{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "### 药物联合作用数据集处理\n",
    "#### 一、基本工作\n",
    "1.导包"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'numpy'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mModuleNotFoundError\u001B[0m                       Traceback (most recent call last)",
      "Input \u001B[1;32mIn [1]\u001B[0m, in \u001B[0;36m<cell line: 4>\u001B[1;34m()\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mwarnings\u001B[39;00m\n\u001B[0;32m      2\u001B[0m warnings\u001B[38;5;241m.\u001B[39mfilterwarnings(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mignore\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m----> 4\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mnumpy\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m \u001B[38;5;21;01mnp\u001B[39;00m\n\u001B[0;32m      5\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mpandas\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m \u001B[38;5;21;01mpd\u001B[39;00m\n\u001B[0;32m      7\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01msklearn\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mpreprocessing\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m MinMaxScaler\n",
      "\u001B[1;31mModuleNotFoundError\u001B[0m: No module named 'numpy'"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.neural_network import MLPRegressor, MLPClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn import model_selection\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from sklearn.metrics.cluster import adjusted_rand_score\n",
    "from sklearn.preprocessing import StandardScaler, scale\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn import decomposition\n",
    "\n",
    "from itertools import cycle\n",
    "from scipy import interp\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "2.读取数据"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "   0     1     2     3     4     5     6     7     8     9     ...      4797  \\\n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0  ...  0.144204   \n",
       "1   1.0   1.0   1.0   1.0   0.0   0.0   0.0   0.0   0.0   1.0  ...  0.144204   \n",
       "2   1.0   1.0   1.0   0.0   0.0   0.0   1.0   0.0   0.0   1.0  ...  0.144204   \n",
       "3   1.0   1.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0  ...  0.144204   \n",
       "4   1.0   1.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0  ...  0.144204   \n",
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       "8   1.0   1.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0  ...  0.144204   \n",
       "9   1.0   1.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0  ...  0.144204   \n",
       "\n",
       "       4798     4799      4800      4801      4802      4803      4804  \\\n",
       "0  0.210953  0.24647 -0.212117 -0.134972  0.121756  0.118738 -0.107353   \n",
       "1  0.210953  0.24647 -0.212117 -0.134972  0.121756  0.118738 -0.107353   \n",
       "2  0.210953  0.24647 -0.212117 -0.134972  0.121756  0.118738 -0.107353   \n",
       "3  0.210953  0.24647 -0.212117 -0.134972  0.121756  0.118738 -0.107353   \n",
       "4  0.210953  0.24647 -0.212117 -0.134972  0.121756  0.118738 -0.107353   \n",
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       "6  0.210953  0.24647 -0.212117 -0.134972  0.121756  0.118738 -0.107353   \n",
       "7  0.210953  0.24647 -0.212117 -0.134972  0.121756  0.118738 -0.107353   \n",
       "8  0.210953  0.24647 -0.212117 -0.134972  0.121756  0.118738 -0.107353   \n",
       "9  0.210953  0.24647 -0.212117 -0.134972  0.121756  0.118738 -0.107353   \n",
       "\n",
       "      4805  4806  \n",
       "0 -0.06814     0  \n",
       "1 -0.06814     0  \n",
       "2 -0.06814     0  \n",
       "3 -0.06814     0  \n",
       "4 -0.06814     1  \n",
       "5 -0.06814     0  \n",
       "6 -0.06814     0  \n",
       "7 -0.06814     0  \n",
       "8 -0.06814     1  \n",
       "9 -0.06814     1  \n",
       "\n",
       "[10 rows x 4807 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_original = pd.read_csv('药物联合作用数据集.csv', header=None, skiprows=1)\n",
    "df_original = df_original.drop(0, axis=1)\n",
    "df_original.columns = range(0, 4807)\n",
    "df_original[4806] = df_original[4806].astype(int)\n",
    "df_original.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "3.划分特征"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "x = df_original.iloc[:, :-1]\n",
    "y = df_original.iloc[:, -1]\n",
    "\n",
    "finger_a = x.iloc[:, 0:881]\n",
    "finger_b = x.iloc[:, 1914:2795]\n",
    "phychem_a = x.iloc[:, 881:936]\n",
    "phychem_b = x.iloc[:, 2795:2850]\n",
    "express_a = x.iloc[:, 936:1914]\n",
    "express_b = x.iloc[:, 2850:3828]\n",
    "cell_line = x.iloc[:, 3828:4806]\n",
    "\n",
    "x_reverse = pd.concat([finger_a, finger_b, phychem_a, phychem_b, express_a, express_b, cell_line], axis=1)\n",
    "y_reverse = y\n",
    "\n",
    "df_reverse = pd.concat([x_reverse, y_reverse], axis=1)\n",
    "\n",
    "# stander = MinMaxScaler()\n",
    "# x = stander.fit_transform(x)\n",
    "\n",
    "# 训练集属性顺序：finger_a phychem_a express_a finger_b phychem_b express_b cell_line\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\n",
    "\n",
    "# clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)\n",
    "# clf.fit(x_train, y_train)\n",
    "\n",
    "x_train_reverse, x_test_reverse, y_train_reverse, y_test_reverse = train_test_split(x_reverse, y_reverse, test_size=0.3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 二、准备工作\n",
    "1.由于该数据集是三类划分，auc无法直接使用有些模型（如LinearSVC）中自带的函数进行计算。所以我们定义一个计算auc的函数（这个函数只对一个分类器有效果，若分类过程中涉及两个分类器如离群点的分类器和非离群点的分类器时，这个函数不适用）。由于auc不是我们重点需要关注的，所以这个函数返回的结果只作为参考。"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 定义计算auc的函数，返回三个类的auc，micro_auc，macro_auc，三个类auc的average\n",
    "# 可能会产生过拟合\n",
    "def calculate_auc(clf, x_test, y_test, paint=False):\n",
    "    y_label = []\n",
    "    for i in range(y_test.shape[0]):\n",
    "        if y_test.iloc[i] == 0:\n",
    "            y_label.append([1, 0, 0])\n",
    "        elif y_test.iloc[i] == 1:\n",
    "            y_label.append([0, 1, 0])\n",
    "        else:\n",
    "            y_label.append([0, 0, 1])\n",
    "\n",
    "    y_proba = np.array(clf.predict_proba(x_test))\n",
    "    y_label = np.array(y_label)\n",
    "    # for i in range(x_test.shape[0]):\n",
    "    #     print(y_label[i])\n",
    "        \n",
    "    # 计算每一类的ROC\n",
    "    fpr = dict()\n",
    "    tpr = dict()\n",
    "    roc_auc = dict()\n",
    "    roc_score = 0\n",
    "    for i in range(3):\n",
    "        fpr[i], tpr[i], _ = metrics.roc_curve(y_label[:, i], y_proba[:, i])\n",
    "        roc_auc[i] = metrics.auc(fpr[i], tpr[i])\n",
    "        roc_score += roc_auc[i]\n",
    "\n",
    "    # 取所有类ROC的平均值作为整个模型的ROC\n",
    "    roc_score /= 3\n",
    "\n",
    "    # micro（方法二）\n",
    "    fpr[\"micro\"], tpr[\"micro\"], _ = metrics.roc_curve(y_label.ravel(), y_proba.ravel())\n",
    "    roc_auc[\"micro\"] = metrics.auc(fpr[\"micro\"], tpr[\"micro\"])\n",
    "\n",
    "    # macro（方法一）\n",
    "    # First aggregate all false positive rates\n",
    "    all_fpr = np.unique(np.concatenate([fpr[i] for i in range(3)]))\n",
    "    # Then interpolate all ROC curves at this points\n",
    "    mean_tpr = np.zeros_like(all_fpr)\n",
    "    for i in range(3):\n",
    "        mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
    "    # Finally average it and compute AUC\n",
    "    mean_tpr /= 3\n",
    "    fpr[\"macro\"] = all_fpr\n",
    "    tpr[\"macro\"] = mean_tpr\n",
    "    roc_auc[\"macro\"] = metrics.auc(fpr[\"macro\"], tpr[\"macro\"])\n",
    "\n",
    "    # Plot all ROC curves\n",
    "    if paint:\n",
    "        lw=2\n",
    "        plt.figure()\n",
    "        plt.plot(fpr[\"micro\"], tpr[\"micro\"],\n",
    "                label='micro-average ROC curve (area = {0:0.2f})'\n",
    "                    ''.format(roc_auc[\"micro\"]),\n",
    "                color='deeppink', linestyle=':', linewidth=4)\n",
    "\n",
    "        plt.plot(fpr[\"macro\"], tpr[\"macro\"],\n",
    "                label='macro-average ROC curve (area = {0:0.2f})'\n",
    "                    ''.format(roc_auc[\"macro\"]),\n",
    "                color='navy', linestyle=':', linewidth=4)\n",
    "\n",
    "        colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])\n",
    "        for i, color in zip(range(3), colors):\n",
    "            plt.plot(fpr[i], tpr[i], color=color, lw=lw,\n",
    "                    label='ROC curve of class {0} (area = {1:0.2f})'\n",
    "                    ''.format(i, roc_auc[i]))\n",
    "\n",
    "        plt.plot([0, 1], [0, 1], 'k--', lw=lw)\n",
    "        plt.xlim([0.0, 1.0])\n",
    "        plt.ylim([0.0, 1.05])\n",
    "        plt.xlabel('False Positive Rate')\n",
    "        plt.ylabel('True Positive Rate')\n",
    "        plt.title('multi-calss ROC')\n",
    "        plt.legend(loc=\"lower right\")\n",
    "        plt.show()\n",
    "\n",
    "    return roc_auc[0], roc_auc[1], roc_auc[2], roc_auc[\"micro\"], roc_auc[\"macro\"], roc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "2.定义k折交叉验证函数，其中一个参数sampled表示是否对训练集进行重采样。这个函数在后文用于定义基线和数据重采样的工作"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# K折交叉验证函数定义，其中带一个参数sampled表示是否对训练集进行重采样\n",
    "def my_cross_validate_score(estimator, X, y, cv = 5, mean = False, sampled=False):\n",
    "    kf = model_selection.StratifiedKFold(n_splits=cv)\n",
    "    #存储k次训练中得到的模型与其对应的分数\n",
    "    res = dict()\n",
    "    accuracy = []\n",
    "    f1_score = []\n",
    "    auc = []\n",
    "    recall_0 = []\n",
    "    recall_1 = []\n",
    "    recall_2 = []\n",
    "    precision_0 = []\n",
    "    precision_1 = []\n",
    "    precision_2 = []\n",
    "\n",
    "    it = 1 \n",
    "    #进行k次训练\n",
    "    for train_index, test_index in kf.split(X, y):\n",
    "        # print('train_index', train_index, 'test_index', test_index)\n",
    "        train_X, train_y = X.iloc[train_index], y.iloc[train_index]\n",
    "        test_X, test_y = X.iloc[test_index], y.iloc[test_index]\n",
    "\n",
    "\n",
    "        if sampled is True:\n",
    "            df_train = pd.concat([train_X, train_y], axis=1)  # 9\n",
    "            df_train_len = len(df_train)\n",
    "            # print(df_train_len)  # 2872\n",
    "\n",
    "            df_train_class_0 = df_train[df_train[4806] == 0]\n",
    "            num_of_class_0 = len(df_train_class_0)\n",
    "            df_train_class_1 = df_train[df_train[4806] == 1]\n",
    "            num_of_class_1 = len(df_train_class_1)\n",
    "            df_train_class_2 = df_train[df_train[4806] == 2]\n",
    "            num_of_class_2 = len(df_train_class_2)\n",
    "\n",
    "            # print(num_of_class_0)  # 2599\n",
    "            # print(num_of_class_1)  # 180\n",
    "            # print(num_of_class_2)  # 93\n",
    "\n",
    "            # 重采样：使得原训练集数据量不变，将类0:类1:类2调整为1:1:1\n",
    "            # 利用DataFrame自带的sample函数进行采样\n",
    "            df_train_sampled = pd.concat([sample(df_train_class_0, df_train_len / (3 * num_of_class_0)), \n",
    "                sample(df_train_class_1, df_train_len / (3 * num_of_class_1)), \n",
    "                sample(df_train_class_2, df_train_len / (3 * num_of_class_2))])\n",
    "            \n",
    "            train_X = df_train_sampled.iloc[:, :-1]\n",
    "            train_y = df_train_sampled.iloc[:, -1]\n",
    "\n",
    "        estimator.fit(train_X, train_y)\n",
    "        \n",
    "        clf_predict = estimator.predict(test_X)\n",
    "        report = metrics.classification_report(test_y, clf_predict, output_dict=True)\n",
    "\n",
    "        # 计算评分\n",
    "        if isinstance(estimator, LinearSVC):\n",
    "            score = estimator.decision_function(test_X)\n",
    "            test_y_hot = label_binarize(test_y, classes=(0, 1, 2))\n",
    "            fpr, tpr, _ = metrics.roc_curve(test_y_hot.ravel(), score.ravel())\n",
    "            auc.append(metrics.auc(fpr, tpr))\n",
    "        # 单独计算auc\n",
    "        else:\n",
    "            _, _, _, _, _, auc_avg = calculate_auc(estimator, test_X, test_y)\n",
    "            auc.append(auc_avg)\n",
    "\n",
    "        # 打印结果报告\n",
    "        accuracy.append(report['accuracy'])\n",
    "        f1_score.append(report['macro avg']['f1-score'])\n",
    "        recall_0.append(report['0']['recall'])\n",
    "        recall_1.append(report['1']['recall'])\n",
    "        recall_2.append(report['2']['recall'])\n",
    "        precision_0.append(report['0']['precision'])\n",
    "        precision_1.append(report['1']['precision'])\n",
    "        precision_2.append(report['2']['precision'])\n",
    "\n",
    "        # print(\"iteration\", it, \".....\")\n",
    "        it += 1\n",
    "        res['accuracy'] = accuracy\n",
    "        res['f1_score'] = f1_score\n",
    "        res['auc'] = auc\n",
    "        res['recall_0'] = recall_0\n",
    "        res['recall_1'] = recall_1\n",
    "        res['recall_2'] = recall_2\n",
    "        res['precision_0'] = precision_0\n",
    "        res['precision_1'] = precision_1\n",
    "        res['precision_2'] = precision_2\n",
    "        \n",
    "    if mean:\n",
    "        for key in res.keys():\n",
    "            res[key] = np.mean(res[key])\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "3.原始数据运行函数，不经过交叉验证，其结果作为参考，并不重要"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# train:test = 7:3 原始数据运行\n",
    "def baseline(clf, x_train, y_train, x_test, y_test):\n",
    "    res = dict()\n",
    "\n",
    "    clf.fit(x_train, y_train)\n",
    "\n",
    "    clf_predict = clf.predict(x_test)\n",
    "    report = metrics.classification_report(y_test, clf_predict, output_dict=True)\n",
    "\n",
    "    # print(y_test)\n",
    "    # print(x_test)\n",
    "\n",
    "    _, _, _, _, _, roc_score = calculate_auc(clf, x_test, y_test, paint=False)\n",
    "\n",
    "    res['accuracy'] = report['accuracy']\n",
    "    res['f1_score'] = report['macro avg']['f1-score']\n",
    "    res['auc'] = roc_score\n",
    "    res['recall_0'] = report['0']['recall']\n",
    "    res['recall_1'] = report['1']['recall']\n",
    "    res['recall_2'] = report['2']['recall']\n",
    "    res['precision_0'] = report['0']['precision']\n",
    "    res['precision_1'] = report['1']['precision']\n",
    "    res['precision_2'] = report['2']['precision']\n",
    "\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "4.定义画图函数，在之后的离群点绘制过程中有用"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def plot_embedding_2d(x, y, label, title=None):\n",
    "\n",
    "    # Plot colors numbers\n",
    "    plt.figure(figsize=(10, 6), dpi=100)\n",
    "    plt.scatter(x, y, c=label, cmap=\"plasma\")\n",
    "    \n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "5.定义八种分类器"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "clf_decision_tree = DecisionTreeClassifier()\n",
    "clf_random_forest = RandomForestClassifier()\n",
    "clf_extra_trees = ExtraTreesClassifier()\n",
    "clf_GBDT = GradientBoostingClassifier()\n",
    "clf_XGB = XGBClassifier()\n",
    "clf_SVC = SVC(probability=True)\n",
    "clf_LinearSVC = LinearSVC()\n",
    "clf_KNN = KNeighborsClassifier()\n",
    "\n",
    "clfs = [clf_decision_tree, clf_random_forest, clf_extra_trees, clf_GBDT, clf_XGB, clf_SVC, clf_LinearSVC, clf_KNN]\n",
    "names = ['decision_tree', 'random_forest', 'extra_trees', 'GBDT', 'XGB', 'SVC', \"LinearSVC\", 'KNN']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 三、基线划定\n",
    "直接用上文提到的十折交叉函数跑数据，不做重采样处理，将其结果定义为基线"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decision_tree\n",
      "{'accuracy': 0.6292829153605016, 'f1_score': 0.3493465124129965, 'auc': 0.5687118740846395, 'recall_0': 0.6602172241445597, 'recall_1': 0.325, 'recall_2': 0.3563636363636364, 'precision_0': 0.9139698195988191, 'precision_1': 0.12326760345624706, 'precision_2': 0.08358115527053064}\n",
      "random_forest\n",
      "{'accuracy': 0.7483846003134796, 'f1_score': 0.4025667723054148, 'auc': 0.6067499411460819, 'recall_0': 0.7980187908496733, 'recall_1': 0.25999999999999995, 'recall_2': 0.30818181818181817, 'precision_0': 0.9160517181458229, 'precision_1': 0.19162689162689162, 'precision_2': 0.1734628379750331}\n",
      "extra_trees\n",
      "{'accuracy': 0.7511931818181818, 'f1_score': 0.42981069802749045, 'auc': 0.631507258460289, 'recall_0': 0.7948877835447904, 'recall_1': 0.35, 'recall_2': 0.30818181818181817, 'precision_0': 0.923489061954118, 'precision_1': 0.3490362557245176, 'precision_2': 0.1845147123407993}\n",
      "GBDT\n",
      "{'accuracy': 0.7621532131661442, 'f1_score': 0.4137902683934544, 'auc': 0.6134460355538228, 'recall_0': 0.813928537101115, 'recall_1': 0.25999999999999995, 'recall_2': 0.2881818181818182, 'precision_0': 0.917471029557524, 'precision_1': 0.2335346008995504, 'precision_2': 0.1458151127080864}\n",
      "[17:07:22] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[17:08:04] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[17:08:45] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[17:09:15] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[17:09:39] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[17:10:03] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[17:10:28] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[17:10:52] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[17:11:15] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[17:11:39] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "XGB\n",
      "{'accuracy': 0.8357983934169277, 'f1_score': 0.4421255047992728, 'auc': 0.6205146918640788, 'recall_0': 0.9026191849288736, 'recall_1': 0.145, 'recall_2': 0.3090909090909091, 'precision_0': 0.9151332999389907, 'precision_1': 0.19220238095238096, 'precision_2': 0.3176792213634319}\n",
      "SVC\n",
      "{'accuracy': 0.9100920846394984, 'f1_score': 0.365587630992146, 'auc': 0.6287516231764925, 'recall_0': 0.9986111111111111, 'recall_1': 0.095, 'recall_2': 0.02, 'precision_0': 0.9131822565933991, 'precision_1': 0.15, 'precision_2': 0.04}\n",
      "LinearSVC\n",
      "{'accuracy': 0.6856994514106584, 'f1_score': 0.3456896760827667, 'auc': 0.807290193371486, 'recall_0': 0.7338872068435218, 'recall_1': 0.23000000000000004, 'recall_2': 0.21818181818181817, 'precision_0': 0.9101584285738742, 'precision_1': 0.1267333521873844, 'precision_2': 0.06549955131683292}\n",
      "KNN\n",
      "{'accuracy': 0.9141643808777429, 'f1_score': 0.518511439392974, 'auc': 0.7613654776762366, 'recall_0': 0.9826725297962321, 'recall_1': 0.3350000000000001, 'recall_2': 0.12454545454545454, 'precision_0': 0.9304570795387385, 'precision_1': 0.7264285714285714, 'precision_2': 0.36}\n"
     ]
    }
   ],
   "source": [
    "# baseline\n",
    "for clf, name in zip(clfs, names):\n",
    "    # if name in ['random_forest', 'extra_trees', 'GBDT', 'XGB', 'SVC', 'LinearSVC', 'KNN']:\n",
    "    #     continue    \n",
    "    res = my_cross_validate_score(clf, x, y, mean=True, cv=10)\n",
    "    print(name)\n",
    "    print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 四、对数据集进行离群点分离处理\n",
    "1.对原数据集进行PCA降维处理，用前文提到的画点函数将其分布画出，查看原数据集三分类的分布"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 降维，准备提取outlier\n",
    "PCA_2d = PCA(n_components=2)\n",
    "x_PCA = PCA_2d.fit_transform(x)\n",
    "plot_embedding_2d(x_PCA[:, 0], x_PCA[:, 1], y, \"原数据集三分类的分布\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>特征</th>\n",
       "      <th>偏度</th>\n",
       "      <th>峰度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-4.609221</td>\n",
       "      <td>19.256986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-2.902408</td>\n",
       "      <td>6.428000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-0.740612</td>\n",
       "      <td>-1.452404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3.504563</td>\n",
       "      <td>10.288411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4802</th>\n",
       "      <td>4802</td>\n",
       "      <td>-0.311210</td>\n",
       "      <td>-1.103204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4803</th>\n",
       "      <td>4803</td>\n",
       "      <td>1.482587</td>\n",
       "      <td>8.288838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4804</th>\n",
       "      <td>4804</td>\n",
       "      <td>0.005615</td>\n",
       "      <td>-0.532641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4805</th>\n",
       "      <td>4805</td>\n",
       "      <td>0.028363</td>\n",
       "      <td>-0.024201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4806</th>\n",
       "      <td>4806</td>\n",
       "      <td>3.415893</td>\n",
       "      <td>11.016874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4807 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        特征        偏度         峰度\n",
       "0        0 -4.609221  19.256986\n",
       "1        1 -2.902408   6.428000\n",
       "2        2 -0.740612  -1.452404\n",
       "3        3  3.504563  10.288411\n",
       "4        4  0.000000   0.000000\n",
       "...    ...       ...        ...\n",
       "4802  4802 -0.311210  -1.103204\n",
       "4803  4803  1.482587   8.288838\n",
       "4804  4804  0.005615  -0.532641\n",
       "4805  4805  0.028363  -0.024201\n",
       "4806  4806  3.415893  11.016874\n",
       "\n",
       "[4807 rows x 3 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看偏度、峰度\n",
    "pd.DataFrame([i for i in zip(range(df_original.shape[1]), df_original.skew(), df_original.kurt())], columns=['特征', '偏度', '峰度'])"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "2.用pca降维之后，使用KMeans算法对降维后的数据进行聚类，使用silhouette_score方法对聚类进行评分，并作图以查看各类数据的分布情况"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n",
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "x_standard = pd.DataFrame(StandardScaler().fit_transform(x.values))\n",
    "x_PCA_standard = pd.DataFrame(PCA_2d.fit_transform(x_standard))\n",
    "\n",
    "kmeans = KMeans(n_clusters=3, random_state=0).fit(x_PCA_standard)\n",
    "\n",
    "plot_embedding_2d(x_PCA_standard.iloc[:, 0], x_PCA_standard.iloc[:, 1], kmeans.labels_, \"KMeans的三分类分布\")\n",
    "\n",
    "# 获取silhouette_score\n",
    "kmeans = KMeans(n_clusters=3, random_state=0)\t\n",
    "# 根据数据data进行聚类，结果存放于result_list中\n",
    "result_list = kmeans.fit_predict(x_PCA_standard)\n",
    "silhouette_score(x_PCA_standard, result_list)\n",
    "\n",
    "# 获取adjusted_rand_score\n",
    "# adjusted_rand_score(y, result_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "3.计算每一类簇的中心位置，并计算各类的点到其簇中心点的距离"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "x_PCA_standard['label'] = kmeans.labels_\n",
    "\n",
    "relative_distance = []\n",
    "for i in range(3):  # 逐一处理\n",
    "    distance = x_PCA_standard.query(\"label == @i\")[[0, 1]] - kmeans.cluster_centers_[i]  # 计算各点至簇中心点的距离\n",
    "    absolute_distance = distance.apply(np.linalg.norm, axis = 1)  # 求出绝对距离\n",
    "    relative_distance.append(absolute_distance / absolute_distance.median())  # 求相对距离并添加\n",
    "    \n",
    "x_PCA_standard['relative_distance'] = pd.concat(relative_distance)  # 合并\n"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "4.定义距离超过2的为离群点，将所有离群点找出"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>label</th>\n",
       "      <th>relative_distance</th>\n",
       "      <th>outlier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.204473</td>\n",
       "      <td>-5.957331</td>\n",
       "      <td>1</td>\n",
       "      <td>2.037219</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5.552363</td>\n",
       "      <td>-4.148582</td>\n",
       "      <td>1</td>\n",
       "      <td>2.349107</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>-2.894105</td>\n",
       "      <td>-22.192122</td>\n",
       "      <td>1</td>\n",
       "      <td>2.172468</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>-2.538032</td>\n",
       "      <td>-21.775383</td>\n",
       "      <td>1</td>\n",
       "      <td>2.050535</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>-1.009756</td>\n",
       "      <td>-24.592681</td>\n",
       "      <td>1</td>\n",
       "      <td>2.505352</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3175</th>\n",
       "      <td>-3.705688</td>\n",
       "      <td>46.629816</td>\n",
       "      <td>2</td>\n",
       "      <td>4.048799</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3176</th>\n",
       "      <td>-3.216037</td>\n",
       "      <td>47.368461</td>\n",
       "      <td>2</td>\n",
       "      <td>4.125106</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3177</th>\n",
       "      <td>-3.928084</td>\n",
       "      <td>46.832725</td>\n",
       "      <td>2</td>\n",
       "      <td>4.067540</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3178</th>\n",
       "      <td>-3.490572</td>\n",
       "      <td>48.603048</td>\n",
       "      <td>2</td>\n",
       "      <td>4.245224</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3179</th>\n",
       "      <td>-3.726909</td>\n",
       "      <td>47.641169</td>\n",
       "      <td>2</td>\n",
       "      <td>4.148658</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>346 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0          1  label  relative_distance  outlier\n",
       "4    -2.204473  -5.957331      1           2.037219        1\n",
       "12    5.552363  -4.148582      1           2.349107        1\n",
       "42   -2.894105 -22.192122      1           2.172468        1\n",
       "57   -2.538032 -21.775383      1           2.050535        1\n",
       "59   -1.009756 -24.592681      1           2.505352        1\n",
       "...        ...        ...    ...                ...      ...\n",
       "3175 -3.705688  46.629816      2           4.048799        1\n",
       "3176 -3.216037  47.368461      2           4.125106        1\n",
       "3177 -3.928084  46.832725      2           4.067540        1\n",
       "3178 -3.490572  48.603048      2           4.245224        1\n",
       "3179 -3.726909  47.641169      2           4.148658        1\n",
       "\n",
       "[346 rows x 5 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_scale = 2\n",
    "x_PCA_standard['outlier'] = x_PCA_standard.relative_distance.apply(lambda x: 1 if x > distance_scale else 0)\n",
    "x_PCA_standard[x_PCA_standard['outlier'] == 1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "作图显示离群点分布情况"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "colors = { 0: 'blue', 1: 'red' }\n",
    "ax.scatter(x_PCA_standard.index, x_PCA_standard.relative_distance, c=x_PCA_standard.outlier.apply(lambda x: colors[x]))\n",
    "ax.set_xlabel('index')\n",
    "ax.set_ylabel('相对距离')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "5.提取出离群点"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>4797</th>\n",
       "      <th>4798</th>\n",
       "      <th>4799</th>\n",
       "      <th>4800</th>\n",
       "      <th>4801</th>\n",
       "      <th>4802</th>\n",
       "      <th>4803</th>\n",
       "      <th>4804</th>\n",
       "      <th>4805</th>\n",
       "      <th>4806</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144204</td>\n",
       "      <td>0.210953</td>\n",
       "      <td>0.246470</td>\n",
       "      <td>-0.212117</td>\n",
       "      <td>-0.134972</td>\n",
       "      <td>0.121756</td>\n",
       "      <td>0.118738</td>\n",
       "      <td>-0.107353</td>\n",
       "      <td>-0.068140</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144204</td>\n",
       "      <td>0.210953</td>\n",
       "      <td>0.246470</td>\n",
       "      <td>-0.212117</td>\n",
       "      <td>-0.134972</td>\n",
       "      <td>0.121756</td>\n",
       "      <td>0.118738</td>\n",
       "      <td>-0.107353</td>\n",
       "      <td>-0.068140</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144204</td>\n",
       "      <td>0.210953</td>\n",
       "      <td>0.246470</td>\n",
       "      <td>-0.212117</td>\n",
       "      <td>-0.134972</td>\n",
       "      <td>0.121756</td>\n",
       "      <td>0.118738</td>\n",
       "      <td>-0.107353</td>\n",
       "      <td>-0.068140</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144204</td>\n",
       "      <td>0.210953</td>\n",
       "      <td>0.246470</td>\n",
       "      <td>-0.212117</td>\n",
       "      <td>-0.134972</td>\n",
       "      <td>0.121756</td>\n",
       "      <td>0.118738</td>\n",
       "      <td>-0.107353</td>\n",
       "      <td>-0.068140</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144204</td>\n",
       "      <td>0.210953</td>\n",
       "      <td>0.246470</td>\n",
       "      <td>-0.212117</td>\n",
       "      <td>-0.134972</td>\n",
       "      <td>0.121756</td>\n",
       "      <td>0.118738</td>\n",
       "      <td>-0.107353</td>\n",
       "      <td>-0.068140</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3175</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.206757</td>\n",
       "      <td>-0.141040</td>\n",
       "      <td>0.084604</td>\n",
       "      <td>-0.075464</td>\n",
       "      <td>-0.266440</td>\n",
       "      <td>0.310790</td>\n",
       "      <td>0.293963</td>\n",
       "      <td>-0.163698</td>\n",
       "      <td>0.009452</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3176</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.206757</td>\n",
       "      <td>-0.141040</td>\n",
       "      <td>0.084604</td>\n",
       "      <td>-0.075464</td>\n",
       "      <td>-0.266440</td>\n",
       "      <td>0.310790</td>\n",
       "      <td>0.293963</td>\n",
       "      <td>-0.163698</td>\n",
       "      <td>0.009452</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3177</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.206757</td>\n",
       "      <td>-0.141040</td>\n",
       "      <td>0.084604</td>\n",
       "      <td>-0.075464</td>\n",
       "      <td>-0.266440</td>\n",
       "      <td>0.310790</td>\n",
       "      <td>0.293963</td>\n",
       "      <td>-0.163698</td>\n",
       "      <td>0.009452</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3178</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.206757</td>\n",
       "      <td>-0.141040</td>\n",
       "      <td>0.084604</td>\n",
       "      <td>-0.075464</td>\n",
       "      <td>-0.266440</td>\n",
       "      <td>0.310790</td>\n",
       "      <td>0.293963</td>\n",
       "      <td>-0.163698</td>\n",
       "      <td>0.009452</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3179</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.206757</td>\n",
       "      <td>-0.141040</td>\n",
       "      <td>0.084604</td>\n",
       "      <td>-0.075464</td>\n",
       "      <td>-0.266440</td>\n",
       "      <td>0.310790</td>\n",
       "      <td>0.293963</td>\n",
       "      <td>-0.163698</td>\n",
       "      <td>0.009452</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>346 rows × 4807 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0     1     2     3     4     5     6     7     8     9     ...  \\\n",
       "4      1.0   1.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0  ...   \n",
       "12     1.0   1.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0  ...   \n",
       "42     1.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0  ...   \n",
       "57     0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0  ...   \n",
       "59     1.0   1.0   1.0   1.0   0.0   0.0   0.0   0.0   0.0   1.0  ...   \n",
       "...    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   \n",
       "3175   1.0   1.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0  ...   \n",
       "3176   1.0   1.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0  ...   \n",
       "3177   1.0   1.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0  ...   \n",
       "3178   1.0   1.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0  ...   \n",
       "3179   1.0   1.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0  ...   \n",
       "\n",
       "          4797      4798      4799      4800      4801      4802      4803  \\\n",
       "4     0.144204  0.210953  0.246470 -0.212117 -0.134972  0.121756  0.118738   \n",
       "12    0.144204  0.210953  0.246470 -0.212117 -0.134972  0.121756  0.118738   \n",
       "42    0.144204  0.210953  0.246470 -0.212117 -0.134972  0.121756  0.118738   \n",
       "57    0.144204  0.210953  0.246470 -0.212117 -0.134972  0.121756  0.118738   \n",
       "59    0.144204  0.210953  0.246470 -0.212117 -0.134972  0.121756  0.118738   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "3175  0.206757 -0.141040  0.084604 -0.075464 -0.266440  0.310790  0.293963   \n",
       "3176  0.206757 -0.141040  0.084604 -0.075464 -0.266440  0.310790  0.293963   \n",
       "3177  0.206757 -0.141040  0.084604 -0.075464 -0.266440  0.310790  0.293963   \n",
       "3178  0.206757 -0.141040  0.084604 -0.075464 -0.266440  0.310790  0.293963   \n",
       "3179  0.206757 -0.141040  0.084604 -0.075464 -0.266440  0.310790  0.293963   \n",
       "\n",
       "          4804      4805  4806  \n",
       "4    -0.107353 -0.068140     1  \n",
       "12   -0.107353 -0.068140     0  \n",
       "42   -0.107353 -0.068140     0  \n",
       "57   -0.107353 -0.068140     0  \n",
       "59   -0.107353 -0.068140     0  \n",
       "...        ...       ...   ...  \n",
       "3175 -0.163698  0.009452     1  \n",
       "3176 -0.163698  0.009452     1  \n",
       "3177 -0.163698  0.009452     1  \n",
       "3178 -0.163698  0.009452     1  \n",
       "3179 -0.163698  0.009452     1  \n",
       "\n",
       "[346 rows x 4807 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_index = x_PCA_standard[x_PCA_standard['outlier'] == 1].index\n",
    "df_outlier = df_original.iloc[outlier_index, :]\n",
    "df_outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284\n",
       "1     46\n",
       "2     16\n",
       "Name: 4806, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_outlier.iloc[:, -1].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "6.将上述过程封装成一个离群点提取函数，在十折交叉验证函数中准备对训练集和测试集进行离群点提取"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def outlier_extract(df, distance_scale=1.5):\n",
    "    x = df.iloc[:, :-1]\n",
    "    y = df.iloc[:, -1]\n",
    "\n",
    "    PCA_2d = PCA(n_components=2)\n",
    "    df_index = df.index\n",
    "\n",
    "    x_standard = pd.DataFrame(StandardScaler().fit_transform(x.values))\n",
    "    x_standard.index = df_index\n",
    "    # print(train_X_standard.index)\n",
    "    x_PCA_standard = pd.DataFrame(PCA_2d.fit_transform(x_standard))\n",
    "    x_PCA_standard.index = df_index\n",
    "\n",
    "    kmeans = KMeans(n_clusters=3, random_state=0).fit(x_PCA_standard)\n",
    "\n",
    "    x_PCA_standard['label'] = kmeans.labels_\n",
    "    relative_distance = []\n",
    "\n",
    "    for i in range(3):  # 逐一处理\n",
    "        distance = x_PCA_standard.query(\"label == @i\")[[0, 1]] - kmeans.cluster_centers_[i]  # 计算各点至簇中心点的距离\n",
    "        absolute_distance = distance.apply(np.linalg.norm, axis = 1)  # 求出绝对距离\n",
    "        relative_distance.append(absolute_distance / absolute_distance.median())  # 求相对距离并添加\n",
    "        \n",
    "    x_PCA_standard['relative_distance'] = pd.concat(relative_distance)  # 合并\n",
    "    # print(train_X_PCA_standard)\n",
    "    x_PCA_standard['outlier'] = x_PCA_standard.relative_distance.apply(lambda x: 1 if x > distance_scale else 0)\n",
    "\n",
    "    x_outlier_index = x_PCA_standard[x_PCA_standard['outlier'] == 1].index\n",
    "    df_without_outlier = df.loc[list(set(df.index) - set(x_outlier_index)), :]\n",
    "    df_outlier = df.loc[x_outlier_index, :]\n",
    "\n",
    "    return df_without_outlier, df_outlier"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "7.定义离群点分离十折交叉函数，其就是在原十折交叉验证函数的基础上对训练集和测试集进行离群点分离，用离群点训练一个模型，用非离群点训练另一个模型，把测试集中的离群点用前者进行预测，非离群点用后者进行预测，合并结果"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def outlier_cross_validate_score(estimator, X, y, cv = 5, mean = False):\n",
    "    kf = model_selection.StratifiedKFold(n_splits=cv)\n",
    "    #存储k次训练中得到的模型与其对应的分数\n",
    "    res = dict()\n",
    "    accuracy = []\n",
    "    f1_score = []\n",
    "    # auc = []\n",
    "    recall_0 = []\n",
    "    recall_1 = []\n",
    "    recall_2 = []\n",
    "    precision_0 = []\n",
    "    precision_1 = []\n",
    "    precision_2 = []\n",
    "\n",
    "    it = 1 \n",
    "    #进行k次训练\n",
    "    for train_index, test_index in kf.split(X, y):\n",
    "        # print('train_index', train_index, 'test_index', test_index)\n",
    "        train_X, train_y = X.iloc[train_index], y.iloc[train_index]\n",
    "        test_X, test_y = X.iloc[test_index], y.iloc[test_index]\n",
    "\n",
    "        df_train = pd.concat([train_X, train_y], axis=1)  # 9\n",
    "        df_train_without_outlier, df_train_outlier = outlier_extract(df_train)\n",
    "        train_X_without_outlier = df_train_without_outlier.iloc[:, :-1]\n",
    "        train_y_without_outlier = df_train_without_outlier.iloc[:, -1]\n",
    "        train_X_outlier = df_train_outlier.iloc[:, :-1]\n",
    "        train_y_outlier = df_train_outlier.iloc[:, -1]\n",
    "\n",
    "        df_test = pd.concat([test_X, test_y], axis=1)  # 9\n",
    "        df_test_without_outlier, df_test_outlier = outlier_extract(df_test)\n",
    "        test_X_without_outlier = df_test_without_outlier.iloc[:, :-1]\n",
    "        test_y_without_outlier = df_test_without_outlier.iloc[:, -1]\n",
    "        test_X_outlier = df_test_outlier.iloc[:, :-1]\n",
    "        test_y_outlier = df_test_outlier.iloc[:, -1]\n",
    "\n",
    "        test_without_outlier_index = df_test_without_outlier.index\n",
    "        test_outlier_index = df_test_outlier.index\n",
    "\n",
    "        clf_without_outlier = None\n",
    "        clf_outlier = None\n",
    "\n",
    "        if isinstance(estimator, DecisionTreeClassifier):\n",
    "            clf_without_outlier = DecisionTreeClassifier()\n",
    "            clf_outlier = DecisionTreeClassifier()\n",
    "        if isinstance(estimator, RandomForestClassifier):\n",
    "            clf_without_outlier = RandomForestClassifier()\n",
    "            clf_outlier = RandomForestClassifier()\n",
    "        if isinstance(estimator, ExtraTreesClassifier):\n",
    "            clf_without_outlier = ExtraTreesClassifier()\n",
    "            clf_outlier = ExtraTreesClassifier()\n",
    "        if isinstance(estimator, GradientBoostingClassifier):\n",
    "            clf_without_outlier = GradientBoostingClassifier()\n",
    "            clf_outlier = GradientBoostingClassifier()\n",
    "        if isinstance(estimator, XGBClassifier):\n",
    "            clf_without_outlier = XGBClassifier()\n",
    "            clf_outlier = XGBClassifier()\n",
    "        if isinstance(estimator, SVC):\n",
    "            clf_without_outlier = SVC(probability=True)\n",
    "            clf_outlier = SVC(probability=True)\n",
    "        if isinstance(estimator, LinearSVC):\n",
    "            clf_without_outlier = LinearSVC()\n",
    "            clf_outlier = LinearSVC()\n",
    "        if isinstance(estimator, KNeighborsClassifier):\n",
    "            clf_without_outlier = KNeighborsClassifier()\n",
    "            clf_outlier = KNeighborsClassifier()\n",
    "        \n",
    "        clf_without_outlier.fit(train_X_without_outlier, train_y_without_outlier)\n",
    "        clf_outlier.fit(train_X_outlier, train_y_outlier)\n",
    "\n",
    "        clf_predict_without_outlier = pd.DataFrame(clf_without_outlier.predict(test_X_without_outlier))\n",
    "        clf_predict_outlier = pd.DataFrame(clf_outlier.predict(test_X_outlier))\n",
    "\n",
    "        clf_predict_without_outlier.index = test_without_outlier_index\n",
    "        clf_predict_outlier.index = test_outlier_index\n",
    "\n",
    "        clf_predict = pd.concat([clf_predict_without_outlier, clf_predict_outlier])\n",
    "        test_y = pd.concat([test_y_without_outlier, test_y_outlier])\n",
    "\n",
    "        report = metrics.classification_report(test_y, clf_predict, output_dict=True)\n",
    "\n",
    "        accuracy.append(report['accuracy'])\n",
    "        f1_score.append(report['macro avg']['f1-score'])\n",
    "        recall_0.append(report['0']['recall'])\n",
    "        recall_1.append(report['1']['recall'])\n",
    "        recall_2.append(report['2']['recall'])\n",
    "        precision_0.append(report['0']['precision'])\n",
    "        precision_1.append(report['1']['precision'])\n",
    "        precision_2.append(report['2']['precision'])\n",
    "\n",
    "        print(\"iteration\", it, \".....\")\n",
    "        it += 1\n",
    "        res['accuracy'] = accuracy\n",
    "        res['f1_score'] = f1_score\n",
    "        # res['auc'] = 0\n",
    "        res['recall_0'] = recall_0\n",
    "        res['recall_1'] = recall_1\n",
    "        res['recall_2'] = recall_2\n",
    "        res['precision_0'] = precision_0\n",
    "        res['precision_1'] = precision_1\n",
    "        res['precision_2'] = precision_2\n",
    "        \n",
    "    if mean:\n",
    "        for key in res.keys():\n",
    "            res[key] = np.mean(res[key])\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "8.训练模型并查看结果"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 1 .....\n",
      "iteration 2 .....\n",
      "iteration 3 .....\n",
      "iteration 4 .....\n",
      "iteration 5 .....\n",
      "iteration 6 .....\n",
      "iteration 7 .....\n",
      "iteration 8 .....\n",
      "iteration 9 .....\n",
      "iteration 10 .....\n",
      "decision_tree\n",
      "{'accuracy': 0.6650558385579938, 'f1_score': 0.3408360869341297, 'recall_0': 0.7104743367935409, 'recall_1': 0.275, 'recall_2': 0.1572727272727273, 'precision_0': 0.9063468679685739, 'precision_1': 0.12234934921756227, 'precision_2': 0.0537468671679198}\n",
      "iteration 1 .....\n",
      "iteration 2 .....\n",
      "iteration 3 .....\n",
      "iteration 4 .....\n",
      "iteration 5 .....\n",
      "iteration 6 .....\n",
      "iteration 7 .....\n",
      "iteration 8 .....\n",
      "iteration 9 .....\n",
      "iteration 10 .....\n",
      "random_forest\n",
      "{'accuracy': 0.7872560736677117, 'f1_score': 0.3390823108136297, 'recall_0': 0.8607086216839678, 'recall_1': 0.09, 'recall_2': 0.08818181818181818, 'precision_0': 0.9031694465202756, 'precision_1': 0.05505211920833908, 'precision_2': 0.08944444444444444}\n",
      "iteration 1 .....\n",
      "iteration 2 .....\n",
      "iteration 3 .....\n",
      "iteration 4 .....\n",
      "iteration 5 .....\n",
      "iteration 6 .....\n",
      "iteration 7 .....\n",
      "iteration 8 .....\n",
      "iteration 9 .....\n",
      "iteration 10 .....\n",
      "extra_trees\n",
      "{'accuracy': 0.7643681426332288, 'f1_score': 0.35974733207171694, 'recall_0': 0.8295391195693963, 'recall_1': 0.16999999999999998, 'recall_2': 0.09727272727272726, 'precision_0': 0.9072797700090647, 'precision_1': 0.2635208257784302, 'precision_2': 0.0842156862745098}\n",
      "iteration 1 .....\n",
      "iteration 2 .....\n",
      "iteration 3 .....\n",
      "iteration 4 .....\n",
      "iteration 5 .....\n",
      "iteration 6 .....\n",
      "iteration 7 .....\n",
      "iteration 8 .....\n",
      "iteration 9 .....\n",
      "iteration 10 .....\n",
      "GBDT\n",
      "{'accuracy': 0.7772276645768026, 'f1_score': 0.3714270435598811, 'recall_0': 0.8430879950019223, 'recall_1': 0.16999999999999998, 'recall_2': 0.11727272727272726, 'precision_0': 0.907333711096945, 'precision_1': 0.21782243487506645, 'precision_2': 0.08956349206349205}\n",
      "[18:00:51] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:01:07] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 1 .....\n",
      "[18:01:13] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:01:34] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 2 .....\n",
      "[18:01:40] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:02:00] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 3 .....\n",
      "[18:02:04] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:02:21] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 4 .....\n",
      "[18:02:28] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:02:49] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 5 .....\n",
      "[18:02:55] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:03:17] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 6 .....\n",
      "[18:03:25] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:03:47] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 7 .....\n",
      "[18:03:53] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:04:11] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 8 .....\n",
      "[18:04:18] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:04:35] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 9 .....\n",
      "[18:04:45] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:04:59] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 10 .....\n",
      "XGB\n",
      "{'accuracy': 0.8455260579937305, 'f1_score': 0.3676079641860563, 'recall_0': 0.9268898981161092, 'recall_1': 0.05500000000000001, 'recall_2': 0.10727272727272727, 'precision_0': 0.9050138382583798, 'precision_1': 0.12941964285714286, 'precision_2': 0.14777777777777779}\n",
      "iteration 1 .....\n",
      "iteration 2 .....\n",
      "iteration 3 .....\n",
      "iteration 4 .....\n",
      "iteration 5 .....\n",
      "iteration 6 .....\n",
      "iteration 7 .....\n",
      "iteration 8 .....\n",
      "iteration 9 .....\n",
      "iteration 10 .....\n",
      "SVC\n",
      "{'accuracy': 0.9088381661442007, 'f1_score': 0.35685248176370543, 'recall_0': 0.9989583333333334, 'recall_1': 0.075, 'recall_2': 0.01, 'precision_0': 0.9108107295637667, 'precision_1': 0.15384615384615383, 'precision_2': 0.1}\n",
      "iteration 1 .....\n",
      "iteration 2 .....\n",
      "iteration 3 .....\n",
      "iteration 4 .....\n",
      "iteration 5 .....\n",
      "iteration 6 .....\n",
      "iteration 7 .....\n",
      "iteration 8 .....\n",
      "iteration 9 .....\n",
      "iteration 10 .....\n",
      "LinearSVC\n",
      "{'accuracy': 0.7787793887147336, 'f1_score': 0.3354514995514564, 'recall_0': 0.8455293637062669, 'recall_1': 0.2, 'recall_2': 0.038181818181818185, 'precision_0': 0.9056686027665733, 'precision_1': 0.07498179271708683, 'precision_2': 0.041233766233766234}\n",
      "iteration 1 .....\n",
      "iteration 2 .....\n",
      "iteration 3 .....\n",
      "iteration 4 .....\n",
      "iteration 5 .....\n",
      "iteration 6 .....\n",
      "iteration 7 .....\n",
      "iteration 8 .....\n",
      "iteration 9 .....\n",
      "iteration 10 .....\n",
      "KNN\n",
      "{'accuracy': 0.8966193181818181, 'f1_score': 0.4367061700667293, 'recall_0': 0.9722858996539794, 'recall_1': 0.24500000000000002, 'recall_2': 0.04818181818181818, 'precision_0': 0.9218256107045535, 'precision_1': 0.4166514625725152, 'precision_2': 0.18666666666666665}\n"
     ]
    }
   ],
   "source": [
    "# outlier\n",
    "for clf, name in zip(clfs, names):\n",
    "    # if name in ['random_forest', 'extra_trees', 'GBDT', 'XGB', 'SVC', 'LinearSVC', 'KNN']:\n",
    "    #     continue    \n",
    "    res = outlier_cross_validate_score(clf, x, y, mean=True, cv=10)\n",
    "    print(name)\n",
    "    print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 五、对数据集进行重采样方法处理\n",
    "1.封装DataFrame自带的sample函数"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 定义采样函数\n",
    "def sample(df, multiple=0.5):\n",
    "    # 创建一个数据结构和之前一致，但空的dataframe\n",
    "    sample_data = df.copy(deep=True)\n",
    "\n",
    "    return sample_data.sample(frac=multiple, replace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "2.使用上文提到的定义基线和重采样的函数，将sampled设置为true，即可进行重采样操作后训练模型并预测"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decision_tree\n",
      "{'accuracy': 0.6857533307210031, 'f1_score': 0.3943061796725999, 'auc': 0.6100133251893597, 'recall_0': 0.7191765186466743, 'recall_1': 0.3350000000000001, 'recall_2': 0.43090909090909096, 'precision_0': 0.9252241006085832, 'precision_1': 0.12215177975876937, 'precision_2': 0.13645009146594284}\n",
      "random_forest\n",
      "{'accuracy': 0.8169954937304075, 'f1_score': 0.4444576860117027, 'auc': 0.6530695352272671, 'recall_0': 0.8742454825067281, 'recall_1': 0.255, 'recall_2': 0.3081818181818182, 'precision_0': 0.9215472274418228, 'precision_1': 0.21946433674684443, 'precision_2': 0.2240668945372053}\n",
      "extra_trees\n",
      "{'accuracy': 0.8408140673981193, 'f1_score': 0.47811328779102935, 'auc': 0.6671279662946232, 'recall_0': 0.8967680699730873, 'recall_1': 0.29500000000000004, 'recall_2': 0.3372727272727273, 'precision_0': 0.9276309378935178, 'precision_1': 0.24379480643661786, 'precision_2': 0.29079299110948725}\n",
      "GBDT\n",
      "{'accuracy': 0.7129516065830721, 'f1_score': 0.4013230593799655, 'auc': 0.6379310639701894, 'recall_0': 0.7489198865820839, 'recall_1': 0.395, 'recall_2': 0.32818181818181824, 'precision_0': 0.9251424174646552, 'precision_1': 0.1686177128883563, 'precision_2': 0.11176929759728219}\n",
      "[18:49:30] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:49:52] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:50:13] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:50:35] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:50:58] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:51:21] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:51:43] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:52:06] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:52:29] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:52:53] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "XGB\n",
      "{'accuracy': 0.743346394984326, 'f1_score': 0.41205963740076956, 'auc': 0.628431451197831, 'recall_0': 0.7855800653594771, 'recall_1': 0.34500000000000003, 'recall_2': 0.3381818181818182, 'precision_0': 0.9229115041665056, 'precision_1': 0.16565931838804948, 'precision_2': 0.1369688644688645}\n",
      "SVC\n",
      "{'accuracy': 0.6556867163009403, 'f1_score': 0.40297106734436905, 'auc': 0.6862710397333466, 'recall_0': 0.6674908688965783, 'recall_1': 0.71, 'recall_2': 0.2190909090909091, 'precision_0': 0.9467592367318302, 'precision_1': 0.2328085599398181, 'precision_2': 0.07227247237055157}\n",
      "LinearSVC\n",
      "{'accuracy': 0.575028409090909, 'f1_score': 0.32908298354915144, 'auc': 0.7230036595878608, 'recall_0': 0.5923875432525951, 'recall_1': 0.43, 'recall_2': 0.3763636363636364, 'precision_0': 0.9166437030289961, 'precision_1': 0.12324128244652792, 'precision_2': 0.06985991261202712}\n",
      "KNN\n",
      "{'accuracy': 0.6531867163009404, 'f1_score': 0.42389090590587497, 'auc': 0.7386149981510878, 'recall_0': 0.6571534986543637, 'recall_1': 0.665, 'recall_2': 0.5209090909090909, 'precision_0': 0.9606882211398038, 'precision_1': 0.20881540752542885, 'precision_2': 0.11167124666290051}\n"
     ]
    }
   ],
   "source": [
    "# sampled\n",
    "for clf, name in zip(clfs, names):\n",
    "    # if name in ['random_forest', 'extra_trees', 'GBDT', 'XGB', 'SVC', 'LinearSVC', 'KNN']:\n",
    "    #     continue    \n",
    "    res = my_cross_validate_score(clf, x, y, mean=True, cv=10, sampled=True)\n",
    "    print(name)\n",
    "    print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 六、对数据集进行特征抽取处理\n",
    "1.查看SelectFromModel默认给出的threshold参数值"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00020807324178110696"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看默认threshold，是所有特征重要性的平均值\n",
    "selector = SelectFromModel(estimator=DecisionTreeClassifier()).fit(x, y)\n",
    "selector.threshold_"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "2.根据其默认的threshold参数值，选取合适的参数查找范围和查找步长。设定参数范围为0-0.08，步长为0.0001，针对**决策树**模型进行相应参数下的特征抽取，并训练评分"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n    使用SelectFromModel进行特征选择\\n    寻找最佳参数threshold\\n'"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "    使用SelectFromModel进行特征选择\n",
    "    寻找最佳参数threshold\n",
    "'''\n",
    "# select\n",
    "# for i in np.arange(0, 0.08, 0.0001):\n",
    "#     selector = SelectFromModel(estimator=DecisionTreeClassifier(), threshold=i).fit(x, y)\n",
    "\n",
    "#     x_selected = pd.DataFrame(selector.transform(x))\n",
    "#     for clf, name in zip(clfs, names):\n",
    "#         if name in ['random_forest', 'extra_trees', 'GBDT', 'XGB', 'SVC', 'LinearSVC', 'KNN']:\n",
    "#             continue    \n",
    "#         if x_selected.empty:\n",
    "#             continue\n",
    "#         res = my_cross_validate_score(clf, x_selected, y, mean=True, cv=10, sampled=False)\n",
    "#         print(i)\n",
    "#         print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "3.绘制出threshold-scores折线图，重点关注recall1和recall2的值"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold_list = []\n",
    "res_pd = pd.DataFrame(columns=['accuracy', 'f1_score', 'auc', 'recall_0', 'recall_1', 'recall_2',\n",
    "       'precision_0', 'precision_1', 'precision_2'])\n",
    "\n",
    "with open('score.txt', 'r') as f:\n",
    "    for i in f.readlines():\n",
    "        if (i.startswith('{') == False):\n",
    "            threshold_list.append(float(i))\n",
    "            continue\n",
    "        else:\n",
    "            tmp = eval(i)\n",
    "            res_pd = pd.concat([res_pd, pd.DataFrame(tmp, index=[0])], axis=0)\n",
    "res_pd.reset_index(inplace=True)\n",
    "res_pd['index'] = threshold_list\n",
    "res_pd.rename(columns={'index' : 'threshold'}, inplace=True)\n",
    "# res_pd\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.plot(threshold_list, res_pd['accuracy'], label='accuracy')\n",
    "plt.plot(threshold_list, res_pd['f1_score'], label='f1')\n",
    "plt.plot(threshold_list, res_pd['auc'], label='auc')\n",
    "plt.plot(threshold_list, res_pd['recall_0'], label='recall_0')\n",
    "plt.plot(threshold_list, res_pd['recall_1'], label='recall_1')\n",
    "plt.plot(threshold_list, res_pd['recall_2'], label='recall_2')\n",
    "plt.plot(threshold_list, res_pd['precision_0'], label='precision_0')\n",
    "plt.plot(threshold_list, res_pd['precision_1'], label='precision_1')\n",
    "plt.plot(threshold_list, res_pd['precision_2'], label='precision_2')\n",
    "\n",
    "plt.xlabel(\"threshold\")\n",
    "plt.ylabel('score')\n",
    "plt.yticks(np.arange(0.0, 1, 0.05))\n",
    "plt.legend(loc=2,bbox_to_anchor=(1.05,1.0),borderaxespad = 0.)\n",
    "plt.title('best threshold for SelectFromModel on DecisionTree')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "4.选择最优的threshold=0.004（recall1值最高）进行特征抽取，将抽取后的数据用八个分类器分别进行训练预测。"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decision_tree\n",
      "{'accuracy': 0.7245748432601881, 'f1_score': 0.43889393426695616, 'auc': 0.6457719867669993, 'recall_0': 0.7558258842752787, 'recall_1': 0.425, 'recall_2': 0.4281818181818181, 'precision_0': 0.9344965276710736, 'precision_1': 0.2411648717383333, 'precision_2': 0.1455717107573244}\n",
      "random_forest\n",
      "{'accuracy': 0.8261206896551723, 'f1_score': 0.4619175128190047, 'auc': 0.6686744416266805, 'recall_0': 0.880102604767397, 'recall_1': 0.31499999999999995, 'recall_2': 0.30818181818181817, 'precision_0': 0.931041419049626, 'precision_1': 0.34831767932947605, 'precision_2': 0.22922254524402827}\n",
      "extra_trees\n",
      "{'accuracy': 0.8480515282131661, 'f1_score': 0.4870689382033052, 'auc': 0.6544596477984582, 'recall_0': 0.9029772202998847, 'recall_1': 0.335, 'recall_2': 0.30818181818181817, 'precision_0': 0.9337848677804313, 'precision_1': 0.4747785547785549, 'precision_2': 0.20529430247172184}\n",
      "GBDT\n",
      "{'accuracy': 0.8132631269592476, 'f1_score': 0.44901606409697925, 'auc': 0.6693631946530509, 'recall_0': 0.8676530661284122, 'recall_1': 0.305, 'recall_2': 0.2772727272727273, 'precision_0': 0.9258206974384284, 'precision_1': 0.2980106839263106, 'precision_2': 0.1827777777777778}\n",
      "[19:31:23] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:31:24] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:31:25] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:31:25] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:31:26] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:31:27] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:31:27] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:31:28] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:31:29] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[19:31:29] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "XGB\n",
      "{'accuracy': 0.8495914968652037, 'f1_score': 0.46177685371118377, 'auc': 0.6765053211525511, 'recall_0': 0.9126934352172242, 'recall_1': 0.21999999999999997, 'recall_2': 0.3090909090909091, 'precision_0': 0.923065752262086, 'precision_1': 0.3033141068634026, 'precision_2': 0.2947572154093893}\n",
      "SVC\n",
      "{'accuracy': 0.9110325235109717, 'f1_score': 0.36709076304834054, 'auc': 0.6218132481261703, 'recall_0': 0.9996527777777778, 'recall_1': 0.095, 'recall_2': 0.02, 'precision_0': 0.9132493206768567, 'precision_1': 0.16363636363636364, 'precision_2': 0.04}\n",
      "LinearSVC\n",
      "{'accuracy': 0.8981798589341692, 'f1_score': 0.32414477912472517, 'auc': 0.9381997522457646, 'recall_0': 0.9916918973471741, 'recall_1': 0.015000000000000003, 'recall_2': 0.0, 'precision_0': 0.9048856300493769, 'precision_1': 0.12, 'precision_2': 0.0}\n",
      "KNN\n",
      "{'accuracy': 0.9047707680250783, 'f1_score': 0.4687736660958593, 'auc': 0.7511477772947599, 'recall_0': 0.9771422049211841, 'recall_1': 0.29500000000000004, 'recall_2': 0.06727272727272729, 'precision_0': 0.9261814072677881, 'precision_1': 0.6291666666666667, 'precision_2': 0.2733333333333333}\n"
     ]
    }
   ],
   "source": [
    "# 选择最优的threshold=0.004\n",
    "# selected\n",
    "selector = SelectFromModel(estimator=DecisionTreeClassifier(), threshold=0.004).fit(x, y)\n",
    "\n",
    "x_selected = pd.DataFrame(selector.transform(x))\n",
    "for clf, name in zip(clfs, names):\n",
    "    # if name in ['random_forest', 'extra_trees', 'GBDT', 'XGB', 'SVC', 'LinearSVC', 'KNN']:\n",
    "    #     continue    \n",
    "    if x_selected.empty:\n",
    "        continue\n",
    "    res = my_cross_validate_score(clf, x_selected, y, mean=True, cv=10, sampled=False)\n",
    "    print(name)\n",
    "    print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 七、对数据进行标签二值化处理\n",
    "1.定义二值化函数，将1、2类统一处理为3类，与0类合并，成为一个二分类数据"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def binary_label(df):\n",
    "    df_label_0 = df[df[4806] == 0]\n",
    "    df_label_1 = df[df[4806] == 1]\n",
    "    df_label_2 = df[df[4806] == 2]\n",
    "    df_label_1_and_2 = pd.concat([df_label_1, df_label_2])\n",
    "\n",
    "    # 1类和2类统一标位3类，与0类组合为二分类，进行识别\n",
    "    df_label_0_and_3 = df.copy(deep=True)\n",
    "    index_label_1_and_2 = df_label_0_and_3[df_label_0_and_3[4806] != 0].index\n",
    "    df_label_0_and_3.loc[index_label_1_and_2, 4806] = 3\n",
    "\n",
    "    return df_label_1_and_2, df_label_0_and_3"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "2.定义交叉函数验证，其难点在于对于3类数据要训练并预测两次（0/3分类一次，1/2分类一次），最后将预测后的结果做整理，然后带入到classification_report函数生成报告"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# K折交叉验证函数（binary）定义\n",
    "def binary_cross_validate_score(estimator, X, y, cv = 5, mean = False):\n",
    "    kf = model_selection.StratifiedKFold(n_splits=cv)\n",
    "    #存储k次训练中得到的模型与其对应的分数\n",
    "    res = dict()\n",
    "    accuracy = []\n",
    "    f1_score = []\n",
    "    auc = []\n",
    "    recall_0 = []\n",
    "    recall_1 = []\n",
    "    recall_2 = []\n",
    "    precision_0 = []\n",
    "    precision_1 = []\n",
    "    precision_2 = []\n",
    "\n",
    "    it = 1 \n",
    "    #进行k次训练\n",
    "    for train_index, test_index in kf.split(X, y):\n",
    "        # print('train_index', train_index, 'test_index', test_index)\n",
    "        train_X, train_y = X.iloc[train_index], y.iloc[train_index]\n",
    "        test_X, test_y = X.iloc[test_index], y.iloc[test_index]\n",
    "\n",
    "        df_train = pd.concat([train_X, train_y], axis=1)\n",
    "        df_train_1_and_2, df_train_0_and_3 = binary_label(df_train)\n",
    "        train_X_1_and_2 = df_train_1_and_2.iloc[:, :-1]\n",
    "        train_y_1_and_2 = df_train_1_and_2.iloc[:, -1]\n",
    "        train_X_0_and_3 = df_train_0_and_3.iloc[:, :-1]\n",
    "        train_y_0_and_3 = df_train_0_and_3.iloc[:, -1]\n",
    "\n",
    "        clf_1_2 = None\n",
    "        clf_0_3 = None\n",
    "\n",
    "        if isinstance(estimator, DecisionTreeClassifier):\n",
    "            clf_1_2 = DecisionTreeClassifier()\n",
    "            clf_0_3 = DecisionTreeClassifier()\n",
    "        if isinstance(estimator, RandomForestClassifier):\n",
    "            clf_1_2 = RandomForestClassifier()\n",
    "            clf_0_3 = RandomForestClassifier()\n",
    "        if isinstance(estimator, ExtraTreesClassifier):\n",
    "            clf_1_2 = ExtraTreesClassifier()\n",
    "            clf_0_3 = ExtraTreesClassifier()\n",
    "        if isinstance(estimator, GradientBoostingClassifier):\n",
    "            clf_1_2 = GradientBoostingClassifier()\n",
    "            clf_0_3 = GradientBoostingClassifier()\n",
    "        if isinstance(estimator, XGBClassifier):\n",
    "            clf_1_2 = XGBClassifier()\n",
    "            clf_0_3 = XGBClassifier()\n",
    "        if isinstance(estimator, SVC):\n",
    "            clf_1_2 = SVC(probability=True)\n",
    "            clf_0_3 = SVC(probability=True)\n",
    "        if isinstance(estimator, LinearSVC):\n",
    "            clf_1_2 = LinearSVC()\n",
    "            clf_0_3 = LinearSVC()\n",
    "        if isinstance(estimator, KNeighborsClassifier):\n",
    "            clf_1_2 = KNeighborsClassifier()\n",
    "            clf_0_3 = KNeighborsClassifier()\n",
    "\n",
    "        clf_0_3.fit(train_X_0_and_3, train_y_0_and_3)\n",
    "        clf_1_2.fit(train_X_1_and_2, train_y_1_and_2)\n",
    "        \n",
    "        df_test = pd.concat([test_X, test_y], axis=1)\n",
    "        test_index = df_test.index\n",
    "        clf_predict_0_3 = pd.DataFrame(clf_0_3.predict(test_X))\n",
    "        clf_predict_0_3.index = test_index\n",
    "\n",
    "        clf_predict_0 = clf_predict_0_3[clf_predict_0_3[0] == 0]\n",
    "        predict_0_index = clf_predict_0.index  # 预测为0的索引\n",
    "\n",
    "        # print(predict_0_index)\n",
    "        # print(test_y.index)\n",
    "        \n",
    "        test_y_0 = test_y.loc[predict_0_index]  # 在真实值中找到预测为0的索引的位置\n",
    "        clf_predict_3 = clf_predict_0_3[clf_predict_0_3[0] == 3]\n",
    "        predict_3_index = clf_predict_3.index  # 预测为3的索引\n",
    "\n",
    "        if not predict_3_index.empty:\n",
    "            test_X_1_and_2 = test_X.loc[predict_3_index, :]\n",
    "            test_y_1_and_2 = test_y.loc[predict_3_index]  # 在真实值中找到预测为3的索引的位置\n",
    "\n",
    "            clf_predict_1_2 = pd.DataFrame(clf_1_2.predict(test_X_1_and_2))\n",
    "            clf_predict_1_2.index = predict_3_index\n",
    "\n",
    "            clf_predict = pd.concat([clf_predict_0, clf_predict_1_2])\n",
    "            test_y = pd.concat([test_y_0, test_y_1_and_2])\n",
    "\n",
    "        else:\n",
    "            clf_predict = clf_predict_0\n",
    "            test_y = test_y_0\n",
    "\n",
    "        report = metrics.classification_report(test_y, clf_predict, output_dict=True)\n",
    "\n",
    "        # if isinstance(estimator, LinearSVC):\n",
    "        #     score = estimator.decision_function(test_X)\n",
    "        #     test_y_hot = label_binarize(test_y, classes=(0, 1, 2))\n",
    "        #     fpr, tpr, _ = metrics.roc_curve(test_y_hot.ravel(), score.ravel())\n",
    "        #     auc.append(metrics.auc(fpr, tpr))\n",
    "            \n",
    "        # else:\n",
    "        #     _, _, _, _, _, auc_avg = calculate_auc(estimator, test_X, test_y)\n",
    "        #     auc.append(auc_avg)\n",
    "\n",
    "        \n",
    "        accuracy.append(report['accuracy'])\n",
    "        f1_score.append(report['macro avg']['f1-score'])\n",
    "        recall_0.append(report['0']['recall'])\n",
    "        recall_1.append(report['1']['recall'])\n",
    "        recall_2.append(report['2']['recall'])\n",
    "        precision_0.append(report['0']['precision'])\n",
    "        precision_1.append(report['1']['precision'])\n",
    "        precision_2.append(report['2']['precision'])\n",
    "\n",
    "        print(\"iteration\", it, \".....\")\n",
    "        it += 1\n",
    "        res['accuracy'] = accuracy\n",
    "        res['f1_score'] = f1_score\n",
    "        res['auc'] = 0\n",
    "        res['recall_0'] = recall_0\n",
    "        res['recall_1'] = recall_1\n",
    "        res['recall_2'] = recall_2\n",
    "        res['precision_0'] = precision_0\n",
    "        res['precision_1'] = precision_1\n",
    "        res['precision_2'] = precision_2\n",
    "        \n",
    "    if mean:\n",
    "        for key in res.keys():\n",
    "            res[key] = np.mean(res[key])\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "3.开始训练、预测"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:37:33] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:37:42] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 1 .....\n",
      "[20:37:44] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:37:53] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 2 .....\n",
      "[20:37:55] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:05] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 3 .....\n",
      "[20:38:08] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:18] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 4 .....\n",
      "[20:38:20] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:30] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 5 .....\n",
      "[20:38:33] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:43] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 6 .....\n",
      "[20:38:46] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:56] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 7 .....\n",
      "[20:38:58] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:39:08] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 8 .....\n",
      "[20:39:11] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:39:21] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 9 .....\n",
      "[20:39:23] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:39:33] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "iteration 10 .....\n",
      "XGB\n",
      "{'accuracy': 0.7931720219435737, 'f1_score': 0.4043776017618411, 'auc': 0.0, 'recall_0': 0.8537461553248751, 'recall_1': 0.16, 'recall_2': 0.32727272727272727, 'precision_0': 0.9128923067208081, 'precision_1': 0.1510277529403673, 'precision_2': 0.1800714056114249}\n",
      "iteration 1 .....\n",
      "iteration 2 .....\n",
      "iteration 3 .....\n",
      "iteration 4 .....\n",
      "iteration 5 .....\n",
      "iteration 6 .....\n",
      "iteration 7 .....\n",
      "iteration 8 .....\n",
      "iteration 9 .....\n",
      "iteration 10 .....\n",
      "SVC\n",
      "{'accuracy': 0.9100920846394984, 'f1_score': 0.365587630992146, 'auc': 0.0, 'recall_0': 0.9986111111111111, 'recall_1': 0.095, 'recall_2': 0.02, 'precision_0': 0.9131822565933991, 'precision_1': 0.15, 'precision_2': 0.04}\n",
      "iteration 1 .....\n",
      "iteration 2 .....\n",
      "iteration 3 .....\n",
      "iteration 4 .....\n",
      "iteration 5 .....\n",
      "iteration 6 .....\n",
      "iteration 7 .....\n",
      "iteration 8 .....\n",
      "iteration 9 .....\n",
      "iteration 10 .....\n",
      "LinearSVC\n",
      "{'accuracy': 0.6330299764890281, 'f1_score': 0.3123324018890591, 'auc': 0.0, 'recall_0': 0.6721393214148403, 'recall_1': 0.31, 'recall_2': 0.17181818181818181, 'precision_0': 0.8989604002215295, 'precision_1': 0.08243389506055525, 'precision_2': 0.04932580599136792}\n",
      "iteration 1 .....\n",
      "iteration 2 .....\n",
      "iteration 3 .....\n",
      "iteration 4 .....\n",
      "iteration 5 .....\n",
      "iteration 6 .....\n",
      "iteration 7 .....\n",
      "iteration 8 .....\n",
      "iteration 9 .....\n",
      "iteration 10 .....\n",
      "KNN\n",
      "{'accuracy': 0.9113430642633228, 'f1_score': 0.5188322092029616, 'auc': 0.0, 'recall_0': 0.9785142733564014, 'recall_1': 0.35, 'recall_2': 0.12454545454545454, 'precision_0': 0.9311179648095361, 'precision_1': 0.6937698412698412, 'precision_2': 0.35}\n"
     ]
    }
   ],
   "source": [
    "# binary\n",
    "for clf, name in zip(clfs, names):\n",
    "    # if name in ['decision_tree', 'random_forest', 'extra_trees', 'GBDT']:\n",
    "    #     continue    \n",
    "    res = binary_cross_validate_score(clf, x, y, mean=True, cv=10)\n",
    "    print(name)\n",
    "    print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 八、获取所有处理下所有模型的各种评分，绘制柱状图（效果不好，仅做参考）\n",
    "1.将之前所有的评分手动录入到几个txt文件中并让其读取"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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",
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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></th>\n",
       "      <th>classifier</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>auc</th>\n",
       "      <th>recall_0</th>\n",
       "      <th>recall_1</th>\n",
       "      <th>recall_2</th>\n",
       "      <th>precision_0</th>\n",
       "      <th>precision_1</th>\n",
       "      <th>precision_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>decision_tree</td>\n",
       "      <td>0.625573</td>\n",
       "      <td>0.351936</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.653979</td>\n",
       "      <td>0.345</td>\n",
       "      <td>0.373636</td>\n",
       "      <td>0.917448</td>\n",
       "      <td>0.106677</td>\n",
       "      <td>0.093969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>random_forest</td>\n",
       "      <td>0.752149</td>\n",
       "      <td>0.418177</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.795933</td>\n",
       "      <td>0.335</td>\n",
       "      <td>0.337273</td>\n",
       "      <td>0.923583</td>\n",
       "      <td>0.209158</td>\n",
       "      <td>0.124503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>extra_trees</td>\n",
       "      <td>0.733944</td>\n",
       "      <td>0.409505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.776533</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.328182</td>\n",
       "      <td>0.921167</td>\n",
       "      <td>0.295385</td>\n",
       "      <td>0.129877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GBDT</td>\n",
       "      <td>0.746487</td>\n",
       "      <td>0.373574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800763</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.219091</td>\n",
       "      <td>0.914319</td>\n",
       "      <td>0.146725</td>\n",
       "      <td>0.090205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>XGB</td>\n",
       "      <td>0.793172</td>\n",
       "      <td>0.404378</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.853746</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.327273</td>\n",
       "      <td>0.912892</td>\n",
       "      <td>0.151028</td>\n",
       "      <td>0.180071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.910092</td>\n",
       "      <td>0.365588</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.998611</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.913182</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LinearSVC</td>\n",
       "      <td>0.63303</td>\n",
       "      <td>0.312332</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.672139</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.171818</td>\n",
       "      <td>0.89896</td>\n",
       "      <td>0.082434</td>\n",
       "      <td>0.049326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.911343</td>\n",
       "      <td>0.518832</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.978514</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.124545</td>\n",
       "      <td>0.931118</td>\n",
       "      <td>0.69377</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      classifier  accuracy  f1_score  auc  recall_0 recall_1  recall_2  \\\n",
       "0  decision_tree  0.625573  0.351936  0.0  0.653979    0.345  0.373636   \n",
       "1  random_forest  0.752149  0.418177  0.0  0.795933    0.335  0.337273   \n",
       "2    extra_trees  0.733944  0.409505  0.0  0.776533     0.33  0.328182   \n",
       "3           GBDT  0.746487  0.373574  0.0  0.800763    0.235  0.219091   \n",
       "4            XGB  0.793172  0.404378  0.0  0.853746     0.16  0.327273   \n",
       "5            SVC  0.910092  0.365588  0.0  0.998611    0.095      0.02   \n",
       "6      LinearSVC   0.63303  0.312332  0.0  0.672139     0.31  0.171818   \n",
       "7            KNN  0.911343  0.518832  0.0  0.978514     0.35  0.124545   \n",
       "\n",
       "  precision_0 precision_1 precision_2  \n",
       "0    0.917448    0.106677    0.093969  \n",
       "1    0.923583    0.209158    0.124503  \n",
       "2    0.921167    0.295385    0.129877  \n",
       "3    0.914319    0.146725    0.090205  \n",
       "4    0.912892    0.151028    0.180071  \n",
       "5    0.913182        0.15        0.04  \n",
       "6     0.89896    0.082434    0.049326  \n",
       "7    0.931118     0.69377        0.35  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_list = ['decision_tree', 'random_forest', 'extra_trees', 'GBDT', 'XGB', 'SVC', 'LinearSVC', 'KNN']\n",
    "score_list = ['baseline', 'outlier', 'sampled', 'selected', 'binary']\n",
    "# score_list = ['baseline']\n",
    "\n",
    "result_pd_list = []\n",
    "\n",
    "for name in score_list:\n",
    "    res_pd = pd.DataFrame(columns=['accuracy', 'f1_score', 'auc', 'recall_0', 'recall_1', 'recall_2',\n",
    "       'precision_0', 'precision_1', 'precision_2'])\n",
    "    with open(name + '.txt', 'r') as f:\n",
    "        for i in f.readlines():\n",
    "            if (i.startswith('{') == True):\n",
    "                tmp = eval(i)\n",
    "                # print(pd.DataFrame(tmp, index=[0]))\n",
    "                res_pd = pd.concat([res_pd, pd.DataFrame(tmp, index=[0])], axis=0)\n",
    "    res_pd.reset_index(inplace=True)\n",
    "    res_pd['index'] = classifier_list\n",
    "    res_pd.rename(columns={'index' : 'classifier'}, inplace=True)\n",
    "    \n",
    "    result_pd_list.append(res_pd)\n",
    "\n",
    "result_pd_list[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "2.定义自动标注函数"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def autolabel(rects):\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        plt.text(rect.get_x() + rect.get_width() / 2.-0.09, 1.01 * height, '%s' % round(float(height), 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "3.定义绘图函数"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def draw_result(accuracy_df, name):\n",
    "    plt.clf()\n",
    "    plt.figure(figsize=(15, 15))\n",
    "    ax=plt.axes()\n",
    "\n",
    "    parameter_list = accuracy_df['classifier']\n",
    "    accuracy_list = accuracy_df['accuracy']\n",
    "    f1_score_list = accuracy_df['f1_score']\n",
    "    auc_list = accuracy_df['auc']\n",
    "    recall_0_list = accuracy_df['recall_0']\n",
    "    recall_1_list = accuracy_df['recall_1']\n",
    "    recall_2_list = accuracy_df['recall_2']\n",
    "    precision_0_list = accuracy_df['precision_0']\n",
    "    precision_1_list = accuracy_df['precision_1']\n",
    "    precision_2_list = accuracy_df['precision_2']\n",
    "\n",
    "    bar_width = 0.1\n",
    "    autolabel(ax.bar(parameter_list, accuracy_list, label='accuracy', width=bar_width, align='center'))\n",
    "    autolabel(ax.bar(np.arange(4) + bar_width, f1_score_list, label='f1_score', width=bar_width, align='center'))\n",
    "    autolabel(ax.bar(np.arange(4) + bar_width * 2, auc_list, label='auc', width=bar_width, align='center'))\n",
    "    autolabel(ax.bar(np.arange(4) + bar_width * 3, recall_0_list, label='recall_0', width=bar_width, align='center'))\n",
    "    autolabel(ax.bar(np.arange(4) + bar_width * 4, recall_1_list, label='recall_1', width=bar_width, align='center'))\n",
    "    autolabel(ax.bar(np.arange(4) + bar_width * 5, recall_2_list, label='recall_2', width=bar_width, align='center'))\n",
    "    autolabel(ax.bar(np.arange(4) + bar_width * 6, precision_0_list, label='precision_0', width=bar_width, align='center'))\n",
    "    autolabel(ax.bar(np.arange(4) + bar_width * 7, precision_1_list, label='precision_1', width=bar_width, align='center'))\n",
    "    autolabel(ax.bar(np.arange(4) + bar_width * 8, precision_2_list, label='precision_2', width=bar_width, align='center'))\n",
    "    ax.set_title(name)\n",
    "    ax.set_xticks(parameter_list)\n",
    "    ax.set_yticks(np.arange(0, 1, 0.05))\n",
    "    ax.set_xlabel('parameters')\n",
    "    ax.set_ylabel('Evaluate score')\n",
    "    ax.grid(axis='y', linewidth=1)\n",
    "    ax.legend(loc=2, bbox_to_anchor=(1.05, 1.0),borderaxespad = 0.)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "4.开始绘图"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XXtMjjzxS2UMEysTpXTDulDUOAAAnsPIPqOKHnF3Yyunu0LJDhw4pPT1d4eHnn76ybt06+fr6KjAw0MOjASquadOmio+P15o1azRlyhQFBgYqNDS0RNy0adMueR0bG6vY2NgScWvWrLnkdZMmTfTuu+8622nAA5zcBeNOWeOA2uD48eNKSUlRz5491apVq6ruDuC1WPkHVPFDzpo2bapmzZq5bXP27NlFyVBOTo6effZZzZgxw3ODABzi7++vESNG8EUVajUnd8G4U9Y4oDoYP368IiMjNX36dLfv79u3T0OGDFFUVJT++Mc/FpWnp6crKiqqRHx6erp69uwpScrIyNDQoUOVlJSk/v376+jRo54ZBGq1zMzMOlFRUcFhYWEhw4cPD4qLi7tq+fLlTSRp1qxZLWfNmtUyOzvbDB06tENYWFhI//79O546dcrrcmVW/gGVfXtnYmKiXnrppUu2d7pTUFCg9evX67nnnpMkzZgxQ5MmTVLz5s090n8AgLOc3gUjnd9lVpY4oDq5+NbIcePGKS0trcTuyAu3RkZEROiee+5RfHy8QkNDS9waecHFt0Hu2LFDM2fOVEREhDIyMrR161b94he/qJSxofY4cOBAvQcffPDIsGHDTkZHRwc3aNCgxD/2Z86cGdCtW7fs5cuXH/7rX//aMjk5uWH//v2zq6K/nuJ132YAFXFhe6ckpaamKigoyG3che2dcXFxl20vISFBN954o4wxkqS1a9dq9uzZio6O1vbt23X//fc72n8AgPPYBQNU/MkX7p5yIZW8DfKmm25SRESENmzYoKSkJEVGRnp4RKiN6tevb+fNm9fqzjvvvPbEiRN1z549W5QHnzlzpo4k7d69u2FERESWJE2ePPnYTTfd5FWJv0TyD0iq+PbO0qxevVr9+vUrer1hw4aiQwB79OihuXPnOj4GAAAApzl5a2Rpt0Faa7VkyRL5+/urXr16nhsMaq3XXnut1V133ZWxbNmyfY0bNy4IDg4+e+TIkbqS9MknnzSVpE6dOp3dvHmzryQ9/vjjga+88orXHUDBtn9AFd/eecHFWzkl6fnnny/1s4rHAgAAVFdO3hpZ2m2QxhjNnj1bTz75pD788EPdc889jo8D1UtlP5rv1ltvPfn73/++/dy5cwMkKSoq6vTTTz/ddu3atU1atGiRL0kPPfTQ0REjRlzrcrlC/P398/7973/vrcw+VgaSf6DQhe2dANz79rEER9u7ekbJQ6AAANVLRZ984c7atWu1bt06zZ49u+g2yODgYLVp00b33XefTpw4wflI8IjBgwefTktL+/LisiFDhuwpHrdy5UqvS/gvRvIPAAAAwK2YmBhFRUXpu+++08cff6zFixdr6tSpJU7+L8utkRs2bCi6jo6O1ty5c5WRkaERI0Zo7ty56tq1qwYNGuSxsQC1nbHWVnUfKiQ8PNwmJydXdTcAoNZg5R/4kZPzgbmA6i4jI0Nr1qxRv379OACzmjHGpFhrw6u6H1eSmpr6TWho6A9V3Y/aIDU1tVVoaGiQu/dY+QcAAABQKm6NBLwDp/0DAAAAAODlWPkHAAAAAFSaXZ06hznZXufduyr16QE1Fck/cAXc5wwAAACgpmPbPwAAAAAA5eRyuUo8+9JdWXEjRoxo36NHj05Tpkxp45meucfKPwAAAIAK4ckXQPn84x//aJ6fn2+2b9++++677w7auXNng27dup2rjM9m5R8AAAAA4NVcLlfIxIkTr+7bt2+wJJ06darOrbfe2iE8PDxkzJgx7SQpOzvbDB06tENYWFhI//79O546dapOZmZmnaioqOCwsLCQ4cOHB/3Ufqxfv77JiBEjMiRp4MCBmevWrfP7qW2WFck/AAAAAMCrpaam+vbp0+d0YmJimiS98sorrbp06XI2OTl5T3p6er3Nmzc3mjlzZkC3bt2yU1JS9sTExGQkJyc3PHDgQL0HH3zwSGJi4tcHDx6sf/DgwZ+0ez47O7tOu3btciSpVatW+enp6fWcGF9ZsO0fAAAAAODVgoODz4wdO/bEhdd79uxpmJyc7JeYmNjk5MmTPvv376+3e/fuhnfffXeGJE2ePPmYJKWlpdWfN29eq7///e8tT5w4UTc7O/snLaD7+voWXGjj1KlTdQoKCn5Kc+VC8g8AAAAAqDRV8Wi+xo0bX5Jlh4SEnHW5XFl/+MMfji1atKhZhw4dcjp16nR28+bNvsOGDTv1+OOPB7Zu3TrvwIED9e+6666McePGZURERFzxML8rCQsLy/7ss8/8brnllqzt27c3DgkJOftT2ywrkn8AAAAAQK3y0EMP/TBy5MigBQsWtGrSpEn++++/v/ehhx46OmLEiGtdLleIv79/3r///e+9n332me/vf//79nPnzg2QpP3799cLCQnJqejnjh49OiMyMrLTd999V2/dunXNtmzZstu5UV0eyT8AAAAAwKslJSXtufh106ZNC1auXLm3eFzxssGDB59OS0v7sixtllZ2sRYtWhQkJibuWbZsWdOnnnrqcMuWLfPLNoKfjuQfAAAAAACHuVyuS24TaNKkSd6nn37634CAgPz7778/o7L7Q/IPAAAAAIDDrrQLoLLxqD8AAAAAALycR5J/Y8w8Y8xGY8zUUt6/1hizwhiTYIx5uaz1AAAAAABA+Tm+7d8Yc5ckH2ttpDHmLWNMsLU2rVjYi5KetdZuMsYsMcZES2pRhnoAAAAAgBps9m/XhTnZ3oN/u7nSHx1YE3ninv9oSe8UXn8iqa+k4kn89ZK2Fl4fkdSsLPWMMRMkTZCkgIAALVu2zNmeA+5EOttcCv/foqZiLgA/cnA+MBdQozEXgBrDE8m/r6RDhdfHJfVyE/OepKeNMZsk3SrpcUl3XKmetfZNSW9KUnh4uB02bJizPQfc+PaxBEfbu3pGlKPtAZWFuQD8yMn5wFxATcZcQG3mcrlCih/q566suIMHD9aNiYm5LiUlpVIPBPTEPf+nJTUqvPZz9xnW2umSPpZ0v6R/WGtPl6UeAAAAAAA11dGjR31Gjx597ZkzZyo93/XEB6bo/JZ9SQqV9E0pcdsltZM0s5z1AAAAAAAoM5fLFTJx4sSr+/btGyxJp06dqnPrrbd2CA8PDxkzZkw7ScrOzjZDhw7tEBYWFtK/f/+Op06dqpOZmVknKioqOCwsLGT48OFBP7UfPj4+dunSpXv9/PwKfmpb5eWJ5H+ppDHGmJmSRkj60hgz3U3c/ydpprU2u5R6KzzQNwAAAABALZOamurbp0+f04mJiWmS9Morr7Tq0qXL2eTk5D3p6en1Nm/e3GjmzJkB3bp1y05JSdkTExOTkZyc3PDAgQP1HnzwwSOJiYlfHzx4sP7Bgwd/0q3zLVq0KGjZsmW+M6MqH8fv+bfWniw8vX+gpD9baw9LSnUT9/QV6mU63TcAAAAAQO0THBx8ZuzYsScuvN6zZ0/D5ORkv8TExCYnT5702b9/f73du3c3vPvuuzMkafLkycckKS0trf68efNa/f3vf2954sSJutnZ2TX29nRPHPgna22Gfjy53+P1AAAAAAA1Q1U8mq9x48aXbLMPCQk563K5sv7whz8cW7RoUbMOHTrkdOrU6ezmzZt9hw0bdurxxx8PbN26dd6BAwfq33XXXRnjxo3LiIiICKnsfjupxn5rAQAAAABARTz00EM/rF69ull4eHjIm2++GdChQ4echx566Oj27dsbu1yukO3bt/vGxsYeu/XWW0/OnDmzTZ8+fa6XpP3799er6r5XlEdW/gEAAAAAqC6KP36vadOmBStXrtxbPK542eDBg0+npaV9WZY2Sysra11PI/kHAAAAAMBhLpfrktsEmjRpkvfpp5/+t6r6Q/IPAAAAAIDDqmJ1/3K45x8AAAAAAC9H8g8AAAAAgJdj2z8AAAAAoNK8fM/QMCfb++OS5ZX+6MCaiJV/AAAAAAC8HMk/AAAAAADlVPw0/9LKLnbs2DGffv36Bf/85z8PHjhw4HVnz541nuvhpUj+AQAAAACoBHPmzGnx8MMPp//f//1fWuvWrXPff//9ppX12dzzDwAAAADwai6XK6Rnz55ZX375ZaPExMS0U6dO1bn77ruDfvjhh3qdO3c+s2DBggPZ2dlmxIgR137//ff1mjZtmv/hhx/uLSgo0NChQ6/Lzs6uc+2115577733vvkp/XjssceOXrg+duxY3cDAwLyfPLgyYuUfAAAAAODVUlNTffv06XM6MTExTZJeeeWVVl26dDmbnJy8Jz09vd7mzZsbzZw5M6Bbt27ZKSkpe2JiYjKSk5MbHjhwoN6DDz54JDEx8euDBw/WP3jwoCML6GvXrvXNzMyse8stt2Q50V5ZsPIPAAAAAPBqwcHBZ8aOHXviwus9e/Y0TE5O9ktMTGxy8uRJn/3799fbvXt3w7vvvjtDkiZPnnxMktLS0urPmzev1d///veWJ06cqJudnf2TF9DT09N9HnrooXYffPDBf39qW+VB8g8AAAAAqDRV8Wi+xo0bF1z8OiQk5KzL5cr6wx/+cGzRokXNOnTokNOpU6ezmzdv9h02bNipxx9/PLB169Z5Bw4cqH/XXXdljBs3LiMiIuKyh/mVxdmzZ01MTMx1zz777KHrr78+56e2Vx5s+wcAAAAA1CoPPfTQD6tXr24WHh4e8uabbwZ06NAh56GHHjq6ffv2xi6XK2T79u2+sbGxx2699daTM2fObNOnT5/rJWn//v31fsrn/vWvf2315ZdfNn7hhRfauFyukDlz5vg7M6IrY+UfAAAAAODVkpKS9lz8umnTpgUrV67cWzyueNngwYNPp6WlfVmWNksru9ijjz569NFHHz16uRhPIfkHAAAAAMBhLpfrktsEmjRpkvfpp59W6n3+FyP5BwAAAADAYVfaBVDZuOcfAAAAAAAvR/IPAAAAAICXY9s/AAAAAKDSfPtYQpiT7V09I6rSHx1YE7HyDwAAAACAlyP5BwAAAACgnMaNG3eNk3Fl8fDDD1/VtWvXzmPGjGlX3rok/wAAAAAAlNNbb7110Mm4K0lISGi8adMmvx07duxq3bp17tKlS5uUpz7JPwAAAADAq8XFxV3Vr1+/4N69e4fceuutHXJzc+VyuUKmTZvW+vrrr+8iSQUFBRo5cmT7CzF5eXkqKCjQmDFj2vXq1auTy+UKOXDgQNG5eS6XK+TCdUFBgWJiYq4NDw8PiYyMvP7YsWM+7uIkaezYsdeEhYWF3HzzzR2PHj3qM2vWrJbjx4+/JiIi4vrrrrvuhi1btjR0N4ZPP/20ybBhwzLq1Kmj22677eRnn31G8g8AAAAAwMX69OlzasuWLXsCAgLyFi5c2PzIkSP1jDH6+uuvv5KkhQsXNs/NzTVbtmzZc/XVV+csWbKk2aJFi5rl5+ebrVu37n744YcPf/75577u2j5y5IjPrl27GiUlJe154oknvj9+/LiPu7hFixY1O3fuXJ2UlJQ9MTExGU8//XSgJG3fvr3xZ599lvbwww8ffv/995u7q5uVlVXn6quvzpWkgICAvPT09HId4M9p/wAAAAAAr9e7d+9sSerevXv2vn37GjRp0iT/iSeeOHLh/d27dzdMSUnxc7lcIdnZ2XU6d+58NiMjw6d3795ZkjRq1KjM/Px8t20HBgbmjx49+od+/foFt27dOveNN95wu9X/yy+/bOhyubIkKSoqKmvZsmXNO3XqdHb48OHHGzRoYIOCgnL27t3bwF1dPz+//OzsbCNJJ0+e9LHWmvKMn+QfAAAAAFBpqurRfJs2bfL95S9/eXLbtm2Nb7vttpONGjUq8PH5cYG+U6dOZ++8887jr7zyynerV6/2M8bYo0eP1l2xYkUzSXr99ddbfPXVV41effXVQ8Xb/s9//lOvZcuW+YmJiWm/+93v2v7zn//0f/jhh38oHte1a9ez//73v5tL+iEhIcG3c+fOZyXJ19e34Er9d7lc2YsWLWoxYcKEjJSUlEbt27c/V57xs+0fAAAAAOD1UlJSfF0uV8jJkyfrjhw58kTx90ePHn3i+++/r9e7d++QJ598su11112XM2rUqExjjMLDw0P+9a9/tXz88cfT3bV9zTXX5K1YsaJZr169OiUkJDQZMmTISXdxI0eOzGzYsGFBWFhYyNKlS/2ffvrpw2Xt/6BBg05/8cUXjX/zm99c88orr7T59a9/fbzMg5dkrLXlia82wsPDbXJyclV3A7XAt48lONre1TOiHG0PqCzMBeBHTs4H5gJqMuZC1TLGpFhrw6u6H1eSmpr6TWhoaIlV8MoUFxd31c0333xq6NChp6qyHz/V6dOnzbvvvtv8xhtvzOrSpUtO8fdTU1NbhYaGBrmry7Z/AAAAAIBXmzlz5ndV3YfyGDp0aIcjR47Uu7hs3bp1X/v5+dnf/OY3GRVpk+QfAAAAAIBqZPny5XudbpN7/gEAAAAA8HIeSf6NMfOMMRuNMVNLed/fGLPSGJNsjHmjsKyuMeaAMSa+8KebJ/oGAAAAAEBt4/i2f2PMXZJ8rLWRxpi3jDHB1tq0YmFjJC201i40xvzLGBMuqUDSImvto073CQAAAABQPTzzzDNhDrdXJY8OrGk8cc9/tKR3Cq8/kdRXUvHk/5ikrsaY5pKukXRQ0i8lDTXG9Je0U9JEa23exZWMMRMkTZCkgIAALVu2zAPdB4qJdLa5FP6/RU3FXAB+5OB8YC6gRmMuoBYbN27cNW+99dZBp+LK4tSpU3V69+7daffu3V+Vt64nkn9fSYcKr49L6uUmJlHSEEm/l7SrMG6LpAHW2u+NMfMl3Sbpw4srWWvflPSmdP5Rf8OGDfNA94FL8Xgz4DzmAvAjHm8GnMdcQG1W1oTeqcQ/Ly9Pd9xxR4dTp075VKS+J+75Py2pUeG1Xymf8bSk31pr/yRpt6TfSNphrf2+8P1kScEe6BsAAAAAoJaJi4u7ql+/fsG9e/cOufXWWzvk5ubK5XKFTJs2rfX111/fRZIKCgo0cuTI9hdi8vLyVFBQoDFjxrTr1atXJ5fLFXLgwIGiBXSXyxVy4bqgoEAxMTHXhoeHh0RGRl5/7NgxH3dxkjR27NhrwsLCQm6++eaOR48e9Zk1a1bL8ePHXxMREXH9ddddd8OWLVsaljaOt99+e3/btm1zKvI78ETyn6LzW/0lKVTSN25i/CV1M8b4SLpRkpW0wBgTWlgWIynVA30DAAAAANRCffr0ObVly5Y9AQEBeQsXLmx+5MiResYYff31119J0sKFC5vn5uaaLVu27Ln66qtzlixZ0mzRokXN8vPzzdatW3c//PDDhz///HNfd20fOXLEZ9euXY2SkpL2PPHEE98fP37c7er8okWLmp07d65OSkrKnpiYmIynn346UJK2b9/e+LPPPkt7+OGHD7///vvN3dWtW7eugoKCcis6fk9s+18qKcEYc5WkwZJGGmOmW2svPvn/BUlvS2ovaaOkRYX//ZckI+lDa+1aD/QNAAAAAFAL9e7dO1uSunfvnr1v374GTZo0yX/iiSeOXHh/9+7dDVNSUvxcLldIdnZ2nc6dO5/NyMjw6d27d5YkjRo1KjM/P99t24GBgfmjR4/+oV+/fsGtW7fOfeONN9xu9f/yyy8bulyuLEmKiorKWrZsWfNOnTqdHT58+PEGDRrYoKCgnL179zZwfPDywMq/tfakzh/6t0lSf2ttarHEX9baJGvtDdZaP2vtQGvtaWvtF9ba7tbabtbaJ5zuFwAAAACg9tq0aZOvJG3btq1xcHDwuUaNGhX4+Py4QN+pU6ezd9555/GkpKQ9L7/88sFu3bqd6dy589ktW7Y0lqTXX3+9xUMPPdTWXdv/+c9/6rVs2TI/MTEx7aqrrsr95z//6e8urmvXrmc3b97sK0kJCQm+nTt3PitJvr6+BQ4PtwRPrPzLWpuhH0/8BwAAAABAUtU9mi8lJcXX5XKFtG7dOnfkyJEnZs2a9bOL3x89evSJFStWNOvdu3eIMUaLFi3aO2DAgKyVK1c2Cw8PD2nUqFHBkiVL9rlr+5prrslbsWJFs7feeisgPz9fv//974+6ixs5cmTmxx9/3DQsLCykWbNm+UuWLNm3aNGi5h4YbgkeSf4BAAAAAKhO4uLi0ocOHXrqwuukpKQ9F7/v4+OjxYsX7y9eb+HChQfctXdx/QYNGtiPP/5475XiJOkf//jHJbcE/P73vz924Xro0KGnLu5jWdorK5J/AAAAAIBXmzlz5ndV3YfyGDp0aIcjR47Uu7hs3bp1X/v5+dmKtknyDwAAAABANbJ8+XK3uwh+Ck886g8AAAAAAFQjJP8AAAAAAHg5kn8AAAAAALwc9/wDAAAAACrNp+uuC3OyvVtu/m+VPDqwpmHlHwAAAACAcho3btw1TsZdyblz58yQIUM6/PznPw+OiIi4/ujRoz7lqU/yDwAAAABAOb311lsHnYy7kvfee6/poEGDMv/v//4v7ZZbbjn5xhtvtCxPfZJ/AAAAAIBXi4uLu6pfv37BvXv3Drn11ls75ObmyuVyhUybNq319ddf30WSCgoKNHLkyPYXYvLy8lRQUKAxY8a069WrVyeXyxVy4MCBolvnXS5XyIXrgoICxcTEXBseHh4SGRl5/bFjx3zcxUnS2LFjrwkLCwu5+eabOx49etRn1qxZLcePH39NRETE9dddd90NW7ZsaehuDL/61a8y//CHPxyTpKNHj9Zt3bp1bnl+ByT/AAAAAACv16dPn1NbtmzZExAQkLdw4cLmR44cqWeM0ddff/2VJC1cuLB5bm6u2bJly56rr746Z8mSJc0WLVrULD8/32zdunX3ww8/fPjzzz/3ddf2kSNHfHbt2tUoKSlpzxNPPPH98ePH3W7JX7RoUbNz587VSUlJ2RMTE5Px9NNPB0rS9u3bG3/22WdpDz/88OH333+/+eXG8dVXX9VPTExsMnbs2BPlGT8H/gEAAAAAvF7v3r2zJal79+7Z+/bta9CkSZP8J5544siF93fv3t0wJSXFz+VyhWRnZ9fp3Lnz2YyMDJ/evXtnSdKoUaMy8/Pz3bYdGBiYP3r06B/69esX3Lp169w33njD7Vb/L7/8sqHL5cqSpKioqKxly5Y179Sp09nhw4cfb9CggQ0KCsrZu3dvg9LGcObMGXPfffdd+7e//W1/gwYNbHnGz8o/AAAAAMDrbdq0yVeStm3b1jg4OPhco0aNCnx8flyg79Sp09k777zzeFJS0p6XX375YLdu3c507tz57JYtWxpL0uuvv97ioYceauuu7f/85z/1WrZsmZ+YmJh21VVX5f7zn//0dxfXtWvXs5s3b/aVpISEBN/OnTuflSRfX9+CsoxhxIgRQffee+8P/fr1yy7X4MXKPwAAAACgElXVo/lSUlJ8XS5XSOvWrXNHjhx5YtasWT+7+P3Ro0efWLFiRbPevXuHGGO0aNGivQMGDMhauXJls/Dw8JBGjRoVLFmyZJ+7tq+55pq8FStWNHvrrbcC8vPz9fvf//6ou7iRI0dmfvzxx03DwsJCmjVrlr9kyZJ9ixYtal6W/r/zzjtN16xZ45+enl7/X//6V6vbb78948knnzxy5ZrnGWvLtVOg2ggPD7fJyclV3Q3UAt8+luBoe1fPiHK0PaCyMBeAHzk5H5gLqMmYC1XLGJNirQ2v6n5cSWpq6jehoaE/VGUf4uLirrr55ptPDR069FRV9sPTUlNTW4WGhga5e4+VfwAAAACAV5s5c+Z3Vd2H8hg6dGiHI0eO1Lu4bN26dV/7+flVePWe5B8AAAAAgGpk+fLle51ukwP/AAAAAACeVFBQUGCquhPervB3XOrBgST/AAAAAABP+uLo0aPN+ALAcwoKCszRo0ebSfqitBi2/QMAAAAAPCYvL+/+w4cPzz18+HBXsQDtKQWSvsjLy7u/tACSfwAAAACAx4SFhR2RdEdV96O241sXAAAAAAC8HMk/AAAAUAHjx49XZGSkpk+f7vb9jIwM3XbbbQoPD9fEiRMvW694WWl1AaCiSP4BAACAcvrggw+Un5+vjRs3au/evUpLSysRs2DBAv3qV79ScnKyTp06peTkZLf13JW5qwsAPwXJPwAAAFBO8fHxGjFihCRp0KBBSkxMLBHTsmVLffHFFzpx4oQOHjyoa665xm09d2Xu6gLAT0HyDwAAAJRTVlaW2rZtK0lq0aKF0tPTS8T07dtX+/fv16xZs9S5c2e1aNHCbT13Ze7qAsBPQfIPAGVwpfs6X3/9dUVHRys6Olo9evTQxIkTtW/fPg0ZMkRRUVH64x//KEnKy8tTu3btimJ37tzpNg4AUL35+fnpzJkzkqTTp0+roKCgRMy0adP0t7/9TU899ZQ6deqkt99+2209d2Xu6gLAT0HyDwBXUJb7OmNjYxUfH6/4+HhFRUXpgQce0KOPPqonn3xSCQkJ+vbbbxUfH68dO3Zo1KhRRbHdunVzGwcAqN7CwsKKtvqnpqYqKCioRExGRoZ27typ/Px8bd68WcYYt/XclbmrCwA/Rd2q7gAAVHfu7sUMDg52G3vo0CGlp6crPDxcX3/9tXr16iVJat26tTIzM/XVV19p+fLlWr9+vbp166Y33njDbRwAoHqLiYlRVFSUvvvuO3388cdavHixpk6deskOsccff1y/+c1vtH//fkVGRmrUqFEqKCi4pN6mTZtkjClR1rFjxxJ1AeCnYOUfAK6gLPd1XjB79mzFxsZKkoYPH65p06bpo48+0qpVq3TLLbeod+/eWrt2rZKSkpSbm6uVK1e6jQMAVG9NmzZVfHy8IiIitH79eoWGhpa4NczlcunLL7/U6dOntWbNGvn5+ZWo16xZM7dl7uoCwE/Byj8AXEFZ7uuUpIKCAq1fv17PPfecJGnq1KlKTEzUSy+9pLFjx8rPz0/du3dXgwYNJEnh4eFKS0tzGwcAqP78/f2Ldob91HoVbQsAyoqVfwC4grLc1ylJCQkJuvHGGy+5L7NHjx46cOCA4uLiJEljxoxRamqq8vPztXTpUoWGhrqNAwAAAJxE8g8AVxATE6MFCxYoLi5O77zzjm644QZNnTq1RNzq1avVr1+/S8peeuklxcXFqXHjxpKkp556SmPGjFGPHj0UGRmpAQMGuI0DAAAAnOSRbf/GmHmSukhaYa0t8VwsY4y/pIWSWktKsdZOLEs9AKgKF+7FXLNmjaZMmaLAwMCiFfuLPf/88yXKpk2bdsnrrl27aseOHVeMAwAAAJzk+Mq/MeYuST7W2khJHYwx7o7EHiNpobU2XFITY0x4GesBQJW4cC9mYGBgVXcFAAAAKDdjrXW2QWNmSVplrV1pjBkpqZG19u1iMb+S1FXSi5I+kjRc0hNlqDdB0gRJCggICJszZ46jfQcAAACAmiImJialcEEVuCJPbPv3lXSo8Pq4pF5uYhIlDZH0e0m7CuOuWM9a+6akNyUpPDzcDhs2zNGOA+58+1iCo+1dPSPK0faAysJcAH7k5HxgLnifXZ06O9pe5927HG3PScwFoObwRPJ/WlKjwms/ub+14GlJv7XWnjTGxEn6TRnrAQAAAACAcvJEgp0iqW/hdaikb9zE+EvqZozxkXSjJFvGegAAAAAAoJw8sfK/VFKCMeYqSYMljTTGTLfWXvxcrBckvS2pvaSNkhbp/BcRF9eL8EDfAMBxTm7vrM5bOwEAAFBzOZ78F27lj5Y0UNKfrbWHJaUWi0mSdEPxusXqZTrdNwAAAAAAaiNPrPzLWpsh6Z3KqgcAAAAAAErHoXoAAAAAAHg5j6z8AwAA7zV+/Hh99dVXGjJkiKZOnVri/ddff11LliyRJJ04cUI33nijevToUaLsjTfekCRNmjRJgwcP1u233668vDx16NBBHTp0kCS9+uqr6tatWyWNDAAA78XKPwAAKLMPPvhA+fn52rhxo/bu3au0tLQSMbGxsYqPj1d8fLyioqL0wAMPuC2TpISEBB0+fFi33367JGnHjh0aNWpUUSyJPwAAziD5BwAAZRYfH68RI0ZIkgYNGqTExMRSYw8dOqT09HSFh4e7LcvNzdUDDzygoKAgLVu2TJK0adMmLV++XC6XS+PHj1deXp5nBwQAQC1B8g8AAMosKytLbdu2lSS1aNFC6enppcbOnj1bsbGxpZbNnz9fXbp00ZQpU5SUlKRXX31VvXv31tq1a5WUlKTc3FytXLnSc4MBAKAWIfkHAABl5ufnpzNnzkiSTp8+rYKCArdxBQUFWr9+vaKjo0st27ZtmyZMmKDAwEDde++9Wr9+vbp37642bdpIksLDw93eVgAAAMqP5B8AAJRZWFhY0Vb/1NRUBQUFuY1LSEjQjTfeKGNMqWUdO3bU3r17JUnJyclq3769xowZo9TUVOXn52vp0qUKDQ317IAAAKglOO0fAACUWUxMjKKiovTdd9/p448/1uLFizV16lRNnz79krjVq1erX79+ly0bP368xo0bp8WLFys3N1fvvfeeMjIyNHr0aFlrdccdd2jAgAGVMi4AALwdyT8AACizpk2bKj4+XmvWrNGUKVMUGBjodnX++eefv2JZkyZN9O67715S1rZtW+3YscPZTgMAAJJ/AABQPv7+/kUn/gMAgJqBe/4BAAAAAPByJP8AAAAAAHg5kn8AAAAAALwc9/wDAABH7OrU2bG2Ou/e5VhbAACAlX8AAAAAALweyT8AAAAAAF6O5B8AAAAAAC9H8g8AAAAAgJcj+QcAAAAAwMuR/AMAAAAA4OVI/gEAAAAA8HIk/wB+kvHjxysyMlLTp093+/7rr7+u6OhoRUdHq0ePHpo4caLy8vLUrl27ovKdO3cqMzNTgwcP1qBBg3TnnXcqJydHkpSenq6oqKjKHBIAAADgdUj+AVTYBx98oPz8fG3cuFF79+5VWlpaiZjY2FjFx8crPj5eUVFReuCBB7Rjxw6NGjWqqLxbt25auHCh4uLi9MknnygwMFCrVq1SRkaGxo4dq6ysrCoYHQAAAOA9SP4BVFh8fLxGjBghSRo0aJASExNLjT106JDS09MVHh6uTZs2afny5XK5XBo/frzy8vI0adIkDRw4UJJ09OhRtW7dWj4+PlqyZImaNm1aKeMBAAAAvBXJP4AKy8rKUtu2bSVJLVq0UHp6eqmxs2fPVmxsrCSpd+/eWrt2rZKSkpSbm6uVK1cWxW3cuFEZGRmKiIhQ06ZN1axZM88OAgAAAKgFSP4BVJifn5/OnDkjSTp9+rQKCgrcxhUUFGj9+vWKjo6WJHXv3l1t2rSRJIWHhxfdLnD8+HFNnjxZb731luc7DwAAANQiJP8AKiwsLKxoq39qaqqCgoLcxiUkJOjGG2+UMUaSNGbMGKWmpio/P19Lly5VaGiocnJydPfdd+uFF15Q+/btK2sIAACHVeQg2AvS09PVs2fPS+InTZqkjz76SJLcHhgLACgbkn8AFRYTE6MFCxYoLi5O77zzjm644QZNnTq1RNzq1avVr1+/otdPPfWUxowZox49eigyMlIDBgzQvHnztHXrVj333HOKjo7WkiVLKnMoAAAHVPQg2AseeeSRoh1l0vkvjw8fPqzbb79dktweGAsAKJu6Vd0BADVX06ZNFR8frzVr1mjKlCkKDAxUaGhoibjnn3/+ktddu3bVjh07LimLjY0tOhOguPj4eMf6DADwHHcHwQYHB7uNvfggWElat26dfH19FRgYKEnKzc3VAw88oNtuu03Lli3TsGHDig6MXb9+vbp166Y33nhDdevyz1kAKAtW/gH8JP7+/hoxYkTRP9YAALVXRQ+CzcnJ0bPPPqsZM2YUvT9//nx16dJFU6ZMUVJSkl599dXLHhgLALg8kn8AAAA4oqIHwc6YMUOTJk1S8+bNi2K2bdumCRMmKDAwUPfee6/Wr19f6oGxAIArI/kHAACAIyp6EOzatWs1e/ZsRUdHa/v27br//vvVsWNH7d27V5KUnJys9u3buz0wFgBQNtwkBQAAAEfExMQoKipK3333nT7++GMtXrxYU6dOLXHyf/GDYDds2FB0HR0drblz5+rUqVMaN26cFi9erNzcXL333nvKyMjQ6NGjZa3VHXfcoQEDBlTa2ACgpvNI8m+MmSepi6QV1toSz3kxxsRKuqfwZXNJmyU9KGlv4Y8kTbbW8vwWoIYKemyFo+19M2OIo+0BAJxX0YNgL3bhkNcmTZro3XffveS9tm3bljgwFgBQNo4n/8aYuyT5WGsjjTFvGWOCrbWX3JBlrX1d0uuF8a9K+oek7pIWWWsfdbpPAAAAqBwXDoIFAFQvxlrrbIPGzJK0ylq70hgzUlIja+3bpcS2lfSKtXaEMWaSzq/+Z0naKWmitTavWPwESRMkKSAgIGzOnDmO9h0AAAAAaoqYmJgUa214VfcDNYMntv37SjpUeH1cUq/LxD6owh0AkrZIGmCt/d4YM1/SbZI+vDjYWvumpDclKTw83A4bNszJfgNufftYgqPtXT0jytH2qqvatO1/V6fOjrXVefcux9pyGnPBM44fP66UlBT17NlTrVq1KrWsJqgtc0Fydj4wF7yPk3NBqt7zgbkA1ByeOO3/tKRGhdd+pX2GMaaOpP6S4guLdlhrvy+8TpYU7IG+AQDgEePHj1dkZGSJg80ueP311xUdHa3o6Gj16NFDEydOVEZGhoYOHaqkpCT1799fR48edVu2b98+DRkyRFFRUfrjH/9YySMDAADewBMr/ymS+kraJClU0p5S4qIkbbY/3newwBjznKQvJMVIKv0kGAAAqpEPPvhA+fn52rhxo8aNG6e0tDQFB1/6HXZsbKxiY2MlSZMnT9bYsWO1Y8cOzZw5UxEREcrIyNDWrVvVsGHDEmXz5s3Tk08+qYiICN1zzz2Kj48vej46UJM4uSusOu8IA4DqyBMr/0sljTHGzJQ0QtKXxhh3yyC/kLThotd/krRA0nZJG621az3QNwAAHBcfH190wNmgQYOKnnPuzqFDh5Senq7w8HDddNNNioiI0IYNG5SUlKTIyEi3ZV9//bV69Tp/F13r1q2VmZlZKeMCAADew/GVf2vtSWNMtKSBkv5srT0sKdVN3P8Ue/2Fzp/4DwBAjZKVlaW2bdtKklq0aKGtW7eWGjt79uyiHQCSZK3VkiVL5O/vr3r16rktGz58uKZNm6aIiAitWrVKL7zwgmcHBAAAvI4nVv5lrc2w1r5TmPgDAODV/Pz8dObMGUnS6dOnVVBQ4DauoKBA69evv2TLvjFGs2fPVvfu3fXhhx+6LZs6daoGDx6suXPnauzYsfLz8/P4mAAAgHfxSPIPAEBtEhYWVrTVPzU1VUFBQW7jEhISdOONN8oYI0l68cUXNX/+fEnSiRMn1Lx5c7dlktSjRw8dOHBAcXFxnh0MAADwSiT/AAD8RDExMVqwYIHi4uL0zjvv6IYbbtDUqVNLxK1evVr9+vUrej1hwgQtWLBA/fr1U35+vgYNGuS2TJJeeuklxcXFqXHjxpU2LgAA4D08cdo/AAC1StOmTRUfH681a9ZoypQpCgwMVGhoaIm455+/9EE2/v7+WrNmzRXLJGnatGnOdhoAANQqJP8AADjA39+/6MR/AACA6oZt/wAAAAAAeDmSfwAAAAAAvBzJPwAAAAAAXo57/gEA8KCgx1Y42t43M4Y42h4AAKgdWPkHAAAAAMDLkfwDAAAAAODlSP4BAAAAAPByJP8AAAAAAHg5kn8AAAAAALwcyT8AAAAAAF6O5B8AAAAAAC9H8g8AAAAAgJcj+QcAAAAAwMuR/AMAAAAA4OVI/gEAAAAA8HIk/wAAAAAAeDmSfwAAAAAAvBzJPwAAAAAAXo7kHwAAAAAAL0fyDwAAAACAlyP5BwAAAADAy5H8AwAAAADg5Uj+AQAAAADwciT/AAAAAAB4OZJ/AAAAAAC8HMk/AAAAAABejuQfAAAAAAAvR/IPAAAAAICXI/kHAAAAAMDLeST5N8bMM8ZsNMZMLeX9WGNMfOHPdmPMG2WpBwAAAAAAys/x5N8Yc5ckH2ttpKQOxpjg4jHW2tettdHW2mhJCZLmlKUeAAAAAAAoP2OtdbZBY2ZJWmWtXWmMGSmpkbX27VJi20p6xVo7oiz1jDETJE2QpICAgLA5c+Y42ncAAAAAqCliYmJSrLXhVd0P1Ax1PdCmr6RDhdfHJfW6TOyDkl4vaz1r7ZuS3pSk8PBwO2zYMCf6C1zWt48lONre1TOiHG2vugp6bIWj7X0zY4ij7TlpV6fOjrXVefcux9pyGnOhYpgLFVOd54Lk7HyoLXNBcnY+1Ja5IFXv+cBcAGoOT9zzf1pSo8Jrv9I+wxhTR1J/SfHlqQcAAAAAAMrHEwl2iqS+hdehkr4pJS5K0mb7430HZa0HAAAAAADKwRPb/pdKSjDGXCVpsKSRxpjp1triJ/j/QtKGy9SL8EDfAAAAAACodRxP/q21J40x0ZIGSvqztfawpFQ3cf9zhXqZTvcNAAAAAIDayBMr/7LWZkh6p7LqAQAAAACA0nGoHgAAAAAAXo7kHwAAAAAAL0fyDwAAAACAlyP5BwAAAADAy5H8AwAAAADg5Uj+AQAAAADwciT/AAAAAAB4OZJ/AAAAAAC8HMk/AAAAAABejuQfAAAAAAAvR/IPAAAAAICXI/kHAAAAAMDLkfwDAAAAAODlSP4BAAAAAPByJP8AAAAAAHg5kn8AAAAAALwcyT8AAAAAAF6O5B8AAAAAAC9H8g8AAAAAgJcj+QcAAAAAwMuR/AMAAAAA4OVI/nFZ48ePV2RkpKZPn37ZuEmTJumjjz6SJOXl5aldu3aKjo5WdHS0du7cWRSXnp6unj17erTPAAAAAIBLkfyjVB988IHy8/O1ceNG7d27V2lpaW7jEhISdPjwYd1+++2SpB07dmjUqFGKj49XfHy8unXrVhT7yCOP6MyZM5XSfwAAAADAeST/KFV8fLxGjBghSRo0aJASExNLxOTm5uqBBx5QUFCQli1bJknatGmTli9fLpfLpfHjxysvL0+StG7dOvn6+iowMLDyBgEAAAAAIPlH6bKystS2bVtJUosWLZSenl4iZv78+erSpYumTJmipKQkvfrqq+rdu7fWrl2rpKQk5ebmauXKlcrJydGzzz6rGTNmVPYwAAAAAKDWI/lHqfz8/Iq26J8+fVoFBQUlYrZt26YJEyYoMDBQ9957r9avX6/u3burTZs2kqTw8HClpaVpxowZmjRpkpo3b16ZQwAAAAAAiOQflxEWFla01T81NVVBQUElYjp27Ki9e/dKkpKTk9W+fXuNGTNGqampys/P19KlSxUaGqq1a9dq9uzZio6O1vbt23X//fdX5lAAAAAAoFarW9UdQPUVExOjqKgofffdd/r444+1ePFiTZ069ZKT/8ePH69x48Zp8eLFys3N1XvvvaeMjAyNHj1a1lrdcccdGjBggAYMGFBUJzo6WnPnzq2KIQEAAABArUTyj1I1bdpU8fHxWrNmjaZMmaLAwECFhoZeEtOkSRO9++67l5S1bdtWO3bsKLXd+Ph4T3QXAAAAAFAKkn9clr+/f9GJ/wAAAACAmol7/gEAAAAA8HIk/wAAAAAAeDm2/aPCdnXq7FhbnXfvcqwtAAAAAMClPLLyb4yZZ4zZaIyZeoW414wxtxde1zXGHDDGxBf+dPNE3wAAAAAAqG0cT/6NMXdJ8rHWRkrqYIwJLiUuSlKgtfajwqLukhZZa6MLf3Y63TcAAAAAAGojY611tkFjZklaZa1daYwZKamRtfbtYjH1JO2UtFLSZ9baZcaYSZIelJRV+N5Ea21esXoTJE2QpICAgLA5c+Y42ncAAAAAqCliYmJSrLXhVd0P1AyeuOffV9Khwuvjknq5iblP0leS/ixpsjGmnaRNkgZYa783xsyXdJukDy+uZK19U9KbkhQeHm6HDRvmge6jrGrLPf/fPpbgaHtXz4hytL3qKuixFY62982MIY625yTmQsUwFyqGuVA9ODkfastckJydD7VlLkjVez4wF4CawxP3/J+W1Kjw2q+Uz+gp6U1r7WFJ/5TUX9IOa+33he8nS3J7uwAAAAAAACgfTyT/KZL6Fl6HSvrGTcx/JHUovA6XtF/SAmNMqDHGR1KMpFQP9A0AAAAAgFrHE9v+l0pKMMZcJWmwpJHGmOnW2otP/p8n6a3CMwHqSRouyV/SvyQZSR9aa9d6oG8AAAAAANQ6jif/1tqTxphoSQMl/blwa39qsZhTku4uVvWQzp/4DwAAAAAAHOSJlX9ZazMkveOJtgEAAAAAQPl44p5/AAAAAABQjZD8AwAAAADg5Uj+AQAAAADwciT/FTR+/HhFRkZq+vTpl42bNGmSPvroI0lSZmamBg8erEGDBunOO+9UTk6O8vLy1K5dO0VHRys6Olo7d+50WxcAAAAAgIoi+a+ADz74QPn5+dq4caP27t2rtLQ0t3EJCQk6fPiwbr/9dknSwoULFRcXp08++USBgYFatWqVduzYoVGjRik+Pl7x8fHq1q2b27oAAAAAAFQUyX8FxMfHa8SIEZKkQYMGKTExsURMbm6uHnjgAQUFBWnZsmWSzq/kDxw4UJJ09OhRtW7dWps2bdLy5cvlcrk0fvx45eXlua0LAAAAAEBFkfxXQFZWltq2bStJatGihdLT00vEzJ8/X126dNGUKVOUlJSkV199tei9jRs3KiMjQxEREerdu7fWrl2rpKQk5ebmauXKlZetCwAAAABAeZH8V4Cfn5/OnDkjSTp9+rQKCgpKxGzbtk0TJkxQYGCg7r33Xq1fv16SdPz4cU2ePFlvvfWWJKl79+5q06aNJCk8PFxpaWml1gUAAAAAoCJI/isgLCysaKt/amqqgoKCSsR07NhRe/fulSQlJyerffv2ysnJ0d13360XXnhB7du3lySNGTNGqampys/P19KlSxUaGuq2LgAAAAAAFVW3qjtQE8XExCgqKkrfffedPv74Yy1evFhTp0695OT/8ePHa9y4cVq8eLFyc3P13nvvad68edq6dauee+45Pffcc4qNjdVTTz2l0aNHy1qrO+64QwMGDNCNN95Yoi4AAAAAABVF8l8BTZs2VXx8vNasWaMpU6YoMDBQoaGhl8Q0adJE77777iVlsbGxio2NLdHejh07rlgXAAAAAICKIvmvIH9//6IT/wEAAAAAqM645x8AAAAAAC9H8g8AAAAAgJdj27/Dgh5b4Wh738wY4mh7AAAAAIDah5V/AAAAAAC8HMk/AAAAAABejuQfAAAAAAAvR/IPAAAAAICXI/kHAAAAAMDLkfwDAAAAAODlSP4BAAAAAPByJP8AAAAAAHg5kn8AAAAAALwcyT8AAAAAAF6O5B8AAAAAAC9H8g8AAAAAgJcj+QcAAAAAwMuR/AMAAAAA4OVI/gEAAAAA8HIk/wAAAAAAeDmSfwAAAAAAvBzJPwAAAAAAXs4jyb8xZp4xZqMxZuoV4l4zxtxe3noAAAAAAKDsHE/+jTF3SfKx1kZK6mCMCS4lLkpSoLX2o/LUAwAAAAAA5WOstc42aMwsSaustSuNMSMlNbLWvl0spp6knZJWSvrMWrusjPUmSJogSQEBAWFz5sxxtO8AAAAAUFPExMSkWGvDq7ofqBnqeqBNX0mHCq+PS+rlJuY+SV9J+rOkycaYdmWpZ619U9KbkhQeHm6HDRvmbM8dEPTYCkfb+2bGEEfbc9KuTp0da6vz7l2OteW0bx9LcLS9q2dEOdpedcVcqBjmgvdhLlRMdZ4LkrPzobbMBcnZ+VBb5oJUvecDcwGoOTxxz/9pSY0Kr/1K+Yyekt601h6W9E9J/ctYDwAAAAAAlJMnEuwUSX0Lr0MlfeMm5j+SOhReh0vaX8Z6AAAAAACgnDyx7X+ppARjzFWSBksaaYyZbq29+AT/eZLeKry3v56k4ZJOFasX4YG+AQAAAABQ6zie/FtrTxpjoiUNlPTnwq39qcViTkm6u3jdYvUyne4bAAAAAAC1kSdW/mWtzZD0TmXVAwAAAAAApeNQPQAAAAAAvBzJPwAAAAAAXo7kHwAAAAAAL0fyDwAAAACAlyP5BwAAAADAy5H8AwAAAADg5Uj+AQAAAADwciT/AAAAAAB4OZJ/AAAAAAC8HMk/AAAAAABejuQfAAAAAAAvR/IPAAAAAICXI/kHAAAAAMDLkfwDAAAAAODlSP4BAAAAAPByJP8AAAAAAHg5kn8AAAAAALwcyT8AAAAAAF6O5B8AAAAAAC9H8g8AAAAAgJcj+QcAAAAAwMuR/AMAAAAA4OVI/gEAAAAA8HIk/wAAAAAAeDmSfwAAAAAAvBzJPwAAAAAAXo7kHwAAAAAAL0fyDwAAAACAlyP5BwAAAADAy5H8AwAAAADg5Uj+AQAAAADwciT/AAAAAAB4OZJ/AAAAAAC8nEeSf2PMPGPMRmPM1FLer2uMOWCMiS/86eauzBN9AwAAAACgtqnrdIPGmLsk+VhrI40xbxljgq21acXCuktaZK199KJ6vYqXAQAAAACAn85Ya51t0JhZklZZa1caY0ZKamStfbtYzCRJD0rKkrRT0kRJE4qXWWvzitWbUBingICAsDlz5jjadwAAAACoKWJiYlKsteFV3Q/UDI6v/EvylXSo8Pq4pF5uYrZIGmCt/d4YM1/SbaWUfXhxJWvtm5LelKTw8HA7bNgwD3T/pwl6bIWj7X0zY4ij7TlpV6fOjrXVefcux9py2rePJTja3tUzohxtr7piLlQMc8H7MBcqpjrPBcnZ+VBb5oLk7HyoLXNBqt7zgbkA1ByeSP5PS2pUeO0n9+cK7LDWniu8TpYULGm1mzIAAAAAAPATeeLAvxRJfQuvQyV94yZmgTEm1BjjIylGUmopZQAAAAAA4CfyxMr/UkkJxpirJA2WNNIYM91ae/HJ/3+S9C9JRtKH1tq1xpjDxcs80DcAAAAAAGodx5N/a+1JY0y0pIGS/mytPaxiq/jW2i90/sT/y5YBAAAAAICfzhMr/7LWZkh6xxNtAwAAAACA8vHEPf8AAAAAAKAaIfkHAAAAAMDLkfwDAAAAAODlSP4BAAAAAPByJP8AAAAAAHg5kn8AAAAAALwcyT8AAAAAAF6O5B8AAAAAAC9H8g8AAAAAgJcj+QcAAAAAwMuR/AMAAAAA4OVI/gEAAAAA8HIk/wAAAAAAeDmSfwAAAAAAvBzJPwAAAAAAXq5uWYKMMf6SrpJ0XFK6tbbAo70CAAAAAACOueLKvzHmUUkfS1ok6WZJf/dwnwAAAAAAgIPKsu3/dmtthKRj1tqFkjp4uE8AAAAAAMBBZUn+Txpj7pPU0Bhzk6QTnu0SAAAAAABwUlmS/19L6ikpQ9IwSeM92SEAAAAAAOCsKx74Z609IunhSugLAAAAAADwgLIc+DevMjoCAAAAAAA8oyzb/o0xprfHewIAAAAAADziitv+JdWXtNYYs1pSliRrrR3n2W4BAAAAAACnlCX5f6LwBwAAAAAA1EBX3PZvrd0vKURSjKTgwtcAAAAAAKCGKMuBfzMljZR0VtKvCl8DAAAAAIAaoizb/sOstTcVXr9hjNngyQ4BAAAAAABnlSX5zzTGjJK0WVKkpEzPdgkAAAAAADipLI/6Gyupl6RXJXWXdJ9HewQAAAAAABx1xZV/a22GMeZ/rbX7jTG3WGszKqNjAAAAAADAGWU58O9tSXcXvrzHGPOGZ7sEAAAAAACcVJZt/9dba/8iSdbaCZKu92yXAAAAAACAk8qS/B83xtxjjLnWGDNCUvaVKhhj5hljNhpjppbyfl1jzAFjTHzhT7ey1AMAAAAAAOVXluT/15Jckv638L9jLxdsjLlLko+1NlJSB2NMsJuw7pIWWWujC392lrEeAAAAAAAop7Ic+HfMGPOItdYaY66VdPwKVaIlvVN4/YmkvpLSisVESBpqjOkvaaekiWWsBwAAAAAAyslYay8fYMzrkjZI6iTpFkmHrbXDLxM/T9Isa22qMWaQpF7W2hnFYnpL+tZa+70xZr6k9yQNK0O9CZImSFJAQEDYnDlzyjlcAAAAAPAOMTExKdba8KruB2qGK678S7rBWhtrjFltre1rjPn8CvGnJTUqvPaT+1sLdlhrzxVeJ0sKLks9a+2bkt6UpPDwcDts2LAydL9yBT22wtH2vpkxxNH2nLSrU2fH2uq8e5djbTnt28cSHG3v6hlRjrZXXTEXKoa54H2YCxVTneeC5Ox8qC1zQXJ2PtSWuSBV7/nAXABqjrLc859njPl/ktKMMS5JuVeIT9H5LfuSFCrpGzcxC4wxocYYH0kxklLLWA8AAAAAAJRTWVb+75EUJeljSZG6woF/kpZKSjDGXCVpsKSRxpjp1tqLT/D/k6R/STKSPrTWrjXGNC1WL6JcIwEAAAAAAG6V5cC/o5I+KHy5rgzxJ40x0ZIGSvqztfawzq/sXxzzhc6f+H+5eplX7j4AAAAAALiSsqz8l5u1NkM/ntzv8XoAAAAAAKB0ZbnnHwAAAAAA1GAk/wAAAAAAeDmSfwAAAAAAvFyZ7vk3xvhLukrScUnp1toCj/YKAAAAAAA45oor/8aYR3X+MX+LJN0s6e8e7hMAAAAAAHBQWbb9326tjZB0zFq7UFIHD/cJAAAAAAA4qCzJ/0ljzH2SGhpjbpJ0wrNdAgAAAAAATipL8v9rST0lZUgaJmm8JzsEAAAAAACcdcUD/6y1RyQ9fOG1MYZt/wAAAAAA1CBlOfBvQbGif3qoLwAAAAAAwANKXfk3xrSTdK2kG4wx/QqLfSXlVkbHAAAAAACAMy637f9aSdGS/Av/aySdkTTO470CAAAAAACOKTX5t9Z+JukzY0x7a+2fKrFPAAAAAADAQVe8599ae8lKvzGmjee6AwAAAAAAnHbF0/6NMc9KukOSX2FRlqTunuwUAAAAAABwzhVX/iX1k9RHUpLOJ/1HPdojAAAAAADgqLIk/3Ukher8yn93SQEe7REAAAAAAHBUWZL/EZJyJD0pKVbSsx7tEQAAAAAAcNQV7/mXFHzR9VxPdQQAAAAAAHhGWZL//oX/bSRpoKQ0SRs81iMAAAAAAOCoKyb/1tppF66NMU9Imu3RHgEAAAAAAEeV5VF/7S56GSCpo+e6AwAAAAAAnFaWbf/TLro+J+k5D/UFAAAAAAB4QFm2/f+mMjoCAAAAAAA8oyyP+gMAAAAAADVYqSv/xpj1kmzxYknWWnuzR3sFAAAAAAAcU2ryb63tX9p7AAAAAACg5mDbPwAAAAAAXq7cyb8xpo0nOgIAAAAAADzjiqf9G2OmS7pdkl9hUZak7p7sFAAAAAAAcE5ZVv6jJPWRlKTzSf9Rj/YIAAAAAAA4qizJfx1JoTq/8t9dUoBHewQAAAAAABxVluR/hKQcSU9KipU03aM9AgAAAAAAjrriPf+SrrLWJhde31eWRo0x8yR1kbTCWlvqlwXGmJ9JWmWt7WmMqStpb+GPJE221u4sy+cBAAAAAIDSlWXl/wFjzHpjzEvGmBuvFGyMuUuSj7U2UlIHY0zwZcL/IqlR4XV3SYustdGFPyT+AAAAAAA44IrJv7X2t9ba/pLmSbrNGLP7ClWiJb1TeP2JpL7ugowxN+v8kwMOFxZFSBpqjEkyxswr3AkAAAAAAAB+ImOtvXyAMd0lDdb55PyopH9baz++TPw8SbOstanGmEGSellrZxSLqS9ptaQ7JS211kYbY3pL+tZa+70xZr6k96y1HxarN0HSBEkKCAgImzNnTjmHCwAAAADeISYmJsVaG17V/UDNUJbV9d9Jek/SX6y1+WWIP60ft/L7yf3ugsckvWatPWGMuVC2w1p7rvA6WVKJ2wWstW9KelOSwsPD7bBhw8rQncoV9NgKR9v7ZsYQR9tz0q5OnR1rq/PuXY615bRvH0twtL2rZ0Q52l51xVyoGOaC92EuVEx1nguSs/OhtswFydn5UFvmglS95wNzAag5St32X7jiL2vtBElrLiT+xpi7r9Bmin7c6h8q6Rs3MQMkPWiMiZfUwxgzV9ICY0yoMcZHUoyk1LIPAwAAAAAAlOZy9/z/v4uuP73oOvYKbS6VNMYYM1PnHxP4pTHmkhP/rbX9LhzsJ2m7tfZ+SX+StEDSdkkbrbVryzIAAAAAAABweWU9VM9cOeQ8a+1JY0y0pIGS/mytPazLrOIXfgEga+0XOn/iPwAAAAAAcNDlkv9AY8xonU/8f3bx9ZUatdZm6McT/wEAAAAAQBW6XPK/RD8eunfxNUk9AAAAAAA1SKnJv7V2WmV2BAAAAAAAeMblDvwDAAAAAABegOQfAAAAAAAvR/IPAAAAAICXI/kHAAAAAMDLkfwDAAAAAODlSP4BAAAAAPByJP8AAAAAAHg5kn8AAAAAALwcyT8AAAAAAF6O5B9AtfHDyr/q+wV/1InPF182Lj09XT179rzkdVRUVNHrjIwM3XbbbQoPD9fEiRMlSZmZmRo8eLAGDRqkO++8Uzk5OZ4ZBAAAAFANkfwDqBay93wu2QK1GfOy8k4cVu7xQ6XGPvLIIzpz5oyk84n+2LFjlZWVVfT+ggUL9Ktf/UrJyck6deqUkpOTtXDhQsXFxemTTz5RYGCgVq1a5fExAQAAANVF3aruAABI0tmDO+Xbqa8kqVFQT5379ivVa9G2RNy6devk6+urwMBASZKPj4+WLFmiYcOGFcW0bNlSX3zxhU6cOKGDBw/qmmuuUXh4eNH7R48eVevWrT08IgAAAKD6YOUfQLVQkHNWPk1aSpLqNGqi/OwTJWJycnL07LPPasaMGUVlTZs2VbNmzS6J69u3r/bv369Zs2apc+fOatGiRdF7GzduVEZGhiIiIjwzEAAAAKAaIvkHUC3Uqd9QNvf8ffg256xkbYmYGTNmaNKkSWrevPll25o2bZr+9re/6amnnlKnTp309ttvS5KOHz+uyZMn66233nK8/wAAAEB1RvIPoFqoH9hRZ7/9SpKUc2Sf6jYruS1/7dq1mj17tqKjo7V9+3bdf//9btvKyMjQzp07lZ+fr82bN8sYo5ycHN1999164YUX1L59e4+OBQAAAKhuSP4BVAuNgyOV9eU6Hf90jrJ2J6heq3bK2LDgkpgNGzYoPj5e8fHx6tGjh+bOneu2rccff1wTJkxQs2bNdPz4cY0aNUrz5s3T1q1b9dxzzyk6OlpLliypjGEBAAAA1QIH/gGoFuo0aKyfjZ6hs/u2qdmNw+Xj56/6rTuUGh8fH1/qa5fLpS+//PKS92NjYxUbG+tklwGP+mHlX5V77IAaXddbzfuMLDUuPT1dt956q7Zt2yZJGj9+vL766isNGTJEU6dO1b59+/S73/1OJ0+elMvl0ssvv6y8vDx16NBBHTqcn2OvvvqqunXrVinjAgAAVYOVfwDVhk9DP/l2jpKPn39VdwWoUhV99OUHH3yg/Px8bdy4UXv37lVaWpoeffRRPfnkk0pISNC3336r+Ph47dixQ6NGjSraSUPiDwCA92PlH0DN8EyzK8eUua1M59oCPKCij76Mj4/XiBEjJEmDBg1SYmKivv76a/Xq1UuS1Lp1a2VmZuqrr77S8uXLtX79enXr1k1vvPGG6tblnwQAAHgzVv4BAKhmKvroy6ysLLVte/5LghYtWig9PV3Dhw/XtGnT9NFHH2nVqlW65ZZb1Lt3b61du1ZJSUnKzc3VypUrK2VcAACg6vA1PwAA1UxFH33p5+dXdAvA6dOnVVBQoKlTpyoxMVEvvfSSxo4dKz8/P3Xv3l0NGjSQJIWHhystLc3zgwIAAFWKlX8AAKqZij76MiwsTImJiZKk1NRUBQUFSZJ69OihAwcOKC4uTpI0ZswYpaamKj8/X0uXLlVoaGjlDAwAAFQZVv4BAKhmGgdH6vDCKco/fUxn9qYoYNgUZWxYIP9+Y4piNmzYUHQdHR2tuXPn6uTJk4qKitJ3332njz/+WJs2bZIkvfTSS4qLi1Pjxo0lSU899ZRGjx4ta63uuOMODRgwoHIHCAAAKh3JPwAA1UxFH33ZtGlTxcfHa82aNZoyZYqaNTt/UOa0adMuie/atat27Njhsf4DAIDqh+QfAIBq6MKjL8vL39+/6MR/AACAC7jnHwAAAAAAL8fKPwAANckzzRxsK9O5tgAAQLXGyj8AAAAAAF6O5B8AAAAAAC9H8g8AAAAAgJcj+QcAAAAAwMt5JPk3xswzxmw0xky9QtzPjDHbylsPAAAAAACUnePJvzHmLkk+1tpISR2MMcGXCf+LpEYVqAcAAAAAAMrIEyv/0ZLeKbz+RFJfd0HGmJslZUk6XJ56AAAAAACgfIy11tkGjZknaZa1NtUYM0hSL2vtjGIx9SWtlnSnpKXW2ugy1psgaYIkBQQEhM2ZM8fRvgMAAABATRETE5NirQ2v6n6gZqjrgTZPq3ArvyQ/ud9d8Jik16y1J4wxZa5nrX1T0puSFB4ebocNG+Zgt50R9NgKR9v7ZsYQR9tz0q5OnR1rq/PuXY615bRvH0twtL2rZ0Q52l515fhcaDjaucaeyXSuLTEXKoq5UDHMherByflQW+aC5Ox8qC3/RpKq93xgLgA1hye2/afoxy37oZK+cRMzQNKDxph4ST2MMXPLWA8AAAAAAJSTJ1b+l0pKMMZcJWmwpJHGmOnW2qIT/K21/S5cG2PirbX3G2OaFqsX4YG+AQAAAABQ6zi+8m+tPanzh/dtktTfWpt6ceLvJj66lHrO7kUEAAAAAKCW8sTKv6y1Gfrx5H6P1wMAAAAAAKXzxD3/AAAAAACgGiH5BwAAAADAy5H8AwAAAADg5Uj+AQAAAADwciT/AAAAAAB4OZJ/AAAAAAC8HMk/vNrx48e1Zs0a/fDDD47EAQCA6oe/9wBwZST/qLHGjx+vyMhITZ8+3e37GRkZGjp0qJKSktS/f38dPXpUr7/+uqKjoxUdHa0ePXpo4sSJbuMumDRpktb85/8qa0gAAKCYH1b+Vd8v+KNOfL7Y7ftO/b3/6KOPKmtIAFAl6lZ1B4CK+OCDD5Sfn6+NGzdq3LhxSktLU3Bw8CUxO3bs0MyZMxUREaGMjAxt3bpVsbGxio2NlSRNnjxZY8eOdRv3i1/8QgkJCTp8+LAGXj+qKoYIAECtl73nc8kWqM2Yl/XDyv+n3OOHVK9F20tinPp7f/vtt1fFEAGg0rDyjxopPj5eI0aMkCQNGjRIiYmJJWJuuukmRUREaMOGDUpKSlJkZGTRe4cOHVJ6errCw8PdxuXm5uqBBx5QUFCQVqclVNq4AADAj84e3CnfTn0lSY2Ceurct1+ViHHq7/2yZcsqbVwAUBVI/lEjZWVlqW3b89/8t2jRQunp6W7jrLVasmSJ/P39Va9evaLy2bNnF60IuIubP3++unTpoilTpmj797v1dsr7nh0QAAAooSDnrHyatJQk1WnURPnZJ9zGOfH3PikpSa+++qpHxwMAVYnkHzWSn5+fzpw5I0k6ffq0CgoK3MYZYzR79mx1795dH374oSSpoKBA69evV3R0dKlx27Zt04QJExQYGKi7ugzU5we2enxMAADgUnXqN5TNzZEk2ZyzkrVu45z4e3/vvfdq/fr1Hh8TAFQVkn/USGFhYUVb/VNTUxUUFFQi5sUXX9T8+fMlSSdOnFDz5s0lSQkJCbrxxhtljCk1rmPHjtq7d68kacfhPWrbNNDDIwIAAMXVD+yos4Vb/XOO7FPdZq1LxDj19z45OVnt27f39JAAoMqQ/KNGiomJ0YIFCxQXF6d33nlHN9xwg6ZOnXpJzIQJE7RgwQL169dP+fn5GjRokCRp9erV6tev32Xjxo8fr/Xr16tfv36av22pJrpGVur4AACA1Dg4UllfrtPxT+coa3eC6rVqp4wNCy6Jcerv/WuvvaZHHnmkUscHAJWJ0/5RIzVt2lTx8fFas2aNpkyZosDAQIWGhl4S4+/vrzVr1pSo+/zzz18xrkmTJnr33XclSd8+xoF/AABUhToNGutno2fo7L5tanbjcPn4+at+6w6XxDj19x4AvB3JP2osf3//ohP/AQCAd/Jp6CffzlFV3Q0AqPFI/uGVXr5nqGNt3XPto461BQAAHPJMM4fby3S2PQCoZrjnHwAAAAAAL0fyDwAAAACAlyP5BwAAAADAy5H8AwAAAADg5Uj+AQAAAADwciT/AAAAAAB4OZJ/AAAAAAC8HMl/DXH8+HGtWbNGP/zwQ1V3BQAAAABQw5D8VwM/rPyrvl/wR534fLHb9zMyMjR06FAlJSWpf//+Onr0qPLy8tSuXTtFR0crOjpaO3fulCSlp6crKiqqqO6+ffs0ZMgQRUVF6Y9//GOljAcAAAAAUL3UreoO1HbZez6XbIHajHlZP6z8f8o9fkj1WrS9JGbHjh2aOXOmIiIilJGRoa1btyogIECjRo3Siy++WBSXkZGhsWPHKisrq6js0Ucf1ZNPPqmIiAjdc889io+PV3R0dGUNDwAAAABQDbDyX8XOHtwp3059JUmNgnrq3LdflYi56aabFBERoQ0bNigpKUmRkZHatGmTli9fLpfLpfHjxysvL08+Pj5asmSJmjZtWlT366+/Vq9evSRJrVu3VmZmZuUMDAAAAABQbZD8V7GCnLPyadJSklSnURPlZ59wG2et1ZIlS+Tv76969eqpd+/eWrt2rZKSkpSbm6uVK1eqadOmatas2SX1hg8frmnTpumjjz7SqlWrdMstt3h6SAAAAACAaobkv4rVqd9QNjdHkmRzzkrWuo0zxmj27Nnq3r27PvzwQ3Xv3l1t2rSRJIWHhystLc1tvalTp2rw4MGaO3euxo4dKz8/P88MBAAAAABQbZH8V7H6gR11tnCrf86RfarbrHWJmBdffFHz58+XJJ04cULNmzfXmDFjlJqaqvz8fC1dulShoaGlfkaPHj104MABxcXFeWYQDuPJBgAAAADgLJL/KtY4OFJZX67T8U/nKGt3guq1aqeMDQsuiZkwYYIWLFigfv36KT8/X4MGDdJTTz2lMWPGqEePHoqMjNSAAQNK/YyXXnpJcXFxaty4saeHc0VTD3+vUfu/0d+OuU/s3T3Z4IL09HT17NlTkkp92gEAAAAAoKQqO+3fGNNCUpikbdbaWrvEW6dBY/1s9Ayd3bdNzW4cLh8/f9Vv3eGSGH9/f61Zs+aSsq5du2rHjh1u24yPj7/k9bRp0xztc0WtOXVKBdZqUfsgPfH99/omJ0dB9etfEuPuyQa/+MUvJEmPPPKIzpw5UxRX/GkHAAAAAAD3PLLyb4yZZ4zZaIyZWsr7/pKWS3JJWm+MCTDG1DXGHDDGxBf+dPNE36ojn4Z+8u0cJR8//6ruikclZWfrF03OP4ng576+2nomu0SMuycbSNK6devk6+urwMBASXL7tAMAAAAAgHuOr/wbY+6S5GOtjTTGvGWMCbbWFj+NrrukOGvtpsIvAnpJOippkbX2Uaf7VKM90+zKMWVuq2of83fGFuhndc//L9fMx0eHzua6jSv+ZIOcnBw9++yz+ve//62YmBhJKnraQZs2bXTfffdp5cqVuuOOOyprKAAAAABQo3hi5T9a0juF159I6ls8wFr7WWHi30/nV/83SoqQNNQYk1S4c6DKbkmAZzQ2dXSu8GkG2QUFKlDZnmwwY8YMTZo0Sc2bNy+KKevTDgAAAAAAkrGlPFquwg0aM0/SLGttqjFmkKRe1toZbuKMpP+VdLWkkZK6SvrWWvu9MWa+pPestR8WqzNB0gRJCggICJszZ46jfYdnrV+/XpmZmYqJidGiRYt01VVX6aabbrok5oMPPpC/v7/69++vN998U71799a7776r8/+7SPv27VOfPn2UnZ2tu+++W+3atdMzzzyj4cOHX/aJBwAAAIC3iYmJSbHWhld1P1AzeCL5/6vOb9/fVHgLQCdr7fOXiX9W0heSllprzxWW/V5SPWvty6XVCw8Pt8nJyY723QlBj61wtL1vGo52rjGHt/3v6tS5XPGn8/M15uABRTRurISsLP2lzVVafeqU/hAQoM67d0k6f9r/iBEjdO7cOXXt2lWzZ88uSvwlKTo6WvHx8friiy80evRoWWt1xx136Lnnnrvks16+Z+hPH2Che6519k6Uq2dEOdpedcVcqJgLc8Fpx48fV0pKinr27KlWrVqVWna5uj/76KxaNG7uWJ+YCxXDXKgevn0swbG2astckJydD47OBcnR+eDkXJCq93xgLlQtYwzJP8rME9v+U/TjVv9QSd8UDzDGPGqMua/wZXNJJyQtMMaEGmN8JMVISvVA31CF/Hx89Pdr2ql7w0Z6+5p26tSwof4QEHBJzIUnG2zYsEGvvfbaJYm/9OOTDC487WDnzp0lEn+gthk/frwiIyM1ffp0t++7e4Smu7LXX3+96PGZPXr00MSJEy+JG7HoDzqWfaJyBwcAAABHeOK++qWSEowxV0kaLGmkMWa6tfbik//flPSOMeZ+nV/1/0TSIUn/kmQkfWitXeuBvqGKNfPx0eCmTau6G0CNUJbV+g8++ED5+fnauHGjxo0bp7S0NAUHB1/SjrtHaDZs2LBEWWxsrGJjYyVJkydP1tixYy+pe2DZTu08/LWiO7gq/XcBAACAn8bx5N9ae9IYEy1poKQ/W2sPq9gqvrU2o/D9i32h808BQC00+7frqroLQKWaevh7/ffcOd3k56fftiy55f7CivuQIUMUFxendevWqW7duiXK1q1bpxUrVig6OlpHjhzR4sWL9eSTT17S1oWzNS48QvOpp55S08Iv4S4uu+DQoUNKT09XePiPuwg3bNig7d/v0kM//7UHfhsAAADwNI+cqF+Y3L9zxUAAqIXWnDqlAmu1qH2Qnvj+e32Tk6Og+vUviSnrav23336roUOH6u2339Ynn3yirVu3uv3M4o/QLK1MkmbPnl20A+DiuGYNm6huHR7EAgAAUBN54p5/AMBlJGVn6xdNzq+8/9zXV1vPZJeIuemmmxQREVG0Mh8ZGem2LDMzU/Hx8XK5XHrxxReVl5fn9jOLP0KztLKCggKtX79e0dHRJep2DrhOa/6T6PBvAwAAAJWB5B8AKtkZW6Cf1T2/gt7Mx0fH8vLdxpVltf6mm27SmDFjlJSUpB9++EEZGRkl2nnxxRc1f/58SdKJEyfUvHlzt2WSlJCQoBtvvLHosM2L406eO62mDZo494sAyuj48eNas2aNfvjhh8uWAQCA0pH8A0Ala2zq6FzhY1azCwpUIPePXC3Lav2DDz6oZcuWKS4uTunp6ZKkqVOnXtLOhAkTtGDBAvXr10/5+fkaNGiQ2zJJWr16tfr16+e+ri3QTdf2dvz3gdpt6uHvNWr/N/rbMfdJfFmfVlEefHEAAKiNuHkTACrZDQ0bKuVMtkIbNdLuc2d1bbH7/aXzK+5t2rTRfffdd8lqffGyBx98UP/7v/+rQ4cOafPmzRo8eLAGDBhwSVsXHqF5pTJJev7550uNc/JZzoDk7PkXv/jFLySdf/TlV199pSFDhpT4Ikxyf5hm/fr1NXLkSOXn58vX11dLlixRVlaWfvWrX+nIkSMKCwvTG2+8oczMzKK4Ot+c1WvDnlF9n3olPgMAgOqI5B8AKtktfn4ac/CAjublKSErS39pc5X+evSo/hAQUBQzYcIEjRgxQnPnzlXXrl01aNAguVyuEmVt27bV6NGjZa3VHXfcUSLxf/meoY71+55rH3WsLUByf/5F8eS/PE+rqOijL//73/8qLi5OAwcOVGxsrFatWqVvvvlGv/rVr/SrX/1Ko0ePVnJyspKSkorixvSMUfzezRoU3NfTvyYAABxB8g8AlczPx0d/v6adPs/K0rgWLRVQt646NWx4SUxZV+u7du2qHTt2eLzPgCcUP//i0Nlct3FlfVpFfHy8RowYIUkaNGiQEhMTSyT/7r5MuLBrQJKOHj2q1q1b69SpU/riiy904sQJHTx4UNdcc80lj788nn1CrRr7O/SbAADA80j+AaAKNPPx0eDC1cuLzf7tuiroDVA1ynv+xZNPPqkPP/xQ99xzj9uyrKwstW3bVpLUokWLcj36UpI2btyojIwMRUREqE2bNlqxYoVmzZqlzp07q0WLFpfEZZ47pV5tb3DqVwEAgMdx4B8AAKgSF86/kKTd586qbb2S98+X52kVfn5+OnPmjCTp9OnTKigocPu57g7TPH78uCZPnqy33npLkjRt2jT97W9/01NPPaVOnTrp7bffviTuL4Mfc+i3AABA5SD5BwAAVeIWPz99dPKkXjySrtWnTqlj/Qb6a7GT+8vztIqwsDAlJiZKklJTUxUUFFTiM919cZCTk6O7775bL7zwgtq3by/p/MGAO3fuVH5+vjZv3ixjzCVxVzcL9OBvBgAA57HtHwAAVAknz7+QpJiYGEVFRem7777Txx9/rMWLF2vq1KmaPn16UYy7wzT/9re/aevWrXruuef03HPPKTY2Vo8//rh+85vfaP/+/YqMjNSoUaM0b968orhzezM1pucw3dH5Fs/8cgAAcBjJPwAAqDJOn39xb68/affOFN3X+1klvn5MbXSzZv92nR78282S3H9xEBsbq9jY2BJtffnll6XG8ehLAEBNQ/IPAAC8RuMGTdTruuiq7gYAANUOyT8AAPB6L98z1NH27rn2UUfbAwDA0zjwDwAAAAAAL0fyDwAAAACAlyP5BwAAAADAy5H8AwAAAADg5Uj+AQAAAADwciT/AAAAAAB4OZJ/AAAAAAC8HMk/AAAAAABejuQfAAAAAAAvR/IPAAAAAICXI/kHAAAAAMDLkfwDAAAAAODlSP4BAAAAAPByJP8AAAAAAHg5kn8AAAAAALwcyT8AAAAAAF6O5B8AAAAAAC9H8g8AAAAAgJcj+QcAAKgEj6ycoWELYvXXz//h9v3MzEwNHjxYgwYN0p133qmcnBzt27dPQ4YMUVRUlP74xz9KkvLy8tSuXTtFR0crOjpaO3fuLGojPT1dPXv2rJTxAABqlipL/o0xLYwxA40xraqqDwAAAJXh4z2fKd8WaNmY13XgxPfad/xgiZiFCxcqLi5On3zyiQIDA7Vq1So9+uijevLJJ5WQkKBvv/1W8fHx2rFjh0aNGqX4+HjFx8erW7duRW088sgjOnPmTGUODQBQQ3gk+TfGzDPGbDTGTC3lfX9JyyW5JK03xgSUpR4AAEBNtPHgdt3eqb8kqV9QbyV9u7NEzKRJkzRw4EBJ0tGjR9W6dWt9/fXX6tWrlySpdevWyszM1KZNm7R8+XK5XC6NHz9eeXl5kqR169bJ19dXgYGBlTQqAEBN4njyb4y5S5KPtTZSUgdjTLCbsO6S4qy1z0laLalXGesBAADUONk5ZxTYJECS1LxRE/2QfbzU2I0bNyojI0MREREaPny4pk2bpo8++kirVq3SLbfcot69e2vt2rVKSkpSbm6uVq5cqZycHD377LOaMWNGZQ0JAFDD1PVAm9GS3im8/kRSX0lpFwdYaz+TJGNMP51f/f+TpOlXqgcAAFAT+dZvpLO55ySd/yKgwFq3ccePH9fkyZP1/vvvS5KmTp2qxMREvfTSSxo7dqz8/PzUvXt3NWjQQJIUHh6utLQ0bd++XZMmTVLz5s0rZTwAgJrH2FL++FS4QWPmSZplrU01xgyS1MtaW+JraGOMkfS/kq6WNLLw+rL1jDETJE2QpICAgLA5c+Y42ncAAABPWL9+vTIzMxUTE6NFixbpqquu0k033XRJTG5urv70pz/pl7/8pXr06FFUfubMGf3P//yPZsyYoQYNGujPf/6z7r77brVr107PPPOMhg8friVLluj8P62kffv2qU+fPvrd735XmUMEUAViYmJSrLXhVd0P1AyeSP7/KmmRtXZT4Vb+Ttba5y8T/6ykLyT1KU+98PBwm5yc7GjfnRD02ApH2/um4WjnGnsm07m2JO3q1NmxttZFz3asLUk6mzHTsbbuufZRx9qSpKtnRDnaXnXFXKgY5oL3YS5UTHWeC1L558Opc1n65cLf6eftwxS/d5NmD3tGy3ev15R+DxTNhddff13/8z//o9DQUElSbGys7rnnHj399NPq2LGjxowZI0n64osvNHr0aFlrdccdd+i555675LOio6MVHx//0wfpAU7OB0fnguTofHByLkhS5927HG3PSd8+luBYW7Xl74KTjDEk/ygzT2z7T9H5LfubJIVK2lM8wBjzqKTvrbXzJTWXdKIs9QAAAGqiJg189c7oWUrYt0WxN45Sa7+W6tK64yUxsbGxio2NLVF32rRpl7zu2rWrduzYUepnVdfEHwBQtTyR/C+VlGCMuUrSYEkjjTHTrbUXn+D/pqR3jDH36/yq/yeSmhSrF+GBvgEAAFSJ5g2b6PbON1d1NwAAtZTjyb+19qQxJlrSQEl/ttYelpRaLCaj8P2LFa/n7F5EAACAauiZZ56p1u0BALyDJ1b+LyT371wx0KF6AAAAAACgdHWqugMAAAAAAMCzSP4BAAAAAPByJP8AAAAAAHg5kn8AAAAAALwcyT8AAAAAAF6O5B8AAAAAAC9H8g8AAAAAgJcj+QcAAAAAwMuR/AMAAAAA4OVI/gEAAAAA8HIk/wAAAAAAeDmSfwAAAAAAvBzJPwAAAAAAXo7kHwAAAAAAL0fyDwAAAACAlyP5BwAAAADAy5H8AwAAAADg5Uj+AQAAAADwciT/AAAAAAB4OZJ/AAAAAAC8HMk/AAAAAABejuQfAAAAAAAvR/IPAAAAAICXI/kHAAAAAMDLkfwDAAAAAODlSP4BAAAAAPByJP8AAAAAAHg5kn8AAAAAALwcyT8AAAAAAF6O5B8AAAAAAC9H8g8AAAAAgJcj+QcAAAAAwMuR/AMAAAAA4OU8kvwbY+YZYzYaY6aW8n4zY8zHxphPjDH/NsbUN8bUNcYcMMbEF/5080TfAAAAgP+/vbsPs7KsFz3+vUOBkeFFjAlfCsraopwAYcxh29BKBOWYAiq+td20I3FD29O+RndqYRcUHDBO1tZN7iOQJYcKCxMpI1KaBBs3CgpalCiwEZWJZBwF0QHmPn+sxTjMiw74LIZ55vu5rrl41r1+973ue1w/5/mt52VJUnuTePEfQrgE6BBjHAp8LITwiSbCPg/cHmMcCWwHLgAGAD+JMWZyP88kPTdJkiRJktqjfBz5zwD35baXA59uGBBj/H6M8be5h72AvwIlwOdCCKtzZw4ck4e5SZIkSZLU7oQYY7IDhjAfuCPGuC6EMBIYHGOc1UzsUGB6jHF4COEsYFuM8ZUQwr3Az2OMDzaInwhMBOjVq9eQuXPnJjp3SZIkSWorxowZsybGWNza81DbkI+j67uAgtx2Ic2cXRBC6AncCVyaa1ofY3w7t/0k0OhygRjj3cDdAMXFxXH06NEJTjsZfW/+VaLjbel8dXKDTa1ObixgQ7/TExtrRWZOYmMBvFV1e2JjXfHRmxIbC+CUWaWJjne0MhcOj7mQPubC4TmacwGSzYd5nR9JbCyAqVOnJjpekpLMh0RzARLNhyRzAeD0P29IdLwkbbt5ZWJjtZe/C1Jrycdp/2t451T/gcCWhgEhhI7Az4BbYoz/nWteEEIYGELoAIwB1uVhbpIkSZIktTv5KP4fAK4JIdwOXA78MYQwvUHMBGAw8PXcnf2vAL4JLACeBipijA/nYW6SJEmSJLU7iZ/2H2N8PYSQAUYA344xbqfBUfwY413AXU10H5D0fCRJkqS02rlzJ2vWrOHMM8/kgx/8YGtPR9JRLB9H/okxVsUY78sV/pIkSZLexZTtr3DVf2/hP1/9W5PPV1dXM2rUKEaOHMnYsWOpqamhqqqKz33uc6xevZrPfvaz7Nixg3379vGRj3yETCZDJpPhmWf89mxJWX6dniRJktSKfvvGG9TGyE/69OXrr7zClpoa+nbseFDMwoULKSsrY8SIEUyaNIlly5bRvXt3br/9dkpKSqiqqmLt2rX06tWLq666ittuu62VViPpaJWXI/+SJEmSWmb1m29yftduAJzTpQtr97zZKGby5MmMGDECgB07dlBUVMRnPvMZSkpKePTRR1m9ejVDhw7l8ccf55e//CWf+tSnmDBhAvv27Tuia5F09LL4lyRJklrRnljLh47JnpDbvUMHXt23v9nYiooKqqqqKCkpASDGyKJFizj++OM59thjOeuss3j44YdZvXo1e/fu5aGHHjoia5B09LP4lyRJklrRceEDvB0jAG/W1lJLbDJu586dXH/99fzgBz+oawshMGfOHAYMGMCDDz7IgAEDOPHEEwEoLi5m48aN+V+ApDbB4l+SJElqRf07d2ZN7lT/P7/9Ficfe2yjmJqaGsaNG8fMmTPp06cPALfddhv33nsvAK+99ho9evTgmmuuYd26dezfv58HHniAgQMHHrmFSDqqWfxLkiRJrWh4YSFLX3+d2/5ayW/eeIOPd+zEv+/YcVDM/PnzWbt2LTNmzCCTybBo0SImTpzIggULGDZsGPv372fkyJF84xvf4JprrmHQoEEMHTqU8847r5VWJelo493+JUmSpFZU2KEDP/zwR/jD7t18secJ9DrmGPp17lz3/Jx/XgGcxvQrFte1/e138OPfPcWYU2/JNtTC9yf9DoDr/v572bZXD/R9x5f/89x8LkXSUcziX5IkSWpl3Tt0YFS3bq09DUkp5mn/kiRJkiSlnMW/JEmSJEkpZ/EvSZIkSVLKWfxLkiRJkpRyFv+SJElSyiwsn83/+cW/sGzt/2vy+erqakaNGsXIkSMZO3YsNTU1AFRWVlJaWloXt3nzZi688EJKS0u54YYb6tobxkk6+ln8S5IkSSny9KaV1MZabhz7H/zt9Vf4a/W2RjELFy6krKyM5cuX07t3b5YtW0ZVVRXjx49n9+7ddXE33XQTt956KytXrmTbtm2Ul5c3GSfp6GfxL0mSJKXIxlfWMfjUDAD9ThnCC6882yhm8uTJjBgxAoAdO3ZQVFREhw4dWLRoEd3qfeXgc889x+DBgwEoKiqiurq6yThJRz+Lf0mSJClFavbuoXuXDwLQpVM33thT1WxsRUUFVVVVlJSU0K1bN7p3737Q85dddhnTpk1j6dKlLFu2jOHDhzcZJ+nod0xrT0CSJElScjodW8DefW8D8PbePcRY22Tczp07uf7661m8eHGzY02ZMoVVq1Yxe/Zsxo8fT2FhYV7mLCn/PPIvSZIkpciHe/0dL2zPnur/0qsv0LNr70YxNTU1jBs3jpkzZ9KnT593HW/QoEFs3bqVsrKyvMxX0pFh8S9JkiSlyIC+5/DEc79l8R++z9pNv+fEnn1ZuvoHB8XMnz+ftWvXMmPGDDKZDIsWLWp2vNmzZ1NWVsZxxx2X76lLyiNP+5ckSZJSpKBjF75y8e38edsaRgy6km7H9eSUE049KGbSpElMmjSpyf7l5eUHPZ42bVqzcdtuXpnInCXln8W/JEmSlDLHdepad8f/+r5zxecSfZ0rPnpTouNJyh9P+5ck5c2ND81i9IJJ/PsfftTk89XV1YwaNYqRI0cyduxYampqAJgwYQJDhw5l+vTpAGzevJkLL7yQ0tJSbrjhhnftK0mSpMYs/iVJefHrv/ye/bGWJdfcxdbXXmHzzhcbxSxcuJCysjKWL19O7969WbZsGffffz/79++noqKCTZs2sXHjRm666SZuvfVWVq5cybZt2ygvL2+yryRJkprmaf+SpLyoePFpLur3WQCG9T2L1due4aM9P3xQzOTJk+u2d+zYQVFRET/+8Y+5/PLLARg5ciSrVq3iueeeY/DgwQAUFRVRXV3dZF9JkiQ1zSP/kqS8eLNmD7279gKgR0FX/vbmzmZjKyoqqKqqoqSkhN27d3PyyScD0LNnTyorK7nsssuYNm0aS5cuZdmyZQwfPrzJvpIkSWqaR/4lSXnRpWMBb+19G8h+EFAbY5NxO3fu5Prrr2fx4sUAFBYWsmfPHgB27dpFbW0tU6ZMYdWqVcyePZvx48dTWFjYZF9JkiQ1zSP/kqS8+GTv01i9bT0Af/rrC5zSvXejmJqaGsaNG8fMmTPp06cPAEOGDGHVqlUArFu3jr59+wIwaNAgtm7dSllZWbN9JUmS1DSLf0lSXpz/iVLu/+Nypj3yH/zyzys47YMf5duPzj0oZv78+axdu5YZM2aQyWRYtGgRY8aMYcGCBZSVlXHfffdx4YUXAjB79mzKyso47rjjmu0rSZKkpnnavyQpL7p26sJ9V9/Bys1PMOnsqygqPIEzij5e9/zUqVMB+MpXvlLXtmHDBjZs2MAFF1zApk2buPDCC/nud78LQAiBF154oa5f/b712yRJktSYxb8kKW96dO7KRaefe8j9CgoK6N+/fx5mJEmS1D552r8kSZIkSSln8S9JkiRJUspZ/EuSJEmSlHJ5Kf5DCPNDCBUhhCnNPN89hPDrEMLyEMIvQggdW9JPkiRJkiQdusSL/xDCJUCHGONQ4GMhhE80EfZ54PYY40hgO3BBC/tJkiRJkqRDlI8j/xngvtz2cuDTDQNijN+PMf4297AX8NeW9JMkSZIkSYcuxBiTHTCE+cAdMcZ1IYSRwOAY46xmYocC02OMw1vSL4QwEZgI0KtXryFz585NdO6SJEmS1FaMGTNmTYyxuLXnobbhmDyMuQsoyG0X0szZBSGEnsCdwKUt7RdjvBu4G6C4uDiOHj06uVknpO/Nv0p0vC2dr05usKnVyY0FbOh3emJjrcjMSWwsgLeqbk9srCs+elNiYwGcMqs00fGOVubC4WlPuTCv8yOJjTV16tTExkqauXB4juZcgGTzIclcgPaTD4nmAiSaD0nmAiSbD0dzLrSXfSSpteTjtP81vHPK/kBgS8OA3A3+fgbcEmP875b2kyRJkiRJhy4fR/4fAFaGEE4CRgFXhhCmxxjr38F/AjAY+HoI4evAXU30K8nD3CRJkiRJancSL/5jjK+HEDLACODbMcbtwLoGMXeRLfgP0qBfsuciSpIkSZLUTuXjyD8xxireuXN/3vtJkiRJkqTm5eOaf0mSJEmSdBSx+JckSZIkKeUs/iVJkiRJSjmLf0mSJEmSUs7iX5IkSZKklLP4lyRJkiQp5Sz+JUmSJElKOYt/SZIkSZJSzuJfkiRJkqSUs/iXJEmSJCnlLP4lSZIkSUo5i39JkiRJklLO4l+SJEmSpJSz+JckSZIkKeUs/iVJkiRJSjmLf0mSJEmSUs7iX5IkSZKklLP4lyRJkiQp5Sz+JUmSJElKOYt/SZIkSZJSzuJfkiRJkqSUs/iXJEmSJCnlLP4lSZIkSUo5i39JkiRJklLO4l+SJEmSpJSz+JckSZIkKeUs/iVJkiRJSjmLf0mSJEmSUs7iX5IkSZKklLP4lyRJkiQp5Sz+JUmSJElKOYt/SZIkSZJSzuJfkiRJ0hFz40OzGL1gEv/+hx81G1NZWUlpaWmT7WeeeeZBbZMnT2bp0qUA3HXXXWQyGTKZDIMGDeK6665LdvJSG2bxL0mSJOmI+PVffs/+WMuSa+5i62uvsHnni41iqqqqGD9+PLt372703I033siePXvqHq9cuZLt27dz0UUXATBp0iTKy8spLy+ntLSUa6+9Nn+LkdqYvBT/IYT5IYSKEMKUd4n5UAhhZb3Hx4QQtoYQynM/n8zH3CRJkiS1jooXn+aifp8FYFjfs1i97ZlGMR06dGDRokV069btoPYVK1bQpUsXevfuDcDevXu59tpr6du3L0uWLDko9qWXXqKyspLi4uI8rURqexIv/kMIlwAdYoxDgY+FED7RRMzxwI+ALvWaBwA/iTFmcj+N/08gSZIkqc16s2YPvbv2AqBHQVf+9ubORjHdunWje/fuB7XV1NTwrW99i1mzZtW13XvvvZxxxhl89atfZfXq1dx55511z82ZM4dJkyblaRVS2xRijMkOGMIdwLIY40MhhCuBghjjPQ1iugEBWBJjzOTaJgNfBnYDzwDXxRj3Neg3EZgI0KtXryFz585NdO6SJEmS8mfevHmUlpZy2mmnUVFRwbZt2xg3blyTsV//+teZMWMGAIsWLeKUU07hnHPOqWu/++67KS4uZvDgwbz44ossXLiQm2++mdraWm655RZmzZpFCOFILu+IGzNmzJoYo6c3qEWOycOYXYCXcts7gcENA2KMrwMNk/EJ4LwY4yshhHuB/wk82KDf3cDdAMXFxXH06NGJT/796nvzrxIdb0vnq5MbbGp1cmMBG/qdnthYKzJzEhsL4K2q2xMb64qP3pTYWACnzGp885o0MhcOT3vKhXmdH0lsrKlTpyY2VtLMhcNzNOcCJJsPSeYCtJ98SDQXINF8SDIXINl8aO1c2BwH8Ldfb2HIzqGUr6zk7BM+xpCKnkDjfaTvfve7HNjf/853vsPLL7/M6tWr2bZtG0uXLmXEiBF07NiR0aNHs2DBAkpKShg9ejS///3vueCCCxgzZkwia5TSIh/F/y6gILddSMsvLVgfY3w7t/0k0OhyAUmSJElt1/mfKOXShf9C5a5XKd/0OHNGT+Xbj87lq8Pe/cZ8jz76aN12JpNh3rx5vPHGG3zxi1/kpz/9KXv37uXnP/85AL/5zW8YNmxYXtchtUX5KP7XAJ8GHgcGAn9pYb8FIYQZwLPAGOB/52FukiRJklpJ105duO/qO1i5+QkmnX0VRYUncEbRx4HGZ61kMpkmz2Sp396/f3/69+8PwIFLgjt27Mj69eu55JJL8rYOqS3KR/H/ALAyhHASMAq4MoQwPcbY7J3/c74J/JjsvQAejDE+nIe5SZIkSWpFPTp35aLTz23taUjtTuLFf4zx9RBCBhgBfDvGuB1Y10xspt72s2Tv+C9JkiRJkhKUjyP/xBirgPvyMbYkSZIkSTo0Lb0ZnyRJkiRJaqMs/iVJkiRJSjmLf0mSJEmSUs7iX5IkqY1ZsmQJ8+fPP+i7zxuqrKyktLS07nF1dTWjRo1i5MiRjB07lpqaGjZv3syFF15IaWkpN9xwA0CTbZKkts/iX5IkqQ3ZsGEDMUYmTJhAVVUVr776aqOYqqoqxo8fz+7du+vaFi5cSFlZGcuXL6d3794sW7aMm266iVtvvZWVK1eybds2ysvLm2yTJLV9Fv+SJEltyJYtW+jfvz8Ap556Klu3bm0U06FDBxYtWkS3bt3q2iZPnsyIESMA2LFjB0VFRTz33HMMHjwYgKKiIqqrq5tskyS1fRb/kiRJbUhNTQ1du3YFoKCg4KCj+wd069aN7t27N9m/oqKCqqoqSkpKuOyyy5g2bRpLly5l2bJlDB8+vMk2SVLbZ/EvSWozkrrOuX7smWeeCcC+ffv4yEc+QiaTIZPJ8Mwzz+RvIdL70LFjR/bt2wdkPwiIMba4786dO7n++uv5wQ9+AMCUKVMYNWoU8+bNY/z48RQWFjbZJklq+yz+JUltQpLXOR9w4403smfPHgDWr1/PVVddRXl5OeXl5Xzyk5/M/6Kkw3DSSSfVneq/fft2evTo0aJ+NTU1jBs3jpkzZ9KnT5+69kGDBrF161bKysretU2S1LZZ/EuS2oQkr3MGWLFiBV26dKF3794APP744/zyl7/kU5/6FBMmTKg7siodbfr168f69ev5zW9+w5/+9Cd69erFihUr3rPf/PnzWbt2LTNmzCCTybBo0SIAZs+eTVlZGccdd1xdbFNtkqS27ZjWnoAkSS3R8Drn1157rVFM/aK/ofrXOdfU1PCtb32LX/ziF4wZMwaAs846i4cffpgTTzyRf/zHf+Shhx7i4osvzsdSpPelU6dOjB8/nk2bNnHOOedQWFhY9yEWwCMrTq3bvvUb7zz+u9Pg54t7Ai/mnv0aj6z4GsM+c6Df1Lp+B9rgmrytQ5J0ZFn8S5LahCSuc168eDEAs2bNYvLkyQedLj1gwAA6deoEQHFxMRs3bkxu8lLCCgoK6s6EkSSpJTztX5LUJiR5nfPDDz/MnDlzyGQyPP3003zpS1/immuuYd26dezfv58HHniAgQMH5mspkiRJR5xH/iVJbUK/fv245557eOONN3j++ee59NJLWbFiBeeee+679qt/nfOMGTOYNGnSQd8WkMlkmDdvHs8++yxXX301MUYuvvhizjvvvHwvSZIk6Yix+JcktQnvdp1z/WucoWXXOTcV+93vZduGnzsjn0uRJEk64iz+JUlthtc5S5IkHR6v+ZckSZIkKeUs/iVJkiRJSjmLf0mSJEmSUs7iX5IkSZKklLP4lyRJkiQp5Sz+JUmSJElKOYt/SZIkSZJSzuJfyoMbH5rF6AWT+Pc//KjZmMrKSkpLSw9qmzBhAkOHDmX69OkHtU+ePJmlS5cCcNddd5HJZMhkMgwaNIjrrrsu+QVIkiRJShWLfylhv/7L79kfa1lyzV1sfe0VNu98sVFMVVUV48ePZ/fu3XVt999/P/v376eiooJNmzaxceNGAFauXMn27du56KKLAJg0aRLl5eWUl5dTWlrKtddee2QWJkmSJKnNsviXElbx4tNc1O+zAAzrexartz3TKKZDhw4sWrSIbt261bWVl5dz+eWXAzBy5EhWrVrF3r17ufbaa+nbty9Lliw5aIyXXnqJyspKiouL87gaSZIkSWlg8S8l7M2aPfTu2guAHgVd+dubOxvFdOvWje7dux/Utnv3bk4++WQAevbsSWVlJffeey9nnHEGX/3qV1m9ejV33nlnXfycOXOYNGlSHlciSZIkKS0s/qWEdelYwFt73wayHwTUxtiifoWFhezZsweAXbt2UVtby1NPPcXEiRPp3bs3//AP/8Dvfvc7AGpra/nd735HJpPJyxokSZIkpYvFv5SwT/Y+jdXb1gPwp7++wCnde7eo35AhQ1i1ahUA69ato2/fvnz84x9n06ZNADz55JP06dMHyN4H4OyzzyaEkIcVSJIkSUqbY1p7AlLanP+JUi5d+C9U7nqV8k2PM2f0VL796Fy+Oix7Y76pU6fWxW7ZsqXu8dtvv80999zD4sWLef7555kwYQIhBB588EFuu+02amtrGTduHFOnTuWRRx7hpJNOaoXVSZIkSWqLLP6lhHXt1IX7rr6DlZufYNLZV1FUeAJnFH28ydgvfOELddudOnVi/PjxbNq0iXPOOYfOnTsDMG7cuEb9hg8fnpe5S5IkSUoni38pD3p07spFp597yP0KCgro379/HmYkSZIkqT3zmn9JkiRJklIuL8V/CGF+CKEihDDlXWI+FEJYeaj9pPZoyZIlzJ8/n0cffbTZmAkTJjB06FCmT58OwObNm7nwwgspLS3lhhtuOCi2srKSM888M69zliRJknT0SLz4DyFcAnSIMQ4FPhZC+EQTMccDPwK6HEo/qT3asGEDMUYmTJhAVVUVr776aqOY+++/n/3791NRUcGmTZvYuHEjN910E7feeisrV65k27ZtlJeX18XfeOONdV8rKEmSJCn9Qmzhd5C3eMAQ7gCWxRgfCiFcCRTEGO9pENMNCMCSGGPmEPpNBCYC9OrVa8jcuXMTnbt0NJo7dy5nnnkmxcXFrFy5kpqamkY3/GsqZunSpcyePZtjjz2Wu+++m4EDB3L22Wezfv16HnvsMbZt28aMGTNaaVWSJEl6v8aMGbMmxljc2vNQ25CPG/51AV7Kbe8EBjcMiDG+DjT8jvKW9LsbuBuguLg4jh49OrFJJ6Xvzb9KdLwtna9ObrCp1cmNBWzod3piY63IzElsLIC3qm5PbKwrPnpTYmMBzOv8yCHFv/TSS5xyyik89dRTVFZW8sorr9CzZ0/gna8NfPDBB7nssssYOHAgBQUFrF27lgkTJvDUU09RUlLCc889x6JFi+jYsSPf+973+MUvfsGYMWPIZw6ZC4fHXDg8pcMWJDYWwPBzX0hsLHPh8BzNuQDJ5kOSuQDJ5kOSuQDJ5kOiuQCJ5kOSuQDJ5kN7yoX6X68sKT/X/O8CCnLbhYfwGofbT0q1jh07sm/fPgBqampo6mydwsLCutP4d+3aRW1tLVOmTGHUqFHMmzeP8ePHU1hYyKxZs5g8eTI9evQ4kkuQJEnKiyTvi9Qwrrq6mlGjRjFy5EjGjh1LTU1Nfhcj5Vk+Cuw1wKdz2wOBLXnuJ6XaSSedxNatWwHYvn17k4X7kCFDWLVqFQDr1q2jb9++AAwaNIitW7dSVlYGwMMPP8ycOXPIZDI8/fTTfOlLXzoia5AkSUpakvdFaipu4cKFlJWVsXz5cnr37s2yZctaYZVScvJx2v8DwMoQwknAKODKEML0GON73cG/Yb+SPMxNanP69evHPffcwxtvvMHzzz/PpZdeyooVKzj33HPrYsaMGUNpaSkvv/wyv/71r3n88ccBmD17NmVlZRx33HEAB30qnslkmDdv3pFdjCRJUkK2bNlC//79ATj11FPZunUrJ5xwwkEx5eXlXH755QCMHDmSVatW8dxzzzF4cPYK46KiIqqrq5uMmzx5ct04O3bsoKio6EgsS8qbxIv/GOPrIYQMMAL4doxxO7CumdjMu/RL9kJEqY3q1KkT48ePZ9OmTZxzzjkUFhbSu3dvAB5ZcWpd3LRv7mfNmoV881udeXJN9g/asM+Qi5vaaNxbv3Fwf0j+2k5JkqR8qampoWvXrgAUFBTw2muvNYrZvXs3J598MgA9e/Zk7dq1XHbZZUybNo2SkhKWLVvGzJkzefDBBxvFHVBRUUFVVRUlJR6bVNuWjyP/xBirgPuOVD8p7QoKCuo+2W5O164dyGQKj9CMJEmSWtf7uS/SqlWrmD17dt19kZqKA9i5cyfXX389ixcvPkKrkvLHm+pJkiRJanOSvC9SU3E1NTWMGzeOmTNn0qdPn/wvSMqzvBz5lyRJkqR8eq/7Ij2y4lR6HF/LtGkv8/jj03niiTe5486TeWTFrfzohzs5/4JjqXj8kwBNxv3bV7/C6tU7ufHf/sDxPc5m0qRJXHHFFa25ZOl9sfiXJEmS1Oa8232RDujS5QN85/YTWbNmD1dc2Z3CwuyJz+O/0PM94y6+uBsXX9wNgOHnlh+RNUn5ZPEvSZIkqU1K8r5I3j9Jaec1/5IkSZIkpZzFvyRJkiRJKWfxL0mSJElSyln8S5IkSZKUchb/kiRJkiSlnMW/JEmSJEkpZ/EvSZIkSVLKWfxLkiRJkpRyFv+SJEmSJKWcxb8kSZIkSSln8S9JkiRJUspZ/EuSJEmSlHIW/5IkSZIkpZzFvyRJkiRJKWfxL0mSJElSyln8S5IkSZKUchb/kiRJkiSlnMW/JEmSJEkpZ/EvSZIkSVLKWfxLkiRJkpRyFv+SJEmSJKWcxb8kSZIkSSln8S9JkiRJUspZ/EuSJEmSlHIW/5IkSZIkpZzFvyRJkiRJKWfxL0mSJElSyln8S5IkSZKUchb/kiRJkiSlXF6K/xDC/BBCRQhhSktjQgjHhBC2hhDKcz+fzMfcJEmSJElqbxIv/kMIlwAdYoxDgY+FED7RwpgBwE9ijJnczzNJz02SJEmSpPYoxBiTHTCEO4BlMcaHQghXAgUxxnveKyb382VgN/AMcF2McV+DfhOBiQC9evUaMnfu3ETnLkmSJEltxZgxY9bEGItbex5qG47Jw5hdgJdy2zuBwS2MeQQ4L8b4SgjhXuB/Ag/W7xRjvBu4G6C4uDiOHj06+dm/T31v/lWi423pfHVyg02tTm4sYEO/0xMba0VmTmJjAbxVdXtiY13x0ZsSGwtgXudHEhurdNiCxMYCGH7uC4mNZS4cHnPh8JgLh8lcOGxJ5kOSuQDJ5kOSuQDJ5kOiuQCJ5kOSuQDJ5oO5cHiSzgWpNeSj+N9F9ig+QCFNX1rQVMz6GOPbubYngUaXC0iSJEmSpEOXjxv+rQE+ndseCGxpYcyCEMLAEEIHYAywLg9zkyRJkiSp3cnHkf8HgJUhhJOAUcCVIYTpMcYp7xJTAqwHfgwE4MEY48N5mJskSZIkSe1O4sV/jPH1EEIGGAF8O8a4nQZH8ZuIqQaqyd7xX5IkSZIkJSgfR/6JMVYB973fGEmSJEmS9P7l45p/SZIkSZJ0FLH4lyRJkiQp5Sz+JUmSJElKOYt/SZIkSZJSzuJfkiRJkqSUs/iXJEmSJCnlLP4lSZIkSUo5i39JkiRJklLO4l+SJEmSpJSz+JckSZIkKeUs/iVJkiRJSjmLf0mSJEmSUs7iX5IkSZKklLP4lyRJkiQp5Sz+JUmSJElKOYt/SZIkSZJSzuJfkiRJkqSUs/iXJEmSJCnlLP4lSZIkSUo5i39JkiRJklLO4l+SJEmSpJSz+JckSZIkKeUs/iVJkiRJSjmLf0mSJEmSUs7iX5IkSZKklLP4lyRJkiQp5Sz+JUmSJElKOYt/SZIkSZJSzuJfkiRJkqSUs/hPoQlL9jB0/m6mP/p28zETJjB06FCmT59+BGcmHVnmgpRlLkhZ5oKk9sziP2Xu37CX/REqJnRhU1UtG1/d3zjm/vvZv38/FRUVbNq0iY0bN7bCTKX8MhekLHNByjIXJLV3Fv8pU75lP5f3PwaAkacew6qtjf+wlZeXc/nll2djRo5k1apVR3SO0pFgLkhZ5oKUZS5Iau8s/lNmd03k5K7Z/6w9CwKVu2PjmN27Ofnkk7MxPXtSWVl5ROcoHQnmgpRlLkhZ5oKk9i4vxX8IYX4IoSKEMOVQYlrST++usGNgz77sH7NdNZHaxn/XKCwsZM+ePdmYXbuora09klOUjghzQcoyF6Qsc0FSe5d48R9CuAToEGMcCnwshPCJlsS0pJ/e25CTPlB3Gtu67fvp26Pxf+IhQ4bUnca2bt06+vbteySnKB0R5oKUZS5IWeaCpPYuxNjEx57vZ8AQ7gCWxRgfCiFcCRTEGO95rxjgzBb0mwhMzD08DfhLopM/sj4I/C0P434A6Ae8DnQHNgHHAy+/S8yfgcYXvklHhrkgZZkLUpa5ILVcnxhjr9aehNqGY/IwZhfgpdz2TmBwC2Pes1+M8W7g7iQn21pCCE/GGIvzNPbxwAjg0Rjj9sONkY4Ec0HKMhekLHNBkvIjH8X/LrJH8gEKafrSgqZiWtJPLRBjrALue78xUltnLkhZ5oKUZS5Ias/yUWCvAT6d2x4IbGlhTEv6SZIkSZKkQ5SPI/8PACtDCCcBo4ArQwjTY4xT3iWmBIhNtKVZKi5fkBJgLkhZ5oKUZS5IUh4kfsM/OPzrqbzGSpIkSZKk5OWl+JckSZIkSUcPb6onSdL7FEIYFEIYdLSOJ0mSZPEPhBCmhhAyh9jne0nGtXAsdwZ11DqcPGpmnL4hhMdCCOUhhKHvf2ZNvkYmhNA3H2Or3RqU+zlax5OOCiHrjhDCqhDC/SGEH4cQnsr9P39RCKFDCOGHIYSnQwhPhhCuzfWbHkJ4PIRQlc+/D5KUZvm44V+7EGP81yTjWmhQ7t+nExxTOtoMA+6JMc7L42tkgHL8VhG9hxDCccC9QBHwDPAT4CvAFcBjwOXAZGBsLv6aGOPw3HY58AQwIMZ4fgihEPg50AV4Psb4T8285swWjnfQ3GKMX26mrQD4GdANeBUYF2Pcl9xvSTokw4G+McZPhxC+CdwCfDbGuCqE8J/AyFzcvwAbgHUhhP+KMU4JIcwD5sUYz2udqUtS29Zui//czQV/BnQAArA6hPBzDt5h6gz8EDgFeA24PMb4Zq5/eYwxk9tudseqQVyn3HgnAduAfwK+BhwLlOb6X9DUzQ6T3hl8/79BpUlLipQQwlQavFeBtzk4j8qbeZ9XAH8FaoDeZIv7/2xiHvXf51fFGIeHEHoCPwJ6AGtijP+aO2o/Izceufl9KPe63YGlMcaZIYQiYFFu3n+MMV4XQrgH+CwwJoTwxxjj5xP5JSqtJgLPxhinhhDuB14HdgH/ATwQY3wRuCWE8BeAGOMP6/UtAe6IMf5b7vGJwJ3Aw8CyEMKHYoyVDV8wxtjS8Q6aWwhhAHBuE23HArUxxmEhhIuBQrJ/06TWkCH74Stk86H+tzt9ENh94EGM8dUQwq/Ifii8/gjNT5JSqz2f9j8R+GWM8bPAXuDvyO4wDQNOzO0wTQTWxRg/DSwG/kczY51BbscKuIfsjlVTrs29xmeAjcAXc+0fz/W9n+yOWyMxxluAWcCsA4V/TglQEWM8v966mlpHwzapvobvowNFynlA31xhDY3fqw3zCJp+nx8HjAMGAFcDZzc1iWbe518DfhpjLAW6hxAuyLVfBPzfekdPbwEWxRj/nmxhfwLZDyqeyeXwoyGED+Tifwj8q4W/WuA0YGzuA7KPAScDc8i+z//ve/R9NsZ4f73He4EvAQuBnkDBIc6l4XhNza2ptrXAsyGE5cD5wJuH+LpSknoBr4cQrgGWAoOBO0MIfyb7oXFFg/hXyX74K0l6n9pz8f9RYF1u+0ngOhrvMPUDVudifkj2yGhTWrpjdQbwX7ntx4HTc9v35v7dCnQ8xHUc7s6gVF9Li5SG79WGeQRNv88rY4y7gP8G9pM9S6Cl6o/3X7yTN8tjjI/XizsNmJR7n3chuxP5a6BDCOG3QL8YY+0hvK4E8Bfge7kzuKaQfe9PAWYCt9aL20P2Qy5CCAfe37sajDWB7Bk1V1Hv6GYzWjJeU3Nrqm0g8FiMcSRwPNkPxaTWUg10jTEuAKaSLeyvJ/v/+ieAmxvE9wR2HsH5SVJqtefifyvQP7c9iOwRnIY7TH8GzsrFfI1sMdSUlu5Y/ZF3Tm8ryT2G994JPCDJnUGpvpYWKQ3fqw3zCJp/nx+u5sZr6r1/c+59PovszuJQYEGMcQRwbgjh1FxsU7kkNWUuMCqE8Cjwz8AQ4OUY4zeA/iGEwbm43wKXhBAeo/m/Ab8le4bKitzjd/sgtiXjNZzbi820bQH+VwjhD2Qvu3my6eGkI+IxsgdKILv/BEDuw9kqoOuBthBCD2AU7+SMJOl9CDHG1p5DqwghfJDstcqB7PWQ/xsYT3bH6HWypybvJXutcRHZ084+H2N8K9e//rX8PcheV9wVeAsYG2OsbiLuwLXQJ5PdITtwzX95jLE8hPAFaHSNZ/059wTuI3sU9pYY46P1x8/FdCF76UH9dexv2BZjfP3wfnNKoybeR8OA75PdEesA/BswggbvVeCXHJxHXyd7yuYPOfh9vjzGmMkdlf8CMDXGeGCMhnP5AryTB7n3/b1kjw49We+a/4PGCCH0Bubn4jbnXufDZHP4WGA7uftxhBA+DvyA7NkL/xBjfP5Qfl+SpMOT+8D1+2Q/MH4T+AzZm2keOGvyamAa2Q8G3iZ78OKnub598YZ/knTY2m3xL0lSa8t9IFZfdYxxdGvMRZIkpZvF/1HInUGlXe4o/U8bNP8lxnhda8xHkiRJSjuLf0mSJEmSUq493/BPkiRJkqR2weJfkiRJkqSUs/iXJClBIYRBIYRBrT0PSZKk+iz+JUlK1qDcjyRJ0lHDG/5JklIphDAVOBs4DtgBfB64B+gD/A24LMa4N/cNK0uAf4oxDgghdAAW1I8Dfgu8DHwoN/xq4FvAvUAR8EyM8cshhJnA2FzMSzHG4SGE4xrG5eZXDjwBDIgxnh9CKAB+BnQDXgXGxRj35eN3I0mS2h+P/EuS0mxljPEzQCUwBvgV8BngdWBwLuZEIMYYB+Qen9BM3NeADwOjgRJgIvBsjHEYcGIIYUCM8RZgFjArxjg8169RXK69BKiIMZ6fe3wGUJuLuwcoTO7XIEmS2rtjWnsCkiTl0Zrcv+uBU4Biskfyi4CC3HPVwB31+uwFPtcwLsa4JYTwcoxxVwghAKcBfx9CyAA9gJNzr9NQc3HPxhjvrxe3Fng2hLAc2AgsO7wlS5IkNeaRf0lSmn0q9++ZwD7gWeAS4KV6MW/GGGvrPb6kmbiG/gJ8L8aYAaYAW3Pte8heakDuQ4Lm4nY1GG8g8FiMcSRwPFDaohVKkiS1gMW/JCnNzspdW98DeBi4AlgF9CR7BL4pj7Uwbi4wKoTwKPDPwIu59t8Cl4QQHiNbwDcX19AW4H+FEP4A9AaebNEKJUmSWsAb/kmSUil3w7/yGGN5K09FkiSp1Vn8S5IkSZKUcp72L0mSJElSyln8S5IkSZKUchb/kiRJkiSlnMW/JEmSJEkpZ/EvSZIkSVLKWfxLkiRJkpRy/x+Gf5N1pYeztQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for res_pd, name in zip(result_pd_list, score_list):\n",
    "    draw_result(res_pd.iloc[:4, :], name)\n",
    "    draw_result(res_pd.iloc[4:8, :], name)\n"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "ce8301e8d6669930eccc46c20d4fb2bc673fb57cee8804ff4cc325597fb77f72"
  },
  "kernelspec": {
   "display_name": "Python 3.8.12 ('dataMiningLab')",
   "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": "3.8.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}