NumberOfTime30-59DaysPastDueNotWorse    149391 non-null int64\n",
      "DebtRatio                               149391 non-null float64\n",
      "MonthlyIncome                           120170 non-null float64\n",
      "NumberOfOpenCreditLinesAndLoans         149391 non-null int64\n",
      "NumberOfTimes90DaysLate                 149391 non-null int64\n",
      "NumberRealEstateLoansOrLines            149391 non-null int64\n",
      "NumberOfTime60-89DaysPastDueNotWorse    149391 non-null int64\n",
      "NumberOfDependents                      149391 non-null float64\n",
      "dtypes: float64(4), int64(7)\n",
      "memory usage: 12.5 MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SeriousDlqin2yrs                        0.000000\n",
       "RevolvingUtilizationOfUnsecuredLines    0.000000\n",
       "age                                     0.000000\n",
       "NumberOfTime30-59DaysPastDueNotWorse    0.000000\n",
       "DebtRatio                               0.000000\n",
       "MonthlyIncome                           0.195601\n",
       "NumberOfOpenCreditLinesAndLoans         0.000000\n",
       "NumberOfTimes90DaysLate                 0.000000\n",
       "NumberRealEstateLoansOrLines            0.000000\n",
       "NumberOfTime60-89DaysPastDueNotWorse    0.000000\n",
       "NumberOfDependents                      0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.info()\n",
    "\n",
    "data.isnull().sum() / data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:52:17.475190Z",
     "start_time": "2019-12-29T03:52:17.403815Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"对于月收入，我们倾向于用随机森林进行填补\"\"\"\n",
    "def fill_missing_rfr(X, y, to_fill):\n",
    "    \"\"\"\n",
    "    用随机森林回归进行填补缺失值, 一次填补一列\n",
    "    \n",
    "    Parameters:\n",
    "    ----------\n",
    "    X: pd.DataFrame  \n",
    "        含有缺失值的特征矩阵\n",
    "    y: array_like  \n",
    "        标签\n",
    "    to_fill: str\n",
    "        要填补缺失值的特征名称\n",
    "    \n",
    "    Returns:\n",
    "    -------\n",
    "    y_predict: array_like\n",
    "        填补好的特征列\n",
    "    \"\"\"\n",
    "    df = X.copy()\n",
    "    y_fill = df.loc[:, to_fill]  # 把需要填补的列，单独取处理\n",
    "    df = pd.concat([df.loc[:, df.columns!=to_fill], pd.DataFrame(y)], axis=1)  # 将标签当成特征进行数据预测\n",
    "    \n",
    "    # 划分训练集和测试集\n",
    "    X_train = df[y_fill.notnull()]\n",
    "    y_train = y_fill[y_fill.notnull()]\n",
    "    \n",
    "    X_test = df[y_fill.isnull()]\n",
    "    \n",
    "    from sklearn.ensemble import RandomForestRegressor as RFR\n",
    "    rfr = RFR(n_estimators=100).fit(X_train, y_train)\n",
    "    y_predict = rfr.predict(X_test)\n",
    "    return y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:54:30.285181Z",
     "start_time": "2019-12-29T03:52:17.815705Z"
    }
   },
   "outputs": [],
   "source": [
    "X = data.iloc[:, 1:]  # 取出特征矩阵\n",
    "y = data.iloc[:, 0]  # 取出标签\n",
    "y_predict = fill_missing_rfr(X, y, to_fill='MonthlyIncome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:54:30.688011Z",
     "start_time": "2019-12-29T03:54:30.661472Z"
    }
   },
   "outputs": [],
   "source": [
    "# 将月收入的缺失值填充进去\n",
    "data.loc[data['MonthlyIncome'].isnull(), 'MonthlyIncome'] = y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:54:31.271788Z",
     "start_time": "2019-12-29T03:54:31.172367Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 149391 entries, 0 to 149390\n",
      "Data columns (total 11 columns):\n",
      "SeriousDlqin2yrs                        149391 non-null int64\n",
      "RevolvingUtilizationOfUnsecuredLines    149391 non-null float64\n",
      "age                                     149391 non-null int64\n",
      "NumberOfTime30-59DaysPastDueNotWorse    149391 non-null int64\n",
      "DebtRatio                               149391 non-null float64\n",
      "MonthlyIncome                           149391 non-null float64\n",
      "NumberOfOpenCreditLinesAndLoans         149391 non-null int64\n",
      "NumberOfTimes90DaysLate                 149391 non-null int64\n",
      "NumberRealEstateLoansOrLines            149391 non-null int64\n",
      "NumberOfTime60-89DaysPastDueNotWorse    149391 non-null int64\n",
      "NumberOfDependents                      149391 non-null float64\n",
      "dtypes: float64(4), int64(7)\n",
      "memory usage: 12.5 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 描述性统计处理异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T12:17:05.856643Z",
     "start_time": "2019-12-27T12:17:05.573710Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>1%</th>\n",
       "      <th>10%</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>99%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <td>149391.0</td>\n",
       "      <td>0.066999</td>\n",
       "      <td>0.250021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <td>149391.0</td>\n",
       "      <td>6.071087</td>\n",
       "      <td>250.263672</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003199</td>\n",
       "      <td>0.030132</td>\n",
       "      <td>0.154235</td>\n",
       "      <td>0.556494</td>\n",
       "      <td>1.093922</td>\n",
       "      <td>50708.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>149391.0</td>\n",
       "      <td>52.306237</td>\n",
       "      <td>14.725962</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <td>149391.0</td>\n",
       "      <td>0.393886</td>\n",
       "      <td>3.852953</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DebtRatio</th>\n",
       "      <td>149391.0</td>\n",
       "      <td>354.436740</td>\n",
       "      <td>2041.843455</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.034991</td>\n",
       "      <td>0.177441</td>\n",
       "      <td>0.368234</td>\n",
       "      <td>0.875279</td>\n",
       "      <td>4985.100000</td>\n",
       "      <td>329664.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <td>149391.0</td>\n",
       "      <td>5424.899449</td>\n",
       "      <td>13244.418183</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>4417.000000</td>\n",
       "      <td>7416.000000</td>\n",
       "      <td>23250.000000</td>\n",
       "      <td>3008750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <td>149391.0</td>\n",
       "      <td>8.480892</td>\n",
       "      <td>5.136515</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <td>149391.0</td>\n",
       "      <td>0.238120</td>\n",
       "      <td>3.826165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <td>149391.0</td>\n",
       "      <td>1.022391</td>\n",
       "      <td>1.130196</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <td>149391.0</td>\n",
       "      <td>0.212503</td>\n",
       "      <td>3.810523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <td>149391.0</td>\n",
       "      <td>0.740393</td>\n",
       "      <td>1.108272</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         count         mean           std  \\\n",
       "SeriousDlqin2yrs                      149391.0     0.066999      0.250021   \n",
       "RevolvingUtilizationOfUnsecuredLines  149391.0     6.071087    250.263672   \n",
       "age                                   149391.0    52.306237     14.725962   \n",
       "NumberOfTime30-59DaysPastDueNotWorse  149391.0     0.393886      3.852953   \n",
       "DebtRatio                             149391.0   354.436740   2041.843455   \n",
       "MonthlyIncome                         149391.0  5424.899449  13244.418183   \n",
       "NumberOfOpenCreditLinesAndLoans       149391.0     8.480892      5.136515   \n",
       "NumberOfTimes90DaysLate               149391.0     0.238120      3.826165   \n",
       "NumberRealEstateLoansOrLines          149391.0     1.022391      1.130196   \n",
       "NumberOfTime60-89DaysPastDueNotWorse  149391.0     0.212503      3.810523   \n",
       "NumberOfDependents                    149391.0     0.740393      1.108272   \n",
       "\n",
       "                                      min    1%        10%          25%  \\\n",
       "SeriousDlqin2yrs                      0.0   0.0   0.000000     0.000000   \n",
       "RevolvingUtilizationOfUnsecuredLines  0.0   0.0   0.003199     0.030132   \n",
       "age                                   0.0  24.0  33.000000    41.000000   \n",
       "NumberOfTime30-59DaysPastDueNotWorse  0.0   0.0   0.000000     0.000000   \n",
       "DebtRatio                             0.0   0.0   0.034991     0.177441   \n",
       "MonthlyIncome                         0.0   0.0   0.180000  1800.000000   \n",
       "NumberOfOpenCreditLinesAndLoans       0.0   0.0   3.000000     5.000000   \n",
       "NumberOfTimes90DaysLate               0.0   0.0   0.000000     0.000000   \n",
       "NumberRealEstateLoansOrLines          0.0   0.0   0.000000     0.000000   \n",
       "NumberOfTime60-89DaysPastDueNotWorse  0.0   0.0   0.000000     0.000000   \n",
       "NumberOfDependents                    0.0   0.0   0.000000     0.000000   \n",
       "\n",
       "                                              50%          75%           99%  \\\n",
       "SeriousDlqin2yrs                         0.000000     0.000000      1.000000   \n",
       "RevolvingUtilizationOfUnsecuredLines     0.154235     0.556494      1.093922   \n",
       "age                                     52.000000    63.000000     87.000000   \n",
       "NumberOfTime30-59DaysPastDueNotWorse     0.000000     0.000000      4.000000   \n",
       "DebtRatio                                0.368234     0.875279   4985.100000   \n",
       "MonthlyIncome                         4417.000000  7416.000000  23250.000000   \n",
       "NumberOfOpenCreditLinesAndLoans          8.000000    11.000000     24.000000   \n",
       "NumberOfTimes90DaysLate                  0.000000     0.000000      3.000000   \n",
       "NumberRealEstateLoansOrLines             1.000000     2.000000      4.000000   \n",
       "NumberOfTime60-89DaysPastDueNotWorse     0.000000     0.000000      2.000000   \n",
       "NumberOfDependents                       0.000000     1.000000      4.000000   \n",
       "\n",
       "                                            max  \n",
       "SeriousDlqin2yrs                            1.0  \n",
       "RevolvingUtilizationOfUnsecuredLines    50708.0  \n",
       "age                                       109.0  \n",
       "NumberOfTime30-59DaysPastDueNotWorse       98.0  \n",
       "DebtRatio                              329664.0  \n",
       "MonthlyIncome                         3008750.0  \n",
       "NumberOfOpenCreditLinesAndLoans            58.0  \n",
       "NumberOfTimes90DaysLate                    98.0  \n",
       "NumberRealEstateLoansOrLines               54.0  \n",
       "NumberOfTime60-89DaysPastDueNotWorse       98.0  \n",
       "NumberOfDependents                         20.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe([0.01, 0.1, 0.25, 0.5, 0.75, .99]).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以看到，异常数据有：  \n",
    "1. 年龄最小为0。\n",
    "2. 90天内逾期但没发展到糟糕情况的最大值为98，这很奇怪，一般而言这个值最大也就90，而这里有大于90的，说明数据存在异常"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T12:17:05.969336Z",
     "start_time": "2019-12-27T12:17:05.950421Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"先处理年龄未到法定年龄的\"\"\"\n",
    "(data['age'] <= 8).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:54:31.700410Z",
     "start_time": "2019-12-29T03:54:31.659447Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"可以看到异常值只有一个，所以我们可以直接删除\"\"\"\n",
    "data = data[data['age'] > 8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T12:17:06.179772Z",
     "start_time": "2019-12-27T12:17:06.164813Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "225"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"处理90天内逾期的\"\"\"\n",
    "(data['NumberOfTime60-89DaysPastDueNotWorse']>90).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T12:17:06.275518Z",
     "start_time": "2019-12-27T12:17:06.253576Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    136\n",
       "0     89\n",
       "Name: SeriousDlqin2yrs, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看逾期超过90天的，坏账情况\n",
    "(data['SeriousDlqin2yrs'][data['NumberOfTime60-89DaysPastDueNotWorse'] > 90]).value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:54:32.070812Z",
     "start_time": "2019-12-29T03:54:32.044505Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"可以看到逾期90天以上的客户并非全是坏客户，所以这里可以直接删除异常数据\"\"\"\n",
    "data = data[data['NumberOfTime60-89DaysPastDueNotWorse']<= 90]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:54:32.571281Z",
     "start_time": "2019-12-29T03:54:32.343491Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
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       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>149165.000000</td>\n",
       "      <td>149165.000000</td>\n",
       "      <td>149165.000000</td>\n",
       "      <td>149165.000000</td>\n",
       "      <td>149165.000000</td>\n",
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       "      <td>149165.000000</td>\n",
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       "      <td>149165.000000</td>\n",
       "      <td>149165.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.066188</td>\n",
       "      <td>6.078770</td>\n",
       "      <td>52.331076</td>\n",
       "      <td>0.246720</td>\n",
       "      <td>354.963542</td>\n",
       "      <td>5.428978e+03</td>\n",
       "      <td>8.493688</td>\n",
       "      <td>0.090725</td>\n",
       "      <td>1.023927</td>\n",
       "      <td>0.065069</td>\n",
       "      <td>0.740911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.248612</td>\n",
       "      <td>250.453111</td>\n",
       "      <td>14.714114</td>\n",
       "      <td>0.698935</td>\n",
       "      <td>2043.344496</td>\n",
       "      <td>1.323148e+04</td>\n",
       "      <td>5.129841</td>\n",
       "      <td>0.486354</td>\n",
       "      <td>1.130350</td>\n",
       "      <td>0.330675</td>\n",
       "      <td>1.108534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030033</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.178211</td>\n",
       "      <td>1.800000e+03</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.153615</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.368619</td>\n",
       "      <td>4.433000e+03</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.553698</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.876994</td>\n",
       "      <td>7.417000e+03</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>50708.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>329664.000000</td>\n",
       "      <td>3.008750e+06</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines            age  \\\n",
       "count     149165.000000                         149165.000000  149165.000000   \n",
       "mean           0.066188                              6.078770      52.331076   \n",
       "std            0.248612                            250.453111      14.714114   \n",
       "min            0.000000                              0.000000      21.000000   \n",
       "25%            0.000000                              0.030033      41.000000   \n",
       "50%            0.000000                              0.153615      52.000000   \n",
       "75%            0.000000                              0.553698      63.000000   \n",
       "max            1.000000                          50708.000000     109.000000   \n",
       "\n",
       "       NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  MonthlyIncome  \\\n",
       "count                         149165.000000  149165.000000   1.491650e+05   \n",
       "mean                               0.246720     354.963542   5.428978e+03   \n",
       "std                                0.698935    2043.344496   1.323148e+04   \n",
       "min                                0.000000       0.000000   0.000000e+00   \n",
       "25%                                0.000000       0.178211   1.800000e+03   \n",
       "50%                                0.000000       0.368619   4.433000e+03   \n",
       "75%                                0.000000       0.876994   7.417000e+03   \n",
       "max                               13.000000  329664.000000   3.008750e+06   \n",
       "\n",
       "       NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "count                    149165.000000            149165.000000   \n",
       "mean                          8.493688                 0.090725   \n",
       "std                           5.129841                 0.486354   \n",
       "min                           0.000000                 0.000000   \n",
       "25%                           5.000000                 0.000000   \n",
       "50%                           8.000000                 0.000000   \n",
       "75%                          11.000000                 0.000000   \n",
       "max                          58.000000                17.000000   \n",
       "\n",
       "       NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "count                 149165.000000                         149165.000000   \n",
       "mean                       1.023927                              0.065069   \n",
       "std                        1.130350                              0.330675   \n",
       "min                        0.000000                              0.000000   \n",
       "25%                        0.000000                              0.000000   \n",
       "50%                        1.000000                              0.000000   \n",
       "75%                        2.000000                              0.000000   \n",
       "max                       54.000000                             11.000000   \n",
       "\n",
       "       NumberOfDependents  \n",
       "count       149165.000000  \n",
       "mean             0.740911  \n",
       "std              1.108534  \n",
       "min              0.000000  \n",
       "25%              0.000000  \n",
       "50%              0.000000  \n",
       "75%              1.000000  \n",
       "max             20.000000  "
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 样本不均衡问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:54:32.989980Z",
     "start_time": "2019-12-29T03:54:32.880515Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "违约占比：6.62% ，守信占比：93.38%\n"
     ]
    }
   ],
   "source": [
    "X = data.iloc[:, 1:]\n",
    "y = data.iloc[:, 0]\n",
    "\n",
    "y.value_counts()\n",
    "print('违约占比：{:.2%} ，守信占比：{:.2%}'.format(y.value_counts()[1] / y.shape[0], \n",
    "                                           y.value_counts()[0] / y.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:54:33.355470Z",
     "start_time": "2019-12-29T03:54:33.330896Z"
    }
   },
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:54:34.356866Z",
     "start_time": "2019-12-29T03:54:33.621864Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    139292\n",
      "0    139292\n",
      "Name: SeriousDlqin2yrs, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\"\"\"通过上采样，解决样本不均衡问题\"\"\"\n",
    "sm = SMOTE(random_state=42)\n",
    "X, y = sm.fit_sample(X, y)\n",
    "\n",
    "n_samples = y.shape[0]\n",
    "\n",
    "value_counts = pd.Series(y).value_counts()\n",
    "print(value_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 划分训练集和测试集，然后保存数据\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:54:34.823138Z",
     "start_time": "2019-12-29T03:54:34.641046Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = pd.DataFrame(X)\n",
    "y = pd.DataFrame(y)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=420)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:54:35.136632Z",
     "start_time": "2019-12-29T03:54:35.091745Z"
    }
   },
   "outputs": [],
   "source": [
    "model_data = pd.concat([y_train, X_train], axis=1)\n",
    "model_data.columns = data.columns\n",
    "model_data.index = range(model_data.shape[0])\n",
    "\n",
    "vali_data = pd.concat([y_test, X_test], axis=1)\n",
    "vali_data.columns = data.columns\n",
    "vali_data.index = range(vali_data.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T13:02:16.334067Z",
     "start_time": "2019-12-27T13:02:08.102428Z"
    }
   },
   "outputs": [],
   "source": [
    "# 保存数据\n",
    "model_data.to_csv('data/model_data.csv')\n",
    "vali_data.to_csv('data/vali_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分箱"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">分箱是把连续型数据离散化。分箱的个数越多，信息损失得越多，一般控制在10个箱子一下。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于评分卡来说，我们分5个箱子就足够了。为了衡量特征的信息量，银行业已经有了相应的函数，information value(IV)。\n",
    "$$IV = \\displaystyle \\sum_{i = 1}^{N}(good\\% - bad\\%)\\times WOE_i$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$WOE_i = ln(\\dfrac{good\\%}{bad\\%})$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在IV中，good%和bad%为**所有**优质客户、坏客户占总的客户百分比。而在$WOE_i$中，good%、bad%则是**对应箱子中**优质客户、坏客户占箱子样本数的百分比。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "WOE又称证据权重(weight of evidence)，WOE越大说明箱子优质客户越多。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IV表示的意义是特征的信息量以及这个特征对模型的共享。\n",
    "![](images/5_3.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IV并非越大越好，当分箱的个数越来越多的时候，信息损失就越大，这是IV越小，所以可以通过观察IV值来确定分箱个数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 等频分箱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:54:49.844202Z",
     "start_time": "2019-12-29T03:54:49.767733Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"pd.qcut 返回两个结果：一个是数据集的对应每个数据的所在的区间，\n",
    "一个是所有的区间(如果retbins=True时，返回的则是区间的边界)\"\"\"\n",
    "model_data['qcut'], updown = pd.qcut(model_data['age'], q=20, retbins=True, duplicates='drop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:54:50.476057Z",
     "start_time": "2019-12-29T03:54:50.339140Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 计算每个箱子中标签为0和标签为1的个数\n",
    "count_y0 = model_data[model_data['SeriousDlqin2yrs'] == 0].groupby('qcut').count()['SeriousDlqin2yrs']\n",
    "count_y1 = model_data[model_data['SeriousDlqin2yrs'] == 1].groupby('qcut').count()['SeriousDlqin2yrs']\n",
    "\n",
    "num_bins = [*zip(updown, updown[1:], count_y0, count_y1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-28T01:42:26.523874Z",
     "start_time": "2019-12-28T01:42:26.512566Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(21.0, 28.0, 4243, 7560),\n",
       " (28.0, 31.0, 3571, 5961),\n",
       " (31.0, 34.0, 4075, 6737),\n",
       " (34.0, 36.0, 2908, 4604),\n",
       " (36.0, 39.0, 5182, 7452),\n",
       " (39.0, 41.0, 3956, 5851),\n",
       " (41.0, 43.0, 4002, 5725),\n",
       " (43.0, 45.0, 4389, 5963),\n",
       " (45.0, 46.0, 2419, 3259),\n",
       " (46.0, 48.0, 4813, 6184),\n",
       " (48.0, 50.0, 4900, 6207),\n",
       " (50.0, 52.0, 4728, 5802),\n",
       " (52.0, 54.0, 4681, 5022),\n",
       " (54.0, 56.0, 4677, 4010),\n",
       " (56.0, 58.0, 4483, 3397),\n",
       " (58.0, 61.0, 6583, 4813),\n",
       " (61.0, 64.0, 6968, 3208),\n",
       " (64.0, 68.0, 6623, 2277),\n",
       " (68.0, 74.0, 6753, 1886),\n",
       " (74.0, 107.0, 7737, 1399)]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-27T13:33:27.788603Z",
     "start_time": "2019-12-27T13:33:27.783607Z"
    }
   },
   "source": [
    "### 确保每个箱子中都有正负标签\n",
    ">我们希望不同属性的人有不同的分数，因此我们希望在同一个箱子内的人的属性是尽量相似的，而不同箱子的人的\n",
    "属性是尽量不同的，即业界常说的”组间差异大，组内差异小“。对于评分卡来说，就是说我们希望一个箱子内的人\n",
    "违约概率是类似的，而不同箱子的人的违约概率差距很大，即WOE差距要大，并且每个箱子中坏客户所占的比重（\n",
    "）也要不同。那我们，可以使用卡方检验来对比两个箱子之间的相似性，如果两个箱子之间卡方检验的P值很\n",
    "大，则说明他们非常相似，那我们就可以将这两个箱子合并为一个箱子。  \n",
    "\n",
    "\n",
    "基于这样的思想，我们总结出我们对一个特征进行分箱的步骤：\n",
    "1. 我们首先把连续型变量分成一组数量较多的分类型变量，比如，将几万个样本分成100组，或50组\n",
    "2. 确保每一组中都要包含两种类别的样本，否则IV值会无法计算\n",
    "3. 我们对相邻的组进行卡方检验，卡方检验的P值很大的组进行合并，直到数据中的组数小于设定的N箱为止\n",
    "4. 我们让一个特征分别分成[2,3,4.....20]箱，观察每个分箱个数下的IV值如何变化，找出最适合的分箱个数\n",
    "5. 分箱完毕后，我们计算每个箱的WOE值， ，观察分箱效果  \n",
    "这些步骤都完成后，我们可以对各个特征都进行分箱，然后观察每个特征的IV值，以此来挑选特征。\n",
    "接下来，我们就以\"age\"为例子，来看看分箱如何完成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-28T01:40:28.610495Z",
     "start_time": "2019-12-28T01:40:28.581455Z"
    }
   },
   "outputs": [],
   "source": [
    "for i in range(20):\n",
    "    # 判断第一组是否有正负标签，如果没有的话向后合并\n",
    "    if 0 in num_bins[0][2:]:\n",
    "        num_bins[0:2] = [(         \n",
    "            num_bins[0][0],  # 区间下限为第一组的下限\n",
    "            num_bins[1][1],  # 区间上限换成第二组的区间上限\n",
    "            num_bins[0][2] + num_bins[1][2],  # 将第一二组标签为0的样本数量合并\n",
    "            num_bins[0][3] + num_bins[1][3]   # 将第一二组标签为1的样本数量合并\n",
    "        )]\n",
    "        continue\n",
    "        \n",
    "    # 判断第二组是否都有标签为0和1的样本，如果没有的话，向前合并，因为我们已经确保第一组含有标签为0和1\n",
    "    for j in range(len(num_bins)):\n",
    "        if 0 in num_bins[i][2:]:\n",
    "            num_bins[j-1: j+1] = [(\n",
    "                num_bins[j-1][0],  \n",
    "                num_bins[j][1],\n",
    "                num_bins[j-1][2] + num_bins[j][2],\n",
    "                num_bins[j-1][3] + num_bins[j][3],\n",
    "            )]\n",
    "            break  # 跳出当前循环的原因是num_bins的len已经发生变换，但循环的j还没有即使更新\n",
    "    else:\n",
    "        break  # 如果第一组和后面的组都含有标签为0，1的样本则跳出循环"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-28T01:40:29.059238Z",
     "start_time": "2019-12-28T01:40:29.034238Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(21.0, 28.0, 4243, 7560),\n",
       " (28.0, 31.0, 3571, 5961),\n",
       " (31.0, 34.0, 4075, 6737),\n",
       " (34.0, 36.0, 2908, 4604),\n",
       " (36.0, 39.0, 5182, 7452),\n",
       " (39.0, 41.0, 3956, 5851),\n",
       " (41.0, 43.0, 4002, 5725),\n",
       " (43.0, 45.0, 4389, 5963),\n",
       " (45.0, 46.0, 2419, 3259),\n",
       " (46.0, 48.0, 4813, 6184),\n",
       " (48.0, 50.0, 4900, 6207),\n",
       " (50.0, 52.0, 4728, 5802),\n",
       " (52.0, 54.0, 4681, 5022),\n",
       " (54.0, 56.0, 4677, 4010),\n",
       " (56.0, 58.0, 4483, 3397),\n",
       " (58.0, 61.0, 6583, 4813),\n",
       " (61.0, 64.0, 6968, 3208),\n",
       " (64.0, 68.0, 6623, 2277),\n",
       " (68.0, 74.0, 6753, 1886),\n",
       " (74.0, 107.0, 7737, 1399)]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定义WOE和IV函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-28T01:50:12.238586Z",
     "start_time": "2019-12-28T01:50:12.211819Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_woe(num_bins):\n",
    "    \"\"\"\n",
    "    通过num_bins构造woe函数\n",
    "    \n",
    "    Parameters:\n",
    "    ----------\n",
    "    num_bins: list\n",
    "        [区间下限, 区间下限, 标签为0的个数, 标签为1的个数]\n",
    "    \n",
    "    Returns:\n",
    "    --------\n",
    "    df: pd.DataFrame\n",
    "        放回一个含有各类信息的woe DataFrame\n",
    "        df.columns = ['min', 'max', 'count_y0', 'count_y1',\n",
    "                    'total', 'percentage', 'bad_rate', \n",
    "                    'good%', 'bad%', 'woe']\n",
    "        其中，total : 箱子中样本的个数\n",
    "              percentage : 箱子中样本个数占总个数的百分比\n",
    "              bad_rate : 箱子中坏样本所占的比例\n",
    "              good% : 好样本占所有好样本的比例\n",
    "              bad% : 坏样本占所有坏样本的比例\n",
    "              woe : woe值\n",
    "    \"\"\"\n",
    "    columns = ['min', 'max', 'count_y0', 'count_y1']\n",
    "    df = pd.DataFrame(num_bins, columns=columns)\n",
    "    df['total'] = df['count_y0'] + df['count_y1']\n",
    "    df['percentage'] = df['total'] / df['total'].sum()\n",
    "    df['bad_rate'] = df['count_y1'] / df['total']\n",
    "    df['good%'] = df['count_y0'] / df['count_y0'].sum()\n",
    "    df['bad%'] = df['count_y1'] / df['count_y1'].sum()\n",
    "    df['woe'] = np.log(df['good%'] / df['bad%'])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-28T01:50:24.780478Z",
     "start_time": "2019-12-28T01:50:24.758488Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_iv(df):\n",
    "    \"\"\"\n",
    "    计算IV值。\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    df : pd.DataFrame\n",
    "        带有woe的df\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    iv : float\n",
    "        \n",
    "    \"\"\"\n",
    "    rate = df['good%'] - df['bad%']\n",
    "    iv = np.dot(rate, df['woe'])\n",
    "    return iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 卡方检验，合并箱子，画出IV曲线 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-28T01:40:35.773769Z",
     "start_time": "2019-12-28T01:40:35.766929Z"
    }
   },
   "outputs": [],
   "source": [
    "num_bins_ = num_bins.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-28T01:40:36.163407Z",
     "start_time": "2019-12-28T01:40:36.143170Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-28T01:40:44.497151Z",
     "start_time": "2019-12-28T01:40:44.470116Z"
    }
   },
   "outputs": [],
   "source": [
    "def merge(num_bins):\n",
    "    \"\"\"\n",
    "    利用卡方检验合并箱子\n",
    "    \n",
    "    Parameters:\n",
    "    -----------\n",
    "    num_bins:list \n",
    "        [区间下限, 区间下限, 标签为0的个数, 标签为1的个数]\n",
    "    \n",
    "    Returns:\n",
    "    -------\n",
    "    axis: list\n",
    "        含有箱子数量的列表，用于绘图当x轴\n",
    "    iv_list: list\n",
    "        IV值\n",
    "    \"\"\"\n",
    "    axis = []\n",
    "    iv_list = []\n",
    "    while len(num_bins) > 2:\n",
    "        for i in range(len(num_bins) -1):\n",
    "            pvs = []  # 用于接收卡方检验返回的P值\n",
    "            p = scipy.stats.chi2_contingency([num_bins[i][2:], num_bins[i+1][2:]])[1]\n",
    "            pvs.append(p)\n",
    "        \n",
    "        max_index = pvs.index(max(pvs))\n",
    "        num_bins[max_index: max_index+2] = [(\n",
    "                        num_bins[max_index][0],\n",
    "                        num_bins[max_index+1][1],\n",
    "                        num_bins[max_index][2] + num_bins[max_index+1][2],\n",
    "                        num_bins[max_index][3] + num_bins[max_index+1][3],\n",
    "        )]\n",
    "        \n",
    "        axis.append(len(num_bins))\n",
    "        woe = get_woe(num_bins)\n",
    "        iv = get_iv(woe)\n",
    "        iv_list.append(iv)\n",
    "        \n",
    "    return axis, iv_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-28T01:40:47.589607Z",
     "start_time": "2019-12-28T01:40:47.188947Z"
    }
   },
   "outputs": [],
   "source": [
    "axisx, IV = merge(num_bins_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-28T01:40:56.512096Z",
     "start_time": "2019-12-28T01:40:56.117838Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.plot(axisx, IV)\n",
    "plt.xticks(axisx)\n",
    "plt.xlabel('number of box')\n",
    "plt.ylabel('iv')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用最佳分箱个数，验证分箱结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-28T01:43:36.208518Z",
     "start_time": "2019-12-28T01:43:36.176663Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_bin(num_bins, n):\n",
    "    \"\"\"\n",
    "    利用卡方检验合并箱子\n",
    "    \n",
    "    Parameters:\n",
    "    -----------\n",
    "    num_bins:list \n",
    "        [区间下限, 区间下限, 标签为0的个数, 标签为1的个数]\n",
    "    n : int\n",
    "        最佳分箱个数\n",
    "    \n",
    "    Returns:\n",
    "    -------\n",
    "    num_bins : list\n",
    "        合并后的num_bins\n",
    "        \n",
    "    \"\"\"\n",
    "    num_bins = num_bins.copy()\n",
    "    while len(num_bins) > n:\n",
    "        for i in range(len(num_bins) -1):\n",
    "            pvs = []  # 用于接收卡方检验返回的P值\n",
    "            p = scipy.stats.chi2_contingency([num_bins[i][2:], num_bins[i+1][2:]])[1]\n",
    "            pvs.append(p)\n",
    "        \n",
    "        max_index = pvs.index(max(pvs))\n",
    "        num_bins[max_index: max_index+2] = [(\n",
    "                        num_bins[max_index][0],\n",
    "                        num_bins[max_index+1][1],\n",
    "                        num_bins[max_index][2] + num_bins[max_index+1][2],\n",
    "                        num_bins[max_index][3] + num_bins[max_index+1][3],\n",
    "        )]\n",
    "    return num_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-28T01:50:30.518932Z",
     "start_time": "2019-12-28T01:50:30.421926Z"
    }
   },
   "outputs": [],
   "source": [
    "after_bins = get_bin(num_bins, n=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-28T01:50:30.967613Z",
     "start_time": "2019-12-28T01:50:30.944058Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(21.0, 64.0, 76578, 91755),\n",
       " (64.0, 68.0, 6623, 2277),\n",
       " (68.0, 74.0, 6753, 1886),\n",
       " (74.0, 107.0, 7737, 1399)]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "after_bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-28T01:50:31.767104Z",
     "start_time": "2019-12-28T01:50:31.732261Z"
    }
   },
   "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>min</th>\n",
       "      <th>max</th>\n",
       "      <th>count_y0</th>\n",
       "      <th>count_y1</th>\n",
       "      <th>total</th>\n",
       "      <th>percentage</th>\n",
       "      <th>bad_rate</th>\n",
       "      <th>good%</th>\n",
       "      <th>bad%</th>\n",
       "      <th>woe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>76578</td>\n",
       "      <td>91755</td>\n",
       "      <td>168333</td>\n",
       "      <td>0.863211</td>\n",
       "      <td>0.545080</td>\n",
       "      <td>0.783880</td>\n",
       "      <td>0.942847</td>\n",
       "      <td>-0.184648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>6623</td>\n",
       "      <td>2277</td>\n",
       "      <td>8900</td>\n",
       "      <td>0.045639</td>\n",
       "      <td>0.255843</td>\n",
       "      <td>0.067795</td>\n",
       "      <td>0.023398</td>\n",
       "      <td>1.063854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>6753</td>\n",
       "      <td>1886</td>\n",
       "      <td>8639</td>\n",
       "      <td>0.044301</td>\n",
       "      <td>0.218312</td>\n",
       "      <td>0.069126</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>1.271693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>7737</td>\n",
       "      <td>1399</td>\n",
       "      <td>9136</td>\n",
       "      <td>0.046849</td>\n",
       "      <td>0.153130</td>\n",
       "      <td>0.079199</td>\n",
       "      <td>0.014376</td>\n",
       "      <td>1.706421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    min    max  count_y0  count_y1   total  percentage  bad_rate     good%  \\\n",
       "0  21.0   64.0     76578     91755  168333    0.863211  0.545080  0.783880   \n",
       "1  64.0   68.0      6623      2277    8900    0.045639  0.255843  0.067795   \n",
       "2  68.0   74.0      6753      1886    8639    0.044301  0.218312  0.069126   \n",
       "3  74.0  107.0      7737      1399    9136    0.046849  0.153130  0.079199   \n",
       "\n",
       "       bad%       woe  \n",
       "0  0.942847 -0.184648  \n",
       "1  0.023398  1.063854  \n",
       "2  0.019380  1.271693  \n",
       "3  0.014376  1.706421  "
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_woe(after_bins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将选取最佳分箱个数的过程包装为类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-28T11:38:19.527042Z",
     "start_time": "2019-12-28T11:38:19.486970Z"
    }
   },
   "outputs": [],
   "source": [
    "def graphforbestbin(df, X, y, n=5, q=20, graph=False):\n",
    "    \"\"\"\n",
    "    实现最优分箱，基于卡方检验\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df : pd.DataFrame\n",
    "        输入的数据\n",
    "    X : 要分箱的列名\n",
    "    y : 标签名\n",
    "    n : 保留分箱的个数\n",
    "    q : 初始分箱个数\n",
    "    \"\"\"\n",
    "    df = df[[X, y]].copy()\n",
    "    \n",
    "    # make num_bins\n",
    "    df['qcut'], updown = pd.qcut(df[X], q=q, retbins=True, duplicates='drop')\n",
    "    count_y0 = df[df[y] == 0].groupby('qcut').count()[y]\n",
    "    count_y1 = df[df[y] == 1].groupby('qcut').count()[y]\n",
    "    num_bins = [*zip(updown, updown[1:], count_y0, count_y1)]\n",
    "    \n",
    "    # 检验num_bins\n",
    "    for i in range(q):\n",
    "        if 0 in num_bins[0][2:]:\n",
    "            num_bins[0: 2] = [(\n",
    "                num_bins[0][0],\n",
    "                num_bins[1][1],\n",
    "                num_bins[0][2] + num_bins[1][2],\n",
    "                num_bins[0][3] + num_bins[1][3],\n",
    "            )]\n",
    "            continue\n",
    "        \n",
    "        for j in range(len(num_bins)):\n",
    "            if 0 in num_bins[j][2:]:\n",
    "                num_bins[j-1 : j+1] = [(\n",
    "                    num_bins[j-1][0],\n",
    "                    num_bins[j][1],\n",
    "                    num_bins[j-1][2] + num_bins[j][2],\n",
    "                    num_bins[j-1][3] + num_bins[j][3],\n",
    "                )]\n",
    "                break\n",
    "                \n",
    "        else:\n",
    "            break\n",
    "            \n",
    "    def _get_woe(num_bins):\n",
    "        \"\"\"\n",
    "        通过num_bins构造woe函数\n",
    "\n",
    "        Parameters:\n",
    "        ----------\n",
    "        num_bins: list\n",
    "            [区间下限, 区间下限, 标签为0的个数, 标签为1的个数]\n",
    "\n",
    "        Returns:\n",
    "        --------\n",
    "        df: pd.DataFrame\n",
    "            放回一个含有各类信息的woe DataFrame\n",
    "            df.columns = ['min', 'max', 'count_y0', 'count_y1',\n",
    "                        'total', 'percentage', 'bad_rate', \n",
    "                        'good%', 'bad%', 'woe']\n",
    "            其中，total : 箱子中样本的个数\n",
    "                  percentage : 箱子中样本个数占总个数的百分比\n",
    "                  bad_rate : 箱子中坏样本所占的比例\n",
    "                  good% : 好样本占所有好样本的比例\n",
    "                  bad% : 坏样本占所有坏样本的比例\n",
    "                  woe : woe值\n",
    "        \"\"\"\n",
    "        columns = ['min', 'max', 'count_y0', 'count_y1']\n",
    "        df = pd.DataFrame(num_bins, columns=columns)\n",
    "        df['total'] = df['count_y0'] + df['count_y1']\n",
    "        df['percentage'] = df['total'] / df['total'].sum()\n",
    "        df['bad_rate'] = df['count_y1'] / df['total']\n",
    "        df['good%'] = df['count_y0'] / df['count_y0'].sum()\n",
    "        df['bad%'] = df['count_y1'] / df['count_y1'].sum()\n",
    "        df['woe'] = np.log(df['good%'] / df['bad%'])\n",
    "        return df\n",
    "\n",
    "    def _get_iv(df):\n",
    "        \"\"\"\n",
    "        计算IV值。\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        df : pd.DataFrame\n",
    "            带有woe的df\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        iv : float\n",
    "\n",
    "        \"\"\"\n",
    "        rate = df['good%'] - df['bad%']\n",
    "        iv = np.dot(rate, df['woe'])\n",
    "        return iv\n",
    "    \n",
    "    IV = []\n",
    "    axisx = []\n",
    "    while len(num_bins) > n:\n",
    "        \n",
    "        pvs = []\n",
    "        for i in range(len(num_bins)-1):\n",
    "            x1 = num_bins[i][2:]\n",
    "            x2 = num_bins[i+1][2:]\n",
    "            pv = scipy.stats.chi2_contingency([x1,x2])[1]\n",
    "            pvs.append(pv)\n",
    "            \n",
    "        i = pvs.index(max(pvs))\n",
    "        num_bins[i:i+2] = [(\n",
    "            num_bins[i][0],\n",
    "            num_bins[i+1][1],\n",
    "            num_bins[i][2]+num_bins[i+1][2],\n",
    "            num_bins[i][3]+num_bins[i+1][3])]\n",
    "        bins_df = pd.DataFrame(_get_woe(num_bins))\n",
    "        axisx.append(len(num_bins))\n",
    "        IV.append(_get_iv(bins_df))\n",
    "        \n",
    "    if graph:\n",
    "        plt.figure()\n",
    "        plt.plot(axisx,IV)\n",
    "        plt.xticks(axisx)\n",
    "        plt.xlabel(\"number of box\")\n",
    "        plt.ylabel(\"IV\")\n",
    "        plt.show()\n",
    "    return bins_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 多所有的特征进行选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-28T11:39:53.117281Z",
     "start_time": "2019-12-28T11:39:50.852384Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RevolvingUtilizationOfUnsecuredLines\n"
     ]
    },
    {
     "data": {
      "image/png": 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a59bpf7z6jMAVAQAAzD7CCDDLHnr6gH6weW+m/beXrdHc+pqAFQEAAIRBGAFm0VAypZs2dmTaLzhtnt7ygqUBKwIAAAiHMALMojt+uVPb9h2XJJlJN25oV1WVBa4KAAAgDMIIMEsOHh/Qp+/dlmlf+aLlWr98XsCKAAAAwiKMALPkU/c8qWP9w5KkuXXV+pvLzg5cEQAAQFiEEWAWbN7To6//alem/VeXnqVFc+oCVgQAABAeYQQoMnfXh+/skHu6fcYpc/T2l50etigAAIASQBgBiux7v+vWr3cezrQ/vGGtahL80wMAAOATEVBExweG9fG7n8i0X9e+WBee2RqwIgAAgNJBGAGK6As/eVrPHRuQJNVWV+mDl68NXBEAAEDpIIwARdJ1oFf//POuTPsvXrlKyxc0BqwIAACgtBBGgCK5+a4tGkymJEltLfV690VnBK4IAACgtBBGgCL4ydbn9OOtz2Xa111+jhpqEwErAgAAKD2xDSNmdrGZDZvZigL3+xMze6CQfaKyDA6n9JG7tmTav7dygS4/99SAFQEAAJSmWIYRMztN0jckFfRXzWb2Z5IuKmSfqDxffqhLXQd6JUlVJt14RbvMLHBVAAAApSd2YcTM2iX9XFJBn49qZoskfbKQfaLyPHe0X5//8VOZ9tUvPV3nnNocsCIAAIDSFaswYmbLJD0k6aikLxa4+1slzZP0bIH7RQX5+x9uVe9gUpI0r7FGf33pWYErAgAAKF2xCiOSFkm6W9IrVMDQYGYXS3q7pM9I2laoflFZfr3zsP7zN3sy7Wtfe7bmNdYGrAgAAKC0xS2MPO7uV7l7T6E6NLM6SbdL6pT04UL1i8qSSrluvLMj0z7n1Gb91wtOC1gRAABA6asOXcBUuHuyCN1eL+ksSZe4+4mpLjQ2s44JLq2eaWGIj//49S49vmc0I990RbsSVSxaBwAAmEzcRkYKyszWSPqApC+7+49D14N46ukb0i0/fDLTvmJ9my5YuSBgRQAAAPEQq5GRQrL0EMjtkg5Lev90+3H39gn675C0drr9Ij4+d99TOtg7KElqqEno796wJnBFAAAA8VCxYUTSOyS9StKV7n44dDGIp6f2HdO//mJHpv0/Xn2GTm1pCFYPAABAnFRyGLlF6VGR9Wa2Puv8Skkys5sldbn7P4coDqXP3XXjxg4lUy5JOm1Bo/78FSsDVwUAABAflRxGRib1Xz/B9esl/VQSYQQ5/ahjnx56+mCm/aE3rlV9TSJgRQAAAPFSsWHE3XM+6sjMHoiuXzSb9SBe+oeSuvn7WzLtV57VqkvOOSVgRQAAAPFTlk/TMrObzWzai9KBk/nizzq1+3CfJKm6ynTDG9dqqo+FBgAAqHRlGUaUnmL1ntBFoDztOdKn2x54OtP+s5ev0BmnzAlYEQAAQDzFdpqWu98o6cYJrk37V9RMz8LJfPzuJ9Q/lJIkLZpTp2tec2bgigAAAOKpXEdGgKL4xfaD+v5jz2baH7jsbM2trwlYEQAAQHwRRoA8DSdTumljR6a9fvk8vfWFywJWBAAAEG+EESBPX3vkGW3deyzTvumKdlVVsWgdAABguggjQB4O9Q7q1nu2Zdr/5UXLdP7yeQErAgAAiD/CCJCHW+95Uj19Q5KkuXXV+p+XrQlcEQAAQPwRRoCT2LynR1975JlM+72XnKnWuXUBKwIAACgPhBFgEu6umzZ2yD3dXt3apLe/bEXQmgAAAMoFYQSYxJ2buvWrHYcz7Q9vaFdtNf9sAAAACoFPVcAEegeG9Ym7t2bal65drFee1RqwIgAAgPJCGAEmcNsDT2vv0X5JUm11lT50+drAFQEAAJQXwgiQw86Dvfo/P+vKtN914SqdtrAxYEUAAADlhzAC5PDRu57QYDIlSVrSXK+/vHh14IoAAADKD2EEGOeBJ5/TfU/sy7Svu/wcNdZWB6wIAACgPBFGgCyDwyl95K4tmfYFKxZow3mnBqwIAACgfBFGgCz/+vAOde7vlSRVmfThK9bKzAJXBQAAUJ4II0DkuWP9+tyPn8q0r/q909Te1hKwIgAAgPJGGAEit/zwSR0fGJYktTTU6P2Xnh24IgAAgPJGGAEk/faZw/rWr3dn2te+9izNb6oNWBEAAED5I4yg4qVSrhvv7Mi01yyZq/96wWkBKwIAAKgMhBFUvG/9Zrc27e7JtG+8ol3VCf5pAAAAFBufuFDRjvYP6ZYfbs2033jeqXrpqoUBKwIAAKgchBFUtM/f95QOHB+UJNXXVOm6N5wTuCIAAIDKQRhBxdq275i+8vCOTPv/vegMtc1rCFcQAABAhSGMoCK5uz743c0aTrkkafmCBv33V64KXBUAAEBlIYygIn3nt3v0SNehTPvGDe2qr0kErAgAAKDyEEZQcXr6hvTxu5/ItC9du1ivOWdxwIoAAAAqE2EEFefWe54cs2j9wxvWBq4IAACgMhFGUFEe392jO365M9N+z6vP1LL5jQErAgAAqFyEEVSMVMr1we9tVrRmXatam/TfL2TROgAAQCiEEVSMr/9qlzbtOpJpf/RN61RbzT8BAACAUPgkhopw8PiA/lfWTusb1rfp5WcsClgRAAAACCOoCP/rh1vV0zckSZpTV60PXs5O6wAAAKERRlD2fr3zkL756O5M+32XnqXFzfUBKwIAAIBEGEGZG06mdP13Nmfaa5bM1X972ekBKwIAAMAIwgjK2ld/sVNb9x7LtD/2lnWqTvBjDwAAUAr4VIayte9ovz5977ZM+8oXL9OLTl8QsCIAAABkI4ygbH3s+0/o+MCwJKmloUYfuGxN4IoAAACQjTCCsvTQ0wd056buTPsDl63Rwjl1ASsCAADAeIQRlJ3B4ZQ+9L3RRevrl8/TH71kecCKAAAAkAthBGXn//y8U537eyVJVSbd/KZ1qqqywFUBAABgPMIIysruwyf0D/c/lWlf/dLTde6yloAVAQAAYCKEEZSVmzZuUf9QSpK0aE6t3v/aswNXBAAAgInENoyY2cVmNmxmK2bYz1vN7Ldm1m9mfWb2czN7YWGqxGz68RP7dO+WfZn2dW84Ry0NNQErAgAAwGRiGUbM7DRJ35CUmGE/75L0LUlrJD0o6QlJr5D0wExDDmZX/1BSN27syLQvWLlAb3nB0oAVAQAA4GRiF0bMrF3SzyW1zrCfBZI+JWmrpDXufom7v1DSxyTNlfS+mdaK2XPbT57WrkN9kqTqKtNH37ROZixaBwAAKGWxCiNmtkzSQ5KOSvriDLv7A0lzJF3l7juzzn82OjJVKya6DvTq9p92ZtrveMVKnb1kbsCKAAAAkI9YhRFJiyTdrfRUqmdn0pG7f0lSs7v/dtylkUUGwzPpH7PD3XXD9zZrMJletL6kuV7vfc2ZgasCAABAPqpDFzBFj7v7VZIKMgXH3Y/nOP266PiLGX8DFN3dj+/Vz586kGnfsGGtmuri9mMNAABQmWL1qc3dk8Xs38wSkq5VelTkS3m+p2OCS6sLVRdyOz4wrI/etSXTfuVZrXr9uiUBKwIAAMBUxG2aVrG9V1K7pNvdvfNkNyOsz923TXuP9kuSahNVuumKdhatAwAAxEisRkaKyczOkXSzpGckXZfv+9y9fYL+OiStLUx1GO/Jvcf0Lw/tyLT/4qLVWrmoKVxBAAAAmDJGRiSZWb2kr0uqlfTH7n4scEmYhLvrQ9/drGTKJUnLFzToLy9iVhwAAEDcMDKS9k+SzpN0vbs/GLoYTO4/f7NHj+w4lGl/5Ip1qq+Z0f6XAAAACKDiR0bM7FpJfyrpLkmfCFsNTqbnxJA+fvcTmfZr1y7WxWtOCVgRAAAApquiw4iZXS3pFklPSbra3T1wSTiJT93zpA72DkqSGmoSumEDy3IAAADiqiynaZnZzZIOu/utk9wzV9JtkkzSJkl/M/5JTO7+wWLWial5bPcR3fF/d2ba73nNGVo2vzFgRQAAAJiJsgwjkq6XtFPShGFE0rmS5kav/3CCewgjJSKZSi9aHxm7Wt3apHe+YlXYogAAADAjsZ2m5e43uru5+44c18zdV5zk/Q9H9034VazaMXVf/9Uz2rS7J9P+6JvXqbY6tj++AAAAUIzDCCrHweMDuuWHT2babzq/Tb+/elHAigAAAFAIhBGUvL//wVb19A1JkubWVev6N5wTuCIAAAAUAmEEJe3RHYf0H7/enWn/9WvP0inN9QErAgAAQKEQRlCyhpMpffC7mzPttac2609eenrAigAAAFBIhBGUrK88vENb9x7LtD/65nWqTvAjCwAAUC74ZIeStLenX5+5d1um/bYXL9eLTp8fsCIAAAAUGmEEJenm729R72BSkjSvsUYfeP2awBUBAACg0AgjKDkPPnVAdz32bKb9gcvWaEFTbcCKAAAAUAyEEZSUgeGkbvje6KL185fP09tevDxgRQAAACgWwghKypd+3qXOA72SpCqTbn7zOlVVWeCqAAAAUAyEEZSMXYdO6B/ufyrTfvvLVmjd0paAFQEAAKCYCCMoGTdt3KL+oZQkadGcOv31a88KXBEAAACKiTCCknDfln2674l9mfYHLz9HzfU1ASsCAABAsRFGEFzfYFI3buzItF+6aoHedH5bwIoAAAAwGwgjCO4LP3lauw/3SZKqq0wffdM6mbFoHQAAoNwRRhDU9v3H9cWfdWbaf37hSp25eG7AigAAADBbCCMIxt314e91aDCZXrTe1lKva159ZuCqAAAAMFsIIwjm+48/qwefPpBp37BhrZrqqgNWBAAAgNlEGEEQxweG9dG7tmTaF53dqte1LwlYEQAAAGYbYQRBfPbebdp3dECSVFtdpZuuaGfROgAAQIUhjGDWbd17VF9+eEem/ZcXrdbpC5vCFQQAAIAgCCOYVamU64Pf2axkyiVJpy9s1F+8anXgqgAAABACYQSz6tu/2a1Hdx7OtG+8ol31NYmAFQEAACAUwghmzZETg/r7H2zNtC9rX6KLzz4lYEUAAAAIiTCCWfPJHz2pg72DkqTG2oRu2LA2cEUAAAAIiTCCWbFt3zF97ZFnMu1rXnOm2uY1BKwIAAAAoRFGMCt++uR+eXrNula1NukdL18ZtiAAAAAERxjBrOg80Jt5/aqzWlVbzY8eAABApeMTIWZF14HjmderFrGnCAAAAAgjmCWd+0dHRla1zglYCQAAAEoFYQRFd3xgWM8dG8i0VzIyAgAAABFGMAt2ZK0Xqa+p0pLm+oDVAAAAoFQQRlB02YvXVy6ao6oqC1gNAAAASgVhBEXXuZ/F6wAAAHg+wgiKrmvMyAhhBAAAAGmEERQdYQQAAAC5EEZQVO4+7rG+hBEAAACkEUZQVPuPD+j4wHCmzcgIAAAARhBGUFRdWaMiC5pqNa+xNmA1AAAAKCWEERRV9mN9eZIWAAAAshFGUFQsXgcAAMBECCMoquzF6ytZvA4AAIAssQ0jZnaxmQ2b2YoZ9nOOmd1lZofN7Fkzu64wFUKSOg9kb3g4J2AlAAAAKDXVoQuYDjM7TdI3JCVm2M9Zkh6UNFfSv0kySR81M7n7x2dcaIUbTqb0zMETmTaP9QUAAEC22IURM2uXdLek1gJ094+SFkh6k7vfGfXfIekjZnaHuz9TgO9RsXYf7tNwyiVJZtJpCxoDVwQAAIBSEqtpWma2TNJDko5K+uIM+1ou6VJJPxkJIpHbJA1K+oOZ9I+xU7SWzW9Qfc2MBrIAAABQZmIVRiQtUnpU5BWSnp1hXy+PjhuzT7p7r6RHJF00w/4r3pjF66wXAQAAwDhxCyOPu/tV7t5TgL4WR8fHclzbLmlVAb5HRetijxEAAABMIlZrRtw9WcDumqPj/hzXeiS15dNJtMYkl9XTKaqcZI+MsHgdAAAA48VtZKSQhqNjX45rg5JYbT1DbHgIAACAycRqZKTARj4p1+e4ViOpNp9O3L091/loxGTt9EqLv96BYe092p9pE0YAAAAwXiWPjHRHxxU5rp0i6cjslVJ+dhwcHRWpq65SW0tDwGoAAABQiio5jGyKji/Kce0CSftmsZayM/ZJWk2qqrKA1QAAAKAUVWwYcfenJD0l6e1mlpmSZWbnKT296mehaisHrBcBAADAyVRsGIl8TtJKSf/bzBrNbKmkf5Fkkv4taGWmwzEvAAAgAElEQVQxRxgBAADAyZTlAnYzu1nSYXe/9SS33ibplZL+VNJVkhLR12fd/cGiFlnmOveP7r6+qpUNDwEAAPB8ZRlGJF0vaaekScOIu7ukt5nZ9yX9cXT6K+7+70Wur6y5uzoZGQEAAMBJxDaMuPuNkm6c4NqUVku7+1clfXXmVUGSDvYO6lj/cKbN7usAAADIpdLXjKAIsp+kNb+xRvOb8tqyBQAAABWGMIKC6zowul6EKVoAAACYCGEEBTd2vQiL1wEAAJAbYQQFlz1Na1UrIyMAAADIjTCCgsveY4TF6wAAAJgIYQQFlUy5dh7MmqbFyAgAAAAmQBhBQe0+fEJDSZckmUkrFhJGAAAAkFvBwoiZnVWovhBf2YvX21oaVF+TCFgNAAAASlkhR0a2mtkOM/uSmV1pZgsL2DdioovF6wAAAMhTIcPIo5KWSnqHpH+XtM/MHjWzj5vZxWZWU8DvhRLVNeaxvoQRAAAATKy6UB25+wVm1izpVZJeHX29QNILJX1AUp+Z/VTSfZLudffNhfreKB2dWRse8iQtAAAATKZgYUSS3P2opI3Rl8xskdKh5DXR8fXRl5vZPkn3SrrH3f+tkHUgnOxpWitb2fAQAAAAEyvq07Tc/YC7f9Pd/x93P1PSSkm3STJJiyX9iaSvFrMGzJ6+waS6e/ozbUZGAAAAMJmCjozkYmZrNTo68ipJLVmXfyPpnmLXgNmRvV6ktrpKbfMaAlYDAACAUlfwMGJmKzQaPi5WegTEosu7JX1H6QByn7sfLPT3RzjZYWTFwkYlqmySuwEAAFDpChZGzOyLSgeQFSOnJB2XdLfS4eNed99aqO+H0tOVtXidJ2kBAADgZAo5MvJOSSmlH/F7r9IB5GF3Hy7g90AJ6xyzxwiL1wEAADC5QoaRtyk99epwAftEjHSyxwgAAACmoJD7jPxHofpC/Li7OvezxwgAAADyV9RH+6JyHOod1NH+0Rl5TNMCAADAyRBGUBDZT9JqaajR/MaagNUAAAAgDgr5NK3kNN/q7l70/U5QXOPXi5jxWF8AAABMrpAhYJckL2B/iJGxT9JivQgAAABOrpAL2FcUqi/ET/YeIyxeBwAAQD5YM4KC6BozTYvF6wAAADg5wghmLJly7Th4ItNmjxEAAADkgzCCGes+0qfB4VSmTRgBAABAPggjmLHsJ2m1tdSroTYRsBoAAADEBWEEM9aVtfP6Sp6kBQAAgDwRRjBj2SMjq1i8DgAAgDwRRjBjXeM2PAQAAADyQRjBjGVveMg0LQAAAOSLMIIZ6R9KqrunL9NezTQtAAAA5IkwghnZcbBX7unXNQnT0vkNYQsCAABAbBBGMCNdWVO0Tl/YpESVBawGAAAAcUIYwYyMfZIW60UAAACQP8IIZoTF6wAAAJguwghmpOvA6IaHjIwAAABgKggjmJEx07RaeZIWAAAA8kcYwbQd7h3UkRNDmTYbHgIAAGAqCCOYtuxRkbn11VrYVBuwGgAAAMQNYQTT1jVuipYZj/UFAABA/mIXRsxsvpndZmZ7zOyYmX3FzJpn0N97zexxM+s1s2fN7DtmtqaQNZerzv0sXgcAAMD0xSqMmFmDpB9JerekxyV9WdIVkr41zf4+JOmzknZL+idJD0p6vaRfmNnqQtRczrJHRlgvAgAAgKmKVRiR9D5JL5H0aXe/zN2vkXSZpEvN7MqpdBSNplwv6Qvu/np3v9bd/4ukyyXNk3RNgWsvO4QRAAAAzETcwsg7JR2XdMPICXd/RNL9kq6aYl9rJdVJunvc+fslDUk6c/pllr9UysetGSGMAAAAYGpiE0bMbImklZLud/fecZfvl3TRFLs8ER3PGXf+LEk1knZNtcZK0t3Tp4HhVKa9YiFhBAAAAFMTmzAiaXF0fCzHte2SWsxswRT62yxpm6TrzOwNZtZkZudK+qqkVHTEBLJHRZY016uprjpgNQAAAIijOH2CHHli1v4c13qiY5ukQ/l05u4pM3uDpPskfX9cX1e6+0P59GNmHRNcKusF8J37maIFAACAmYnTyMhwdOzLcW0wOjZOsc+3Sjo9et0tqV9Si6R3mtnCKVdYQVi8DgAAgJmKUxgZ+fRbn+NaTXTMewtwM1sn6ROSHpW0yt2XSmqV9Bmln9D1r/n04+7tub6UnjpWtjoJIwAAAJihOIWR7ui4Ise1U6LjkSn091al//zvdPcuSXL34+7+15IekvQGM5s3zVrLXvaGh6tb5wSsBAAAAHEVmzDi7gck7ZH0ohyXL4iO+6bQ5anR8ckc17ZIsqx7kKV/KKk9R0ZnyzEyAgAAgOmITRiJ3C3pQjM7e+SEmdVJeoukre6ea3H7REbuzbXQ/PzomNdi+ErzzKETck+/rkmYls1vCFsQAAAAYiluYeQLSj9292tmtszM6iXdrvRTtO6YYl+bo+PHRqZjmVm9md2g9C7vv3X3qYy0VIzsKVqnLWhUdSJuP0YAAAAoBXF6tK/cfZOZvV/pReY7ld4pvU7SI5I+OXKfmd0s6bC73zpJd9+R9BtJb5b0RjM7IGmh0ovhU5L+pih/iDIwdvE660UAAAAwPbH7lba7f17SKyV9W+kQ8iFJF7r7YNZt10t6z0n6GZR0saRPKR1sFkoakPQzSZe5+48LX3156GKPEQAAABRArEZGRkQbEk64KaG7W579HFV6BIRRkCnIHhlZxeJ1AAAATFPsRkYQHhseAgAAoBAII5iSIycGdah3dEbcSqZpAQAAYJoII5iS7FGROXXVap1TF7AaAAAAxBlhBFPSOW7xulley3MAAACA5yGMYEpYLwIAAIBCIYxgSggjAAAAKBTCCKZke9bu66ta2fAQAAAA00cYQd5SKdeOg+wxAgAAgMIgjCBve4/2q38olWmvIIwAAABgBggjyFv2k7QWN9dpTl11wGoAAAAQd4QR5K3rwOh6ERavAwAAYKYII8hb55gnabF4HQAAADNDGEHesqdprW5lZAQAAAAzQxhB3thjBAAAAIVEGEFeBoaT2n34RKZNGAEAAMBMEUaQl2cOnlDK06+rq0zLFzSGLQgAAACxRxhBXrIXr5+2oFE1CX50AAAAMDN8okReWC8CAACAQiOMIC9d+wkjAAAAKCzCCPLSmbXh4apW9hgBAADAzBFGkBemaQEAAKDQCCM4qZ6+IR04Pphpr2LDQwAAABQAYQQnlT0q0lSb0Clz6wJWAwAAgHJBGMFJdWWtF1nZ2iQzC1gNAAAAygVhBCc19klaLF4HAABAYRBGcFLbs6ZprWLxOgAAAAqEMIKTyh4ZYfE6AAAACoUwgkm5O4/1BQAAQFEQRjCpvUf71TeUzLQJIwAAACgUwggmlT1Fq3VunebW1wSsBgAAAOWEMIJJdTJFCwAAAEVCGMGkOrNGRlazeB0AAAAFRBjBpMZseMjICAAAAAqIMIJJjX2SFhseAgAAoHAII5jQ4HBKuw73ZdqMjAAAAKCQCCOY0DOHTiiZcklSosp02oLGwBUBAACgnBBGMKHsKVrL5zeotpofFwAAABQOny4xIRavAwAAoJgII5hQ9mN9V7WyeB0AAACFRRjBhNjwEAAAAMVEGMGEsteMrCKMAAAAoMBiF0bMbL6Z3WZme8zsmJl9xcyaC9Bvwsx+a2aHzWxRIWqNs2P9Q9p/bCDTZpoWAAAACq06dAFTYWYNkn4k6SXRcZukqyW1SXrtDLt/r6TzJV3j7gdm2FfsZY+KNNQktLi5LmA1AAAAKEdxGxl5n9JB5NPufpm7XyPpMkmXmtmV0+3UzJZJuknS45JuK0ilMdc1br2ImQWsBgAAAOUobmHknZKOS7ph5IS7PyLpfklXzaDff5A0R+lRkeSMKiwT28c8SYv1IgAAACi82IQRM1siaaWk+929d9zl+yVdNM1+N0h6s6RvuPsDM6mxnLB4HQAAAMUWmzAiaXF0fCzHte2SWsxswVQ6NLN6pUdFJGmXmX3GzP7OzJbPoM6yMGbDQ0ZGAAAAUARxWsA+8sSs/Tmu9UTHNkmHptDnX0k6PXp9bdb5vzWzq91948k6MLOOCS6tnkIdJcXd1ZU9TWsRT9ICAABA4cVpZGQ4OvbluDYYHRvz7czM6jQaQL6r9JO0Vkp6j9J/L18zs6XTKzXenjs2oN7B0aUzK5imBQAAgCKI08jIyK/q63Ncq4mOtVPo72WSFkraIemP3H1kU41/tPSjoz4v6W2SPj1ZJ+7enut8NGKydgr1lIzOrFGRRXNq1dJQM8ndAAAAwPTEaWSkOzquyHHtlOh4ZAr9tUXHb2YFkRH3R8czp9Bf2ejMXi/CqAgAAACKJDZhJNqIcI+kF+W4fEF03DeFLo9Fx64c10aGAgZzXCt7rBcBAADAbIhNGIncLelCMzt75ES09uMtkra6e67F7RMZeSrXshzX1kXHiRanl7UxGx7yJC0AAAAUSdzCyBckpZReXL4sejTv7UpPubpjKh25+05Jv5L0ruyF6tHjga+XNCDpzkIVHifjd18HAAAAiiFOC9jl7pvM7P2SPiNpp6QhSXWSHpH0yZH7zOxmSYfd/daTdHmNpHskbTKz7yoddDZIWiLpOnffW/g/RWkbSqb0zKETmfZqRkYAAABQJHEbGZG7f17SKyV9W+kQ8iFJF7p79vqO65V+RO/J+vqlpJdK+oGkyyRdrfQ+Je92908UuPRY2HXohIZTLkmqMmn5gryflgwAAABMSaxGRka4+0OSHprkuk2hry2S/qQQdZWD7Clay+Y3qq46EbAaAAAAlLPYjYyguLL3GFnFFC0AAAAUEWEEY3SyeB0AAACzhDCCMbqyNjxcRRgBAABAERFGMMbYaVpseAgAAIDiIYwg4/jAsJ47NpBpM00LAAAAxUQYQcaOrPUi9TVVWtJcH7AaAAAAlDvCCDK27x9dL7Jy0RxVVeX9hGQAAABgyggjyMjeY4TF6wAAACg2wggyunisLwAAAGYRYQQZ2U/SIowAAACg2AgjkCS5+9hpWuy+DgAAgCIjjECStP/4gI4PDGfajIwAAACg2AgjkCR1ZU3RWtBUq3mNtQGrAQAAQCUgjECS1MmTtAAAADDLCCOQxJO0AAAAMPsII5A07klaLF4HAADALCCMQJLUeWB09/VVi+YErAQAAACVgjACDSdTeubgiUybx/oCAABgNhBGoN2H+zScckmSmXTagsbAFQEAAKASEEYwZorWsvkNqq9JBKwGAAAAlYIwgrGL11kvAgAAgFlCGMGYx/qyxwgAAABmC2EEY0ZGWLwOAACA2UIYARseAgAAIAjCSIXrHRjW3qP9mTZhBAAAALOFMFLhskdF6qqr1NbSELAaAAAAVBLCSIUbP0WrqsoCVgMAAIBKQhipcKwXAQAAQCiEkQpHGAEAAEAohJEK17l/dPf1Va1seAgAAIDZQxipYO6uTkZGAAAAEAhhpIId7B3Usf7hTJvd1wEAADCbCCMVLHvn9fmNNZrfVBuwGgAAAFQawkgF6zowul6EKVoAAACYbYSRCjZ2vQiL1wEAADC7CCMVLHua1qpWRkYAAAAwuwgjFSx7jxEWrwMAAGC2EUYqVDLl2nkwa5oWIyMAAACYZYSRCrX78AkNJV2SZCatWEgYAQAAwOwijFSo7MXrbS0Nqq9JBKwGAAAAlYgwUqG6WLwOAACAwAgjFaqTPUYAAAAQWOzCiJnNN7PbzGyPmR0zs6+YWXMB+/+JmT1QqP5KFU/SAgAAQGixCiNm1iDpR5LeLelxSV+WdIWkbxWo/z+TdFEh+ip12dO0Vray4SEAAABmX6zCiKT3SXqJpE+7+2Xufo2kyyRdamZXzqRjM1sk6ZMFqLHk9Q0m1d3Tn2kzMgIAAIAQ4hZG3inpuKQbRk64+yOS7pd01Qz7vlXSPEnPzrCfkpc9Rau2ukpt8xoCVgMAAIBKFZswYmZLJK2UdL+79467fL9mML3KzC6W9HZJn5G0bbr9xEV2GFmxsFGJKgtYDQAAACpVbMKIpMXR8bEc17ZLajGzBVPt1MzqJN0uqVPSh6dfXnx08SQtAAAAlIDq0AVMwcgTs/bnuNYTHdskHZpiv9dLOkvSJe5+wmxqowRm1jHBpdVTrGPWdI7ZY4TF6wAAAAgjTiMjw9GxL8e1wejYOJUOzWyNpA9I+rK7/3gGtcVK9u7rjIwAAAAglDiNjIx8gq7Pca0mOtbm25mlh0Bul3RY0vunW5S7t0/Qf4ektdPtt1jcXZ37R6dp8SQtAAAAhBKnMNIdHVfkuHZKdDwyhf7eIelVkq5098MzqCtWDvUO6mj/cKbNNC0AAACEEpsw4u4HzGyPpBfluHxBdNw3hS5vUXpUZL2Zrc86v1KSzOxmSV3u/s/TqbdUZT9Jq6WhRvMbaya5GwAAACie2ISRyN2S/tzMznb3J6XM07DeImmru+da3D6RkSdvXT/B9esl/VRSWYWR8etFprpgHwAAACiUOC1gl6QvSEpJ+pqZLTOzeqXXfbRJumMqHbm75fpSOoD8NGpfVOg/QGhjn6TFehEAAACEE6uREXffZGbvV3pzwp2ShiTVSXpE0idH7oumWB1291uDFFrCsvcYYfE6AAAAQorbyIjc/fOSXinp20qHkA9JutDdB7Nuu17SewKUV/K6xkzTYvE6AAAAwonVyMgId39I0kOTXJ/2QohynJo1Iply7Th4ItNmjxEAAACEFLuREUxf95E+DQ6nMm3CCAAAAEIijFSQ7CdptbXUq6E2EbAaAAAAVDrCSAXJ3nl9JU/SAgAAQGCEkQqSvXh9FYvXAQAAEBhhpIJ0jdvwEAAAAAiJMFJBsjc8ZJoWAAAAQiOMVIj+oaS6e/oy7dVM0wIAAEBghJEKseNgr9zTr2sSpqXzG8IWBAAAgIpHGKkQXVlTtE5f2KRE1bT3hQQAAAAKgjBSITrHPEmL9SIAAAAIjzBSIVi8DgAAgFJDGKkQXQdGNzxkZAQAAAClgDBSIcZM02rlSVoAAAAIjzBSAQ73DurIiaFMmw0PAQAAUAoIIxUge1Rkbn21FjbVBqwGAAAASCOMVIDO/WPXi5jxWF8AAACERxipAF2sFwEAAEAJIoxUgOwwwnoRAAAAlArCSAUYs8cIYQQAAAAlgjBS5lIpV9fB7GlahBEAAACUBsJImevu6dPgcCrTXrGQMAIAAIDSQBgpc9nrRZY016uprjpgNQAAAMAowkiZy14vwhQtAAAAlBLCSJnjSVoAAAAoVYSRMtdJGAEAAECJIoyUuezd11ez4SEAAABKCGGkjPUPJbXnSF+mzcgIAAAASglhpIw9c+iE3NOvaxKmZfMbwhYEAAAAZCGMlLHsKVqnLWhUdYL/uQEAAFA6+HRaxsYuXme9CAAAAEoLYaSMdbHHCAAAAEoYYaSM8VhfAAAAlDLCSBnL3vBwFWEEAAAAJYYwUqaOnBjUod7BTHsl07QAAABQYggjZSp7itacumq1zqkLWA0AAADwfISRMjV+8bqZBawGAAAAeD7CSJnqYvE6AAAAShxhpEwRRgAAAFDqCCNlanvW7uurWtnwEAAAAKWHMFKGUinXjoM81hcAAACljTBShvYe7Vf/UCrTXkEYAQAAQAkijJShzqwnaS1urtOcuuqA1QAAAAC5xS6MmNl8M7vNzPaY2TEz+4qZNc+gv7ea2W/NrN/M+szs52b2wkLWPNu6DoyuF2HxOgAAAEpVrH5lbmYNkn4k6SXRcZukqyW1SXrtNPp7l6T/Lalf0oOSFkh6haQHzOw8d99RmMpnV+eYJ2mxeB0AAAClKW4jI+9TOoh82t0vc/drJF0m6VIzu3IqHZnZAkmfkrRV0hp3v8TdXyjpY5LmRt8rlrKnaa1uZWQEAAAApSluYeSdko5LumHkhLs/Iul+SVdNsa8/kDRH0lXuvjPr/GejY2ynag0MJ1UVbbjONC0AAACUqthM0zKzJZJWSrrT3XvHXb5f0t9MpT93/5KZfd3dj4+7VBMdh6dXaXhff9fL1D+U1K5DJ7SkpT50OQAAAEBOsQkjkhZHx8dyXNsuqcXMFrj7oXw7zBFEJOl10fEXU6yvpNTXJHTm4rmhywAAAAAmFKcwMvLErP05rvVExzZJeYeR8cwsIelapUdFvpTnezomuLR6unUAAAAAlSBOa0ZGpk315bg2GB0bZ/g93iupXdLt7t45w74AAAAATCJOIyMj60RyLYIYWedRO93OzewcSTdLekbSdfm+z93bJ+ivQ9La6dYDAAAAlLs4jYx0R8cVOa6dEh2PTKdjM6uX9HWlw8wfu/ux6fQDAAAAIH+xCSPufkDSHkkvynH5gui4b5rd/5Ok8yTd4O4PTrMPAAAAAFMQmzASuVvShWZ29sgJM6uT9BZJW9091+L2SZnZtZL+VNJdkj5RoDoBAAAAnETcwsgXJKUkfc3MlkXTq25X+ilad0y1MzO7WtItkp6SdLW7eyGLBQAAADCxOC1gl7tvMrP3S/qMpJ2ShiTVSXpE0idH7jOzmyUddvdbJ+rLzOZKuk2SSdok6W/MbPz3+2Ch/wwAAAAA0mIVRiTJ3T9vZr9W+jG8SyTdI+kWdx/Muu16pcPKhGFE0rmSRnYF/MMJ7iGMAAAAAEUSuzAiSe7+kKSHJrluE13LuudhpUdFAAAAAAQQtzUjAAAAAMoEYQQAAABAEIQRAAAAAEEQRgAAAAAEQRgBAAAAEARhBAAAAEAQxqbjxWFmR+vq6uauXr06dCkAAABAwWzfvl0DAwPH3L15pn0RRorEzPZKapS0K1AJIyloe6Dvnws15a8U66Km/JViXdSUv1Ksi5ryU4o1SaVZFzXlrxTrCl3Tckkn3H3JTDsijJQpM+uQJHdvD13LCGrKXynWRU35K8W6qCl/pVgXNeWnFGuSSrMuaspfKdZVijVNF2tGAAAAAARBGAEAAAAQBGEEAAAAQBCEEQAAAABBEEYAAAAABMHTtAAAAAAEwcgIAAAAgCAIIwAAAACCIIwAAAAACIIwAgAAACAIwggAAACAIAgjAAAAAIIgjAAAAAAIgjBSZsxsvZndY2ZHzWzAzLaZ2R+FrkuSzOxSM/ucmf2Lmf1PM1sSuqZsZnaTmQXdeMfMfmNmnuPrgZB1ZTOzf4xquiTQ918xwd9R9tdXAtX2OjN70MwOm9mh6PXlIWrJqukVZrYxqumomX3HzNYFquViMxs2sxUTXP9vZrbZzPrM7FEze1nomqJ7mszsMTO7sdj1nKwmM5tjZrea2TPRPYfM7DYzawxZV3R9hZldZ2ZfNrNPm9nLQ9c07t6zzazfzP40VE1mds0k/9163v2zUdO4+15iZkkzu6NYteRTl5k9cLL/zs92TdG1U8zsjujf34noM9ZHzKy+WPXkUVNr9O+tK/r5/p2ZvaOY9RRadegCUDhmdr6khyQ1Svq/knolXSTp381syN2/HbC2z0m6RtJBScck/Zmka83sYnfvCFXXCDM7W9IHAtdQK6ld0k8kPTzuctfsV/R8ZvZiSe+W9J/ufl+gMo5I+tgE19qU/tl6bvbKSTOziyR9X9JmSV+R1CzpCkkbzexKd/9WgJr+//buPf6yud7j+OttTNJFSDES47gmnNwlt8gtg1zCodzJkXsmlTANXUwcDkmKcjlHF5eQaxmUUp1BhMGgQ45bBqGGGZfP+ePz3TPbnv2b+f1+89v7+5uf9/PxWI81+7vXXusz67f3Wt/7+jTwY/J8XAi8E9gN2EzS5hHR+j3rZCxLAj8FhvXw/lHAt4FHgbOA9YEbJH0kIh6sEVPZRsC5wCrAZZ2Io7cxSRoGXANsADwOXA80fpNLkN+3rsdV3t8YuAKYD3gE+BfgCElfioiTasTUxvdKfB3Ti5hWB54jv+Ot/l4ppsZ2w4CzgSnAFzsRSx/iuhD4bQ8fPRDoSGFkNr+/twM3Ah8EfkbmsdYDjgVWA7apENMiwB+AkcAl5HVha+BcSatExBGdiGnARYSXIbKQBZF/AJs2pW1G/mjvqhjXjiWG4wCVtG2BV4Gra5+3Es9NJcaoGMMaJYZda5+PHuIbBtxG3qiWqh1PDzF+D3gZ+ECFY98K/AmYtyltUWByjd8fWfB4jszcv7cpfY1yjh5s/B67EMuHSxxRlpEt748EpgIPAQuVtOHAncA1NWIq28wHXNS0zZjK52n/kn46MLykvQe4t6SvVimuBchM0ARgkZK2KPBnYBqweI2/X8v2ezdtu1fF79TdwHWd/B719zwBh5dtvjiY4mr53Lpl+yO7HRNwQElfsyX9wk79/noR09klfaemtPmAW0r6Bt36rs3J4m5aQ4SkFYG1ga9GxPhGekT8irxRrVpqPWo4BpgQEWOj/FIi4koyY7t+pZimk7Q32YL0WOVQVi/rO6tG0bODyYzsSRHxaO1gWklaCtgH+G5EPF4hhNWBX0XEa42EiHiaLKAsVyGeUcBCwLcj4tmmmG4HrgWW7UZckpYgK0peBL7fw2Z7AG8DxkbE8yXOV4FTgC0kvbdCTJAFkR2Bowby+HMQ0x7k9eGwcn6IiBeAH5X3V+/hc52Oay9Kq2RETC5xPQ2cSRYq16kQU/P2i5Ctbh27xvcmJknzAyvSpWt8X86TpA8AY4FJwGmDJa42xgJPAN+tENPqwDMRcVtL+nVlPaDX09nFJGlesqX79mhqeY+IqeT3HWCrgYypU1wYGSIi4n7gXeTFv9Vw4LU26d2yB/CZNunDyWbOappuUv9Ddq2paQ3yojOpchwzkbQ4cALZ/WJc3Wh69FWydv1blY4/BfhQc0K5WaxAnYLuSmV9e5v3Jpf1Il2IYxGya9H6wJM9bLMeWYt3VUv6jeR9aoMKMTVsBHSji+tsY4qIDYD1G5U6TYaXdSeu8705Vz8pcd3TQ1wDfZ3vy98PslD7HrKrcKf0JqZ/JbvHT+hgHH2NqeF04N1kQXfaIIprujIGaTPgxIh4pUJMU4CFJb2/Jb0xBm+gr/Ozi+mDZL6v9nWp6AUAABCvSURBVDV+jrkwMoRExNRGbVmDpKWB5cmWidcrxXVPRLwpgy1pI7KW4ZoaMTX5D7KLwb7AG5VjWYPsZjdeOdD4eeXA4zUqxwVwKnmjug0YK+nrktauHNN05eawB3B+RDxTKYzLgK0lHS5poVKrdTZ5wzivQjyNjOlCbd5bsay7UUi6OyJ2KzX4PVkUeDwinmtOLC1cU8nxB92OCWDniPjDAB+7J72KKSLaZey3KOvfD3xYs48rIv4WEb9rTiutAAcCz3cgrt7+/ZC0CXltOJnOtkj0JqbGtXxPSY8rJ2q4S9JBkjqRH+vVeZK0FbAD2R1o3TIY+rOShs/qc52Oq43R5LjTH81uww7F9HMy3/xfkpaR9G7lBEGHAveRlZrdjGmwXOPnmAsjQ19jUHa7wXJdJ2krSacDvyT7znZ8kNwsYtkE+CzZ7ai1Rq/bsbwNWJXs6jA/cA7ZlWZz4FZJa1WM7aPAzuXlTmSXla8Af1CXZhfqhQPIbj7tWga75TByQPGp5FiNx8h+6icAHRnAOxuNCoCDmhPL934D4L6I6PiNqpeVIAsAPRUiXyB/FwOmtxUz3azA6e+xJK1Ltt7c2FrpMxD6Gpek5cpkBHcDywB7RMRLNWKSNB9573sQ+NpAxtDPmNYs63XIyS5+QH73zyQLS12PqRSCGsdeCjgeOAK4ALilTStAV+JqVbrhjgLO6UCrSK9iiohbyHvNZuT4thfJCULuArYY6OtFL/b3JDkh0FaSplfYKGfWG11eXj+QMXWKCyNDWMnA7kf+UC6qHE7DycAhZMbxQrLWrOuablL3AyfWiKHFYmTf0MMiYt2IODIidiPHssxDztZRyzFlPRH4BLAksD15gz9e0na1AoPpXaEOBMZHxH0VQ1kHaExlOpm8UYkcczDgffl74QrgL2RrzbWS9pX0DeDy8v4PK8TUk9fIQfXtTCNnCLQW5bt/Jtmq++XK4TR8iuz6ugw5y89A1xb3xTFkP/79O5GB7YeXyC5/y0TEARFxKDlAeQJwsOpMd78D2aXzdXIA+1LkzFAXkde0syvE1M5B5PW0WsWqpHeSeSqAV8jCQJAz7e3W7XjK+MTvkF21fiPpSEkHk3mJFYF7I6Lm76/XXBgZokoT+fnkD2XfWl202liFvMDdQN6wamWyGzep/cpgr6oi4q8RsUlEnN6S/nvgSjJDuUC34yqFtsaMbDtExPiIeCwiLidbSSC7uNW0NfABsjWpipIpvIDsUvTxiHgf2XS+F5kpu0ZdeA5Es5L52pzM6GxJnp8vk93tnqfvA0c76Z9AT/P0DycrL2xmx5IF3dMHS6YjIr4NjADGkH3dx3f7uw/TJ3U5mqxJ/3W3j99ORBwaETtFxD+a0qaQrafDyYqLbhtV1uMi4j/LvejOiNidHIuwbRlbWU25vu5NThBSc/KU48j8yzeABSNicXK8yMPAtyTtNKsPd8ix5IQDi5Jjo84APlLeqzV+ss9cGBm6TicH0x5XZs8ZFCLijXLTHEV2Ixnd7RtV003qrNZ+zoPUJPK3OrLCsd9LZgQnRMQDzW9ExN1kC0CNmaKa7UrWUrUOfu6mj5EFohMi4maY/l0/n2wNfB9ZqOuqiHg4ItYmaz63IAu2kBmPF7sdzyw8QZvvd5kBcGE69PyFuZmkDclKlT8zeFpFAIiIpyLia2SmbWVmVFx0RXk2zNnk+ILRs9l8MGh0r1umwrEbXSAvbPPeTeS9p0ZczTYlr6Fdf1ZTi52BOyLimEYlZkRMZMb3e/duBxQRr0c+S2QxYBOy5wlkT4bB0iNmtvzQwyFI0p5kU+L11OmrPhNJap4BJiKmSroJ+Bx5MXyoi+F8ncxgvyapuYvWhiXWE0uMX+1iTEhaOiLaPdxw4bLu6BNee9Do693TQxfnJbvRVFEKstsAv2yubaxgRFk/0Oa9iWU9oOMe+iIi7pP0NHmzepKsrBhM7iJb/0ZGxCNN6auRNcZPV4lqkJI0gpzBago5yL56F6TWa3xxLVmbvHyXwxlFXs9vJiu8GunvKesdJC0LXBoRf+pWUKWFYWqbMTSD9RrfGMBe7Rpf7EJ2I7uichwjyK6HbxIRD0maRt1r/LPATZL2L0nHRETtSXl6zYWRIaZMfXc28Fdg99pfRkkLkZmxM5l5bMaCZd3t6X0bzyzoaZrHxhiJrhVGSl/+oyQt1jyjUGme3oy8ELfL6HZURLwk6VHy6c5vUmaLWpDMcNSyFflwv59XjAFmDL5uV4PYaDJ/ts173XQI2bf4yNI1ZDC5mpwUYT/e/Lv7t7L+TdcjGqRKAfxyMmO0c2uLZQ2SzgE+JmmllgJJ7Wv8xmVptU1ZHiKfA9RxpUX+XnKii++0vN14FsREuu9uctzIEsxcKbgyOZ6r2nTz5R64PfDbijMlNjxDm2u8pJXICs6q1/gyiH1n4NbSlXqu4W5aQ4ikD5M39QC2j6YHndUS+QCzx4AvSlqlkS5peWA7cuq6Xs8zPkAxbRwRal0os600ve6mG8haqOnP8ChdDU4ClgZ+0Y9pEAfKz4D1JG3eFNswZsT6sypRpcaUprX7hN9HDiI+stS4orQt+bDIqWQtbRWlUuBwMrMzmAauAxARt5LTRh8laXsASZ8iB61Oou4g6EGj/O4uJR9wOy4iLq4cUsMd5IDZ4xoJJRN5RHnZ1Rl9IuK8Hq7xS5dN9i5p53UxrEnkvfDo0rIFgKT1gCPJrqY1uiFdQuYZjmueXljSJ8nuUVdH++mku2VtslBb+xoPcA+wpqQ9y/258fiEH5T3r+vxk91xPDCMuaNr4pu4ZWRo+Q7ZDD2BbIbeoeX9c3voBtRpnwduAf4o6RJy1pydyAvggRXiGXQi4kZJVwH7SlqfnAVpJXJmk0eZ0Q+0hpOAbYGrJV1OdvPZmJyM4DrqNp1vCjxV6Xs9XUQ8Ieks8rs+SdJkssWmMR7qaxHxt2oB5piCBcnW0sEymUWr3cmHHF4m6WVyiuspZMaxtfvPW9VnyMkIXgbeaOlmCtmf/bLuh8U55DTpYyRtShZONiAH158WEXdUiGlQiYg3JI0GfgrcJ2kCeX1Yl7wX7hcRT1WI6x5Jp5BTtn9Y0i3kfWdbckbAL3U7phablvWtVaNIJ5JdXc8DzpA0lRkPFbyfGYWSrpO0Mnl9uLhU7sxVXBgZWhrTiq5VllY30HPf/46JiAml9udEcuajV4HxZJ/GB7sdzyD2aTLTuCd5wXuULAiMi5aHwXVTRDxb/n5Hk83lo8g+/KeSEyRUyShKWpJ8GF6NzFc7h5AtJPuQk0fMQ9akfT8izqgVVOlOdwhwQ0TUfshojyJikqRVye5a6wCPAGN9jXiTxjV+ftpnEs+nwu8hIqZJ2pKc2WcHshAyEdg1In7a7XgGq4i4WNJLzJhp7CWyy924iPhjxbhGS7oT+Hfy/gN5jx4dEffXiqvYhGx17tbDR3sUEb8rXeGPJX+LC5KVc9cBX4mInqYn74Zvkl3qahce+0WucDIzMzMzsxo8ZsTMzMzMzKpwYcTMzMzMzKpwYcTMzMzMzKpwYcTMzMzMzKpwYcTMzMzMzKpwYcTMzMzMzKpwYcTMzMzMzKpwYcTMzMzMzKpwYcTMzMzMzKpwYcTMzMzMzKpwYcTMzPpF0s2SHqkdRydImkfSaEkPSZom6W+Svt6P/QzZc2RmNhDmrR2AmZnZIHQIMA64HrgEGAGsVDUiM7MhyIURMzOzme0CPBwRWzYSJKliPGZmQ5K7aZmZmc1sBPB/zQkREZViMTMbslwYMTMzm5lbQczMusCFETOzuZikRyT9VtI7JJ0h6SlJf5d0haQRTduNkRSSRrZ8fmRJH9O0v5slHS7pmfJ6TUknSnpR0v2SVm7Zx/qSbpf0iqQHJR3cQ6yrS7qqxDdF0g2S1miz3XmSovx7eUnnS3pc0mfn4DwtIekCSc+WY98sacN2xy3HXgrYqPG63bnr4/F7e44+Ws7LPyU9J+kiSUu1bHNUiefoprThkiaVczti5j2bmQ1OHjNiZjb3GwZcCawA/ARYE9gWmB/YvB/7WxNYArgK2IscxP08cGl5/SXgM2XbhYFrgduAHwKfBM6QtHBEjG3sUNLGZbsXgP8GhpPjMn4jaa2ImNgahKR1y7FfBu4FJvfj/0LJzP8eWAz4OfAssBMwXtIuEXFZ2fRKZnTNOrjEemHTrv7en+PT+3O0dYnvZeDHwALAzsAnJH00Ih4um54G7A58RdIPI+KZEu9ywEER8WQ/4zQz676I8OLFixcvc+kCPAK8QWbWFypp8wC3l/R3lLQxQAAjWz4/sqSPadrfNGDJ8vq5Nq9vKv++uXz23Kb9LQI8BkwF3t8Uz4PAU8BiTduuVz5/QUtM55X0/wVOBobP4Tn6Rdnfbi3/7+fIQtYCPZzXmwfg79PbczQ/8DTwErB807bblc+Pb9nvWsDrwFlkYec54FZAtb+TXrx48dKXxd20zMzmfgIOjYjnASLiDWB8SX9fP/Z3d0T8tfz7xTavW8dTjGn8IyImAz8C3gZsUpJXA5YFngAOLl2+TiRbCKYCG/QQx8SIOCoiXu3H/wEASQuV49wWERc1xfkIcDqwIDCqv/vvgzFNx253jrYA3g98LyImNW17BfBrYBNJizelTwDOBPYnC2/vAg6ICA+yN7O5irtpmZnN/Z6NiPEtaVPKenYDsYe1SXtpNq+bvRARj7WkPVDWS5b1smW9Wlla9TTG4cuzOG5vLU+2zPy5zXt3lfWKA3CcWenNOVqhrHuKcyMyziea0o8BdgS2Ab4ZEfcMTLhmZt3jlhEzs7nfX+bgs0vOfpNZen0Waa33mC9EhNosb2+344holzHvr1m1GHS6NaEv56gvcS5MjisBWLofcZmZVefCiJnZ3G9aL7ZpZGRbW8Q3bN2wjxaWtGhL2nJl3RgM/lBZr9CyHZJ2lPR5SZ26H00ix86s2ua9Vcr6gTbvDaTenKNGDH2J8/vkffwHwK6StpvTQM3Mus2FETOzt4ZnyvpDjYSSQT58APZ9XNM+FwT2AV4DbizJfwIeBnaRtFzTth8ELgD2K+NcBlwZR3MNsJakXZqOvSRwGDlD1lWdOHaL2Z2j68m/0YGSlm3adhTwceDGiHiiKX0fcqa0E4BDyAkCzir7NjOba3jMiJnZW8N1ZAvKyZJeAeYDxpFT3m41B/v9J7CvpBWA+8u+RgLjGpnniHhD0gFkoeAOSZeS41B2Igdxf2EOjt8bBwNrABdJ+jQ589SOZBenXSLixQ4fvzfn6GVJe5NT+94m6eIS345kIeVzjZ2V54icAkwETomIVyUdBPwKOBXYu8P/HzOzAeOWETOzt4CI+Av5zIrXyOdpnAacC3x+Dnc9GfgU+QyP/ckB818gn0XSfPwbgfWBW4AdyOeU3A1sWN7rmIh4FFibfL7Jx5uOvWnMeMZIJ/X2HF1NDlS/HdiNbPm4GFg7Ih5q2vQschawgxozjUXEDeQzZvaStGVH/zdmZgNIngXQzMzMzMxqcMuImZmZmZlV4cKImZmZmZlV4cKImZmZmZlV4cKImZmZmZlV4cKImZmZmZlV4cKImZmZmZlV4cKImZmZmZlV4cKImZmZmZlV4cKImZmZmZlV4cKImZmZmZlV4cKImZmZmZlV4cKImZmZmZlV4cKImZmZmZlV4cKImZmZmZlV4cKImZmZmZlV4cKImZmZmZlV4cKImZmZmZlV8f8iLXkfXQVWqgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfTime30-59DaysPastDueNotWorse\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DebtRatio\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MonthlyIncome\n"
     ]
    },
    {
     "data": {
      "image/png": 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NV6s+efEWve4FfTKr+dcdAAAgURIZWoDlYGd2RB/+h+16aPdw2fU3vHCDPvGGzVrd1ahtiQAAAOKF0ALEzNRMXv/rJ7v0xR8/punc7CrbvSvadM0bt+jCzX0R9g4AAKD5CC1AjDz07LA+/M0HtTM7Wnb9rS8+Th97/ZnsWA8AAJYlQgsQAxPTOX3hvsd08z8/oVx+dnRlw8p2XfvmF+jc03oj7B0AAEC0CC1AxH719H79yTe36/E9Y2XXL/2PJ+gjrz1dK9oZXQEAAMsboQWIyKGpnK6/5zf6ygO/lc8Oruj41R369JvP0u+dsja6zgEAAMQIoQWIwM+f2Ksr79yuJ/ceLF0zky77vY368O+fps5W/mkCAAAU8c0IaKLxyRl9+oc7dev//1TZ9ZPXdukzbzlLv7txdUQ9AwAAiC9CC9Ak9z82pCvv3K7dBw6VrqVMet8rT9blrzlV7S3pCHsHAAAQX4QWoMFGJqZ17fcf0d/96zNl109d363r3vJCvfD4YyLqGQAAQDIQWoAG2rpzQB/91sPKjkyUrmVSpv923in6v87bpLYMoysAAABHQ2gBGuDAwSl98ru/1rf+bXfZ9c0bevSZt5ylzRtWRtQzAACA5CG0AHX2w4f79bFv79DQ2GTpWms6pQ++5vn641eerJZ0KsLeAQAAJA+hBaiTobFJffw7O/T97f1l1194/DG67i1n6dT1KyLqGQAAQLIRWoBFcnd958Hn9Inv7ND+g9Ol622ZlD504Wl698tPUjplEfYQAAAg2QgtwCIMjEzoY99+WPf+eqDs+jkbV+vTbzlLJ63tiqhnAAAASwehBVgAd9c3f/msPvW9X2tkYqZ0vbM1rSsvOl3/5SUnKsXoCgAAQF0QWoAa7T5wSB/91kP6p0cHy66/7JQ1+os3n6XjV3dG1DMAAIClidACVCmfd33jX5/W/3vXTo1Nzo6udLdldNXrz9AfnX28zBhdAQAAqDdCC3AUIxPT+pdHh/S3P3tSP3tiX9m9c09bp2vf9AJtOKYjot4BAAAsfYQWoILfDo3rvkcGtHXnHm377T7N5L3sfk97Rh//g8168+88j9EVAACABiO0AJKmc3n94sn92rpzQPft3KMnBsfnLXvhmet1zRu3qLenvYk9BAAAWL4ILVi29o9P6SeP7tF9j+zRPz06qNHQKmBzreps0Xmn9eoPXrRB5566jtEVAACAJiK0YNlwdz22Z0z3PbJH9z0yoF89vV9znvoqc3rfCr369F6df0avXnT8KjaIBAAAiAihBUvaxHROP//tPm19pPDY17P7D81btjWT0u9tWqPzT+/Veaf36rhVLF0MAAAQB4QWLDl7Rib0498UHvu6//EhHZzKzVu2d0Wbzj+jV68+fb1edsoadbbyTwIAACBu+IaGxHN37XhuRPc9skdbdw7owWeHj1j+rONWFh77On29Nm/oYed6AACAmCO0IJEOTs3ogcf3auvOwrLEAyOT85btaEnrFc9fq/PP6NV5p/Wy6hcAAEDCEFqQGM/uP6gf79yj+3bu0U937dXUTH7ess87piN47KtXLzl5jdpb0k3sKQAAAOqJ0ILYyuVd//7MgcLeKY/s0c7s6LxlUyb9zgmr9OozCo99nbq+m2WJAQAAlghCC2LnicExffHHj+snvxnUvvGpecutaM/oVaeu0/ln9OpVp/ZqdVdrE3sJAACAZiG0IFbcXe/82jY9s6/y0sQnr+3Sq0/v1avP6NXZG1erJZ1qcg8BAADQbIQWxMre8amywJJJmc45aXUhqJzeq5PXdUfYOwAAAESB0IJYyQ5PlF53tKT1s4+er5UdLRH2CAAAAFHj2RrESji0HHtMO4EFAAAAhBbES/9IKLSsZD8VAAAAEFoQM9nh2fksfT0dEfYEAAAAcUFoQaz0DzPSAgAAgHKEFsRKeE7LekILAAAARGhBzJRNxO8htAAAAIDQghhx97LHw/oYaQEAAIAILYiRkYkZHZrOlc6Z0wIAAACJ0IIYCT8a1ppOaXVXa4S9AQAAQFwQWhAb/eHljle2y8wi7A0AAADiInGhxcxWmdmNZrbbzEbN7BYz66lDvVeb2ZM1lH+XmbmZbVxs2yjIMp8FAAAAFWSi7kAtzKxD0t2Szg6Oj0p6u6QNki5cRL2vl/RxSc9UWX6tpOsW2h4qY48WAAAAVJK0kZbLVQgsN7j7Re7+AUkXSbrAzN62kArN7K2Svqna/ltcL2nNQtrD/BhpAQAAQCVJCy3vlTQm6eriBXffJmmrpEtqrczM3iDp7yV9W9JPq3zPeZLeoSpHZVC9/pFQaGGPFgAAAAQSE1rMrE/SSZK2uvv4nNtbJZ27gGqPk3SVpEslTVfRhzZJN0l6StKnF9AejiAbmojP42EAAAAoStKclvXBcXuFe7skrTSz1e6+r4Y6b3b3nKRqV6q6StKpKjySdmwN7aAK5Y+HdUTYEwAAAMRJkkJLcYWwwQr3hoPjBklVh5ZiYKmGmZ0u6UpJf+vud5vZZdW+N1THjnlubaq1rqVmfHJGIxMzpXNGWgAAAFCUmMfDJBW/0R6qcG8qOHY2sP2bVAhHlzewjWUrG5rPkk6Z1na3RdgbAAAAxEmSRlqK81gq/Qm+JTg2ZAt1M3u3pFdJutTd9y60HnffPE/9OySdudB6l4Lwo2HrV7QpnWJjSQAAABQkaaTlueC4scK93uB4oN6Nmtk6FfZkucvdb693/SjoZ7ljAAAAzCMxIy3uPmRmuyW9uMLtc4LjQAOa/oik1ZIGzeya0PWzguMVZjYs6S/dve6habkoXzmMSfgAAACYlZjQErhL0nvM7DR3/41UWob4TZJ2unulSfqLVdxE8p3z3P/vwfHLasBIz3IRHmlZzx4tAAAACEnS42GS9CVJeUm3m9lxZtauwgT5DZJua0SD7n6Zu9vcH0nvCoqcFFx7shHtLxfhOS2sHAYAAICwRI20uPuDZnaFpM+qsMHjtKQ2SdtUmHciSQoe49rv7tdH0lHULLx6GHNaAAAAEJa0kRa5+xckvVLSnSqElT+V9Ap3nwoVu0rS+yPoHhaIkRYAAADMJ1EjLUXu/oCkB45wv+b1ct393BrL3yLpllrbweEmpnPaOz6bORlpAQAAQFjiRlqw9OwZmSy9NpN6VxBaAAAAMIvQgsj1h5Y7XtvdptYMv5YAAACYxbdDRC48CZ/5LAAAAJiL0ILIsUcLAAAAjoTQgsixchgAAACOhNCCyIXntLByGAAAAOYitCBy2dDqYYy0AAAAYC5CCyKXDY+09HRE2BMAAADEEaEFkZrO5bVnlJEWAAAAzI/QgkgNjk7KffacOS0AAACYi9CCSIWXO17V2aL2lnSEvQEAAEAcEVoQqfByx30rmc8CAACAwxFaEKmy5Y572iLsCQAAAOKK0IJIMdICAACAoyG0IFLZkdnQwsphAAAAqITQgkiVj7QQWgAAAHA4QgsiFV49jJEWAAAAVEJoQWTyedcAj4cBAADgKAgtiMzQ+KRm8rM7SzIRHwAAAJUQWhCZ8HyWFW0ZdbdlIuwNAAAA4orQgsiE57Os59EwAAAAzIPQgshkmYQPAACAKhBaEJnwHi19PYQWAAAAVEZoQWQYaQEAAEA1CC2ITP/wodJrVg4DAADAfAgtiAwjLQAAAKgGoQWRcPey1cP6CC0AAACYB6EFkThwcFqTM/nSOSMtAAAAmA+hBZEIj7K0t6S0sqMlwt4AAAAgzggtiER2JDQJv6ddZhZhbwAAABBnhBZEgvksAAAAqBahBZEYKFs5jOWOAQAAMD9CCyLBSAsAAACqRWhBJLIj7NECAACA6hBaEImykZYeQgsAAADmR2hBJLLMaQEAAECVCC1outGJaY1NzpTOmdMCAACAIyG0oOnCoywtadOartYIewMAAIC4I7Sg6cLzWXpXtCuVYmNJAAAAzI/QgqZj5TAAAADUgtCCpsuyRwsAAABqQGhB0/UPM9ICAACA6hFa0HTZ4UOl130sdwwAAICjILSg6RhpAQAAQC0SF1rMbJWZ3Whmu81s1MxuMbOeOtR7tZk9eYT7J5nZN81sr5lNmdnTZvbBxba7HIUn4jOnBQAAAEeTiboDtTCzDkl3Szo7OD4q6e2SNki6cBH1vl7SxyU9M8/94yT9XNI6SQ9Jek7SuZI+Z2YZd79+oW0vN4emcjpwcLp0zkgLAAAAjiZpIy2XqxBYbnD3i9z9A5IuknSBmb1tIRWa2VslfVNH/m/xaUmrJb3d3c9y94sknSPpkKSrzCy9kLaXo/AoS8qkdd1tEfYGAAAASZC00PJeSWOSri5ecPdtkrZKuqTWyszsDZL+XtK3Jf10njIrJV0s6Yvu/vVQu9uDdldJ2lhr28tVeLnjdSvalEkn7VcQAAAAzZaYb4xm1ifpJElb3X18zu2tKjyuVavjJF0l6VJJ05UKuPuwpGMkXVnhdktwnFlA28tSdoSVwwAAAFCbJM1pWR8ct1e4t0vSSjNb7e77aqjzZnfPSZKZzVvI3Wc0J5iYWZekl6swv+Xpahozsx3z3NpUzfuXgrKVw3qYzwIAAICjS8xIi6TiCmGDFe4NB8cNtVRYDCwL9H5JnZJucndfRD3LSvjxMFYOAwAAQDWSNNJSHOk4VOHeVHDsbEZHzOwEFR4r65f0uWrf5+6b56lvh6Qz69O7eGOPFgAAANQqSaGlOI+l0jfd4tyS1kZ3wsxSkm6R1K3CamKjjW5zKWGkBQAAALVK0uNhzwXHjRXu9QbHA03ox/8j6TxJf+Xu/18T2ltSykdamIgPAACAo0tMaHH3IUm7Jb24wu1zguNAI/tgZq+W9GcqLAbwPxrZ1lI0NZPX0Nhk6byPifgAAACoQmJCS+AuSa8ws9OKF8ysTdKbJO1090qT9OvCzE6V9A8q7BPzn9290twaHMGe0Ymy894eNpYEAADA0SUttHxJUl7S7WZ2nJm1S7pJhVXDbmtUo2Z2rKR7VdhI8lJ3f7xRbS1l4fksa7pa1d6SjrA3AAAASIokTcSXuz9oZldI+qykp1TYELJN0jZJ1xXLmdk1kva7+/V1avpaSSdIelTSS83spXPu3+nu/1antpasfibhAwAAYAESFVokyd2/YGa/lPRBSX2S7pH0GXefChW7SoVQU6/Q8rLgeGpQ91yPSyK0HEWW5Y4BAACwAIkLLZLk7g9IeuAI9+ff3n7+95x7hHun1lofDsdICwAAABYiaXNakGDZkdm1C1juGAAAANUitKBpykZaWO4YAAAAVSK0oGmyPB4GAACABSC0oClyedee0dDGkoQWAAAAVInQgqYYGptULu+lcx4PAwAAQLUILWiK8HyWnvaMutoSuXAdAAAAIkBoQVNkh1k5DAAAAAtDaEFTsEcLAAAAForQgqYIrxx2LKEFAAAANSC0oCkYaQEAAMBCEVrQFIy0AAAAYKEILWiK/pHZifjrWe4YAAAANSC0oOHcXQPDsxtLsnoYAAAAakFoQcPtG5/SVC5fOmdOCwAAAGpBaEHDhSfhd7am1dPOxpIAAACoHqEFDZeds3KYmUXYGwAAACQNoQUN1z/CymEAAABYOEILGi47PLtyWF8Pk/ABAABQG0ILGq6fPVoAAACwCIQWNFx4Tst6QgsAAABqRGhBw2XDc1rYWBIAAAA1IrSgodz9sNXDAAAAgFoQWtBQIxMzOjiVK50zpwUAAAC1IrSgocKjLK3plFZ3tUbYGwAAACQRoQUN1R9e7piNJQEAALAAhBY0FPNZAAAAsFiEFjQUe7QAAABgsQgtaChGWgAAALBYhBY0VH9oj5Y+9mgBAADAAhBa0FADPB4GAACARSK0oKHKVw/riLAnAAAASCpCCxpmfHJGIxMzpXNGWgAAALAQhBY0TDY0nyWdMq3tbouwNwAAAEgqQgsaJrxy2PoVbUqn2FgSAAAAtSO0oGH6We4YAAAAdUBoQcNkQ5Pwj2USPgAAABaI0IKGCY+0rGePFgAAACwQoQUNMzDCHi0AAABYPEILGoY5LQAAAKgHQgsaJrx6GCMtAAAAWChCCxpiYjqnveNTpXNGWgAAALBQhBY0xJ6RydJrM6l3BaEFAAAAC0NoQUP0h5Y7XtvdptYMv2oAAABYmMR9kzSzVWZ2o5nwJR+1AAAgAElEQVTtNrNRM7vFzHrqUO/VZvbkEe63m9mfm9mTZnbQzL5tZhsW2+5SlWXlMAAAANRJ3UKLmZ1ar7qO0EaHpLsl/VdJD0n6mqQ3SPrmIut9vaSPH+G+SbpD0kclPSfpJkkvlnSPmbUupu2lqmzlMPZoAQAAwCLUc6RlZzAK8WUze5uZralj3UWXSzpb0g3ufpG7f0DSRZIuMLO3LaRCM3urCqHnSP8t/g9JfyDpTkkvc/f/W9LLJZ0s6X8spN2lLstyxwAAAKiTeoaWX0h6nqR3S/qGpAEz+4WZXWtm55lZSx3aeK+kMUlXFy+4+zZJWyVdUmtlZvYGSX8v6duSfnqUdl3Sh9zdg3afUuFz1tzuckBoAQAAQL3ULbS4+zmS1ki6WNIXJO2Q9B8kfUTSjyTtN7Pvm9nlZral1vrNrE/SSZK2uvv4nNtbJZ27gG4fJ+kqSZdKmp6nXZP0EkkPufuTFdp9oZmtWkDbS1o/c1oAAABQJ5l6VubuI5K+G/zIzNZKerWk84Pja4MfN7MBSfdKusfdv15F9euD4/YK93ZJWmlmq919Xw1dvtndc0Ff5yvTI6njCO1KhTC1v4Z2l7xsaPWwvp6OCHsCAACApKtraJnL3YdUmMB+hySZ2YmSPizpv6kQQv6LpLdLqia0FFcIG6xwbzg4bpBUdWgpBpY6tfuro1VkZjvmubWpin4kxnQurz2js/u0MNICAACAxWhoaJEkMztTs6Mtr5K0MnT7V5LuqbKqmeB4qMK94tbrnQvpY0zbTazB0UkVZv4UMKcFAAAAi1H30GJmGzUbUs5TYUSl+OzVs5L+UYWg8iN331tD1cV5LJW+ARcn+Tdi+eG6tevumytdD0Zgzqy9a/EUXu54VWeL2lvSEfYGAAAASVe30GJmf6VCUNlYvKTCSl93qRBS7nX3nYto4rnguLHCvd7geGAR9Vfk7iNmNtbsdpOsfOUw5rMAAABgceo50vJeSXkVlj6+V4Wg8lN3nzniu6rk7kNmtluFTR3nOic4DtSjrQq2R9RuImVHwhtLtkXYEwAAACwF9dyn5Q8lrXP3/+juH3P3f65XYAm5S9IrzOy04gUza5P0Jkk73b3SZPl6+L6kE83sNXOu/5GkEUn/3qB2E6ls5TBGWgAAALBI9dyn5R/cvdHL/n5JhdGc283sODNrl3STCqt33dbAdr8maVTSX5vZmWaWNrNrVBh9ucPdK+7xslyF57SwchgAAAAWq+Grh9WTuz9oZldI+qykp1TYELJN0jZJ1xXLBYFiv7tfX6d2+83sXSoEox0qrCTWIekJSX9SjzaWkvI5LYQWAAAALE49Hw9rCnf/gqRXSrpThbDyp5Je4e5ToWJXSXp/ndu9U9LvSrpV0i8l/aWk/9CE0aXEYaQFAAAA9VTP1cOq2aixEnf3mvrh7g9IeuAI9+fd3v4I7zm3ijI7JL2z1rqXk3zeNTBCaAEAAED91PPxsGck+VFLYUkbGp/UTH7214CJ+AAAAFisuoUWd99Yr7qQXOH5LCvaMupuS9S0KQAAAMRQ4ua0IN76mYQPAACAOiO0oK7C81kILQAAAKgHQgvqqmykpYfQAgAAgMUjtKCusix3DAAAgDojtKCu+ocPlV6zchgAAADqgdCCumKkBQAAAPVGaEHduDurhwEAAKDuCC2omwMHpzU5ky+dM9ICAACAeiC0oG7CoyztLSmt7GiJsDcAAABYKggtqJuyPVp62mVmEfYGAAAASwWhBXXDfBYAAAA0AqEFdZMNLXd8LMsdAwAAoE4ILagbRloAAADQCIQW1E12hD1aAAAAUH+EFtRN2UhLD6EFAAAA9UFoQd1kh8MjLcxpAQAAQH0QWlAXoxPTGpucKZ0zpwUAAAD1QmhBXYT3aGlJm9Z0tUbYGwAAACwlhBbURXg+S++KdqVSbCwJAACA+iC0oC76h1k5DAAAAI1BaEFdZNmjBQAAAA1CaEFdMNICAACARiG0oC6yw4dKr/tY7hgAAAB1RGhBXTDSAgAAgEYhtKAusiPMaQEAAEBjEFqwaBPTOR04OF06Z6QFAAAA9URowaKFVw5LmbSuuy3C3gAAAGCpIbRg0cLzWdataFMmza8VAAAA6odvl1i07AgrhwEAAKBxCC1YtLKVw3qYzwIAAID6IrRg0cJzWlg5DAAAAPVGaMGisUcLAAAAGonQgkVjpAUAAACNRGjBopWPtDARHwAAAPVFaMGiTM3ktXd8snTex0R8AAAA1BmhBYuyZ3RC7rPnvT1sLAkAAID6IrRgUcLzWdZ0taq9JR1hbwAAALAUEVqwKP1MwgcAAECDEVqwKFmWOwYAAECDEVqwKIy0AAAAoNEILViU7Mih0muWOwYAAEAjEFqwKGUjLSx3DAAAgAZIXGgxs1VmdqOZ7TazUTO7xcx6FlHfGWb2PTPbb2b9ZvbRecqdbWb3mtmgmY2Y2S/M7B0L/yRLwwBzWgAAANBgmag7UAsz65B0t6Szg+Ojkt4uaYOkCxdQ36mS7pe0QtLXJZmkT5mZ3P3aULkzJG2VlJX0DUktkl4n6W/MbJ27X7+Yz5VUubxrYHR2Y8n1hBYAAAA0QKJCi6TLVQgsN7j7FZJkZrdJ+rmZvc3d76ixvi9KWi3pYnf/TlDfDkmfNLPb3P3poNyfShqT9DvuPhqU65T0K0kfM7Mb3MNbLC4PQ2OTyuVnPzaPhwEAAKARkvZ42HtVCA9XFy+4+zYVRkEuqaUiMzte0gWSflwMLIEbJU1JenPo2u9I+pdiYAnaPSjpXyQdI2ldbR9jaQjPZ+lpz6irLWkZGAAAAEmQmNBiZn2STpK01d3H59zeKuncGqt8WXD8bvhiUPe2OfUdlHR6hTq2SJqQNFRj20tCdpiVwwAAANB4iQktktYHx+0V7u2StNLMVtexvpND59+S9AIz+4yZ9QY/n5L0Ekm3unu+hnaXDPZoAQAAQDMk6Xme4gphgxXuDQfHDZL21am+DaHzv5C0SdKHg5+ir0j6QJXtFefLVLKp2jriJMvKYQAAAGiCJI20zATHQxXuTQXHzjrWF67rdEkXBa/3Bz9SYU7Mq2toc0lhpAUAAADNkKSRluI8lkrfjluCY2sd6wvX9RVJqyS9RdK33N3N7PWSbpP0j2Z2irs/e7QG3X1zpevBCMyZNfQ9FrIjjLQAAACg8ZI00vJccNxY4V5vcDxQx/oOSJKZnSjpHEk3uvudxaWN3f37Kjwq1ibpP9fQ7pIRfjxsPcsdAwAAoEESE1rcfUjSbkkvrnD7nOA4UEOVDwbH+eor1nVscPxNhXK/Do4bKtxb0tx9zpwWVg8DAABAYyQmtATukvQKMzuteMHM2iS9SdJOd680qb4id39M0mOS3mFmpUfBzOwsFR7V+ufgUrHOSpPlXxQc91b9CZaIfeNTmsrNLprGnBYAAAA0StJCy5ck5SXdbmbHmVm7pJtUGOm4bQH1fV6FvV9uNrNOM3uepK9KMklfD8o8I2lE0rvNrDQqY2Yvl/SJ4PTuBbSdaOFJ+J2tafW0J2l6FAAAAJIkUd803f1BM7tC0mclPSVpWoU5JdskXVcsZ2bXSNrv7tcfpcobJb1S0mWSLpGUDn4+5+73B21OBXuyXCfpF2a2V4WJ+sUlk//G3R+cW/FSl52zcpiZRdgbAAAALGWJCi2S5O5fMLNfSvqgpD5J90j6jLtPhYpdpUKoOWJoCSbV/6GZfV/SpcHlW9z9G3PK/aWZPSvp/ZLOUiEoPa7CaMyfL/5TJU8/K4cBAACgSRIXWiTJ3R+Q9MAR7tf0Z393v1XSrUcp83eS/q6Wepey7PDs9jZ9PUzCBwAAQOMkbU4LYqJ/mJEWAAAANAehBQsyMFI+pwUAAABoFEILFiQ80tLHxpIAAABoIEILajZ3Y0lGWgAAANBIhBbUbGRiRgencqVz5rQAAACgkQgtqFl4lKU1ndLqrtYIewMAAICljtCCmvWHlztmY0kAAAA0GKEFNWM+CwAAAJqJ0IKasUcLAAAAmonQgpqxRwsAAACaidCCmrFHCwAAAJqJ0IKaZXk8DAAAAE1EaEHNylcP64iwJwAAAFgOCC2oyfjkjEYmZkrnjLQAAACg0QgtqEk2NAk/nTKt7W6LsDcAAABYDggtqEl4Psv6FW1Kp9hYEgAAAI1FaEFN+tlYEgAAAE1GaEFNwnu0HMskfAAAADQBoQU1Ca8ctp49WgAAANAEhBbUhD1aAAAA0GyEFtSEOS0AAABoNkILasJICwAAAJqN0IKqTUzntHd8qnTOSAsAAACagdCCqu0ZmSy9NpN6VxBaAAAA0HiEFlQtvHLY2u42tWb49QEAAEDj8a0TVcuOMJ8FAAAAzUdoQdXCk/D72KMFAAAATUJoQdVY7hgAAABRILSgallCCwAAACJAaEHV+pnTAgAAgAgQWlC1bGj1sL6ejgh7AgAAgOWE0IKqTOfy2jM6u08LIy0AAABoFkILqjI4Oin32XPmtAAAAKBZCC2oSnjlsFWdLWpvSUfYGwAAACwnhBZUZWAkvHIY81kAAADQPIQWVKVsj5aetgh7AgAAgOWG0IKqlK0cxkgLAAAAmojQgqqER1pYOQwAAADNRGhBVbLhx8MILQAAAGgiQguqwkgLAAAAokJowVHl8162ehihBQAAAM1EaMFRDY1PaiY/u7MkE/EBAADQTIQWHNXA8GTp9Yq2jLrbMhH2BgAAAMsNoQVH1V+23DGPhgEAAKC5EhdazGyVmd1oZrvNbNTMbjGznkXUd4aZfc/M9ptZv5l9tIr3fMjM3Mzeu9B2kyQ7wsphAAAAiE6invMxsw5Jd0s6Ozg+KuntkjZIunAB9Z0q6X5JKyR9XZJJ+pSZyd2vnec9x0v6hKRfSPpq7Z8iecIrh/X1EFoAAADQXIkKLZIuVyGw3ODuV0iSmd0m6edm9jZ3v6PG+r4oabWki939O0F9OyR90sxuc/enK7znf0rqlPR+d88v9IMkSZbljgEAABChpD0e9l5JY5KuLl5w922Stkq6pJaKghGTCyT9uBhYAjdKmpL05grveYOkiyX9jbv/rObeJ1T5nBZWDgMAAEBzJSa0mFmfpJMkbXX38Tm3t0o6t8YqXxYcvxu+GNS9bW59ZtalwijLiKSP1NhWojHSAgAAgCglJrRIWh8ct1e4t0vSSjNbXcf6Tp5z7WpJJ0j6V0mXm9lfmNlramgvkdy9fE4LoQUAAABNlqQ5LcUVwgYr3BsOjhsk7atTfRuKJ2Z2oqQPBqfnBz+SdKWZfUXSH1czvyWYL1PJpqp6HIHhQ9OanJn9aIy0AAAAoNmSNNIyExwPVbg3FRw761hfuK4PS2qT1C/pjZKK82H+VdJ7JL2/hnYTJTzK0t6S0sqOlgh7AwAAgOUoSSMtxXkslf7UX/wm3VrH+sJ1/afgeJm73xO8ftbMHpT0jArB5fNHa9DdN1e6HozAnFlNp5stO2e5YzOLsDcAAABYjpI00vJccNxY4V5vcDxQx/rCdW2QNCDp3nAhdx+U9JCk59fQbqIwnwUAAABRS0xocfchSbslvbjC7XOC40ANVT4YHOerL1zXqKQn3d0rlG3R7ONpS042tNzxsSx3DAAAgAgkJrQE7pL0CjM7rXjBzNokvUnSzmDkoyru/pikxyS9w8xKj4KZ2VkqPKr1z6HiD0l63tw6zKxdhUn0802wTzxGWgAAABC1pIWWL0nKS7rdzI4LQsNNKjy+ddsC6vu8Cnu/3GxmnWb2PElflWSSvh4qd4ek48zsfXPe/2eSuoP7S1J2hD1aAAAAEK0kTcSXuz9oZldI+qykpyRNq7Cq1zZJ1xXLmdk1kva7+/VHqfJGSa+UdJmkSySlg5/Pufv9oXJflnSppL8yszeqMEJzjqSXqvCY2c2L/nAx1T9nIj4AAADQbEkbaZG7f0GFoHGnCmHlTyW9wt3D80quUhXLEHvBH0p6p6SfSLpP0iXufvmcclOSXhPUe6Kk9wXHr0m6wN0rLZu8JIRXD2NOCwAAAKKQqJGWInd/QNIDR7hf07q87n6rpFuPUuaQpGuDn2VhdGJaY5MzpXPmtAAAACAKiRtpQfMMhOaztKRNa7pq2QYHAAAAqA9CC+YVns/Su6JdqRQbSwIAAKD5CC2YV/8wK4cBAAAgeoQWzCvLHi0AAACIAUIL5sVICwAAAOKA0IJ5ZYdnV3LuY7ljAAAARITQgnkx0gIAAIA4ILRgXuElj5nTAgAAgKgQWlDRxHRO+w9Ol84ZaQEAAEBUCC2oKLxyWMqkdd1tEfYGAAAAyxmhBRWF57OsW9GmTJpfFQAAAESDb6KoKDvCymEAAACIB0ILKipbOayH+SwAAACIDqEFFYXntLByGAAAAKJEaEFF7NECAACAuCC0oCL2aAEAAEBcEFpQUflICxPxAQAAEB1CCw4zNZPX0Nhk6ZzHwwAAABAlQgsOs2d0Qu6z5709bCwJAACA6BBacJjwymFrulrVlklH2BsAAAAsd4QWHKaf5Y4BAAAQI4QWHCbLcscAAACIEUILDsNICwAAAOKE0ILDZEcOlV6z3DEAAACiRmjBYcKPh/X1MNICAACAaBFacBjmtAAAACBOCC0ok8u7BkZnN5ZcT2gBAABAxAgtKDM0NqlcfnZnSR4PAwAAQNQILSgTXjmspz2jrrZMhL0BAAAACC2YIzvMymEAAACIF0ILyrBHCwAAAOKG0IIyrBwGAACAuCG0oEx2hJEWAAAAxAuhBWX6GWkBAABAzBBaUCb8eNh6ljsGAABADBBaUOLuc+a0sHoYAAAAokdoQcm+8SlN5fKlc+a0AAAAIA4ILSgJz2fpbE2rp52NJQEAABA9QgtKsnP2aDGzCHsDAAAAFBBaUNI/wsphAAAAiB9CC0oGwiMtPUzCBwAAQDwQWlDCHi0AAACII0ILSrIjh0qvWTkMAAAAcZG40GJmq8zsRjPbbWajZnaLmfUsor4zzOx7ZrbfzPrN7KNVvu9dZuZmtnGhbcdNf9njYYQWAAAAxEOi1rQ1sw5Jd0s6Ozg+KuntkjZIunAB9Z0q6X5JKyR9XZJJ+pSZyd2vPcL71kq6ruYPEGNzN5ZkpAUAAABxkajQIulyFQLLDe5+hSSZ2W2Sfm5mb3P3O2qs74uSVku62N2/E9S3Q9Inzew2d396nvddL2nNgj5BTI1MzOjgVK50zpwWAAAAxEXSHg97r6QxSVcXL7j7NklbJV1SS0VmdrykCyT9uBhYAjdKmpL05nned56kd0h6pqaex1x4lKU1ndLqrtYIewMAAADMSkxoMbM+SSdJ2uru43Nub5V0bo1Vviw4fjd8Mah7W6X6zKxN0k2SnpL06Rrbi7X+4fJJ+GwsCQAAgLhITGiRtD44bq9wb5eklWa2uo71nVzh+lWSTpX0f0qaG5wSbWCE+SwAAACIpyTNaSmuEDZY4d5wcNwgaV+d6tsQvmBmp0u6UtLfuvvdZnZZle2E69gxz61NtdZVb+zRAgAAgLhK0kjLTHA8VOHeVHDsrGN9c+u6SYUwc3kNbSQGK4cBAAAgrpI00lJ8HKvSN+qW4FjL7PGj1Veqy8zeLelVki519701tFHG3TdXuh6MwJy50HrrgT1aAAAAEFdJGml5LjhurHCvNzgeqGN9ByTJzNapsCfLXe5+ew31J0qWx8MAAAAQU4kZaXH3ITPbLenFFW6fExwHaqjyweD4Ys1ZQSyor1jXR1TYy2XQzK4JlTkrOF5hZsOS/tLdawlNsVK+elhHhD0BAAAAyiUmtATukvQeMzvN3X8jlZYhfpOkne5eaVJ9Re7+mJk9JukdZnatu08F9Z2lwqNaNwdFi5tIvnOeqv57cPyyahvpiY3xyRmNTMyUzhlpAQAAQJwk6fEwSfqSpLyk283sODNrV2GC/AZJty2gvs+rsPfLzWbWaWbPk/RVSSbp65Lk7pe5u839kfSuoI6TgmtPLu6jRScbWu44nTKt7W6LsDcAAABAuUSNtLj7g2Z2haTPqrDB47SkNhU2g7yuWC54jGu/u19/lCpvlPRKSZdJukRSOvj5nLvfX/cPEFPh+SzrV7QpnWJjSQAAAMRHokKLJLn7F8zsl5I+KKlP0j2SPlN8vCtwlQqh5oihxd1d0h+a2fclXRpcvsXdv1H/nscXyx0DAAAgzhIXWiTJ3R+Q9MAR7tc0VODut0q6tcb33CLpllreE1fhx8OOZRI+AAAAYiZpc1rQAOUrhzHSAgAAgHghtKD88TA2lgQAAEDMEFqgfua0AAAAIMYILSgbaWGPFgAAAMQNoWWZm5jOae/47MJrjLQAAAAgbggty9yekcnSazOpdwWhBQAAAPFCaFnmwssdr+1uU2uGXwkAAADEC99Ql7nwcsfMZwEAAEAcEVqWOZY7BgAAQNwRWpY5ljsGAABA3BFalrksoQUAAAAxR2hZ5vpH2KMFAAAA8UZoWeayoYn4fT0dEfYEAAAAqIzQsozN5PIaHJ3dp4WRFgAAAMQRoWUZGxybVN5nz5nTAgAAgDgitCxj4ZXDVnW2qL0lHWFvAAAAgMoILctY+cphzGcBAABAPBFalrGRQ9NqSZsk5rMAAAAgvjJRdwDR+aNzTtDbfvd47R2f0lQuH3V3AAAAgIoILctcKmVat6It6m4AAAAA8+LxMAAAAACxRmgBAAAAEGuEFgAAAACxRmgBAAAAEGuEFgAAAACxRmgBAAAAEGuEFgAAAACxRmgBAAAAEGuEFgAAAACxRmgBAAAAEGuEFgAAAACxRmgBAAAAEGuEFgAAAACxRmgBAAAAEGuEFgAAAACxRmgBAAAAEGvm7lH3Ydkzs5G2trYVmzZtirorAAAAQF3s2rVLk5OTo+7es9i6CC0xYGZZSZ2Snomg+WJS2hVB20cSx37Rp+rEsU9SPPtFn6oXx37Rp+rFsV/0qTpx7JMUz37Rp8MdL+mgu/cttiJCyzJnZjskyd03R92XsDj2iz5VJ459kuLZL/pUvTj2iz5VL479ok/ViWOfpHj2iz41FnNaAAAAAMQaoQUAAABArBFaAAAAAMQaoQUAAABArBFaAAAAAMQaq4cBAAAAiDVGWgAAAADEGqEFAAAAQKwRWgAAAADEGqEFAAAAQKwRWgAAAADEGqEFAAAAQKwRWgAAAADEGqFlGTOzF5rZPWY2YmaTZvaomf1RDPp1gZl93sy+amZ/YmZ9UfcpzMz+zMwi3eDIzH5lZl7h5ydR9ivMzL4Y9Ok1EbW/cZ7/RuGfWyLq2++b2f1mtt/M9gWvXx9FX0J9ermZfTfo04iZ/aOZbYmoL+eZ2YyZbZzn/jvN7GEzO2RmvzCzl0bdp6BMl5ltN7NPNLo/1fTLzLrN7Hozezoos8/MbjSzzqj6FNzfaGYfNbOvmdkNZvayRvanmj7NKXuamU2Y2WVR9svMPnCE/+86rHwz+jSn3NlmljOz2xrVl6P1ycx+crT/n292n4J7vWZ2W/Bv72Dw/eqTZtbeqP5U2a91wb+53wa/4/9uZu9udJ/qJRN1BxANM3uRpAckdUr6uaRxSedK+oaZTbv7nRH16/OSPiBpr6RRSe+S9KH/3d59h81Rlnsc//4MAbEgIEqREg5V2hFCE0OXJgGlCBxQOohIl4iIQGgqUQ4cEBEEDHAOFoqAVE0ARRFPqNJD0ACHHroGQsl9/rifTZZl3+TNm3dnNuT3ua69JvvM7Mz9TnZnnj6SNoyI++uIqZmk5YAjao5hTmBF4Cbg1pbV/6g+oveStDrwdeDyiBhVUxgvAyf1sG4R8rv1XHXhJEkbANcA9wEjgXmArYHfStohIi6tIaYvA78gz8dFwIeBnYFNJG0aEa3fs07GsjjwK2BAD+sPB34IPAacBQwBRkn6TEQ8UkdMZRsB5wErA5d3Io4ZiUvSAOBaYF3gSeAGoPG7XJT8zlUaU1m/AXAlMBcwHvg34FBJ346Ik+uIqY2flvg6qhdxrQa8SH7PW71cU0yN7QYAZwMTgW91IpZexnQR8KceProf0JFCy3R+ex8EbgQWA35N5q/WAY4GVgW26kRMvYhrAeA2YBBwKXld2BI4T9LKEXFop+LqNxHh12z4Igss/wQ2bkrbhPyB31NTTNuV4x8DqKRtDbwFXFP3OSvx3FRijBpjGFxi2Knu89FDfAOA28mb2RJ1x9NDjD8FXgc+VcOxbwXuAuZoSlsQmFDHb48soLxIFgI+3pQ+uJyjRxq/xwpiWbHEEeU1qGX9IGASMA6Yr6QNBO4Grq0jprLNXMDFTdsM74JztU9JPx0YWNI+Btxf0letIaZ5yIzSGGCBkrYg8DfgTWCROv7/Wrbfo2nb3ev6/yvb3Atc3+nvUl/OFXBI2eZb3RJTy+fWLtsfVnVMwL4lffWW9Is69dvrZVxnl/Ttm9LmAm4p6etW9V3r68vdw2ZDkpYH1gS+GxGjG+kR8XvyhrZKqUWp2lHAmIg4PsqvKSKuIjPAQ2qI510k7UG2Rj1RcyirleXdtUbRswPIDO/JEfFY3cG0krQEsCfwk4h4soYQVgN+HxFvNxIi4lmyILNMDfEMBeYDfhgRLzTFdAdwHbB0FXFJWpSsTHkVOKeHzXYF5gSOj4iXSpxvAacAm0n6eA0xQRZYtgMO78/jz2Rcu5LXiIPLOSIiXgF+Xtav1sPnOhnT7pRWzoiYUGJ6FjiTLHyuVUNMzdsvQLbidfQa35u4JM0NLE9F1/kZOVeSPgUcD4wFTuuGmNo4HngK+EkNMa0GPB8Rt7ekX1+W/X49nV5ckuYgW8/viKbW/IiYRH7nAbbo77j6mwsts6GIeAj4CHmjaDUQeLtNehV2Bb7SJn0g2bxam6ab2f+SXXrqNJi8MI2tOY73kLQIcALZ7WNEvdH06Ltkbf0Pajr+RODTzQnlhrIc9RSIVyjLO9qsm1CWC1QQxwJkd6YhwNM9bLMOWSN4dUv6jeT9bN0aYmpYH6iqW+1044qIdYEhjeQu630AABExSURBVAqgJgPLsr+v8705V78sMd3XQ0z9fZ2fkf8/yMLvx8guyp3Um7j+nezCP6bDscxITA2nAx8lC8RvdklMU5QxUpsAJ0bEGzXENBGYX9InW9IbYwQ7cZ2fXlyLkfm+uq/zM8WFltlURExq1L41SFoSWJZs7Xinhpjui4h3ZcQlrU/WWlxbdTwt/pPs2rAXMLnmWAaTXftGKwdMv6QcQD245rgATiVvZrcDx0s6SdKaNcc0RbmJ7ApcEBHP1xTG5cCWkg6RNF+pITubvKmMrCGeRuZ1vjbrli/LKgpT90bEzqU1oCcLAk9GxIvNiaXFbBI5PqLqmAB2iIjb+vnY09KruCKiXSFgs7L8S9UxRcRzEfHn5rTSorAf8FIdMTXFsRF5bfgRnW/d6E1cjev5bpKeVE46cY+k/SV1Iu/Wq3MlaQtgW7Ib0tplUPdXJQ2c1uc6GVMbw8hxsT+f3oYdiuk3ZP76vyUtJemjykmODgIeJCs/q46rW67zM8WFFmvWGGDebtBfpSRtIel04Hdkv96ODvSbTiwbAV8luzu11hBWHcucwCpkF4u5gXPJLjybArdKWqPG2D4L7FDebk92lfkOcJsqnE1pOvYluxe1a2WsysHkoOhTybEkT5D96E8AOjIQeToaFQX7NyeW7/26wIMR0fGbWS8rSuYBeipsvkL+LvpNbytvqq7k6evxJK1Ntgjd2FpBVHVMkpYpkyrcCywF7BoRr9URk6S5yPveI8Bx/RlDO72Ma/WyXIucuONn5Pf/TLJgVXlMpbDUOPYSwLHAocCFwC1tWhY6HlOr0v13KHBuB1pZehVTRNxC3ms2IcffvUpOdHIPsFknrhe92OfT5ORGW0iaUrmjnElwWHl7Q3/H1d9caDEgpy4E9iZ/VBfXHA7khfFAMoN5EVkLV7mmm9lDwIl1xNBiIbLf6sERsXZEHBYRO5NjbT5Azk5Sl6PK8gHg88DiwDZkRuBYSV+sKzCY0gVrP2B0RDxYYyhrAY0pXieQNzSRYyL6dZxBL10J/J1s/blO0l6SvgdcUdafX0NMPXmbnBygnTfJ2RCtjfL9P5NsKT6y5nAAvkR2uV2KnNGoE7XPvXUUOc5gn05kdPvoNbK74VIRsW9EHEQOtB4DHKB6HgWwLdmd9B1yIP4S5GxYF5PXtbNriKnV/uT1tLbKV0kfJvNTAG+QBYYgZxbcuY6YyhjKH5NdxP4o6TBJB5D5ieWB+yOizt9gr7jQYo3m+QvIH9VedXQNa2Nl8iI4iryx1ZUZb9zM9i4D1moVEY9HxEYRcXpL+l+Aq8iM5zxVx1UKd43Z57aNiNER8UREXEG2ukB2ravTlsCnyNapWpSM44VkV6YNI+ITZHP97mTm7Vp1+BkarUombVMyM7Q5eX6OJLv5vcSMD4DtpH8BPT3nYCBZyWHtHU0Wik/vhsxJRPwQWBgYTvbDH131dx+mTExzBFkz/4eqj9+TiDgoIraPiH82pU0kW2QHkpUcVRtaliMi4r/K/ejuiNiFHCuxdRn/WYtyfd2DnOikzklgjiHzL98D5o2IRcjxLI8CP5C0/bQ+3EFHkxMnLEiO3zoD+ExZV9cYzxniQotBDqr7NHBMmTGodhExudxYh5LdV4ZVfUNrupmd1doPu0uNJX/Tg2o49sfJDOOYiHi4eUVE3Eu2KNQxM1aznchar9ZB3FX6HFlwOiEiboYp3/ULyNbFT5CFv0pFxKMRsSZZi7oZWQCGzJy8WnU80/AUbb7fZbbD+enQsytmdZLWIytg/kZ3tLIAEBHPRMRxZOZuJaZWcFSiPFvnbHL8w7DpbN4tGt36lqrh2I3ulxe1WXcTef+pI66GjclraOXPumqxA3BnRBzVqOyMiAeY+v3epY6gIuKdyGexLARsRPZmgewd0Q09bKbLD5eczUnajWzGvIF6+tO/iyQ1z3YTEZMk3QR8jbxgjqswnJPIjPjbkpq7hq1XYj2xxPjdCmNC0pIR0e4hkvOXZcefuNtGoy96Tw+3nIPsvlOLUuDdCvhdc81lDRYuy4fbrHugLPt1XMaMiIgHJT1L3tCeJis0usk9ZGvioIgY35S+Kln7/GwtUXUxSQuTs3ZNJCcMqLX7U+s1vriOrJ1etuJwhpLX85vJirFG+sfKcltJSwOXRcRdVQZWWiwmtRnn063X+cZA/Nqu88COZNe1K2uMAfI6/56JOSJinKQ3qfEaX+J4AbhJ0j4l6aiIqHuCoV5xoWU2VqYFPBt4HNilzi+tpPnITNuZvHfsyLxlWfW0x41nPvQ0/WVjDEdlhZYy1uBwSQs1z6BUmsU3IS/Y7TLEHRURr0l6jHzS9ruU2bHmJTMmddmCfIjib2qMAaYOIm9XG9lopn+hzboqHUj2ez6sdEfpJteQkzvszbt/d/9Rln+sPKIuVgrrV5CZqB1aW0FriOdc4HOSVmgpuNR9jd+gvFptVV7jyOcoVaK08t9PTtrx45bVjWdpPED17iXHtSzKeysQVyLHnNUyFX+5B24D/KnGmSEbnqfNNV7SCmRFaN3XeMpg/B2AW0s37lmCu4fNpiStSGYAAtgmmh4qV4fIB8U9AXxL0sqNdEnLAl8kp/Pr9Tzt/RTTBhGh1hdldpmm91UaRdZoTXkGSunicDKwJPDbPkwP2V9+DawjadOm2AYwNdZf1xJVakzzWnef9QfJgdCHlRpclLYmH8o5iaz1rUWpPDiEzBB10wB8ACLiVnI67cMlbQMg6Uvk4Nux1DuYu6uU395l5IOER0TEJTWHBHAnOej3mEZCyWweWt5WOntRRIzs4Rq/ZNlkj5I2ssq4yO/yE8ARpaUMAEnrAIeR3Vzr6AJ1KZlnOKZ52mVJXyC7Zl0T7afZrsKaZOG37ms8wH3A6pJ2K/fnxiMlflbWX9/jJ6tzLDCAWadbJOCWltnZj8km8DFkE/i2LevP66ELUid9A7gF+KukS8lZgrYnL5L7VRxLV4qIGyVdDewlaQg569MK5CwujzG1j2odTga2Bq6RdAXZvWgDclKF66m3yX5j4JkavtPvEhFPSTqL/K6PlTSBbAFqjNc6LiKeqy3AHO8wL9ny2g0TcrSzC/kwycslvU5O/T2RzGC2djuanX2FnFjhdWBySxdXyD73l1cc07nk9PHDJW1MFmLWJScIOC0i7qw4nq4UEZMlDQN+BTwoaQx5jVibvB/uHRHP1BDXfZJOIaezX1HSLeS9Z2tyFsRvVx1Tk43L8tYaY2g4kexiOxI4Q9Ikpj648SGmFl5qIWkl8vpwSakImmW40DL7aky5ukZ5tRpFz+MTOiIixpSapBPJmZ7eAkaT/S0fqTKWLvdlMnO5G3lhfIwsMIyIlofuVSkiXij/f0eQzfRDyTEGp5KTPNSSoZS0OPnQwaozaD05kGxx2ZOcAOMDZM3cORFxRl1BlW58BwKjIqLuh7n2KCLGSlqF7Ca2FjAeON7XiPdoXOPnpn1m8gIq/k1ExJuSNidnMdqWLKw8AOwUEb+qMpZuFxGXSHqNqbOrvUZ29RsREX+tMa5hku4Gvk7egyDv08Mi4qG64iLvhZNpM5akahHx59L9/mjydzgvWYl3PfCdiOhp2vaqfJ/syldnIbNP5IopMzMzMzPrZh7TYmZmZmZmXc2FFjMzMzMz62outJiZmZmZWVdzocXMzMzMzLqaCy1mZmZmZtbVXGgxMzMzM7Ou5kKLmZmZmZl1NRdazMzMzMysq7nQYmZmZmZmXc2FFjMzMzMz62outJiZWUdJulnS+Lrj6ARJH5A0TNI4SW9Kek7SSX3Yz/v2HJmZ9Yc56g7AzMxsFnYgMAK4AbgUWBhYodaIzMzeh1xoMTMz67sdgUcjYvNGgiTVGI+Z2fuSu4eZmZn13cLA/zUnRETUFIuZ2fuWCy1mZmZ951YVM7MKuNBiZjYbkDRe0p8kfUjSGZKekfSypCslLdy03XBJIWlQy+cHlfThTfu7WdIhkp4v71eXdKKkVyU9JGmlln0MkXSHpDckPSLpgB5iXU3S1SW+iZJGSRrcZruRkqL8e1lJF0h6UtJXZ+I8LSrpQkkvlGPfLGm9dsctx14CWL/xvt25m8Hj9/Ycfbacl39JelHSxZKWaNnm8BLPEU1pAyWNLed24ffu2cysO3lMi5nZ7GMAcBWwHPBLYHVga2BuYNM+7G91YFHgamB3cjD6S8Bl5f23ga+UbecHrgNuB84HvgCcIWn+iDi+sUNJG5TtXgH+BxhIjhv5o6Q1IuKB1iAkrV2O/TpwPzChD38LJdP/F2Ah4DfAC8D2wGhJO0bE5WXTq5jaJeyAEutFTbt6uS/Hp/fnaMsS3+vAL4B5gB2Az0v6bEQ8WjY9DdgF+I6k8yPi+RLvMsD+EfF0H+M0M6uc3PXWzOz9r0ynuzjwIDAkIl6S9AFgDLAq8JGImFhaUo4FloyI8U2fHwT8AzguIoaX/S0CLB0Rj0t6EfhIy/t7ImJDSTcD6wPnR8ReZX8LAHcBnwQWi4jnSjwPAx8FPhMRz5Rt1wH+DFwUEbs2xTQS2A0YTxaUjoyIt2biHP0WGArsEhEXN/3dd5LdwJaIiFfbnNfxEbFBX49b9nMzvTtHc5N/74eAwRExtmz7ReAK4MaI2Lhpv2sAtwHnAEcB44CHgM957I2ZzUrcPczMbPYh4KCIeAkgIiYDo0v6J/qwv3sj4vHy71fbvG8d7zG88Y+ImAD8HJgT2KgkrwosDTwFHFC6mp1ItjhMAtbtIY4HIuLwmSywzFeOc3ujwFLiHA+cDsxLFmg6bXjTsdudo83IQsxPGwWWsu2VwB+AjSQt0pQ+BjgT2AcYSRYs93WBxcxmNe4eZmY2+3ghIka3pE0sy+kNKB/QJu216bxv9kpEPNGS9nBZLl6WS5flquXVqqcxGEdO47i9tSxZkfe3NuvuKcvl++E409Kbc7RcWfYU5/pknE81pR8FbAdsBXw/Iu7rn3DNzKrjlhYzs9nH32fis4tPf5Npemcaaa33om9GhNq8PthuxxHRLgPfV9Nqgeh068SMnKMZiXN+ctwLwJJ9iMvMrHYutJiZzT7e7MU2jQxva0v8eq0bzqD5JS3YkrZMWTYGtY8ry+VatkPSdpK+Uca9dMJYYDKwSpt1K5flw23W9afenKNGDDMS5znk/f5nwE5l/IuZ2SzFhRYzM2v2fFl+upFQMtKH9MO+j2na57zAnsDbwI0l+S7gUWBHScs0bbsYcCGwdxmH0+/KOJ9rgTUk7dh07MWBg8kZwa7uxLFbTO8c3UD+H+0naemmbYcCG5ID8Z9qSt+TnBnuBOBA4BHgrLJvM7NZhse0mJlZs+vJFpkfSXoDmAsYQU4FvMVM7PdfwF6SliNnr9oCGASMaGSyI2KypH3JwsOdki4jx8lsTw5G/+ZMHL83DgAGAxdL+jLwIjkWZB5gx9aZwzqgN+fodUl7kFMe3y7pkhLfdmRh5muNnZXnsJwCPACcEhFvSdof+D1wKrBHh/8eM7N+45YWMzObIiL+Tj7z423yeSSnAecB35jJXU8AvkQ+A2UfcuD/N8lnuTQf/0ZgCHALsC35nJd7gfXKuo6JiMeANcnnw2zYdOyNm57R0km9PUfXkAPu7wB2JltSLgHWjIhxTZueRc56tn9jZrWIGEU+o2d3SZt39K8xM+tHfk6LmZmZmZl1Nbe0mJmZmZlZV3OhxczMzMzMupoLLWZmZmZm1tVcaDEzMzMzs67mQouZmZmZmXU1F1rMzMzMzKyrudBiZmZmZmZdzYUWMzMzMzPrai60mJmZmZlZV3OhxczMzMzMupoLLWZmZmZm1tVcaDEzMzMzs67mQouZmZmZmXU1F1rMzMzMzKyrudBiZmZmZmZdzYUWMzMzMzPrai60mJmZmZlZV/t/fQJTJ8iZencAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfOpenCreditLinesAndLoans\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfTimes90DaysLate\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberRealEstateLoansOrLines\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumberOfTime60-89DaysPastDueNotWorse\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "UnboundLocalError",
     "evalue": "local variable 'bins_df' referenced before assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-308-951dd6c4934d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmodel_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mgraphforbestbin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"SeriousDlqin2yrs\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mq\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgraph\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-305-5a65473b8b10>\u001b[0m in \u001b[0;36mgraphforbestbin\u001b[1;34m(df, X, y, n, q, graph)\u001b[0m\n\u001b[0;32m    123\u001b[0m         \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"IV\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    124\u001b[0m         \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 125\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mbins_df\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mUnboundLocalError\u001b[0m: local variable 'bins_df' referenced before assignment"
     ]
    }
   ],
   "source": [
    "model_data.columns\n",
    "for i in model_data.columns[1:-1]:\n",
    "    print(i)\n",
    "    graphforbestbin(model_data,i,\"SeriousDlqin2yrs\",n=2,q=20, graph=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到并非所有的特征可以进行分箱的，有一些特征根本不符合分箱的要求。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:04:53.833170Z",
     "start_time": "2019-12-29T03:04:53.388239Z"
    }
   },
   "outputs": [],
   "source": [
    "tmp = graphforbestbin(model_data, 'age', 'SeriousDlqin2yrs', n=2, q=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:05:44.106949Z",
     "start_time": "2019-12-29T03:05:44.082984Z"
    }
   },
   "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>min</th>\n",
       "      <th>max</th>\n",
       "      <th>count_y0</th>\n",
       "      <th>count_y1</th>\n",
       "      <th>total</th>\n",
       "      <th>percentage</th>\n",
       "      <th>bad_rate</th>\n",
       "      <th>good%</th>\n",
       "      <th>bad%</th>\n",
       "      <th>woe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>69610</td>\n",
       "      <td>88547</td>\n",
       "      <td>158157</td>\n",
       "      <td>0.811028</td>\n",
       "      <td>0.559868</td>\n",
       "      <td>0.712553</td>\n",
       "      <td>0.909882</td>\n",
       "      <td>-0.244461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>28081</td>\n",
       "      <td>8770</td>\n",
       "      <td>36851</td>\n",
       "      <td>0.188972</td>\n",
       "      <td>0.237985</td>\n",
       "      <td>0.287447</td>\n",
       "      <td>0.090118</td>\n",
       "      <td>1.159921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    min    max  count_y0  count_y1   total  percentage  bad_rate     good%  \\\n",
       "0  21.0   61.0     69610     88547  158157    0.811028  0.559868  0.712553   \n",
       "1  61.0  107.0     28081      8770   36851    0.188972  0.237985  0.287447   \n",
       "\n",
       "       bad%       woe  \n",
       "0  0.909882 -0.244461  \n",
       "1  0.090118  1.159921  "
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:05:35.703010Z",
     "start_time": "2019-12-29T03:05:35.679318Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{21.0, 61.0, 107.0}"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(tmp['min']).union(tmp['max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:00:26.389576Z",
     "start_time": "2019-12-29T03:00:26.357176Z"
    }
   },
   "outputs": [],
   "source": [
    "auto_col_bins = {\"RevolvingUtilizationOfUnsecuredLines\":6,\n",
    "        \"age\":5,\n",
    "        \"DebtRatio\":4,\n",
    "        \"MonthlyIncome\":3,\n",
    "        \"NumberOfOpenCreditLinesAndLoans\":5}\n",
    "#不能使用自动分箱的变量\n",
    "hand_bins = {\"NumberOfTime30-59DaysPastDueNotWorse\":[0,1,2,13]\n",
    "        ,\"NumberOfTimes90DaysLate\":[0,1,2,17]\n",
    "        ,\"NumberRealEstateLoansOrLines\":[0,1,2,4,54]\n",
    "        ,\"NumberOfTime60-89DaysPastDueNotWorse\":[0,1,2,8]\n",
    "        ,\"NumberOfDependents\":[0,1,2,3]}\n",
    "#保证区间覆盖使用 np.inf替换最大值，用-np.inf替换最小值\n",
    "hand_bins = {k:[-np.inf,*v[:-1],np.inf] for k,v in hand_bins.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:00:31.444762Z",
     "start_time": "2019-12-29T03:00:31.435547Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'NumberOfTime30-59DaysPastDueNotWorse': [-inf, 0, 1, 2, inf],\n",
       " 'NumberOfTimes90DaysLate': [-inf, 0, 1, 2, inf],\n",
       " 'NumberRealEstateLoansOrLines': [-inf, 0, 1, 2, 4, inf],\n",
       " 'NumberOfTime60-89DaysPastDueNotWorse': [-inf, 0, 1, 2, inf],\n",
       " 'NumberOfDependents': [-inf, 0, 1, 2, inf]}"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hand_bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来对所有特征按照选择的箱体个数和手写的分箱范围进行分箱："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:11:24.225658Z",
     "start_time": "2019-12-29T03:11:22.432453Z"
    }
   },
   "outputs": [],
   "source": [
    "bins_of_col = {}\n",
    "for col in auto_col_bins:\n",
    "    bins_df = graphforbestbin(\n",
    "        model_data, \n",
    "        col,\n",
    "        'SeriousDlqin2yrs',\n",
    "        n=auto_col_bins[col],\n",
    "        q=20,\n",
    "        graph=False\n",
    "                        )\n",
    "    bins_list = sorted(set(bins_df['min']).union(bins_df['max']))  # 合并区间，这里用集合是因为最小值区间上限=最大值区间下限\n",
    "    bins_list[0], bins_list[-1] = -np.inf, np.inf\n",
    "    bins_of_col[col] = bins_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:13:35.321922Z",
     "start_time": "2019-12-29T03:13:35.309578Z"
    }
   },
   "outputs": [],
   "source": [
    "bins_of_col.update(hand_bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:13:40.209481Z",
     "start_time": "2019-12-29T03:13:40.197176Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RevolvingUtilizationOfUnsecuredLines': [-inf,\n",
       "  0.09896531250000001,\n",
       "  0.29805387844466347,\n",
       "  0.46520709114394176,\n",
       "  0.9824034417564677,\n",
       "  0.9999998999999999,\n",
       "  inf],\n",
       " 'age': [-inf, 36.0, 54.0, 61.0, 74.0, inf],\n",
       " 'DebtRatio': [-inf,\n",
       "  0.017500875550000003,\n",
       "  0.40166383662187843,\n",
       "  1.4742210817767465,\n",
       "  inf],\n",
       " 'MonthlyIncome': [-inf, 0.10055111559029495, 6166.0, inf],\n",
       " 'NumberOfOpenCreditLinesAndLoans': [-inf, 1.0, 3.0, 5.0, 17.0, inf],\n",
       " 'NumberOfTime30-59DaysPastDueNotWorse': [-inf, 0, 1, 2, inf],\n",
       " 'NumberOfTimes90DaysLate': [-inf, 0, 1, 2, inf],\n",
       " 'NumberRealEstateLoansOrLines': [-inf, 0, 1, 2, 4, inf],\n",
       " 'NumberOfTime60-89DaysPastDueNotWorse': [-inf, 0, 1, 2, inf],\n",
       " 'NumberOfDependents': [-inf, 0, 1, 2, inf]}"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins_of_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 计算各箱的WOE值，并映射到数据中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:16:36.336432Z",
     "start_time": "2019-12-29T03:16:36.238393Z"
    },
    "scrolled": true
   },
   "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>age</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>cut</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>(48.5, 58.757]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>(58.757, 64.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>(64.0, 74.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>(48.5, 58.757]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>(58.757, 64.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>(48.5, 58.757]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>(74.0, inf]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>(48.5, 58.757]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>(58.757, 64.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>(48.5, 58.757]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>(48.5, 58.757]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>(74.0, inf]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>(48.5, 58.757]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>(58.757, 64.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>(58.757, 64.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>(58.757, 64.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194978</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194979</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>(58.757, 64.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194980</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194981</th>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194982</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194983</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>(48.5, 58.757]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194984</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194985</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194986</th>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>(48.5, 58.757]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194987</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194988</th>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>(58.757, 64.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194989</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>(58.757, 64.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194990</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>(48.5, 58.757]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194991</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>(48.5, 58.757]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194992</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194993</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194994</th>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>(58.757, 64.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194995</th>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>(48.5, 58.757]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194996</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194997</th>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>(74.0, inf]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194998</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194999</th>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>(74.0, inf]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195000</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195001</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195002</th>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>(74.0, inf]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195003</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195004</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>(48.5, 58.757]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195005</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 48.5]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195006</th>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>(58.757, 64.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195007</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>(48.5, 58.757]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195008 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  SeriousDlqin2yrs             cut\n",
       "0        53                 0  (48.5, 58.757]\n",
       "1        63                 0  (58.757, 64.0]\n",
       "2        39                 1    (-inf, 48.5]\n",
       "3        73                 0    (64.0, 74.0]\n",
       "4        53                 1  (48.5, 58.757]\n",
       "5        42                 0    (-inf, 48.5]\n",
       "6        37                 1    (-inf, 48.5]\n",
       "7        26                 0    (-inf, 48.5]\n",
       "8        28                 1    (-inf, 48.5]\n",
       "9        32                 0    (-inf, 48.5]\n",
       "10       63                 0  (58.757, 64.0]\n",
       "11       32                 0    (-inf, 48.5]\n",
       "12       51                 0  (48.5, 58.757]\n",
       "13       75                 0     (74.0, inf]\n",
       "14       33                 1    (-inf, 48.5]\n",
       "15       56                 0  (48.5, 58.757]\n",
       "16       38                 0    (-inf, 48.5]\n",
       "17       63                 0  (58.757, 64.0]\n",
       "18       53                 1  (48.5, 58.757]\n",
       "19       40                 1    (-inf, 48.5]\n",
       "20       23                 0    (-inf, 48.5]\n",
       "21       53                 1  (48.5, 58.757]\n",
       "22       79                 0     (74.0, inf]\n",
       "23       45                 1    (-inf, 48.5]\n",
       "24       52                 0  (48.5, 58.757]\n",
       "25       63                 0  (58.757, 64.0]\n",
       "26       62                 1  (58.757, 64.0]\n",
       "27       36                 0    (-inf, 48.5]\n",
       "28       63                 0  (58.757, 64.0]\n",
       "29       28                 1    (-inf, 48.5]\n",
       "...     ...               ...             ...\n",
       "194978   43                 0    (-inf, 48.5]\n",
       "194979   63                 1  (58.757, 64.0]\n",
       "194980   45                 1    (-inf, 48.5]\n",
       "194981   45                 0    (-inf, 48.5]\n",
       "194982   36                 0    (-inf, 48.5]\n",
       "194983   52                 0  (48.5, 58.757]\n",
       "194984   47                 0    (-inf, 48.5]\n",
       "194985   26                 1    (-inf, 48.5]\n",
       "194986   51                 0  (48.5, 58.757]\n",
       "194987   29                 0    (-inf, 48.5]\n",
       "194988   64                 0  (58.757, 64.0]\n",
       "194989   59                 1  (58.757, 64.0]\n",
       "194990   52                 0  (48.5, 58.757]\n",
       "194991   53                 0  (48.5, 58.757]\n",
       "194992   28                 1    (-inf, 48.5]\n",
       "194993   32                 0    (-inf, 48.5]\n",
       "194994   63                 0  (58.757, 64.0]\n",
       "194995   49                 1  (48.5, 58.757]\n",
       "194996   48                 0    (-inf, 48.5]\n",
       "194997   79                 0     (74.0, inf]\n",
       "194998   30                 1    (-inf, 48.5]\n",
       "194999   78                 0     (74.0, inf]\n",
       "195000   48                 1    (-inf, 48.5]\n",
       "195001   41                 1    (-inf, 48.5]\n",
       "195002   77                 0     (74.0, inf]\n",
       "195003   32                 1    (-inf, 48.5]\n",
       "195004   50                 1  (48.5, 58.757]\n",
       "195005   46                 1    (-inf, 48.5]\n",
       "195006   64                 0  (58.757, 64.0]\n",
       "195007   53                 1  (48.5, 58.757]\n",
       "\n",
       "[195008 rows x 3 columns]"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = model_data.copy()\n",
    "#函数pd.cut，可以根据已知的分箱间隔把数据分箱\n",
    "#参数为 pd.cut(数据，以列表表示的分箱间隔)\n",
    "data = data[[\"age\",\"SeriousDlqin2yrs\"]].copy()\n",
    "data[\"cut\"] = pd.cut(data[\"age\"],[-np.inf, 48.49986200790144, 58.757170160044694, 64.0,\n",
    "74.0, np.inf])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:17:14.290463Z",
     "start_time": "2019-12-29T03:17:14.035807Z"
    }
   },
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cut</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(-inf, 48.5]</th>\n",
       "      <td>39558</td>\n",
       "      <td>59296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(48.5, 58.757]</th>\n",
       "      <td>23469</td>\n",
       "      <td>24438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(58.757, 64.0]</th>\n",
       "      <td>13551</td>\n",
       "      <td>8021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(64.0, 74.0]</th>\n",
       "      <td>13376</td>\n",
       "      <td>4163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(74.0, inf]</th>\n",
       "      <td>7737</td>\n",
       "      <td>1399</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "SeriousDlqin2yrs      0      1\n",
       "cut                           \n",
       "(-inf, 48.5]      39558  59296\n",
       "(48.5, 58.757]    23469  24438\n",
       "(58.757, 64.0]    13551   8021\n",
       "(64.0, 74.0]      13376   4163\n",
       "(74.0, inf]        7737   1399"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将数据按分箱结果聚合，并取出其中的标签值\n",
    "data.groupby(\"cut\")[\"SeriousDlqin2yrs\"].value_counts()\n",
    "#使用unstack()来将树状结构变成表状结构\n",
    "data.groupby(\"cut\")[\"SeriousDlqin2yrs\"].value_counts().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:17:58.524014Z",
     "start_time": "2019-12-29T03:17:58.425714Z"
    }
   },
   "outputs": [],
   "source": [
    "bins_df = data.groupby(\"cut\")[\"SeriousDlqin2yrs\"].value_counts().unstack()\n",
    "bins_df[\"woe\"] = np.log((bins_df[0]/bins_df[0].sum())/(bins_df[1]/bins_df[1].sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:18:49.764699Z",
     "start_time": "2019-12-29T03:18:49.736526Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>SeriousDlqin2yrs</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>woe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cut</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(-inf, 48.5]</th>\n",
       "      <td>39558</td>\n",
       "      <td>59296</td>\n",
       "      <td>-0.408610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(48.5, 58.757]</th>\n",
       "      <td>23469</td>\n",
       "      <td>24438</td>\n",
       "      <td>-0.044295</td>\n",
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       "    <tr>\n",
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       "      <td>8021</td>\n",
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       "    </tr>\n",
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       "      <th>(64.0, 74.0]</th>\n",
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       "      <th>(74.0, inf]</th>\n",
       "      <td>7737</td>\n",
       "      <td>1399</td>\n",
       "      <td>1.706421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "SeriousDlqin2yrs      0      1       woe\n",
       "cut                                     \n",
       "(-inf, 48.5]      39558  59296 -0.408610\n",
       "(48.5, 58.757]    23469  24438 -0.044295\n",
       "(58.757, 64.0]    13551   8021  0.520561\n",
       "(64.0, 74.0]      13376   4163  1.163390\n",
       "(74.0, inf]        7737   1399  1.706421"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将上述过程包装成函数\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:45:13.238022Z",
     "start_time": "2019-12-29T03:45:13.224201Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_woe(df, col, y_col, bins):\n",
    "    \"\"\"\n",
    "    获取某一特征分箱后的woe值\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df : pd.DataFrame\n",
    "        原始数据集\n",
    "    col : str\n",
    "        进行分箱的特征的列名\n",
    "    y_col : str\n",
    "        标签名\n",
    "    bins : 分箱依据\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "\n",
    "    \"\"\"\n",
    "    data = df[[col, y_col]].copy()\n",
    "    data['cut'] = pd.cut(data[col], bins)\n",
    "    bins_df = data.groupby('cut')[y_col].value_counts().unstack()\n",
    "    woe = bins_df['woe'] = np.log(\n",
    "        (bins_df[0] / bins_df[0].sum()) / (bins_df[1] / bins_df[1].sum())\n",
    "    )\n",
    "    return woe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:46:42.887294Z",
     "start_time": "2019-12-29T03:46:41.682105Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 将所有特征的woe值映射到字典中\n",
    "woe_all = dict()\n",
    "\n",
    "for col in bins_of_col:\n",
    "    woe_all[col] = get_woe(model_data, col, 'SeriousDlqin2yrs', bins_of_col[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:46:46.020550Z",
     "start_time": "2019-12-29T03:46:45.989455Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RevolvingUtilizationOfUnsecuredLines': cut\n",
       " (-inf, 0.099]     2.198001\n",
       " (0.099, 0.298]    0.672793\n",
       " (0.298, 0.465]   -0.128400\n",
       " (0.465, 0.982]   -1.074203\n",
       " (0.982, 1.0]     -0.477724\n",
       " (1.0, inf]       -2.034521\n",
       " dtype: float64, 'age': cut\n",
       " (-inf, 36.0]   -0.522752\n",
       " (36.0, 54.0]   -0.279383\n",
       " (54.0, 61.0]    0.249486\n",
       " (61.0, 74.0]    1.011397\n",
       " (74.0, inf]     1.706421\n",
       " dtype: float64, 'DebtRatio': cut\n",
       " (-inf, 0.0175]     1.523095\n",
       " (0.0175, 0.402]    0.035575\n",
       " (0.402, 1.474]    -0.388036\n",
       " (1.474, inf]       0.174886\n",
       " dtype: float64, 'MonthlyIncome': cut\n",
       " (-inf, 0.101]      1.472165\n",
       " (0.101, 6166.0]   -0.222063\n",
       " (6166.0, inf]      0.272875\n",
       " dtype: float64, 'NumberOfOpenCreditLinesAndLoans': cut\n",
       " (-inf, 1.0]   -0.844417\n",
       " (1.0, 3.0]    -0.331191\n",
       " (3.0, 5.0]    -0.057269\n",
       " (5.0, 17.0]    0.123964\n",
       " (17.0, inf]    0.473404\n",
       " dtype: float64, 'NumberOfTime30-59DaysPastDueNotWorse': cut\n",
       " (-inf, 0.0]    0.353726\n",
       " (0.0, 1.0]    -0.874436\n",
       " (1.0, 2.0]    -1.380844\n",
       " (2.0, inf]    -1.541019\n",
       " dtype: float64, 'NumberOfTimes90DaysLate': cut\n",
       " (-inf, 0.0]    0.234408\n",
       " (0.0, 1.0]    -1.741944\n",
       " (1.0, 2.0]    -2.263892\n",
       " (2.0, inf]    -2.428133\n",
       " dtype: float64, 'NumberRealEstateLoansOrLines': cut\n",
       " (-inf, 0.0]   -0.393594\n",
       " (0.0, 1.0]     0.194687\n",
       " (1.0, 2.0]     0.618129\n",
       " (2.0, 4.0]     0.389247\n",
       " (4.0, inf]    -0.299090\n",
       " dtype: float64, 'NumberOfTime60-89DaysPastDueNotWorse': cut\n",
       " (-inf, 0.0]    0.125295\n",
       " (0.0, 1.0]    -1.389862\n",
       " (1.0, 2.0]    -1.774595\n",
       " (2.0, inf]    -1.818446\n",
       " dtype: float64, 'NumberOfDependents': cut\n",
       " (-inf, 0.0]    0.628335\n",
       " (0.0, 1.0]    -0.582955\n",
       " (1.0, 2.0]    -0.526937\n",
       " (2.0, inf]    -0.478678\n",
       " dtype: float64}"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woe_all"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将woe映射到原始数据集中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:55:12.715489Z",
     "start_time": "2019-12-29T03:55:10.972094Z"
    }
   },
   "outputs": [],
   "source": [
    "#不希望覆盖掉原本的数据，创建一个新的DataFrame，索引和原始数据model_data一模一样\n",
    "model_woe = pd.DataFrame(index=model_data.index)\n",
    "\n",
    "for col in bins_of_col:\n",
    "    model_woe[col] = pd.cut(model_data[col], bins_of_col[col]).map(woe_all[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T03:55:34.015129Z",
     "start_time": "2019-12-29T03:55:33.960181Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.198001</td>\n",
       "      <td>-0.279383</td>\n",
       "      <td>0.035575</td>\n",
       "      <td>-0.222063</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.672793</td>\n",
       "      <td>1.011397</td>\n",
       "      <td>0.035575</td>\n",
       "      <td>-0.222063</td>\n",
       "      <td>-0.057269</td>\n",
       "      <td>0.353726</td>\n",
       "      <td>0.234408</td>\n",
       "      <td>-0.393594</td>\n",
       "      <td>0.125295</td>\n",
       "      <td>0.628335</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.034521</td>\n",
       "      <td>-0.279383</td>\n",
       "      <td>-0.388036</td>\n",
       "      <td>-0.222063</td>\n",
       "      <td>-0.057269</td>\n",
       "      <td>-0.874436</td>\n",
       "      <td>-1.741944</td>\n",
       "      <td>-0.393594</td>\n",
       "      <td>-1.774595</td>\n",
       "      <td>-0.478678</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.198001</td>\n",
       "      <td>1.011397</td>\n",
       "      <td>-0.388036</td>\n",
       "      <td>-0.222063</td>\n",
       "      <td>0.123964</td>\n",
       "      <td>0.353726</td>\n",
       "      <td>0.234408</td>\n",
       "      <td>0.618129</td>\n",
       "      <td>0.125295</td>\n",
       "      <td>0.628335</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.074203</td>\n",
       "      <td>-0.279383</td>\n",
       "      <td>-0.388036</td>\n",
       "      <td>0.272875</td>\n",
       "      <td>0.123964</td>\n",
       "      <td>0.353726</td>\n",
       "      <td>0.234408</td>\n",
       "      <td>0.618129</td>\n",
       "      <td>0.125295</td>\n",
       "      <td>-0.582955</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RevolvingUtilizationOfUnsecuredLines       age  DebtRatio  MonthlyIncome  \\\n",
       "0                              2.198001 -0.279383   0.035575      -0.222063   \n",
       "1                              0.672793  1.011397   0.035575      -0.222063   \n",
       "2                             -2.034521 -0.279383  -0.388036      -0.222063   \n",
       "3                              2.198001  1.011397  -0.388036      -0.222063   \n",
       "4                             -1.074203 -0.279383  -0.388036       0.272875   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTime30-59DaysPastDueNotWorse  \\\n",
       "0                        -0.057269                              0.353726   \n",
       "1                        -0.057269                              0.353726   \n",
       "2                        -0.057269                             -0.874436   \n",
       "3                         0.123964                              0.353726   \n",
       "4                         0.123964                              0.353726   \n",
       "\n",
       "   NumberOfTimes90DaysLate  NumberRealEstateLoansOrLines  \\\n",
       "0                 0.234408                     -0.393594   \n",
       "1                 0.234408                     -0.393594   \n",
       "2                -1.741944                     -0.393594   \n",
       "3                 0.234408                      0.618129   \n",
       "4                 0.234408                      0.618129   \n",
       "\n",
       "   NumberOfTime60-89DaysPastDueNotWorse  NumberOfDependents  SeriousDlqin2yrs  \n",
       "0                              0.125295            0.628335                 0  \n",
       "1                              0.125295            0.628335                 0  \n",
       "2                             -1.774595           -0.478678                 1  \n",
       "3                              0.125295            0.628335                 0  \n",
       "4                              0.125295           -0.582955                 1  "
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将标签补充到数据中\n",
    "model_woe[\"SeriousDlqin2yrs\"] = model_data[\"SeriousDlqin2yrs\"]\n",
    "#这就是我们的建模数据了\n",
    "model_woe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  建模于验证\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T04:52:07.489661Z",
     "start_time": "2019-12-29T04:52:07.237011Z"
    }
   },
   "outputs": [],
   "source": [
    "vail_data = pd.read_csv('data/vali_data.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T04:52:07.883903Z",
     "start_time": "2019-12-29T04:52:07.857447Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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></th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000481</td>\n",
       "      <td>2080.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.478906</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0.181301</td>\n",
       "      <td>29373.217358</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.057460</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.021830</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.011585</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0.139685</td>\n",
       "      <td>5583.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.663034</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0.399663</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.201706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "0                 0                              0.000000   58   \n",
       "1                 1                              0.478906   39   \n",
       "2                 0                              0.057460   64   \n",
       "3                 0                              0.011585   52   \n",
       "4                 1                              0.663034   53   \n",
       "\n",
       "   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                                     0   0.000481    2080.000000   \n",
       "1                                     0   0.181301   29373.217358   \n",
       "2                                     0   0.021830    6000.000000   \n",
       "3                                     0   0.139685    5583.000000   \n",
       "4                                     0   0.399663    4800.000000   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                                4                        0   \n",
       "1                               11                        0   \n",
       "2                                4                        0   \n",
       "3                                8                        0   \n",
       "4                               12                        0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                             0                                     0   \n",
       "1                             2                                     0   \n",
       "2                             0                                     0   \n",
       "3                             1                                     0   \n",
       "4                             0                                     0   \n",
       "\n",
       "   NumberOfDependents  \n",
       "0            0.000000  \n",
       "1            3.000000  \n",
       "2            0.000000  \n",
       "3            0.000000  \n",
       "4            0.201706  "
      ]
     },
     "execution_count": 449,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vail_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T04:57:08.317018Z",
     "start_time": "2019-12-29T04:57:07.556791Z"
    }
   },
   "outputs": [],
   "source": [
    "vail_woe = pd.DataFrame(index=vail_data.index)\n",
    "for col in bins_of_col:\n",
    "    vail_woe[col] = pd.cut(vail_data[col], bins_of_col[col]).map(woe_all[col])\n",
    "\n",
    "vail_woe['SeriousDlqin2yrs'] = vail_data['SeriousDlqin2yrs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T04:57:12.302686Z",
     "start_time": "2019-12-29T04:57:12.137532Z"
    }
   },
   "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",
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       "    }\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>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>83576.000000</td>\n",
       "      <td>83576.000000</td>\n",
       "      <td>83576.000000</td>\n",
       "      <td>83576.000000</td>\n",
       "      <td>83576.000000</td>\n",
       "      <td>83576.000000</td>\n",
       "      <td>83576.000000</td>\n",
       "      <td>83576.000000</td>\n",
       "      <td>83576.000000</td>\n",
       "      <td>83576.000000</td>\n",
       "      <td>83576.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.007560</td>\n",
       "      <td>0.024371</td>\n",
       "      <td>0.011545</td>\n",
       "      <td>0.010301</td>\n",
       "      <td>-0.003525</td>\n",
       "      <td>-0.028983</td>\n",
       "      <td>-0.063987</td>\n",
       "      <td>-0.000609</td>\n",
       "      <td>-0.017227</td>\n",
       "      <td>0.009595</td>\n",
       "      <td>0.502237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.484381</td>\n",
       "      <td>0.616758</td>\n",
       "      <td>0.408296</td>\n",
       "      <td>0.402196</td>\n",
       "      <td>0.274125</td>\n",
       "      <td>0.651366</td>\n",
       "      <td>0.761119</td>\n",
       "      <td>0.391107</td>\n",
       "      <td>0.457618</td>\n",
       "      <td>0.585176</td>\n",
       "      <td>0.499998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.034521</td>\n",
       "      <td>-0.522752</td>\n",
       "      <td>-0.388036</td>\n",
       "      <td>-0.222063</td>\n",
       "      <td>-0.844417</td>\n",
       "      <td>-1.541019</td>\n",
       "      <td>-2.428133</td>\n",
       "      <td>-0.393594</td>\n",
       "      <td>-1.818446</td>\n",
       "      <td>-0.582955</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.074203</td>\n",
       "      <td>-0.279383</td>\n",
       "      <td>-0.388036</td>\n",
       "      <td>-0.222063</td>\n",
       "      <td>-0.057269</td>\n",
       "      <td>-0.874436</td>\n",
       "      <td>0.234408</td>\n",
       "      <td>-0.393594</td>\n",
       "      <td>0.125295</td>\n",
       "      <td>-0.526937</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.477724</td>\n",
       "      <td>-0.279383</td>\n",
       "      <td>0.035575</td>\n",
       "      <td>-0.222063</td>\n",
       "      <td>0.123964</td>\n",
       "      <td>0.353726</td>\n",
       "      <td>0.234408</td>\n",
       "      <td>0.194687</td>\n",
       "      <td>0.125295</td>\n",
       "      <td>-0.478678</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.672793</td>\n",
       "      <td>0.249486</td>\n",
       "      <td>0.035575</td>\n",
       "      <td>0.272875</td>\n",
       "      <td>0.123964</td>\n",
       "      <td>0.353726</td>\n",
       "      <td>0.234408</td>\n",
       "      <td>0.194687</td>\n",
       "      <td>0.125295</td>\n",
       "      <td>0.628335</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.198001</td>\n",
       "      <td>1.706421</td>\n",
       "      <td>1.523095</td>\n",
       "      <td>1.472165</td>\n",
       "      <td>0.473404</td>\n",
       "      <td>0.353726</td>\n",
       "      <td>0.234408</td>\n",
       "      <td>0.618129</td>\n",
       "      <td>0.125295</td>\n",
       "      <td>0.628335</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       RevolvingUtilizationOfUnsecuredLines           age     DebtRatio  \\\n",
       "count                          83576.000000  83576.000000  83576.000000   \n",
       "mean                              -0.007560      0.024371      0.011545   \n",
       "std                                1.484381      0.616758      0.408296   \n",
       "min                               -2.034521     -0.522752     -0.388036   \n",
       "25%                               -1.074203     -0.279383     -0.388036   \n",
       "50%                               -0.477724     -0.279383      0.035575   \n",
       "75%                                0.672793      0.249486      0.035575   \n",
       "max                                2.198001      1.706421      1.523095   \n",
       "\n",
       "       MonthlyIncome  NumberOfOpenCreditLinesAndLoans  \\\n",
       "count   83576.000000                     83576.000000   \n",
       "mean        0.010301                        -0.003525   \n",
       "std         0.402196                         0.274125   \n",
       "min        -0.222063                        -0.844417   \n",
       "25%        -0.222063                        -0.057269   \n",
       "50%        -0.222063                         0.123964   \n",
       "75%         0.272875                         0.123964   \n",
       "max         1.472165                         0.473404   \n",
       "\n",
       "       NumberOfTime30-59DaysPastDueNotWorse  NumberOfTimes90DaysLate  \\\n",
       "count                          83576.000000             83576.000000   \n",
       "mean                              -0.028983                -0.063987   \n",
       "std                                0.651366                 0.761119   \n",
       "min                               -1.541019                -2.428133   \n",
       "25%                               -0.874436                 0.234408   \n",
       "50%                                0.353726                 0.234408   \n",
       "75%                                0.353726                 0.234408   \n",
       "max                                0.353726                 0.234408   \n",
       "\n",
       "       NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "count                  83576.000000                          83576.000000   \n",
       "mean                      -0.000609                             -0.017227   \n",
       "std                        0.391107                              0.457618   \n",
       "min                       -0.393594                             -1.818446   \n",
       "25%                       -0.393594                              0.125295   \n",
       "50%                        0.194687                              0.125295   \n",
       "75%                        0.194687                              0.125295   \n",
       "max                        0.618129                              0.125295   \n",
       "\n",
       "       NumberOfDependents  SeriousDlqin2yrs  \n",
       "count        83576.000000      83576.000000  \n",
       "mean             0.009595          0.502237  \n",
       "std              0.585176          0.499998  \n",
       "min             -0.582955          0.000000  \n",
       "25%             -0.526937          0.000000  \n",
       "50%             -0.478678          1.000000  \n",
       "75%              0.628335          1.000000  \n",
       "max              0.628335          1.000000  "
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vail_woe.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T04:59:29.116639Z",
     "start_time": "2019-12-29T04:59:29.039894Z"
    }
   },
   "outputs": [],
   "source": [
    "# 划分训练集和测试集\n",
    "X_train = model_woe.iloc[:,:-1]\n",
    "y_train = model_woe.iloc[:,-1]\n",
    "\n",
    "X_vail = vail_woe.iloc[:,:-1]\n",
    "y_vail = vail_woe.iloc[:,-1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T04:58:16.889402Z",
     "start_time": "2019-12-29T04:58:16.868856Z"
    }
   },
   "source": [
    "\n",
    "开始建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T05:10:50.628069Z",
     "start_time": "2019-12-29T05:10:43.378738Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40187374365846656"
      ]
     },
     "execution_count": 471,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression as LR\n",
    "lr = LR(solver='liblinear' ,max_iter=500).fit(X, y.values.ravel())\n",
    "lr.score(X_vail, y_vail.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-29T05:19:11.503863Z",
     "start_time": "2019-12-29T05:19:10.657201Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a196633c50>"
      ]
     },
     "execution_count": 474,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scikitplot as skplt\n",
    "#%%cmd\n",
    "#pip install scikit-plot\n",
    "vali_proba_df = pd.DataFrame(lr.predict_proba(X_vail))\n",
    "skplt.metrics.plot_roc(y_vail, vali_proba_df,\n",
    "        plot_micro=False,figsize=(6,6),\n",
    "        plot_macro=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
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 "nbformat": 4,
 "nbformat_minor": 2
}