交易数据异常检测.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "#encoding = utf-8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "data = pd.read_csv('C:/Users/ywb/Desktop/creditcard.csv')\n",
    "scale = StandardScaler()\n",
    "scaled_amount = scale.fit_transform(data.Amount.values.reshape(-1,1))\n",
    "data = data.drop(['Time','Amount'],axis=1)\n",
    "data['normamount'] = scaled_amount.reshape((-1,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    284315\n",
      "1       492\n",
      "Name: Class, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x124356d8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(data.Class.value_counts())\n",
    "data.Class.value_counts().plot(kind= 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 样本不均衡问题的解决办法\n",
    "### 1 下采样\n",
    "### 2 过采样（SMOTE数据生成）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Class</th>\n",
       "      <th>normamount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0</td>\n",
       "      <td>0.244964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.342475</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "\n",
       "         V8        V9       V10     ...           V21       V22       V23  \\\n",
       "0  0.098698  0.363787  0.090794     ...     -0.018307  0.277838 -0.110474   \n",
       "1  0.085102 -0.255425 -0.166974     ...     -0.225775 -0.638672  0.101288   \n",
       "\n",
       "        V24       V25       V26       V27       V28  Class  normamount  \n",
       "0  0.066928  0.128539 -0.189115  0.133558 -0.021053      0    0.244964  \n",
       "1 -0.339846  0.167170  0.125895 -0.008983  0.014724      0   -0.342475  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 下采样法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(199364, 1)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils import shuffle\n",
    "# from sklearn.cross_validation import train_test_split\n",
    "X = data.iloc[:,data.columns != 'Class']\n",
    "Y = data.iloc[:,data.columns == 'Class']\n",
    "\n",
    "#整体的划分\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.3,shuffle = True)\n",
    "print(y_train.shape)\n",
    "#下采样\n",
    "neg_num = np.sum(data.Class != 0)\n",
    "under_sample_positive = data[data.Class==0].sample(n=neg_num,replace=False)\n",
    "\n",
    "under_sample_data = under_sample_positive.append(data[data.Class==1])\n",
    "\n",
    "#生成下采样数据\n",
    "under_sample_data = shuffle(under_sample_data)\n",
    "\n",
    "under_x_train,under_x_test,under_y_train,under_y_test = train_test_split(under_sample_data.iloc[:,under_sample_data.columns!='Class'],\n",
    "                                                                        under_sample_data.iloc[:,under_sample_data.columns =='Class'],\n",
    "                                                                        test_size = 0.2,random_state = 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.cross_validation import KFold\n",
    "from sklearn.metrics import recall_score,confusion_matrix,roc_curve,auc,precision_score,precision_recall_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c值为  ： 0.06\n",
      "---------------------------------------------------------\n",
      "\n",
      "当前迭代轮数：0   当前迭代recall：0.9850746268656716\n",
      "当前迭代轮数：1   当前迭代recall：0.9701492537313433\n",
      "当前迭代轮数：2   当前迭代recall：0.9696969696969697\n",
      "当前迭代轮数：3   当前迭代recall：1.0\n",
      "当前迭代轮数：4   当前迭代recall：0.9565217391304348\n",
      "\n",
      "c值为0.06时，平均recall值为0.9762885178848839\n",
      "---------------------------------------------------------\n",
      "\n",
      "c值为  ： 0.065\n",
      "---------------------------------------------------------\n",
      "\n",
      "当前迭代轮数：0   当前迭代recall：0.9850746268656716\n",
      "当前迭代轮数：1   当前迭代recall：0.9701492537313433\n",
      "当前迭代轮数：2   当前迭代recall：0.9696969696969697\n",
      "当前迭代轮数：3   当前迭代recall：1.0\n",
      "当前迭代轮数：4   当前迭代recall：0.9565217391304348\n",
      "\n",
      "c值为0.065时，平均recall值为0.9762885178848839\n",
      "---------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def para_select():\n",
    "    fold = KFold(len(under_x_train),n_folds=5,shuffle=True,random_state=11)\n",
    "    c_para_set = [0.06,0.065]\n",
    "    for c_pa in c_para_set:\n",
    "        print('c值为  ：',c_pa)\n",
    "        print('---------------------------------------------------------\\n')\n",
    "        recall_all =[]\n",
    "        for i,slices in enumerate(fold):\n",
    "            Lr = LogisticRegression(C=c_pa,penalty='l1')\n",
    "            Lr.fit(X=under_x_train.iloc[slices[0],:], y=under_y_train.iloc[slices[0],:])\n",
    "            under_y_pred = Lr.predict(under_x_train.iloc[slices[1],:])\n",
    "            this_score = recall_score(under_y_pred,under_y_train.iloc[slices[1],:])\n",
    "            recall_all.append(this_score)\n",
    "            print('当前迭代轮数：{}   当前迭代recall：{}'.format(i,this_score))\n",
    "        print('\\nc值为{}时，平均recall值为{}'.format(c_pa,np.mean(recall_all)))\n",
    "        print('---------------------------------------------------------\\n')\n",
    "\n",
    "    print('最好的L1惩罚项系数为c={}'.format(0.06))\n",
    "    return\n",
    "\n",
    "fold = KFold(len(under_x_train),n_folds=5,shuffle=True,random_state=11)\n",
    "c_para_set = [0.06,0.065]\n",
    "for c_pa in c_para_set:\n",
    "    print('c值为  ：',c_pa)\n",
    "    print('---------------------------------------------------------\\n')\n",
    "    recall_all =[]\n",
    "    for i,slices in enumerate(fold):\n",
    "        Lr = LogisticRegression(C=c_pa,penalty='l1')\n",
    "        Lr.fit(X=under_x_train.iloc[slices[0],:], y=under_y_train.iloc[slices[0],:])\n",
    "        under_y_pred = Lr.predict(under_x_train.iloc[slices[1],:])\n",
    "        this_score = recall_score(under_y_pred,under_y_train.iloc[slices[1],:])\n",
    "        recall_all.append(this_score)\n",
    "        print('当前迭代轮数：{}   当前迭代recall：{}'.format(i,this_score))\n",
    "    print('\\nc值为{}时，平均recall值为{}'.format(c_pa,np.mean(recall_all)))\n",
    "    print('---------------------------------------------------------\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.97369280680992\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "model = LogisticRegression(C=0.06,penalty='l1')\n",
    "# model = svm.SVC(probability=True)\n",
    "model.fit(X=under_x_train.iloc[slices[0],:], y=under_y_train.iloc[slices[0],:])\n",
    "\n",
    "#对整体数据分割得到的测试集进行测试\n",
    "all_predprob = model.predict_log_proba(x_test.values)\n",
    "all_pred = model.predict(x_test.values)\n",
    "# print(metrics.accuracy_score(all_pred,y_test))\n",
    "my_fpr,my_tpr,threshold = roc_curve(y_test,all_predprob[:,1],pos_label=1)\n",
    "\n",
    "print(auc(my_fpr,my_tpr))\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.style.use('ggplot')\n",
    "plt.plot(my_fpr,my_tpr,'r-',label ='Roc curve(area=%0.3f)'%auc(my_fpr,my_tpr)) \n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('False Positive Rate',fontsize= 20)\n",
    "plt.ylabel('True  Positive Rate',fontsize= 20)\n",
    "plt.title('ROC Curve',fontsize = 22)\n",
    "\n",
    "plt.plot([0,1],[0,1],'b--')\n",
    "plt.xlim((-0.01,1))\n",
    "\n",
    "plt.plot()\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[83803,  1494],\n",
       "       [   18,   128]], dtype=int64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "cmt = confusion_matrix(y_test, all_pred,labels=[0,1])\n",
    "cmt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[83803  1494]\n",
      " [   18   128]]\n",
      "0.8767123287671232\n",
      "0.07891491985203453\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    import itertools\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "# Compute confusion matrix\n",
    "plot_confusion_matrix(cmt,[0,1])\n",
    "print(recall_score(y_test, all_pred))\n",
    "print(precision_score(y_test, all_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Recall')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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REZEq7/vvg4iPd/LRR2Hk5sLmzUdo0MDkuedU6a4iKWGSiuXOxkpN9n2ek40152UwDPL/7GGavo+xDQITn4iIiEgVt2OHg3vuiWLr1mDCw02uvDKLSZNcNGhgBjq0WkEJk1QchwPr2y+xvv3Sr9kYdyu2QSMBsH7dhTl9KkbjswMQoIiIiEjVtG+fnYwMg44d84iNNcnONnjssWOMGZNFZKQV6PBqFSVMUmFsk+/FOrjPv9HuwOhxfmACEhEREanCTBNWrw4hPt7JihUh9O2by/vvJ9OwocmXXx4NdHi1lhImqTBGXEeMuI6BDkNERESkylu0KIxnn63D3r0OYmO93H57Jtde6wp0WIISJhERERGRgNi+3UHr1l7CwiySk23ExJhMnZrKyJHZhIQEOjo5QWXFRUREREQqidvtG0269NJYhg1rwEcf+cqD33iji48+SuLyy5UsVTUaYRIRERERqWAeD8yYUYd588JJSrLTokUeDz98jOHDcwCwaRijylLCJCIiIiJSASwL9uxxEBeXh8MB33wTQo8euUyalMWAAW4lSdWEEiYRERERkXJ07JjBggXhJCQ4OXTIxubNR6hb1+LDD5MIDg50dFJSSphERERERMrBvn12XnopgkWLwsjOttG9ey533plBaKhv3yQlS9WTEiYRERERkVLKzYX0dBuxsSZZWQYffBDGZZdlM3FiFuec4wl0eFIOlDCJiIiIiJTQwYM23nnHydy54Zx/fi6vvJJKu3Z5bNt2hIgIK9DhSTlSwiQiIiIicoY2bAhm1iwny5eHYppw4YVuxo7Nyj+vZKnmUcIkIiIiInIKGRkG4eEWdjt8+WUI69YFc+utmVx3XRbNm3sDHZ5UMBUzFBERERE5iZ9+cnDffXXp2bMhX33l2032L3/JZOPGIzzwQIaSpVpCI0wiIiIiIsd5vfDZZ6EkJDhZty6EkBCLSy/N5uyzfclRZKSm3NU2SphEREREpNbLyYHQUN/njz8eid0ODz54jLFjs4iOVpJUmylhEhEREZFaybJg/fpg4uOdbNoUzNq1RwgJgYULk2nSxIvdHugIpSpQwiQiIiIitUpmpm+/pIQEJz/9FERUlMnYsVm43QYhIZbWJokfJUwiIiIlYB09DKnJGG07BToUESkhrxfsdti2LYj774/inHNyefbZVC69NIewME27k5NTwiQiIgJYnlyszWvBk1vQ+NMPWLt2gsMBGL62xIMA2Gd9XD7Pu3cXuDIKjpMOY737KrYX5mGEO8vlOURqs7w8WL48lPh4Jx06eHjssXT69s1l2bKjdO7swTACHaFUdUqYRESkxrO2rMNKTS5oSE/DWroAwiPAdnyHjYxjxT7e6D2AE++qrOMJ02mfc+8urP17/ds+mgth4fl9kZ7mlyz5XfvDJozzBp7Rc4lIUUeO2Jg7N5x33nFy+LCdpk3zGDkyG/D9F+zSxRPgCKW6UMIkIiLVlvX7Hqw9P/m3LZztm3NjO75a250DecW8MWrUBKNpi4Jjmx1j6GUFSRRAeARGaFj+oRkUhLVlPebyRQXP+eWnkJNV8JxQbCKEKwOj67m+zxs3x8rzYOt7IdSN9vX1/UaszxZg1IvFcuf4rjt6CGvZImw3TS32ayEiviIOJ/4e8cwzdZg3z8nAgTk8+WQaF13kVhEHKRUlTCJSI1lZLqwNq7ENGhHoUKQULFcG1vYtvnc/J9q+WQ6H9uP3jict5eQdhIRi9Bns12QMGAr1YgoaHEF+idAZi20E2S6shfFFThmDLy70IiyMjt3g7NYFbTY7RlT0qfvPdmEB5jP3FTlljbkBo269kscsUsO5XAaLFvmKOPz732l06+bhjjsymTw5k9atVcBBykYJk4hUe5bbjfXFYsjNKWhb+oHvY+ceGLENAxWa/IFlWfD7Ht+GJyfa9u/FWvkZOILzlwmxb+/JO+B44lPQIbQ/B6ND10IXGBARiVFBCxNsl4zF+tOfi54IDimf52zbGWPcrZDrzm+yVnwCacngCCpoSzwI+/Zi9OxX9ucUqaZ273aQkBDOggXhZGTY6NTJQ3a27/+hKt1JeVHCJCLViuX1Yr33Olbh9SY/7yhYf+L4w481reYFwDK9WF9/hu2iURX3HB4PuLMLGkwTa2E8VqFElt9/gaOHT95Bm46+NUUA0fUx6tTFGD7a/5qoGIyQkPINvBSMkNCK6zs4xH+kCjAdQVjvzcJ8aHLBdMFjqUD5FZ8QqW7cbvjzn2NxuQwuuSSbiRNd9OqlIg5S/pQwSZVjZWdhzZmJ7ZZ/BDoUCTDLsrASXsI6tK8g8UlKhGPHp2Gd1cz3MSIS6jfC9n8PYtSpC4C58jOsd1+FoKCT9FyzWKaZP3XN8uZh5WRjzX0VK8tVcNG2Db7z9RthnNO77M/5v++x9v7s37bo7eIfcOJ75QjyJUOjJ2JERhWcrxOF0aR5meOqqYxu58GRA76ayMdZq5cXuc5KS4E/fF/Ml/+Jce5AbDdr/ZNUb0eP+oo4rF8fzNy5KYSEwCuvpNKhg4f69c1Ahyc1mBImqXKsea9hbfwG6/wLMbr0DHQ4UonMea/71q3YjidHFr43iQAdu/k+NmkOLeKwjb0Jo36jgMQZSNbB3+Hw/oJjr4n1+tP5x4l/fECzlv7HIades2PleeDAb/5rh778FOuXnwq+LwCHD5y8g5ZtMc4bVHAc5MA4d2Dp1gpJPiOmAca42/zazKBgrK8+xfvQ5ILGYr4v1oZVcJqEyfp9D6QXjNxax1Kw4l/E9vRsjMJrv0QqkWXBpk1BJCQ4+fTTMDwegwED3KSmGkRHW1xwgfv0nYiUkRImqXIs9/EffoWn8UiNYy55H2v1MjAKVSNL9r3dN3oPKGg7Ow5jxBUYTf/wxj/ALNPEvPUybHc/hdGmY8U8x3f/xfp+o3/bN5+f/OL6jTD6XkS4M5wsVxYEB2NcMDw/UbG+W485858QVpC4WClJWCuXgLfgL7PW5x8WG4/f96VpS19RhY5dC1+BUQtG9KoKo/cAX1nywpq1gtiGGL365zeZj/8VmrXE/O+q/Dbr0/fA7sgfuT3qzsY8euSkz2NtWI0x7PLyfwEiZ+DLL0OYODGGOnVMJkxwMWGCi7g4rU2SyqWESaoEM+FFOLEmIOnkv7Sl+jLXf421+F3/9UTHv8/G+Rf6XWv0/xNG206VGV4RlmX5djosxHz9aV8hgvypgb74zbmvYn/4xZL3n54GZqFEZc0XWGu+8C9nfeL/QuGqanXqYpw3CKNfoa+bzQ6NmmLYbETExpKTlFTsc5svPV5QOKDw/7Xg4+uC7HYIc2KbdKf/A5u1wIiuX5KXKRXMaN0eo3X7018Y1wF2/4j1xoyi57r1ASAoJBh3wyYYfQbnF0mxft7um2aZloy5cqmvbeM38PN2bC++hxEWXm6vReSEX36xk5DgpHlzLzfe6OKCC9z8+99pXHppNk6ndfoORCqAEiYJrCZn+xY3Z2flN1lpKb49Uxo2DmBgUlrW7p2Y82aBWegvgPt/BfAv8xzXEaPbuQGv8GXleSDxkF+bOWsG7D95lbYTr8Fq0Bh2bsXo1OP0/f+4DTy5Bf2vXg47tp6yf8D3NerZ17d+paxad8S4YLhf5TXiOkJsA4xLx1VYRTkJPNtfH4XUPyTRhg0anJX/fY+KjSXpj4l2uBNr8TtYK05SVCI5EQrvXyVSBl6vbyQpPt7JqlWhOBwWkyb51mAGB8M112SdpgeRiqWESQLKCAr2lc8txDynt29Nhk27y1V1VkoS5rzXwVPoTfjRI5B4EM7pXfA9jG2EEdce27ArAhPocZbbDb/8z299jvnuK0USphOMy68rdGDDOPcCjBjfKIuVnoo5dSLENijoPy8P68uP/f8AsHU9HPz95P1fN4WCOtpgNGuF0bJNaV7aaRl1Io8/n9Q2RkgoNGpa8sed1Qzbc+/6JfvWmi+wFr8DYc7yDFFquX/8I4r588Np1MjL3/+ezrhxWTRsqCIOUnUoYRIphmWaWKuWYRs8MtChVAlWXh7WkvlQqPKalXgItm+GJmcXTOlyRvgW+d90V5UYtbC++y9WmK9UtfXpe8UnL7fc7X8c1+GMFrpbq5bj3fmd7+C33ZBy/K/0J9ZmWb5f+rZ/PAnhhaYwRdbzrxInUgUZ4U6gIDmytGmulJFlwZYtviIOf/97Bs2bexk/3sVFF+UwdGhObShsKtWQEiaptqz9v8KRAxU2pctautA3HSUyCqNn3wp5jqrM+nEbVkqhKTqpR7E+ne9ba2Yv9KOjfiNs//gnhrNO5Qd5Ksfjsea8XOSU7e6n/BsaNi558hJeBzr3gNTkghGqMCe0jsV2y90Y0bGliVpEpEbKzoaPPgojPt7JDz8EExFhMnJkDs2be+nZ0wN4Ah2iSLGUMEm1ZT56B1B+mzZa2zZgZWYUHK/72vfxWAqBHyepWJbbDZmFNoL15GI+93D+6Ehhtr8+ghFXMVXhypPRqz/G2a2LFG+gbjSGM6Ls/Tsc2O98pMz9iIjUdDk50LdvQxIT7bRt62H69DSuvDKbiAgVcZDqQQmTCGAlHcH8zxMnP1nVRk4qgPnPqSedqmZcOg7j/EJFCIKDMSKrx5QcwzCggQqHiIhUNq8XvvoqhA0bgnnggQxCQ+GOOzJo3z6PPn1yqQKztUVKJOAJ03fffcfs2bMxTZOLLrqIyy67zO98UlISM2fOxOVyYZom48aNo0ePU1elkprH2rUT8/03/SuvlaW/w/t9U6lOHB+femZcfTNG13N9bT9sxpr7KkaDs8rlOasKc+kHWJu+8W88vB/adfFPjmx2jG7nqXSwiIickZQUg/feczJnTji//+6gYUMvU6ZkUq+exfXXq9KdVF8BTZhM0+TNN9/kwQcfJCYmhvvuu49evXrRtGlBNZ8PPviA888/n6FDh7J//36efPJJJUy1kLVrB/y6C7r0Ktin5vdfSteX6fVt5JibW+ScUb9R/h4kxDbgTCYLmLP+jbVhdblNDaxo1tZ1vsIEhfdvqReL7U+XYbTrHLjARKT6yHVjuY9vLv6/HzDXfIH9L/cHNiYJqNWrQ5g0KRq32+D8893cd186I0aoiIPUDAFNmHbv3k2jRo1o2ND3BrVv375s3LjRL2EyDIOsLN9fJbKysqhXr3pMB5KKYZt8H8bxn75mwktY27eUvBMLyM31bZBaeNPUoGA4u3XJu9uwuuQxBNrZrbH/34OBjkJEqpvjWwWY01SivrbLyYE5c2zY7SEMHeqmW7dcxo1zce21WbRvn3f6DkSqkYAmTCkpKcTEFJTtjYmJYdeuXX7XjBkzhieeeIJly5bhdrt56KGHKjtMqWSGzcACzEfuKNii5sS+OaWY9+x98THYUSixOjFsFNsQo22nEvVl7diKOXM6eKveLwPzv6uw4l8A8zR7V5gmdO5ZOUGJSI1idDsPrr7Zt7n4cdaH71TJn4lSMX7/3c6cOeHMmxdOaqqdiy8OZ+hQN5GRFk88kR7o8EQqREATJssqOuHpj/u2fPvttwwaNIhRo0bx888/89JLLzFjxgxsJ6ZlHbdixQpWrFgBwFNPPUVsbNUp6etwOKpUPFWd2f8istJTIdft125v1ISwRgXriY6FhpJrt532a3v08D5szVoS0rt/QaPNTtifRmE/xWPdkZGkAVF1owg6fl2W6xgZnlzCR431bQYJuBYmAJT79/hU942Z5SLny0+xCr1pyd36X3Lz8nBeOfG0fQf36kew7skaST9vpDTO/L6JhebX+7VkWiauxe/qvqsF7r/fzrPP2rDZ4NJLLaZMMRkwwI5h6HsvZ646/p4KaMIUExNDcnLBwvvk5OQiU+6++uor7r/fNy+6bdu2eDweMjIyqFu3rt91Q4YMYciQIfnHSUlJVBWxsbFVKp5qYfAlJ212Ffo6mjk5WF7T72tr7d5ZZJqelZmB1aYzOcNG+7XnAJzi+2Kl+0qMp0z7v/xpKCf+qppz4SiMiEgAjLRUrG9XlPv3+FT3jbn+a6y3Xih6osFZRV7nyZzutUv1pZ83UhpluW/M7Gygav3elfKRmmrw/vvhXHVVFvXqWbRvH8qddwYxfryLxo1N/byRUqlK903jxmdWTTegCVPr1q05dOgQiYnQndIkAAAgAElEQVSJREdHs3btWu644w6/a2JjY9m+fTuDBg1i//79eDweIiMjAxSxVHXmR3Phf9+D4T8CSePmJe+sTQeMkWPgxMLmE6Jj85Ol4li7dkKWC6Nr75I/7x/7SkvBfOFRcGcXNGb71vXZHpsJ0Q0K2oMCXvhSRESquR9+CCI+PpzFi8PJyTGoX9/kiiuyGTkyh5Ejc07fgUgNE9B3V3a7nRtuuIHp06djmiaDBw+mWbNmzJ8/n9atW9OrVy8mTJjAa6+9xpIlSwCYMmVKkWl7IvksC9p2wv6PJ8vclREajnH5daV6rPn0vUDpNtW10pIhMwOP6xhWairWb3tg/17o0BWjbqER2Dp1oWETjD9MTxURESmN7GwYOzaWzZuDCQszGT06i4kTXXTqpDVqUrsF/M/RPXr0KFImfOzYsfmfN23alMcff7yyw5LqIj0V7yO3FxwfPQwt4io/Do8H86tPy9yNleXCvPdm8OaR8odztkvHYcR1KPNziIiInLB/v50NG4K54opswsKgfXsPl16azZgxWdSteyaba4jUfAFPmERKyzhvIFaWCwrvltSwMUaPvpUbSL1YyPNgzXu9xA+1ftqOdeRAQUO2C7x5GBcMJ7LPADKOr6MiJBRatSungEVEpDYzTfjmmxDi48NZsSKUoCC48MIcoqIsnn76WKDDE6lylDBJtWW0Pwd7+3MCHQa2oZdh9b3QL28zZz5RdO3TSZgzn8hfj1SY0aUnoecPJrOKLIoUESkp6w9bHFiL50CdKGx/+nOAIhKATZuC+Otf67F3r4OYGC9/+Usm112XRVSURpNEiqOESaQcFCkCEREJnly/JmvHVqxdO/yvc+dgDBqBMfKqgjaHA6OOfxVIEZEqzTQxV3yUf2h98TGkHD35tYUSJislCZIT/fv59/0YN/4NW5/BFRVtrbNjhwPTNOjSxUPjxl7q1/dy110ZXHxxNiEhgY5OpOpTwiRSScwFb8GB3/wr+NkMaNEWo15M8Q8UEanKYuqDZWHNf7PIKePScfmfWx/PBcD7ylMFF2xZe9IuraUfgBKmMsnNhSVLwoiPd7JpUzAXXZTD22+n0LixyYcfJp++AxHJp4RJpKKkpWB+OKfg+FgK9OyL/bZ7AxeTiEg5s10wHKv3BWD5T8EjNAzjxB52gAlYm9bA4f0F1zRqgtG+K0b3Pr7jbBfmq//COP/UyZJlmvDbHvAUbHBurVqGtWE1ttcWn7J6qOX1Qpp/WR3zpccwzr8Q27DLT/1iq4m33nLywgsRJCXZadEij4cfPsZVVxWd/i0iZ0YJk0gFMJqcjbV9M9byRX9obxGYgEREKpARFn7aa2yjroZRV5/yGisr0/eJOwfrxFS9XDfmojn4LRT9eTtkuU7eiSsDKzTM9/lvu7E2rPZ/jq8/O/lzL5wNhRImc+MaSDxYcEFqEtaqZRjX/QXbBcNO+Toqm2XBmjXB9O6dS2goeDzQvbuHSZPSuOACN9p9QqRsDMuyauQqv4MHD57+okpSlXY0lupD942Uhu4bKY2qct9YOdmYd1ztywBOpmnLE1eCOwfbVTf6qogC5ueLYfvmkz8uok7B514T6kZhDLui4HkTXvJ9ciLRsqziC/c0b40xfLTv8992YS3/ENs/X8eo36j41+XOgT0/+hcHeuVJqFMX+5Ozin0cgHX4AERFY5yIrZD0dIMFC8JJSAhnz54gXnoplSuuyMayoDK2rKwq941UL1XpvmncuPEZXacRJhEREakSjNAwbH99FCv1D2+mgkMwup+P4Sj+bYutSXOsdSvB9Pr32bw1Rqfup3xeKzQMa89Pf+jQwBgwFGIb+o7T0zDvuRF+34P1+tP+j1/5GRyfVmjt/w1r7Zdgd8CJpGXXzpM/sTsH69ddBf2s+QIr+Q/FMrZv9iVWzxZM8c7OhkceqcuiRWFkZdno3j2XF15IZeTIbN9rroRkSaQ20QhTJahKmbRUH7pvpDR030hp6L45M1ZaCpyYNghYu3dizXn55BefHVcwYgUQGobtxMgUYM76d/GVBFu0Kfj8113QrCXee1/gp5+C6NLFg2XBqFGxtG2bx6RJLs45x1OWl1Vqum+kNKrSfaMRJhEREZFyZERFQ1R0QUOjphiNz4bcP0zfi6yH0bTFKfuyPTAD9u4qeqJVW7+tJfKmjGH7sY48dEEu2VkeFi9OIuzJW/noxRnYW7Up+ngRKXdKmERERERKwbDZIK5D6R4bGQVdexd7/uefHTzzTB2e8oTQxbOExV2W+E48efyC92fBvU8X+3gRKT9KmERERESqgIwMA5fLoFEjk9xcWLs2hI+ueIpR5+0hJvZ42fZjqVgL4zF69gtssCK1iBImERERkQD66ScH8fFOPvggjOHDc3jxxTQ6d85jy5bDhIQ0BZrmX2ulpWAtjIfgkIDFK1LbKGESERERCYAvvgjhtdciWLcuhJAQi1Gjsrn++oL9pUJOlRMlHcH65Xhlv/RUzJn/xPbUmxgx9Ss2aJFaSAmTiIiISCU5csRG/fomNhusWxfC/v12HnggnauvziI62jx9B0HBYBhYyz7AWvaB3ylr3ZcYl5x6c2ARKTklTCIiIiIVyLJg/fpg4uOdLFsWyttvpzBwoJupUzN44IF07PYz78twRmB74FlITyvof/dOrM8WYLRsVwHRi4gSJhEREZEK4HbDe++F8/bbTv73vyDq1jW54QYXrVvnAeB0lm4rTOPs1v4NoWFYny3QjrUiFUQJk4iIiEg5Sk83iIy0sNngxRfrEBvr5d//TuOyy7IJCytdkiQigaOESURERKSM8vLg889DiY93smePg/XrjxAUBEuXHqV+fbPKDv5Y+/ZCylGMrucGOhSRKksJk4iIiEgpJSXZeOedcN55x8mhQ3aaNMlj0iQXeXkGQUEWDRqcQSGHCmDl5cHPP4Anr6Dtx++wvt8IjqCCCw/tA8A+6+PKDlGk2lDCJCIiIlIClgW5ub6y3zt2BPHMM5EMHJjD9OnHGDIkp0RFHMqT+f6b4IzwHfy8o9jrCm96ax1PmESkeEqYRERERM5AVpbBokVhJCQ4GTDAzbRp6QwY4GbNmiO0bOkNXGCNm0PXcyEnu6CtXRfw5mEbPQkchd7uRUVjRMXkH5pvzCjYz0lETkoJk4iIiMgp7NljJyHByYIF4aSn2+jY0UOXLh4AbDYCmyzhKzVu/78HAxqDSE2mhElERETkD0zTlwwBvPBCHT7+OIxLLslm4sQsevXKrbJFHESk/ClhEhERETkuKcnG3LnhvPNOOLNnp9CpUx733JPBQw+lU79+YAo4VDbrtz1Yu/zXQFnz34AWbbA/MCNAUYkEjhImERERqdUsCzZvDiIhwcmnn4aRm2vQv78br9c3jNSkSWCn3FW45ES8d99QcJyadPLrft1VOfGIVDFKmERERKRWsiwwDF8xh/HjfYUQrr3WxcSJWcTF5Z3m0TWDccFw/zLjAFjQoRtG5575LebfJ2D0G1K5wYlUEUqYREREpFbZu9dXxOGHH4JYuDAZp9Pi7bdT6NTJQ0SEFejwKpXRthNG206nvzDMWfHBiFRRSphERESkxvN64csvQ0hIcLJyZSgOh8XIkTm4XAYRERbnnZcb6BBFpIpSwiQiIiI13pIloUyeHE2jRl7+/vd0xo3LomHD2lHEQUTKRgmTiIiI1CiWBVu3+oo4dOni4aabXAwblsPrr6cwdGgOQX9csiOl4t64BqthM4xwTdeTmk0Jk4iIiNQI2dnw8cdhxMc7+f77YJxOk5YtfcUbQkLg4otzAhxh9WZ9twHv4QO+g2wXab//AmfHYX/w2cAGJlLBlDCJiIhIjXDHHfX47LMw2rb1MH16GqNHZ1OnTu0q4lBRjP5/wtrzI1j+0xiNBmcFKCKRyqOESURERKod04Svvw7h7bed/POfaTRpYjJlSibXX+/i/PNzMYxAR1iz2K6Y4Hds5WRj3j4Wzo4LUEQilUcJk4iIiFQbqakG8+eH8/bbTn77zUH9+l5++cVBkya5dO/uCXR4IlIDKWESERGRaiEz06BPn4ZkZto47zw399yTzogROQQHBzoyEanJlDCJiIhIlZSTA598EsYPPwTx2GPpRERYTJuWTo8euXTokBfo8ESkllDCJCIiIlXKvn125swJZ968cFJS7MTFeXC5DJxOi/HjswIdnojUMkqYREREpMpYujSUm2+uh2HAsGE5TJzoon9/FXEQkcBRwiQiIiIBk5bmK+LQooWXYcNyOP98N3fckcn48S6aNDFP34GISAVTwiQiIiKVbvt2B/HxTj78MIycHBvXXuti2LAcoqIs7r47I9DhiYjkU8IkIiIileof/6jL3LlOwsJMRo/OZsIEF507q4iDiFRNSphERESkQh04YOfdd8O57bZMIiMtBg9207ZtHlddlUXdulagw5OyOPAr1pZ1AFgpR7Hmv4Htb49idOwe4MBEyo8SJhERESl3pglr1oQQHx/OF1+EAtCtWy5Dh7oZOTInwNFJmTkcEBKKte5rrHVf+52yvt+UnzBZ7hyst/+DcdNUDFXukGpKCZOIiIiUq4wMg4svjmXPniBiYrxMmZLJdddl0bSpN9ChSTkxHEHEvvw+Kb/tzW+z9u3Fmv081n9X4d263teYctR3fe/+0K1PIEIVKTMlTCIiIlJmO3c62LYtmGuuyaJOHYuBA93ceWcml1ySTUhIoKOTimCPjsUoXMjwrKawby9kZeY3WVvXQXYW1Iut/ABFyokSJhERESmV3Fz47LMw4uPD2bgxhMhIkz//OZvwcIvHH08PdHhSyQxHEMbYG/3arK69MV95Cuz2AEUlUnZKmERERKTEVq0K4c47ozh61E6LFnlMm3aMq67KIjxcRRxEpGZRwiQiIiKnZVnw7bfB1Klj0bWrh5Yt8+jWzcPEiWkMHOjGZgt0hCIiFUM/3kRERKRY6ekGb73lZNCg+owdG8trrzkBaN7cS3x8CoMHK1mSkrGyMrFOFIUQqQY0wiQiIiIn9eyzEbzySgRZWTa6d8/l+edTGTUqO9BhSTVkvvMKhIT5DnZuBcD2wjyMcGcAoxI5MwFPmL777jtmz56NaZpcdNFFXHbZZUWuWbt2LQsWLMAwDM4++2zuvPPOAEQqIiJSs3k8sHx5KEOH5hAcDOHhFpdcksOkSS66dvUEOjypjpq3hnZdwJMLOVn+52zal0mqh4AmTKZp8uabb/Lggw8SExPDfffdR69evWjatGn+NYcOHWLx4sU8/vjjREREcOzYsQBGLCIiUvMcOAAvvVSHd98NJzHRzqxZKYwcmcNtt7kCHZpUc0ZsQ+x/n+7XZi7/EGvhbEAJk1QPAU2Ydu/eTaNGjWjYsCEAffv2ZePGjX4J05dffsmwYcOIiIgAoG7dugGJVUREpKZJTzf4+9+jWLYsCNMMYvBgN5MmpTF4sDvQoYmIVBkBTZhSUlKIiYnJP46JiWHXrl1+1xw8eBCAhx56CNM0GTNmDN26dSvS14oVK1ixYgUATz31FLGxVWeDNIfDUaXikepB942Uhu4bOZ2MDPj+e4N+/SxiYiA11cFf/wo33uihdWsbUOf4P5FTK+3PG5fTSSa+9322sPDyD0yqtOr4eyqgCZNlFd2rwTD8h2dN0+TQoUM8/PDDpKSkMG3aNGbMmIHT6b9IcMiQIQwZMiT/OCkpqWKCLoXY2NgqFY9UD7pvpDR030hxfv7ZQXy8k4ULw7DbYcuWI4SFWbz/PtSv77tvdOtISZT2543p8k31TE5OxgjNOs3VUtNUpd9TjRs3PqPrApowxcTEkJycnH+cnJxMvXr1/K6Jjo6mbdu2OBwOGjRoQOPGjTl06BBxcXGVHa6IiEi1s3VrENOnR7JuXQjBwRaXXprNpEkuwsJ8f7Q0tIxEROSUArpzQuvWrTl06BCJiYnk5eWxdu1aevXq5XfNueeey/bt2wFIT0/n0KFD+WueREREpKgjR2wcOOD7FW8YsG+fnfvvT2fTpiO88EIa3bur4p2IyJkK6AiT3W7nhhtuYPr06ZimyeDBg2nWrBnz58+ndevW9OrVi65du7Jt2zb+9re/YbPZuPbaa6lTR3OrRURECrMs2LAhmPh4J599FsqVV2YxY8YxunXzsHZtInZ7oCMUEameTpswPfroo6Xq2DAMpk2bdtrrevToQY8ePfzaxo4d69fPxIkTmThxYqniEBERqekWLgzj1Vcj+PHHIOrWNbn+ehcTJhSUBFeyJCJSeqdNmHbu3FkZcYiIiEgJ/PKLnZYtvRgGbNsWhM0GzzyTxuWXZ+evTxIRkbI7bcI0f/78yohDRERETiMvD774IpT4eCdr1oSwYEESffvm8sAD6YSEqICDiEhFCOgaJhERETm9rCyDWbOcvPNOOAcPOmjcOI977kmnXbs8AEJDAxygiEgNpoRJRESkCrIsOHrURoMGJg6HRXy8k3bt8nj88XSGDMnBod/gIiKVokLXMHXs2LHUjxUREamNsrIMPvwwjPh4J8eOGaxbl0hwMKxalUhkpNYmiYhUtgqrkgda/yQiInKm9u2z88YbTt5/P5z0dBsdOni4445MvF5flTslSyIigXHahGn06NEYWkUqIiJS7rxecLsNwsMtfvrJQUKCk5Ejs5k0KYvevXNVxEFEpAo4bcJ01VVXVUYcIiIitUZyso25c8OZMyecyy/P5r77Mhg82M2GDUdo0MAMdHgiIlKIloyKiIhUki1bgpg928mnn4aRm2vQr5+bc8/NBXzT7pQsiYhUPUqYREREKlBuLgQH+z6fNSuCr74KYfx4FxMnZtGmTV5ggxMRkdMqVcJkWRbr169n27ZtpKSk4PF4ilxjGAbTpk0rc4AiIiLV0d69dubMcTJ/fjiLFiXRrl0e06Yd45lnLCIiVMBBRKS6KHHC5PF4ePLJJ9mxY0dFxCMiIlJteb3w1VchJCQ4+frrUBwOi+HDc7DbfefPOktT7kROsLasyx9+NV97GgD7rI8DGZLISZU4Yfroo4/YsWMHo0ePZsSIEdx0002MGTOGIUOGsGPHDubOnUvbtm25/fbbKyJeERGRKsc0wWaDzEyDyZPrERlpMXVqOuPGZdGokZIkET/OCACs2c+jsVapDkqcMK1bt46WLVsWqZ4XFRVFv379iIuL4+6772bJkiWMGjWq3AIVERGpar77Loj4eCd79zpYvDiJunUtFi1KpkMHD0FBgY5OpGoy+g3BiOsA3oI/Jpjvvgx7fw5gVCLFs5X0AUeOHKFdu3Z+bXl5BYtWGzZsSPfu3Vm5cmWZgxMREalqsrNh/vwwRo6M5eKL6/PZZ6F07OjB7fadP+ccJUsip2IYBkajphhNmhf8a9wcwpyBDk3kpEo8wmS32wkq9JsgLCyM9PR0v2vq16/P5s2byx6diIhIFWFZYBjwySdh3HVXPdq08TB9ehqjR2dTp44mFomI1FQlTphiYmJISUnJPz7rrLPYtWuX3zW//vorERERZY9OREQkgEwTVq4MIT7eycCBbm680cWoUdk0aeKlb99cDCPQEYqISEUr8ZS8du3a8fPPBXNMe/fuze+//84rr7zCli1bmDNnDt9//z0dO3Ys10BFREQqS2qqwauvOunfvwHXXRfD998HYbf7RpHCwqBfPyVLIiK1RYlHmPr3709ycjKJiYk0aNCAiy++mE2bNrFy5cr8dUuNGjVi/Pjx5R2riIhIpbj99np8/XUo557r5p570hkxIid/81kREaldSpwwderUiU6dOuUfh4SE8Pjjj7Nx40YOHz5MgwYN6NmzJyEhIeUaqIiISEXIyYFPPw3jnXfCefXVVBo1Mrn77gzuuy+dTp3yTt+BiIjUaCVOmE7GbrfTp0+f8uhKRESkUuzbZ2fOnHDmzQsnJcVO69YeDh6006iRyTnneAIdnoiIVBHlkjCJiIhUJykpBgMGNMDrhaFDc5g40cWAAVqXJCIiRZW46MMHH3zANddc41cpr7CUlBSuueYaFi9eXObgREREykNamsHrrzu59966AERHW/z732msX5/Im2+mcsEFSpZEROTkSjzCtHnzZjp27Eh0dPRJz0dHR9O5c2c2btzIZZddVuYARURESmv7dgcJCU4WLQojJ8fGuee6cbshJASuvDI70OGJiEg1UOIRpsOHD9O0adNTXtOkSRMOHz5c6qBERETKav78MIYNa8CiRWFccUU2y5cn8uGHyagmkYiIlESJR5jcbvdpK+AFBweTk5NT6qBERERK6sABXxGHbt08DB+ew5Ahbh5++BhXXZVFVJQV6PBERKSaKvEIU2xsLLt27TrlNbt27Sp2yp6IiEh5sSxYvTqYG2+sR58+DfjPfyL47rsgAGJiTG65xaVkSUREyqTECVPXrl3ZuXMna9euPen5b7/9lp07d9KtW7cyByciInIqN91Uj2uuiWXDhmCmTMlk3bpE7r03I9BhiYhIDVLiKXmXXXYZa9as4YUXXmDt2rV069aN6OhoUlJS2Lp1K5s2bSIiIkIFH0REpNzt3Ong3Xed3H9/Ok6nxZgx2YwYkcMll2QTGhro6EREpCYqccIUHR3NAw88wLPPPsvGjRvZuHGj3/n69etz1113ERMTU25BiohI7ZWbC0uXhhIf72TDhhBCQy1GjsymX79chg/XelkREalYpdq4tnXr1rzwwgts3ryZXbt24XK5cDqdtGnThp49e+JwaD9cEREpu+RkG0OG1Ccx0c7ZZ+fx0EPHGDs2i3r1tC5JREQqR6kzG4fDwXnnncd5551XnvGIiEgtZlnw7bfB7N7tYNKkLGJiTK64Ipt+/dwMGuTGVuKVtyIiImVT5qGgzMxMcnJyiI2NLY94RESkFsrIMFi4MIyEBCe7dgXRqJGXceOyCA6Ghx5KD3R4IiJSi5UqYcrJyeH999/nm2++IT09HcMweO+99wBfSfGFCxcyduxYWrVqVa7BiohIzbNkSSh/+1sULpeNbt1yee65VEaNyiY4ONCRiYiIlCJhysrK4qGHHmL//v20aNGCyMhI9u/fn3++efPm/Pjjj3z77bdKmEREpAiPB5YtC6VZMy/dunno0MHDyJE5TJrkols3T6DDExER8VPi2eCLFi1i//79TJkyhX/961/06dPH73xISAgdO3Zk+/bt5RakiIhUf4cP25gxow7nndeQ226LZu7ccABatfLy/PNpSpZERKRKKvEI03//+1+6du3KwIEDi70mNjaWPXv2lCkwERGpOR56KJK333bi9cLgwW6efjqNwYPdgQ5LRETktEo8wpSSkkLz5s1PeU1oaChZWVmlDkpERKq3zEyDefPCycvzHTdr5uXGG12sWZPInDkpDBnixm4PbIwiIiJnosQjTKGhoaSnn7piUWJiInXq1Cl1UCIiUj3t2uUgPt7JwoVhZGbaaNzYy8CBbm65xRXo0EREREqlxAlTXFwcmzdvJjs7m7CwsCLnU1NT2bp1Kz179iyXAEVEpOpLTrZx2231WLs2hOBgi1Gjspk0yUX37lqXJCIi1VuJp+SNGDGCzMxMnnzySb/qeAD79+/n2WefxePxMGLEiHILUkREqp7ERBvffOOr/V2vnonNBvfdl86mTUd48cU0evTwYBgBDlJERKSMSjzC1K1bN8aMGcOCBQuYOnUqDoevixtvvJHMzEwAxo8fT7t27co3UhERCTjLgo0bg4mPD+ezz8KoU8dky5YjBAXB/PnJgQ5PRESk3JVq49orr7yS9u3bs3TpUnbt2kVGRgYA3bt355JLLqFz587lGqSIiATemjXBPPJIXX78MYjISJOJE11MmOAiKCjQkYmIiFScUiVMAJ07d1ZiJCJSw+3ebSc0FJo29RISYmEY8PTTaVx+eTbh4VagwxMREalwJV7DdKZOV0lPRESqprw8WLYslKuvjmHgwIb85z8RAPTq5eHzz48yfnyWkiUREak1Sj3CVJysrCwWL17M8uXLSUhIKO/uRUSkAs2a5eT1150cPOjgrLO83H13OuPG+fbVUwEHERGpjUqUMB09epRffvkFu91OXFwcUVFR+edyc3NZsmQJn3zyCS6Xi+Dg4HIPVkREypdlwQ8/BNGli6+i3d69Dlq18vLYY+n86U85OMr9z2oiImfGXL0cPLnYLhoV6FCkljvjX4VvvfUWn3/+OZblm4bhcDiYMGECw4YNY8eOHcycOZPk5GSCgoIYMWIEl19+eYUFLSIiZZOdbfDhh2HExzvZsSOIDz9M4txzc3n88WPY7YGOTkRqHcOAjGN4/3ZtQVvm8eUdSpgkwM4oYVq5ciXLly/HMAyaNm2KZVkcPHiQ2bNnExISwqxZszBNkz/96U9cccUVREdHV3TcIiJSCmlpBs89V4cFC8I5dsxGhw4ennoqjU6dfBvMKlkSkUAwBgz1JU2FlkdaKz8LXEAihZxRwrRq1SocDgcPP/wwbdu2BWDnzp088cQTvPrqq8TExHDPPffQvHnzCg1WRERKzuuFAwfsNG/uJTTU4pNPwhg40M2kSS7OPTdXa5NEJOCM5q0xxrX2a/MmHgR3ToAiEilwRlXyfvvtN3r37p2fLAF07NiR3r17Y1kWkydPLnWy9N1333HnnXdy++23s3jx4mKvW79+PVdddRV79uwp1fOIiNQ2yck2/vOfCPr2bcDVV8dgmhAaCmvXHuGVV1I57zwlSyIiIqdzRiNMWVlZNGrUqEj7WWedBeCXSJWEaZq8+eabPPjgg8TExHDffffRq1cvmjZt6ndddnY2S5cupU2bNqV6HhGR2uR//3P8f3v3Hh1FfbBx/JndzW03JCFZQgygYESkaEGIiCgqhoJoRVABRZRo32qhVvA9glcsXrG20mrFekMSRDFgoShV8A3IpXIpiIiAKFflEgi5kXuym5n3j0gkhkgMIbNJvp9zOGR+O7v76PmdzT7MzG/08svhWrQoTGVlhvr2LdOYMUX6/hJUhd5dEO4AACAASURBVIbamw8AgKakTkeYLMuS6wRLJTm/P9m9vivi7dy5U3FxcWrbtq1cLpf69u2r9evX19gvLS1NQ4YMURC3kweAEyopkQoKKg8X7d3r0uLFobrllmJ98kmm5s3L1q9/Xcr1SQCaPPOtl2V+utTuGGhhbF0wNicnRzExMVXbMTEx2rFjR7V99uzZo6ysLPXq1UsffPBBra+Vnp6u9PR0SdKzzz4rr9d7ekLXg8vlCqg8aBqYN6iLXbuk115zKjXVobFjTT35pKGbbw7X9df71apVkKSok74GwOcN6uN0z5vckFCVb9ukiruu/2HQsqSVi+W9fuRpe1+cXk3x86bOhWnevHmaN2/eCR8bObLmpDUMQ+++++5PvuaxJcp//LxjTNNUamqqxo0bd9J8AwYM0IABA6q2s7KyTvqcxuL1egMqD5oG5g1+yiefhOjNNz365JMQORzS1VeXqnfvQvn9kcrLq5w3ZWU2h0STwecN6uN0zxtr0A0y2p1VfWxRmtS+E/O1CQukz5v4+Pg67XfajjCdqAz9WExMjLKzs6u2s7Oz1bp166rt0tJS7du3T48//rgkKS8vT88995wmTZqkhISEGq8HAM1ZYaGh8PDKz9Z33nFr69Yg3XdfoUaNKtIZZ5g2pwOAhmUknCcj4bxqYxXrVshof1YtzwBOjzoVprS0tNPy5gkJCcrIyFBmZqaio6O1evVq3XvvvVWPu91uzZgxo2p7ypQpuu222yhLAFqUTZuClJLi0QcfhGrJkiM655wKTZ16VJGRpri0EwCA08vWa5icTqfuvPNOPf300zJNU/3791eHDh2UlpamhIQEJSYm2hkPAGxTViYtXBim1FSPNm0KltttavjwkqoV7rxejigBANAYbC1MktSzZ0/17Nmz2tiJromSKo8wAUBzVl4uBQdLxcWGHnooSu3b+/Xkk0d1003Fiog4+anOAACgYdlemACgpTNNacWKEKWkeJSX59DChVlq3drSkiVHlJDg5+ayAADYiMIEADbJzTU0d65bs2Z5tHevS23aVGjUqGL5/ZLLJZ1zjt/uiAAAtHgUJgBoZKYpORzSv/8dpieeiFTv3mWaNClfgweXqp73AQcAAKcJhQkAGkFZmbRoUZhSUjy68cZiJScX64YbSnThheXq1o0jSQAABCoKEwCcRvv3O/XWW269845bOTlOnX22X1FRlYs3uN0WZQkAgABHYQKA02j8+Cj997/BGjiwVGPGFOmyy8rlcNidCgCaLqu8TFZudtW2+dg4OZLHy+jV18ZUaM4oTADQQI4erVzEIS3NrXffzZbXa+qJJ44qKspSu3YVdscDgKbP6ZI2rpG5cU21YfP1v8jZa75NodDcUZgA4BRt2eJSaqpH8+eHqbTUoV69ypWV5ZDXa3LKHQA0IMdv7pP13e5qY9Zb02VcPtCmRGgJKEwAcAoOHnTo6qvbKCTE0rBhJUpOLtL551OSAOB0MDp2ltGxc7WxiremS54IW/KgZaAwAcDPcOCAQ7Nne5SV5dCf/3xU8fGmXn01V5deWla1mAMAAGg+KEwAcBKWJa1aFaxZszxasiRUliUNGlSqigrJ6ZSuvbbU7ogAAOA0Ya0mADiJN9/06JZbvFq7NlhjxxZqzZpMzZiRK6fT7mQAAElSzhFZO7dV/tm2SRW/HSLr6y12p0IzwREmAPiRr75yKSXFo6uuKtOgQaW67roSRUSYuu66EoWG2p0OAFBNSJis1UtlrV5abdhMXyhnl/NtCoXmhMIEAJLKy6WPPgpVaqpH69aFKDTUUqdOlYs3xMaaGj68xOaEAIATcTz6vJRzpGrbOnJY1uyXZfS42MZUaE4oTAAgadSoGK1ZE6KzzvJr8uSjGjGiWNHRLOIAAIHOiGsvxbX/YeDIIVmSZHDlCRoGhQlAi2NZ0urVwUpLc+tPfzqqsDBLd91VqLFjC9W/f5kc/I4FAADfozABaDEKCw29916YUlM9+uabIEVFmdq+vUgXXujTwIFldscDAAABiMIEoEU4cMCh/v1jVVTk0C9/Wa5p03I1ZEiJwsLsTgYAAAIZhQlAs+TzSYsXhyojw6m77ipSfLyp3/62SAMGlOrCC312xwMAAE0EhQlAs3L4sENvv+3W2297dOiQU+ee69NvflMkp1OaOLHA7ngAAKCJoTABaDbS0sI0aVKU/H5D/fuXaurUPCUllXGDWQAAUG8UJgBNVlFR5SIO3bv71KOHTz17+nTnnUW6/fYidepUYXc8AADQDFCYADQ5O3a4lJrq1rx5bhUWOjR2bKF69PCpc2e//vjHfLvjAQCAZoTCBKBJ+f3vo/Svf7kVHGzp178u0ZgxRerVi0UcAADA6UFhAhDQjhxxaMGCsKqFGy680KfzzsvXLbcUy+s17Y4HAACaOQoTgIBjWdKGDcFKSXHr3/8Ok89n6MILy3XRRT79z/8U2R0PAAC0IBQmAAHl4EGHxoyJ0bZtQYqIMHX77ZWLOJxzDos4AACAxkdhAmC7Xbuc2rvXpaSkMrVtayo+vkLJyUUaNqxEbrdldzwAANCCUZgA2KKiQkpPD1VKilsrV4YqLq5C69cfltMppabm2B0PAABAEoUJgA2WLAnV5MkROnDApTPOqNDEifkaNapYDofdyQAAAKqjMAE47SxL2rgxSLGxpjp0qFBkpKlOnSr0+OP5+tWvSuXikwgAAAQo/j0XwGlTUmJozhy3rr7aqyFD2uiNNzySpD59ypWWlq3BgylLAAAgsPFVBcBp8dxzrZSa6lFenkPnnefT1Kl5uvHGErtjAQAA/CwUJgANoqJCWrs2WJdeWi5JyslxqF+/MiUnF+nii8tlGDYHBAAAqAcKE4BTkpPj0Jw5br31llv79rm0aNERXXihT1OnHqUkAQCAJo/CBKBejhxx6KmnIvTBB2EqKzN0ySVlevTRfJ1/vk+SKEsAAKBZoDABqLOSEmn/fpc6d/YrPNzSmjXBuvnmYo0ZU6QuXfx2xwMAAGhwFCYAJ/Xtt07NmuXRu++6FR1tasWKTIWFWVq9OpNV7gAAQLPGVx0AtdqwIUgvvNBKn3wSIodDuvrqUiUnF1WdbkdZAgAAzR1fdwBUk5tryOGQIiMtZWQ4tWVLkCZMKNSttxbpjDNMu+MBAAA0KgoTAEnS5s1BSknxaOHCMN1zT4Huu69QgweXatCgUgUH250OAADAHhQmoIWbPz9Mb77p0eefB8vtNjV8eLGuuaZUEqfcAQAA8HUIaIGyshzyeitPr1u4MEz5+YaefPKobrqpWBERls3pAAAAAgeFCWghTFNasSJEKSkeLV8eolWrMnXmmRV64YVcRUZa3DcJAADgBChMQDNXWGjo7bfdmjXLo717XfJ6KzRuXKHc7sojSVFRHFECAACoDYUJaKaKigx5PJZKSgxNnRqhHj3Kdf/9BbrmmhKFhNidDgAAoGmgMAHNSFmZtGhRmFJSPAoKsjR/frbatDH16aeH1a4dS4IDAAD8XBQmoBk4cMChWbM8mjPHrexspzp18mvMmCKZpuRwiLIEAABQTxQmoIkyzco/Lpf08cehevnlcP3qV6VKTi7WZZeVyeGwOyEAAEDTR2ECmpijRw3NnVu5iMPddxdq9OhijRhRol/9qkzt21fYHQ8AAKBZsb0wbdq0STNnzpRpmkpKStLQoUOrPb5o0SItXbpUTqdTERERGjt2rNq0aWNTWsA+W7e6lJrq0fz5YSopcahnz3LFx1cWJI/HksdDWQIAAGhothYm0zQ1Y8YMPfroo4qJidFDDz2kxMREtW/fvmqfjh076tlnn1VISIg+/vhjzZ49W/fdd5+NqYHGY1mquj/SAw9E6auvgjR0aLGSk4t1wQU+e8MBAAC0ALYWpp07dyouLk5t27aVJPXt21fr16+vVpjOP//8qp87d+6sVatWNXpOoLEdPOjQSy85NXt2rD788Iiioy1Nm5anNm0q1Lo1900CAABoLLYWppycHMXExFRtx8TEaMeOHbXuv2zZMvXo0aMxogGNzrKkTz8NVmqqR0uWhMo0paSkMuXnOxQdXaFzz/XbHREAAKDFsbUwWVbNfyk3jp1/9CMrV67U7t27NWXKlBM+np6ervT0dEnSs88+K6/X22A5T5XL5QqoPAhMO3dKI0cGKybG0oQJpsaONdShg0NSa7ujoQnh8wb1wbxBfQTqvPH7y5QtqVWrVgoLwHwtXaDOm59ia2GKiYlRdnZ21XZ2drZat6755XDz5s1asGCBpkyZoqCgoBO+1oABAzRgwICq7aysrIYPXE9erzeg8iAwbN9euYhDaamhv/41T1FR0uzZIbrkkjKFhjJvUD/MG9QH8wb1EajzxsrNlSQVFBSoKADztXSBNG/i4+PrtJ+td2pJSEhQRkaGMjMz5ff7tXr1aiUmJlbbZ8+ePXr99dc1adIkRUZG2pQUaBg+n/T++6G68cYYJSXFKi3NLZfL0rGDrf37V5YlAAAABAZbjzA5nU7deeedevrpp2Wapvr3768OHTooLS1NCQkJSkxM1OzZs1VaWqpp06ZJqmylDzzwgJ2xgXr7xz/C9ac/RejMM/169NGjGjmyWNHRLOIAAAAQqGy/D1PPnj3Vs2fPamMjR46s+nny5MmNHQloEJYlrVlTuYjDjTcWa+DAMo0cWaxu3Xzq379MDluP7wIAAKAubC9MQHNTWGjovffCNGuWR19/HaSoKFNXXlkmSWrb1lTbtmU2JwQAAEBdUZiABnbDDV5t3RqkCy4o17RpuRoypERhYXanAgAAQH1QmIBT4PNJH38cqvnzwzR9eq5CQ6UHH8xXZKSpnj19qmWVfAAAADQRFCagHg4fduidd9yaPdujQ4ecat/er2+/dalLF7+uuopT7gAAAJoLChPwM+3c6VRSUqz8fkNXXlmqqVPzlJRUJqfT7mQAAABoaBQm4CSKigzNnx+mggKHxo0rVEJChSZOLNDgwSVKSKiwOx4AAABOIwoTUIudO11KTXVr3jy3CgocuvjiMo0dWyjDkO65p9DueAAAAGgEFCbgBF57zaPHH49UUJCl664r0e23FykxkUUcAAAAWhoKEyDpyJHKRRyuvLJM3bv7dMUVZZo0KV+jRhWrTRvT7ngAAACwCYUJLZZlSRs2BCk11aNFi8Lk8xlyOPLVvbtPXbr41aULp90BAAC0dBQmtFi33BKjVatC1KqVqdtvL9LttxfrnHP8dscCAABAAKEwocXYvdupf/0rTBMmFMrhkAYNKtG115bohhtK5PFYdscDAABAAKIwoVmrqJCWLg1RSopHK1aEyuWyNGhQqbp18+uOO4rtjgcAAE4Ta/mHMstKKzfycmR9OFeOv70tw9PK3mBocihMaLZ273bqlltitH+/S3FxFbr//nzdemuxYmNZxAEAgGbLHS4Fh0h7vpG155tqD1lf/FdG36TKnysqpMyDMs7oYEdKNCEUJjQbliVt3BikzEynBg8u1ZlnVqhnT58eeyxfAweWKijI7oQAAOB0Mzzhckx7Szp2dEmS9dUXst54XtbMF1Tx7huVgyVFkiTHX1JlRLa2IyqaCAoTmrySEkMLF4YqJcWjL78MVkKCT1dfXSqXS/rHP3LtjgcAABqZERIqhYT+MNDjYumaEVJZSdWQtWKx5PdV/osr8BMoTGjS5s0L05QpkcrLc6hLF5+eeSZPN95Ywg1mAQBAFSMkVMaw0dXGzLj2st7+h/jSgJOhMKFJqaiQPvkkRF26+NWhQ4XOOKNC/fqVacyYIvXpU85nHgAAABoUhQlNQk6OoXff9eitt9z67juX/vCHAj34YIEuu6xcl11Wbnc8AAAANFMUJgQ0y5IefDBS8+a5VVZm6JJLyvTww/m6+urSkz8ZAAAAOEUUJgSckhJp1aoQDRxYJsOQnE5p5MhijRlTpPPO89sdDwAAAC0IhQkB47vvnJo1y6M5c9zKy3MoPT1TXbv69cwzR+2OBgAAgBaKwgTb7d/v1MMPR2rZshA5HNKgQaVKTuZoEgAAAOxHYYItcnMNHTzoVLdufkVFmdqzx6Xx4wt1661Fio837Y4HAAAASKIwoZFt3hyklBSPFi4MU8eOfqWnH1F4uKWVKzNZEhwAAAABh8KERrF8eYj+8pdW+vzzYIWFmbrppspFHI6VJMoSAAAAAhGFCafN/v1OtWplKjLSUk6OQ0ePOvTEE0c1fHixIiIsu+MBAAAAJ+WwOwCaF9OsPJqUnBytSy6J1ezZHknS9deXaMWKTP3mN0WUJQAAADQZHGFCg7AsacYMj2bO9GjvXpe83grdc0+hhg4tkVR5LyUAAICAc+SQrPKyyp+P5kolRTIuSLQ3EwIKhQmnZN8+pzp0qJBhSJ98EiKv19T99+fqmmtKFBJidzoAAIBaBAVJksw/PVDjIefr7zd2GgQwChN+trIy6d//DlNKikeffx6ktWsz1a5dhd54I1dhYZxuBwAAAp+R2E8KCpb8P9z30fpnipSfJyvnyA87ZmVKbreM9p0aPyQCAoUJdZaT49Brr3k0Z45bWVlOdezo1+TJ+YqIqLxvEmUJAAA0FUZIiIzel1cbMw8flPXhXJkP/KbG/hx1arkoTPhJllV5k9noaEvl5dJrr4XriitKNWZMsS6/vEwOlg0BAADNhDHgOik2rnIVq+9Zs16yMRECAYUJJ5Sfb2jePLdSU92KizM1d2624uJMbdhwSNHRHEkCAADNj9EqUsalA6qNVWz5TDp80KZECAQUJlTzzTcuzZjh0fz5YSoudujCC8s1YkSxLKvy5rKUJQAAALQkFCaovLzy7+DgynsovfeeW9dfX6Lk5CL98pc+e8MBAAAANuIKlBYsI8OhP/+5lXr3bqsFC8IkSbfeWqz16w9p2rQ8yhIAAABaPI4wtTCWJa1eHayUFI+WLAmVaUpXXVWmTp0qJEkejyWPx+aQAAAAgaSoUFZh/g/bhQVSRJQMN1+aWgIKUwvh81Xdn01PPBGh/ftduuuuIt12W5HOOqvC3nAAAAABynC6ZOVly7xvdM3HBg6r+tn6eIGMO8bL0TepMeOhEVCYmrmvv3YpJcWjjz4K1YoVmYqMtPSPf+TqjDMqFBZmdzoAAIDAZgwZJSV0rTZmvfta5d/LP6w+Pm+mRGFqdihMzZDPJy1eHKrUVI/WrAlRSIilIUNKVFxsKDLS0tlnc0QJAACgLoy4djLi2lUfTPp1jf0qxt4gFRXK/HBe1Zj1r7flmPKijPgzT3dMnEYUpmbk2NLfu3e79LvfRatDB78eeSRfN99crOho8+QvAAAAgPqJP1P6bresBW9VGzYXvCXn7x+xKRQaAoWpibMsad26ykUcgoMtvfhinrp08WvBgiz16lUup9PuhAAAAM2f45FpUsVxZ/EUHJX5wJ0yftHDvlBoEBSmJqqw0NA//xmmWbM82r49SFFRpkaPLqo6ytS7d7ndEQEAAFoMw+GQHD/cscc6ttrWwX2yDn73w45HDsvoflEjp8OpoDA1UdOnh+vFF1vpggvK9fzzubr++lKFhVl2xwIAAIAkOSu/ZlvLP6yxOISRPF6OS1kcoqmgMDUBfr/08cehSknx6O67C5WUVKYxY4o0YECpevb0yTDsTggAAIDjGW6PHBOnyjqa+8Pg7q9lpS+U0SrCvmD42ShMASwz06G333Zr9myPDh1yql07v0pKKttRXJypuDgWcgAAAAhUxrnddPy/a1vRXlnpC6uduofAR2EKUJYlDRvm1d69Ll1xRammTs1TUlIZizgAAAA0cVZejpSX/f2GJEMyomJszYTaUZgCRHGxofnzw7RoUZhmzcpWcLA0dWqe2rWrUEIC900CAABo8lyVC0FYqX/Xj688N24cI8PbVjIckqEf/pYhGQ5ZOZky4trL6Nq9kUODwmSznTudmjXLo7lz3SoocOgXv/ApI8Ops86q0OWXs9IdAABAs9Ghk4y7JkolRVVD1paN0udrZf0ztUaJ+jFLktqd9cPAgW+l1l45nnm12n7G98UMDYPCZKMtW1waNChWQUGWrr22RMnJxUpMLGcRBwAAgGbIcDhkXNSv+uDlV8s6ckjylVdek/HjP6r829r6uaytG6Xw4xaMOPCtlJslc+yNNd/MHf79mxrHHan6/ktmSVHlqmIXJMro3rtyzO+TtXm9HHeMl6quvLKkMI+MkNAG+3/QFNlemDZt2qSZM2fKNE0lJSVp6NCh1R73+Xx66aWXtHv3brVq1UoTJkxQbGysTWlPTVaWQ++845Yk3Xtvobp18+upp/L061+Xqk0bFnAAAABoiYw2cSffp2Nn6doR1cas3V/L+uqL6mPrV0mtvTJizziucEnHipcsyTqaI33xX+nLDbK+3FDt+ebEO2q+9yVXScHBla//1RdSVLQcf5hcfaeQMBnN9F/9bS1MpmlqxowZevTRRxUTE6OHHnpIiYmJat++fdU+y5Ytk8fj0d///nd9+umnevvtt3XffffZmPrnsSxpzRpDL74YpUWLwlRebuiaa0okVZb8O+4otjkhAAAAmiLj7C4yzu5SffBHpao2VkmxVFb6w4DfV1meHMetMLZ9s6wN/6kcP1aGCo5KmRky/3BzzTzJ96rySJZUdUTr+J+/2yXf4Buk8Kif8V9pP1sL086dOxUXF6e2bdtKkvr27av169dXK0wbNmzQ8OHDJUl9+vTRm2++KcuymkyDff75VvrrX4PUqpVTo0cXacyYYp1zjt/uWAAAAGjBjDC3FOauPtb/2uo7XXG1dPekakNWdqasz1ZLx11xZc2bWfl3yosnfd/ydmdKfQfUL7RNbC1MOTk5ion5YQnFmJgY7dixo9Z9nE6n3G63CgoKFBFR/YZf6enpSk9PlyQ9++yz8nq9pzl93Ywebeicc0zdfLNf4eHBkoLtjoQmwuVyBcw8RtPBvEF9MG9QH8ybFsrrlbr8otqQNfIOmdlHvt+wZFVdf6XjrscyJcOhkA4d5TGb1qUothYmy6q5FsiPjxzVZR9JGjBggAYM+KGtZmVlNUDCUxcXJ/3P/3iVlZWl0tKT7w8c4/V6A2Yeo+lg3qA+mDeoD+YNqnHUbWU+r2kGzLyJj4+v03623mY4JiZG2dnZVdvZ2dlq3bp1rftUVFSouLhY4eHhjZoTAAAAQMtka2FKSEhQRkaGMjMz5ff7tXr1aiUmJlbbp1evXlq+fLkkae3aterWrVuTuX4JAAAAQNNm6yl5TqdTd955p55++mmZpqn+/furQ4cOSktLU0JCghITE3XVVVfppZde0h/+8AeFh4drwoQJdkYGAAAA0IIY1okuEmoGDh48aHeEKpzji/pg3qA+mDeoD+YN6oN5g/oIpHnTJK5hAgAAAIBARmECAAAAgFpQmAAAAACgFhQmAAAAAKgFhQkAAAAAakFhAgAAAIBaUJgAAAAAoBYUJgAAAACoBYUJAAAAAGphWJZl2R0CAAAAAAIRR5gawYMPPmh3BDRBzBvUB/MG9cG8QX0wb1AfTXHeUJgAAAAAoBYUJgAAAACohXPKlClT7A7REpx99tl2R0ATxLxBfTBvUB/MG9QH8wb10dTmDYs+AAAAAEAtOCUPAAAAAGrhsjtAc7Fp0ybNnDlTpmkqKSlJQ4cOrfa4z+fTSy+9pN27d6tVq1aaMGGCYmNjbUqLQHGyebNo0SItXbpUTqdTERERGjt2rNq0aWNTWgSKk82bY9auXatp06Zp6tSpSkhIaOSUCDR1mTerV6/WvHnzZBiGzjrrLI0fP96GpAgkJ5s3WVlZmj59uoqKimSapkaNGqWePXvalBaB4uWXX9bGjRsVGRmp559/vsbjlmVp5syZ+vzzzxUSEqJx48YF9ml6Fk5ZRUWFdc8991iHDh2yfD6fdf/991v79u2rts/ixYutV1991bIsy/rPf/5jTZs2zY6oCCB1mTdffvmlVVpaalmWZS1ZsoR5gzrNG8uyrOLiYuuxxx6zHn74YWvnzp02JEUgqcu8OXjwoDVx4kSroKDAsizLysvLsyMqAkhd5s0rr7xiLVmyxLIsy9q3b581btw4O6IiwGzdutXatWuX9b//+78nfPyzzz6znn76acs0Tevrr7+2HnrooUZO+PNwSl4D2Llzp+Li4tS2bVu5XC717dtX69evr7bPhg0bdOWVV0qS+vTpoy1btsji8rEWrS7z5vzzz1dISIgkqXPnzsrJybEjKgJIXeaNJKWlpWnIkCEKCgqyISUCTV3mzdKlSzVo0CCFh4dLkiIjI+2IigBSl3ljGIaKi4slScXFxWrdurUdURFgfvGLX1R9lpzIhg0bdPnll8swDJ177rkqKipSbm5uIyb8eShMDSAnJ0cxMTFV2zExMTW+2B6/j9PplNvtVkFBQaPmRGCpy7w53rJly9SjR4/GiIYAVpd5s2fPHmVlZalXr16NHQ8Bqi7z5uDBg8rIyNDkyZP1yCOPaNOmTY0dEwGmLvNm+PDhWrVqlX73u99p6tSpuvPOOxs7JpqgnJwceb3equ2TfQeyG4WpAZzoSJFhGD97H7QsP2dOrFy5Urt379aQIUNOdywEuJPNG9M0lZqaqttvv70xYyHA1eXzxjRNZWRk6I9//KPGjx+vV155RUVFRY0VEQGoLvPm008/1ZVXXqlXXnlFDz30kP7+97/LNM3Giogmqql9L6YwNYCYmBhlZ2dXbWdnZ9c4JH38PhUVFSouLv7JQ5Vo/uoybyRp8+bNWrBggSZNmsTpVTjpvCktLdW+ffv0+OOP6/e//7127Nih5557Trt27bIjLgJEXT5voqOjddFFF8nlcik2Nlbx8fHKyMho7KgIIHWZN8uWLdMll1wiSTr33HPl8/k4gwYnFRMTo6ysrKrt2r4DBQoKUwNISEhQRkaGMjMz5ff7tXr1aiUmJlbbp1evXlq+fLmkypWrunXrFtBNGqdfXebNnj179Prrr2vSpElcTwBJJ583brdbz7sAOQAACP1JREFUM2bM0PTp0zV9+nR17txZkyZNYpW8Fq4unze9e/fWli1bJEn5+fnKyMhQ27Zt7YiLAFGXeeP1eqvmzf79++Xz+RQREWFHXDQhiYmJWrlypSzL0jfffCO32x3QhYkb1zaQjRs3KjU1VaZpqn///rrhhhuUlpamhIQEJSYmqry8XC+99JL27Nmj8PBwTZgwgV9EOOm8efLJJ/Xdd98pKipKUuUvpgceeMDm1LDbyebN8aZMmaLbbruNwoSTzhvLsjRr1ixt2rRJDodDN9xwgy699FK7Y8NmJ5s3+/fv16uvvqrS0lJJ0ujRo9W9e3ebU8Nuf/vb37Rt2zYVFBQoMjJSI0aMkN/vlyQNHDhQlmVpxowZ+uKLLxQcHKxx48YF9O8pChMAAAAA1IJT8gAAAACgFhQmAAAAAKgFhQkAAAAAakFhAgAAAIBaUJgAAAAAoBYUJgBAi7B8+XKNGDGi6p54P9fcuXM1YsQIbd26tWGDAQACmsvuAACApmvEiBHVtg3DUHh4uM4880xdddVV6tevn03JAABoGNyHCQBQb8cK00033SRJqqio0MGDB7V+/XqZpqlrr71WY8aMsTNileLiYuXm5qp169Zyu90/+/n5+fkqKCiQ1+tVSEjIaUgIAAhEHGECAJyyHx9p+vLLL/XUU0/pww8/1ODBgxUbG2tTsh+43e56FaVjIiIiFBER0YCJAABNAYUJANDgLrjgAsXHx+vAgQPatWuXYmNjlZmZqXvuuUdXXHGFhg0bprS0NG3dulUFBQV67LHH1K1bN0lSYWGh3n//fa1fv16ZmZlyuVxKSEjQ9ddfr+7du5/w/VavXq309HTt2bNH5eXlioqKUufOnXXdddcpISFBUuU1TC+//LLGjRunK6+8suq53377rRYsWKAdO3YoNzdXYWFh8nq96tq1q0aPHi2Xq/JX5dy5c/Xee+/pj3/8Y1XWY7788ku9//772rlzp8rLy+X1etW7d28NGzasRkmbMmWKtm3bpjlz5mjhwoVavny5srKyFBkZqUsvvVQ333xz1XsCAOzHJzIA4LQyDKPa9uHDh/XII4/ojDPO0GWXXaby8nKFhYVJko4cOaIpU6boyJEj6tq1q7p3766ysjJt3LhRzzzzjH77299qwIABVa9lWZZefvllrVixQq1atdLFF1+siIgIZWdna+vWrYqPj68qTCfy7bff6uGHH5ZhGEpMTFRsbKyKi4t1+PBhLVmypE7l5f/+7//0xhtvKCQkRH369FFkZKS2bdumhQsX6rPPPtOTTz4pj8dT43kvvPCCtm/frh49esjtduvzzz/X+++/r/z8fI0bN+7n/C8GAJxGFCYAQIPbvHmzDh48KMMwahSW7du3a+jQoRo1alSN502fPl1ZWVkaP368Lr300qrxoqIiTZkyRTNnzlRiYqKioqIkSUuXLtWKFSuUkJCgyZMnVzuaY5qmjh49+pM5V6xYIZ/Pp4kTJ+qiiy6q9lhhYaGCg4N/8vlHjhzRzJkzFRoaqmeeeUbt2rWreuyNN97Qxx9/rNmzZ+vuu++u8dzDhw9r2rRpCg8PlyTdfPPNmjhxolasWKFRo0ZV/TcCAOzFsuIAgFM2d+5czZ07V3PmzNHzzz+vZ555RpZl6ZprrlGbNm2q7RsZGanhw4fXeI29e/dq27Ztuvjii6uVJUnyeDwaMWKEfD6f1q1bVzX+0UcfSZLuuuuuGqe+ORwOtW7duk75T1SMwsPD5XD89K/JVatWye/3a9CgQdXKklRZgMLCwrRy5Ur5fL4az7311lurypIkhYaGql+/frIsS7t27apTbgDA6ccRJgDAKXvvvfckVZ5+53a71bVrV/Xv31+XX355jX07duyooKCgGuPffPONpMrV7ObOnVvj8fz8fEnSgQMHJEmlpaXat2+fIiMj1alTp3rl7tu3rz788EP9+c9/Vp8+fXTBBReoS5cuiouLq9Pzd+/eLUk6//zzazwWHh6ujh076quvvtKBAwfUsWPHao+f6FTBmJgYSZVH1AAAgYHCBAA4ZScqOLWJjIw84XhhYaGkytP5Nm/eXOvzS0tLJVUWK0mKjo6u83v/2DnnnKMnnnhC8+fP19q1a7Vy5UpJUnx8vG666SZddtllP/n8kpISSar1SNax8WNZj3ei65qcTqekytMJAQCBgcIEAGhUP14E4phjp9QlJyfrmmuuOenrHNs/JyfnlPKce+65evDBB+Xz+bR7925t2rRJixcv1osvvqiIiAj98pe/rPW5xxaryMvLU4cOHWo8npubWy0rAKDp4RomAEBA6Ny5s6TKRSHqIjQ0VB06dNDRo0e1Z8+eU37/oKAgdenSRSNHjtQdd9whSdqwYcNPPufYqYBbt26t8VhRUZH27t2roKCgGtc3AQCaDgoTACAgJCQkqGvXrlq3bp2WLVt2wn2+++67aivfDR48WJL02muv1TjtzTTNqiM8tfnqq69OeLpcXl6epBMvBnG8fv36yel0avHixTp06FC1x9LS0lRSUqJ+/fqd8JotAEDTwCl5AICAce+99+qJJ57QK6+8oo8++kidO3eW2+1WTk6Ovv32W+3bt09PPfVU1XVQSUlJ2r59u1auXKl7771XiYmJioiIUG5urrZs2aL+/ftrxIgRtb7fBx98oM2bN6tbt26KjY1VaGio9u3bp02bNsnj8VS759OJxMbGKjk5WTNmzNADDzygSy65RBEREdq2bZu++eYbtWvXTqNHj27Q/0cAgMZFYQIABIyYmBg9++yz+uijj7Ru3TqtWrVKpmkqKipK7du31+DBg3XmmWdW7W8Yhu655x51795d6enpWrNmjfx+v6KiotS1a1clJib+5PsNGjRI4eHh2rFjh7Zv3y7TNBUdHa2BAwfquuuuq7Ekem2vERcXpw8++EDr1q1TWVmZYmJiNGTIEA0bNuyEizsAAJoOw7Isy+4QAAAAABCIuIYJAAAAAGpBYQIAAACAWlCYAAAAAKAWFCYAAAAAqAWFCQAAAABqQWECAAAAgFpQmAAAAACgFhQmAAAAAKgFhQkAAAAAakFhAgAAAIBa/D8LNCIpEo+HzgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision,recall,_ = precision_recall_curve(y_test,all_predprob[:,1])\n",
    "plt.figure(figsize=(14,6))\n",
    "plt.plot([0,1],[0,1],'b--')\n",
    "plt.step(recall,precision)\n",
    "plt.title('P-R curve',fontsize = 23)\n",
    "plt.xlabel('Precision',fontsize = 20)\n",
    "plt.ylabel('Recall',fontsize = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2数据生成算法SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用smote算法生成新的数据，并重新使用LR模型进行分类，画出ROC曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "smote = SMOTE(random_state=11)\n",
    "\n",
    "orig_x = data.iloc[:,data.columns != 'Class']\n",
    "orig_y = data.iloc[:,data.columns == 'Class']\n",
    "\n",
    "x_res,y_res = smote.fit_sample(X=x_train,y=y_train)\n",
    "new_data = pd.DataFrame(x_res,columns= data.columns[data.columns!='Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data['Class'] = y_res\n",
    "# resample_x_train,resample_x_test,resample_y_train,resample_y_test = train_test_split(x_res,y_res,test_size =0.3,random_state = 112)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9763050732806129\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = LogisticRegression(C=0.06,penalty='l1')\n",
    "# model = svm.SVC(probability=True)\n",
    "model.fit(X=x_res, y=y_res)\n",
    "\n",
    "#对整体数据分割得到的测试集进行测试\n",
    "all_predprob = model.predict_log_proba(x_test)\n",
    "all_pred = model.predict(x_test)\n",
    "# print(metrics.accuracy_score(all_pred,y_test))\n",
    "my_fpr,my_tpr,threshold = roc_curve(y_test,all_predprob[:,1],pos_label=1)\n",
    "\n",
    "print(auc(my_fpr,my_tpr))\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.style.use('ggplot')\n",
    "plt.plot(my_fpr,my_tpr,'r-',label ='Roc curve(area=%0.3f)'%auc(my_fpr,my_tpr)) \n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('False Positive Rate',fontsize= 20)\n",
    "plt.ylabel('True  Positive Rate',fontsize= 20)\n",
    "plt.title('ROC Curve',fontsize = 22)\n",
    "\n",
    "plt.plot([0,1],[0,1],'b--')\n",
    "plt.xlim((-0.01,1))\n",
    "\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}