2StepSelect.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 准备"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 库 导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import numpy as np\n",
    "    import pandas as pd\n",
    "    import matplotlib.pyplot as plt\n",
    "    import seaborn as sns\n",
    "    from sklearn import datasets\n",
    "    import warnings\n",
    "    import GEOparse\n",
    "    from typing import Tuple, List\n",
    "    from sklearn.metrics import  roc_curve, auc, precision_recall_curve, confusion_matrix, classification_report, accuracy_score, roc_auc_score, f1_score, precision_score, recall_score, confusion_matrix,fbeta_score\n",
    "    from imblearn.over_sampling import SMOTE\n",
    "    sm = SMOTE(random_state=42)\n",
    "    from multiprocessing import Pool, cpu_count\n",
    "    import ast\n",
    "except:\n",
    "    print(\"Some modules are not loaded\")\n",
    "    %pip install GEOparse\n",
    "    %pip install imblearn\n",
    "    %pip install seaborn\n",
    "    %pip install scikit-learn\n",
    "    %pip install pandas\n",
    "    %pip install numpy\n",
    "    %pip install matplotlib\n",
    "    %pip install warnings\n",
    "    %pip install ast\n",
    "    import numpy as np\n",
    "    import pandas as pd\n",
    "    import matplotlib.pyplot as plt\n",
    "    import seaborn as sns\n",
    "    from sklearn import datasets\n",
    "    import warnings\n",
    "    import GEOparse\n",
    "    from typing import Tuple, List\n",
    "    from sklearn.metrics import  roc_curve, auc, precision_recall_curve, confusion_matrix, classification_report, accuracy_score, roc_auc_score, f1_score, precision_score, recall_score, confusion_matrix,fbeta_score\n",
    "    from imblearn.over_sampling import SMOTE\n",
    "    sm = SMOTE(random_state=42)\n",
    "    from multiprocessing import Pool, cpu_count\n",
    "    import ast\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "from sklearn.feature_selection import f_classif\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "from sklearn.linear_model import LinearRegression, RidgeClassifier, Lasso, LassoCV, LogisticRegression, LogisticRegressionCV, ElasticNet,ElasticNetCV,SGDClassifier,RidgeClassifierCV\n",
    "from sklearn.svm import SVC, LinearSVC, NuSVC"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基础设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "GSE_Train = \"GSE162330\" # 用于训练的数据集\n",
    "Skip_Paint = True # 是否绘制不必要的绘图\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据 导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "GPL10558 = GEOparse.get_GEO(geo=\"GPL10558\", destdir=\"./datasets\", silent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpl = GEOparse.get_GEO(geo='GPL10558', destdir=\"./datasets\", silent=True)\n",
    "gsedata = pd.read_pickle(\"./datasets/GSE162330.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sample name                      0\n",
       "organism                         0\n",
       "characteristics: sample class    0\n",
       "description                      0\n",
       "chip name or GEO platform id     0\n",
       "                                ..\n",
       "WSB1                             0\n",
       "CD96                             0\n",
       "ENOX2                            0\n",
       "THOC3                            0\n",
       "CFAP74                           0\n",
       "Length: 19952, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsedata.isnull().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sample name                      0\n",
       "organism                         0\n",
       "characteristics: sample class    0\n",
       "description                      0\n",
       "chip name or GEO platform id     0\n",
       "                                ..\n",
       "WSB1                             0\n",
       "CD96                             0\n",
       "ENOX2                            0\n",
       "THOC3                            0\n",
       "CFAP74                           0\n",
       "Length: 19952, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop the rows with missing values more than 50%\n",
    "gsedata = gsedata.dropna(axis=1, thresh=0.5*len(gsedata.index))\n",
    "gsedata = gsedata.dropna(axis=0, thresh=0.5*len(gsedata.columns))\n",
    "\n",
    "gsedata.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "v    1069\n",
       "c     467\n",
       "b     215\n",
       "Name: characteristics: sample class, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsedata['characteristics: sample class'].value_counts() # 看一下感染这个列的分布"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 函数 准备"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 定义存储结果的DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_results = pd.DataFrame(columns=['method', 'model', 'n', 'acc', 'auc', 'f1', 'precision', 'recall', 'confusionmatrix','coef', 'coef_rounded', 'found_coef', 'multiple_times','intercept', 'gene_id', 'gene_symbol', 'gb_acc'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 可接受的权重的范围筛选器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_allowed_numbers(max_n=25,allowed_factors=[1,2,3,4,5]):\n",
    "    result = []\n",
    "    for x in allowed_factors:\n",
    "        for y in allowed_factors:\n",
    "            result.append(x*y)\n",
    "        result.append(x)\n",
    "    result = list(set(result))\n",
    "    return result\n",
    "\n",
    "def is_all_in_list(list,n=25,allowed_factors=[2,3,5]):\n",
    "    for i in list:\n",
    "        if i not in get_allowed_numbers(n,allowed_factors):\n",
    "            return False\n",
    "    return list"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 画图"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分数分布图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_score_distribution(ax, y_score, final_target):                                 # 这个函数负责处理分数分布图这一个小图\n",
    "    ax.scatter(range(len(y_score)), y_score, c=final_target, cmap='bwr', alpha=0.5)     # 画散点图，x轴是样本序号，y轴是分数，颜色是感染情况，颜色映射是蓝白红，透明度是0.5\n",
    "                                                                                        # x是样本序号：分开画，免得都黏在一起了，透明度的设置是一样的原因。\n",
    "    ax.set_title('分数分布图')\n",
    "    ax.set_xlabel('样本序号')\n",
    "    ax.set_ylabel('分数')\n",
    "    plt.rcParams['font.sans-serif']=['SimHei']                                          # 中文乱码\n",
    "    plt.rcParams['axes.unicode_minus']=False                                            # 负号乱码\n",
    "    labels = ['细菌感染', '病毒感染']                                                   # 图例标签，这里是感染情况，以后如果换了的话得改。 #todo 将这个改为传入参数\n",
    "    ax.legend(labels, loc='upper right')                                                # 图例\n",
    "    ax.plot([0, len(y_score)], [0.5, 0.5], color='black', lw=1, linestyle='--')         # 以0.5为界，画一条虚线，表示分数大于0.5的是细菌感染，小于0.5的是病毒感染，这个以后也得改\n",
    "                                                                                        # 因为不同的模型（如linear regression 和 ridge regression）的判断标准不一样，所以这个界限也不一样。\n",
    "                                                                                        # 现在的做法是直接在y_score 上做手脚，还是……蛮不优雅的，但是先这样吧。\n",
    "                                                                                        # todo 将这个改为传入参数"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ROC曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc_curve(ax, y_score, final_target):\n",
    "    from sklearn.metrics import auc\n",
    "    fpr, tpr, thresholds = roc_curve(final_target, y_score)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    ax.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "    ax.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "    ax.set_xlabel('False Positive Rate 假阳性率')\n",
    "    ax.set_ylabel('True Positive Rate 真阳性率')\n",
    "    ax.set_title('ROC 曲线（受试者工作特征曲线）')\n",
    "    ax.legend(loc=\"lower right\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PR曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_pr_curve(ax, y_score, final_target):\n",
    "    precision, recall, thresholds = precision_recall_curve(final_target, y_score)\n",
    "    ax.plot(recall, precision, color='darkorange', lw=2, label='PR curve')\n",
    "    ax.set_xlabel('Recall 召回率')\n",
    "    ax.set_ylabel('Precision 准确率')\n",
    "    ax.set_title('PR 曲线（准确率-召回率曲线）')\n",
    "    ax.set_xlim([0.0, 1.0])\n",
    "    ax.set_ylim([0.0, 1.05])\n",
    "    ax.legend(loc=\"lower left\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "混淆矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(ax, y_score, final_target):\n",
    "    y_pred = np.where(y_score > 0.5, 1, 0)\n",
    "    cnf_matrix = confusion_matrix(final_target, y_pred)\n",
    "    ax.imshow(cnf_matrix, interpolation='nearest',cmap=plt.cm.Blues) # type: ignore\n",
    "    ax.set_title('Confusion Matrix 混淆矩阵')\n",
    "    tick_marks = np.arange(2)\n",
    "    plt.xticks(tick_marks, ['病毒感染', '细菌感染'], rotation=45)\n",
    "    plt.yticks(tick_marks, ['病毒感染', '细菌感染'])\n",
    "    thresh = cnf_matrix.max() / 2.\n",
    "    for i in range(cnf_matrix.shape[0]):\n",
    "        for j in range(cnf_matrix.shape[1]):\n",
    "            plt.text(j, i, cnf_matrix[i, j],horizontalalignment=\"center\",color=\"white\" if cnf_matrix[i, j] > thresh else \"black\",fontsize=20)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label 感染情况')\n",
    "    plt.xlabel('Predicted label 预测情况')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分数分布图、ROC曲线、PR曲线和混淆矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_all(score, target, title=\"分数分布图、ROC曲线、PR曲线和混淆矩阵\", feature=None, weight=None):\n",
    "    fig, axs = plt.subplots(2, 2, figsize=(12, 12))                                                     # 规定画布大小为1200*1200px，分成2*2的4个小图\n",
    "    fig.suptitle(title, fontsize=16)                                                                    # 设置标题\n",
    "    plot_score_distribution(axs[0,0], score, target)                                                    # 第一个小图（左上角）是分数分布图 这里的axs[0,0]是指第一行第一列的小图，注意计数是从0开始的。\n",
    "    plot_roc_curve(axs[0,1], score, target)                                                             # 第二个小图（右上角）是ROC曲线\n",
    "    plot_pr_curve(axs[1,0], score, target)                                                              # 第三个小图（左下角）是PR曲线\n",
    "    plot_confusion_matrix(axs[1,1], score, target)                                                      # 第四个小图（右下角）是混淆矩阵\n",
    "    if feature or weight:                                                                               # 如果有feature或者weight的话，就在图的下方加上文字\n",
    "        try:\n",
    "            # add margin at bottom\n",
    "            fig.subplots_adjust(bottom=0.12)                                                            # 调整图的下边距，这样文字就不会挡住图了\n",
    "            # add feature and weight at the bottom\n",
    "            fig.text(0.5, 0.04, '使用feature:'+str(feature), ha='center', va='bottom', fontsize=12)     # 在图的下方加上文字，这里的0.5,0.04是指文字的位置，ha='center'是指文字居中，va='bottom'是指文字在底部，fontsize=12是指文字大小\n",
    "            fig.text(0.5, 0.02, '对应权重:'+str(weight), ha='center', va='bottom', fontsize=8)          # 同上\n",
    "        except:\n",
    "            pass                                                                                        # 如果出错了就算了\n",
    "    return fig, axs"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 给定 基因 权重 和数据，计算分数并画图（可能需要修改）"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最简单"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def testfeature(feature,weight,data,gpl=gpl,title=\"分数分布图、ROC曲线、PR曲线和混淆矩阵\"):\n",
    "    ID = []\n",
    "    for i in range(len(feature)):\n",
    "        ID.append(gpl.table[gpl.table['Gene Symbol'] == feature[i]]['ID'].values[0]) # type: ignore\n",
    "    ID = np.array(ID).reshape(-1)\n",
    "    testdata = data[ID]\n",
    "    n = len(testdata)\n",
    "    y_score = np.zeros(n)\n",
    "    y_pred = np.zeros(n)\n",
    "    for i in range(len(testdata)):\n",
    "        y_score [i] = np.dot(testdata.iloc[i],weight)\n",
    "        y_pred [i] = np.where(y_score[i] > 0.5, 1, 0)\n",
    "    try:\n",
    "        target = data['target'].values\n",
    "    except:\n",
    "        target = gsedata['target']\n",
    "    acc = accuracy_score(target, y_pred)\n",
    "    print(\"Accuracy: \", acc)\n",
    "    fig, axs = plot_all(y_score, target, title, feature=feature, weight=weight)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "权重由放大得到"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def testfeaturewithtimes(feature,weight,data,times=1,gpl=gpl,title=\"分数分布图、ROC曲线、PR曲线和混淆矩阵\"):\n",
    "    ID = []\n",
    "    for i in range(len(feature)):\n",
    "        ID.append(gpl.table[gpl.table['Gene Symbol'] == feature[i]]['ID'].values[0]) # type: ignore\n",
    "    ID = np.array(ID).reshape(-1)\n",
    "    testdata = data[ID]\n",
    "    n = len(testdata)\n",
    "    y_score = np.zeros(n)\n",
    "    y_pred = np.zeros(n)\n",
    "    for i in range(len(testdata)):\n",
    "        y_score [i] = np.dot(testdata.iloc[i],weight)\n",
    "        # 结果除以times\n",
    "        y_score [i] = y_score [i] / times\n",
    "        y_pred [i] = np.where(y_score[i] > 0.5, 1, 0)\n",
    "    try:\n",
    "        target = data['target'].values\n",
    "    except:\n",
    "        target = gsedata['target']\n",
    "    acc = accuracy_score(target, y_pred)\n",
    "    print(\"Accuracy: \", acc)\n",
    "    fig, axs = plot_all(y_score, target, title, feature=feature, weight=weight)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "画出按照倍数比例画出各种基因在不同条件下的表达量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_boxplot(data_higher, gpl, gsedata, data_higher_times):\n",
    "    fig, ax = plt.subplots(1,50, figsize=(50,15))\n",
    "    for i in range(50):\n",
    "        sns.boxplot(\n",
    "            x='target', \n",
    "            y=data_higher.iloc[:,i],\n",
    "            data=gsedata,\n",
    "            ax=ax[i],\n",
    "            )\n",
    "        ax[i].set_title(gpl.table[gpl.table['ID'] == data_higher.columns[i]]['Gene Symbol'].values[0])\n",
    "\n",
    "        # 最上面加上对应的倍数\n",
    "        ax[i].text(0.1, 0.98,\n",
    "                   str('%.2f' % data_higher_times[i]),\n",
    "                   transform=ax[i].transAxes,\n",
    "                   fontsize=16,\n",
    "                   verticalalignment='top',\n",
    "                   bbox=dict(boxstyle='round', facecolor='white', alpha=0.5))\n",
    "\n",
    "        # 显示优化\n",
    "        ax[i].set_yscale('log') #对数坐标\n",
    "        ax[i].set_ylim(10, 40000) # y轴的范围\n",
    "        if i != 0: # 不显示x，y轴的标签， 不然太挤了没意义\n",
    "            ax[i].set_xlabel('')\n",
    "            ax[i].set_yticklabels([])\n",
    "            ax[i].set_ylabel('')\n",
    "            ax[i].set_yticks([])\n",
    "        else:\n",
    "            ax[i].set_xlabel('')\n",
    "            ax[i].set_ylabel('表达量')\n",
    "        ax[i].tick_params(axis='x', rotation=90) # 不然画不下\n",
    "    plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 筛选"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按照n增加而计算准确率等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_best_n(ax,X_train, y_train, X_test, y_test, method, model, max_features=20):\n",
    "    train_acc,test_acc,train_roc,test_roc = [],[],[],[]\n",
    "    for i in range(1, max_features):\n",
    "        if method == 'mannual':\n",
    "            reselect = X_train.iloc[:, :i]\n",
    "            model.fit(reselect, y_train) # use the selected features to train the data\n",
    "            y_pred = model.predict(X_test.iloc[:, :i])\n",
    "            train_acc.append(model.score(reselect, y_train))\n",
    "            test_acc.append(model.score(X_test.iloc[:, :i], y_test))\n",
    "            train_roc.append(roc_auc_score(y_train, model.predict(reselect)))\n",
    "            test_roc.append(roc_auc_score(y_test, model.predict(X_test.iloc[:, :i])))\n",
    "        else:\n",
    "            reselect = SelectKBest(score_func=method, k=i)\n",
    "            reselect.fit(X_train, y_train)\n",
    "            model.fit(X_train.iloc[:, reselect.get_support(indices=True)], y_train) # use the selected features to train the data\n",
    "            y_pred = model.predict(X_test.iloc[:, reselect.get_support(indices=True)])\n",
    "            train_acc.append(model.score(X_train.iloc[:, reselect.get_support(indices=True)], y_train))\n",
    "            test_acc.append(model.score(X_test.iloc[:, reselect.get_support(indices=True)], y_test))\n",
    "            train_roc.append(roc_auc_score(y_train, model.predict(X_train.iloc[:, reselect.get_support(indices=True)])))\n",
    "            test_roc.append(roc_auc_score(y_test, model.predict(X_test.iloc[:, reselect.get_support(indices=True)])))\n",
    "    # ax.plot(range(1, max_features), train_acc, label='train_acc')\n",
    "    ax.plot(range(1, max_features), test_acc, label='test_acc')\n",
    "    # ax.plot(range(1, max_features), train_roc, label='train_roc')\n",
    "    ax.plot(range(1, max_features), test_roc, label='test_roc')\n",
    "    if method == 'mannual':\n",
    "        ax.set_title('使用'+str(model) + '按照表达比选择特征')\n",
    "    else:\n",
    "        ax.set_title('使用'+str(model) + '和' + method.__name__ + '选择特征')\n",
    "    plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "    ax.legend()\n",
    "    # grid\n",
    "    ax.grid()\n",
    "    # mark the maximum value\n",
    "    ax.scatter(test_acc.index(max(test_acc)) + 1, max(test_acc), marker='o', color='b')\n",
    "    ax.text(test_acc.index(max(test_acc)) + 1, max(test_acc), str(test_acc.index(max(test_acc)) + 1) + ', ' + str('%.2f%%' % (max(test_acc) * 100)))\n",
    "    ax.scatter(test_roc.index(max(test_roc)) + 1, max(test_roc), marker='o', color='r')\n",
    "    ax.text(test_roc.index(max(test_roc)) + 1, max(test_roc), str(test_roc.index(max(test_roc)) + 1) + ', ' + str('%.2f%%' % (max(test_roc) * 100)))\n",
    "    # y is 0.5 to 1\n",
    "    ax.set_ylim(0.5, 1)\n",
    "\n",
    "    if method != 'mannual':\n",
    "        select = SelectKBest(score_func=method, k=test_acc.index(max(test_acc)) + 1).fit(X_train, y_train)\n",
    "        X_train_new = select.transform(X_train)\n",
    "        X_test_new = select.transform(X_test)\n",
    "        model.fit(X_train_new, y_train)\n",
    "        y_pred = model.predict(X_test_new)\n",
    "        gene_id = X_train.columns[select.get_support(indices=True)]\n",
    "    else:\n",
    "        X_train_new = X_train.iloc[:, :test_acc.index(max(test_acc)) + 1]\n",
    "        X_test_new = X_test.iloc[:, :test_acc.index(max(test_acc)) + 1]\n",
    "        model.fit(X_train_new, y_train)\n",
    "        y_pred = model.predict(X_test_new)\n",
    "        gene_id = X_train_new.columns\n",
    "    gene_symbol = [gpl.table[gpl.table['ID'] == i]['Gene Symbol'].values[0] for i in gene_id] # type: ignore\n",
    "    # print('使用' + str(model) + '和' + method.__name__ + '选择特征后的特征为：', gene_symbol)\n",
    "    # print('使用' + str(model) + '和' + method.__name__ + '选择特征后的特征权重为：', model.coef_)\n",
    "    \n",
    "    n = test_acc.index(max(test_acc)) + 1\n",
    "    best_acc = max(test_acc)\n",
    "\n",
    "    return best_acc, n, gene_symbol, model.coef_\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "筛选哪些基因比较有差异"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_higher_columns(data, diff=2.0):\n",
    "    data_mean = data.groupby('target').mean()\n",
    "    higher_columns = []\n",
    "    higher_times = []\n",
    "    for i in range(data_mean.shape[1]):\n",
    "        if diff*data_mean.iloc[0,i] < data_mean.iloc[1,i]  or data_mean.iloc[0,i] > diff*data_mean.iloc[1,i]:\n",
    "            higher_columns.append(data_mean.columns[i])\n",
    "            higher_times.append(data_mean.iloc[1,i]/data_mean.iloc[0,i])\n",
    "    return higher_columns, higher_times"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "排序有差异的基因"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sort_higher_columns(data, higher_columns, higher_times):\n",
    "    # sort the columns and times together\n",
    "    data_higher_times, data_higher_columns = zip(*sorted(zip(higher_times, higher_columns), reverse=True))\n",
    "    # convert data_higher_columns to list\n",
    "    data_higher_columns = list(data_higher_columns)\n",
    "    # add target column to the top of data_higher_columns\n",
    "    data_higher_columns.append('target')\n",
    "    # get the new data\n",
    "    data_higher = data[data_higher_columns]\n",
    "    return data_higher, data_higher_times"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_best_acc(test_acc, n, gene_symbol, coef):\n",
    "    best_acc = max(test_acc)\n",
    "    best_i = 0\n",
    "    i = 0\n",
    "    print('最好的准确率为：', best_acc)\n",
    "    for acc in test_acc:\n",
    "        if acc == best_acc:\n",
    "            print('='*60)\n",
    "            print('对应的特征数为：', n[i],'方法为：', methods[i // len(models)].__name__,'特征为：', gene_symbol[i])\n",
    "            print('模型为：', models[i % len(models)],'特征权重为：', coef[i])\n",
    "            weights_avg = np.mean(coef[i])\n",
    "            new_coefs = []\n",
    "            for j in range(len(coef[i])):\n",
    "                # new_coefs.append(10*(coef[i][j]/weights_avg)) 保留2位小数\n",
    "                # new_coefs.append(round(10*(coef[i][j]/weights_avg), 2)) type numpy.ndarray doesn't define __round__ method\n",
    "                new_coefs.append(np.round(10*(coef[i][j]/weights_avg), 2))\n",
    "            print('比例大致为：', new_coefs)\n",
    "        i += 1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不记得了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_symbols(gene_symbols):\n",
    "    gene_symbols = gene_symbols.reset_index(drop=True)\n",
    "    symbol = []\n",
    "    for i in range(len(gene_symbols)):\n",
    "        for j in range(len(gene_symbols[i])):\n",
    "            if gene_symbols[i][j] not in symbol:\n",
    "                symbol.append(gene_symbols[i][j])\n",
    "            else:\n",
    "                pass\n",
    "    return symbol\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "test all\n",
    "输入筛选方法和训练模型等 输出结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input: methods, models, n_features, X_train, X_test, y_train, y_test, gpl\n",
    "# output: results\n",
    "\n",
    "\n",
    "def test_all(methods, models, n_features, X_train, X_test, y_train, y_test, gpl):\n",
    "    import time\n",
    "    global final_results\n",
    "    for method in methods:\n",
    "        for n in n_features:\n",
    "            reselect = SelectKBest(score_func=method, k=n)\n",
    "            reselect.fit(X_train, y_train)\n",
    "            for model in models:\n",
    "                # log the time\n",
    "                time_start = time.time()\n",
    "                model.fit(reselect.transform(X_train), y_train)\n",
    "                y_score = model.predict(reselect.transform(X_test))\n",
    "                y_pred = np.array([1 if i > 0.5 else 0 for i in y_score], dtype=int)\n",
    "                acc = accuracy_score(y_test, y_pred)\n",
    "                auc = roc_auc_score(y_test, model.predict(reselect.transform(X_test)))\n",
    "                f1 = f1_score(y_test, y_pred)\n",
    "                precision = precision_score(y_test, y_pred)\n",
    "                recall = recall_score(y_test, y_pred)\n",
    "                confusionmatrix = confusion_matrix(y_test, y_pred)\n",
    "                coef = model.coef_\n",
    "\n",
    "                weights_avg = np.mean(coef)\n",
    "                min_times = 0.5 / np.min(np.abs(coef)) if np.min(coef) != 0 else 0.5 / np.mean(np.abs(coef))\n",
    "                max_times = 25 / np.max(np.abs(coef))\n",
    "                multipletimes = np.linspace(min_times, max_times, num=1000)\n",
    "                found_coef = False\n",
    "                coef_roundeds = []\n",
    "                differences = []\n",
    "                for multipletime in multipletimes:\n",
    "                    coef_rounded = np.round(coef.ravel() * multipletime).tolist()\n",
    "                    try:\n",
    "                        coef_rounded = [int(i) for i in coef_rounded]\n",
    "                        abs_coef_rounded = [abs(i) for i in coef_rounded]\n",
    "                        if is_all_in_list(abs_coef_rounded):\n",
    "                            coef_roundeds.append(coef_rounded)\n",
    "                            difference = np.mean(np.abs(np.array(coef_rounded) - np.array(coef.ravel() * multipletime))) / np.mean(np.abs(np.array(coef_rounded)))\n",
    "                            differences.append(difference)\n",
    "                            found_coef = True\n",
    "                        else:\n",
    "                            pass\n",
    "                    except:\n",
    "                        pass\n",
    "                if not found_coef:\n",
    "                    coef_rounded = np.round(100 * coef.ravel() / weights_avg).tolist()\n",
    "                    try:\n",
    "                        coef_rounded = [int(i) for i in coef_rounded]\n",
    "                    except:\n",
    "                        pass\n",
    "                    multiple_time = 100\n",
    "                else:\n",
    "                    best_index = differences.index(min(differences))\n",
    "                    coef_rounded = coef_roundeds[best_index]\n",
    "                    multiple_time = np.mean(np.abs(coef_rounded)) / np.mean(np.abs(coef.ravel()))\n",
    "                    # is all > 0\n",
    "                    # if not all(i > 0 for i in coef_rounded):\n",
    "                    #     found_coef = False\n",
    "\n",
    "                intercept = model.intercept_\n",
    "                gene_id = X_train.columns[reselect.get_support(indices=True)]\n",
    "                gene_id = np.array(gene_id).reshape(-1)\n",
    "                gene_symbol = [gpl.table[gpl.table['ID'] == i]['Gene Symbol'].values[0] for i in gene_id]\n",
    "                gb_acc = [gpl.table[gpl.table['ID'] == i]['GB_ACC'].values[0] for i in gene_id]\n",
    "                final_results = final_results.append({\n",
    "                    'method': method.__name__,\n",
    "                    'model': model.__class__.__name__,\n",
    "                    'n': n,\n",
    "                    'acc': acc,\n",
    "                    'auc': auc,\n",
    "                    'f1': f1,\n",
    "                    'precision': precision,\n",
    "                    'recall': recall,\n",
    "                    'confusionmatrix': confusionmatrix,\n",
    "                    'coef': coef,\n",
    "                    'coef_rounded': coef_rounded,\n",
    "                    'found_coef': found_coef,\n",
    "                    'multiple_times': multiple_time,\n",
    "                    'intercept': intercept,\n",
    "                    'gene_id': gene_id,\n",
    "                    'gene_symbol': gene_symbol,\n",
    "                    'gb_acc': gb_acc\n",
    "                }, ignore_index=True)\n",
    "                time_end = time.time()\n",
    "                if time_end - time_start > 2:\n",
    "                    print('SLOW!!!:method:', method.__name__, 'n:', n, 'model:', model.__class__.__name__,'time cost', time_end - time_start, 's')\n",
    "    return results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot_all_results 未使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input: results, methods, models, gpl\n",
    "# output: figure\n",
    "def plot_all_results(results, methods, models, gpl):\n",
    "    # Group the results by method and model\n",
    "    groups = results.groupby(['method', 'model'])\n",
    "\n",
    "    # Create a figure with multiple subplots\n",
    "    fig, axs = plt.subplots(len(methods), len(models) + 1, sharex=True, sharey=True, figsize=(5*len(models), 3*(len(methods)+1)))\n",
    "\n",
    "    # Iterate over the subplots and plot the acc and roc values\n",
    "    for i, method in enumerate(methods):\n",
    "        for j, model in enumerate(models):\n",
    "            group = groups.get_group((method.__name__, model.__class__.__name__))\n",
    "            axs[i, j].plot(group['n'], group['acc'], label='acc')\n",
    "            axs[i, j].plot(group['n'], group['auc'], label='auc')\n",
    "            axs[i, j].set_xlabel('n')\n",
    "            axs[i, j].set_ylabel('score')\n",
    "            axs[i, j].set_ylim([0.5, 1]) # Set y-axis limits\n",
    "            axs[i, j].legend()\n",
    "            # Find the maximum acc and roc values for this method and model\n",
    "            max_acc_idx = group['acc'].idxmax()\n",
    "            max_auc_idx = group['auc'].idxmax()\n",
    "            max_acc = group.loc[max_acc_idx, 'acc']\n",
    "            max_auc = group.loc[max_auc_idx, 'auc']\n",
    "            max_n_acc = group.loc[max_acc_idx, 'n']\n",
    "            max_n_auc = group.loc[max_auc_idx, 'n']\n",
    "\n",
    "            # Add annotations with the maximum acc and roc values\n",
    "            axs[i, j].annotate(f'Max Acc: {max_acc:.2f} (n={max_n_acc})',\n",
    "                                xy=(max_n_acc, max_acc),\n",
    "                                xytext=(max_n_acc, max_acc + 0.1),\n",
    "                                ha='center',\n",
    "                                arrowprops=dict(arrowstyle='->'))\n",
    "            axs[i, j].annotate(f'Max Roc: {max_auc:.2f} (n={max_n_auc})',\n",
    "                                xy=(max_n_auc, max_auc),\n",
    "                                xytext=(max_n_auc, max_auc - 0.1),\n",
    "                                ha='center',\n",
    "                                arrowprops=dict(arrowstyle='->'))\n",
    "\n",
    "            axs[i, j].set_title(f'{method.__name__} - {model.__class__.__name__}')\n",
    "            # grid\n",
    "            axs[i, j].grid()\n",
    "        # Create a table of gene symbols for the first model in the method's group\n",
    "        first_model = groups.get_group((method.__name__, models[0].__class__.__name__))\n",
    "        # gene_symbols = first_model['gene_symbol'].reset_index(drop=True)\n",
    "        # symbol = get_symbols(gene_symbols)\n",
    "        gene_ids = first_model['gene_id'].reset_index(drop=True)\n",
    "        id = get_symbols(gene_ids)\n",
    "        # cell_text = np.array(symbol).reshape(-1, 1)\n",
    "        cell_text = np.array([id]).T\n",
    "        row_labels = [f'Gene {i}' for i in range(len(id))]\n",
    "        col_labels = ['Gene Symbol', 'Gene ID']\n",
    "        table = axs[i, len(models)].table(cellText=cell_text, rowLabels=row_labels, colLabels=col_labels, loc='center')\n",
    "        axs[i, len(models)].axis('off')\n",
    "        axs[i, len(models)].set_title(f'{method.__name__} - selected genes')\n",
    "\n",
    "    # Iterate over the first column of subplots and set their y-axis labels to be the method names\n",
    "    for i, method in enumerate(methods):\n",
    "        axs[i, 0].set_ylabel(method.__name__)\n",
    "\n",
    "    # Add a main title to the figure\n",
    "    fig.suptitle('Accuracy and AOC by method and model')\n",
    "\n",
    "    # Adjust the layout of the subplots\n",
    "    fig.tight_layout()\n",
    "\n",
    "    # Show the figure\n",
    "    plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "给定数据、选取基因和单向权重，计算分数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cal_score_with_feature(data, gpl, feature, weight, times, intercept,model):\n",
    "    ID = []\n",
    "    for i in range(len(feature)):\n",
    "        ID.append(gpl.table[gpl.table['Gene Symbol'] == feature[i]]['ID'].values[0])\n",
    "    ID = np.array(ID).reshape(-1)\n",
    "    ID.astype('str')\n",
    "    data = data[ID]\n",
    "    data.columns = feature\n",
    "    data_weight = data * weight\n",
    "    data_weight_sum = data_weight.sum(axis=1)\n",
    "    if model in ['LogisticRegression','LogisticRegressionCV','logisticregression','LR','lr']:\n",
    "        data_weight_z = data_weight_sum / times + intercept\n",
    "        data_weight_score = 1 / (1 + np.exp(-data_weight_z))\n",
    "    elif model in ['LinearRegression','RidgeClassifier','Lasso','LassoCV','ElasticNet']:\n",
    "        data_weight_score = data_weight_sum / times + intercept\n",
    "    elif model in ['NuSVC','LinearSVC','SVC']:\n",
    "        data_weight_score = data_weight_sum / times + intercept\n",
    "    else:\n",
    "        print('The model',model,'is not supported yet.')\n",
    "        data_weight_score = data_weight_sum / times + intercept\n",
    "    return data_weight_score"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一次性完成基因筛选和模型训练\n",
    "通过调控C，使用LogisticRegression or LinearSVC 一次性完成基因筛选和模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_classifier(clf, method, model, X_train, y_train, X_test, y_test, params, max=15):\n",
    "    global final_results\n",
    "\n",
    "    acc_scores, auc_scores, f1_scores, precision_scores, recall_scores, n_features = [], [], [], [], [], []\n",
    "\n",
    "    for param in params:\n",
    "        clf.set_params(**param)\n",
    "        clf.fit(X_train, y_train)\n",
    "        nonzero_coef_indices = np.flatnonzero(clf.coef_)  # Indices where coef_ != 0\n",
    "        n = len(nonzero_coef_indices)\n",
    "        y_pred = clf.predict(X_test)\n",
    "        y_pred = np.array([1 if i > 0.5 else 0 for i in y_pred], dtype=int)\n",
    "        \n",
    "        if 5 <= n <= max:\n",
    "            cm = confusion_matrix(y_test, y_pred)\n",
    "            auc = roc_auc_score(y_test, y_pred)\n",
    "            f1 = f1_score(y_test, y_pred)\n",
    "            f2 = fbeta_score(y_test, y_pred, beta=2)\n",
    "            f05 = fbeta_score(y_test, y_pred, beta=0.5)\n",
    "            precision = precision_score(y_test, y_pred)\n",
    "            recall = recall_score(y_test, y_pred)\n",
    "            \n",
    "            acc = clf.score(X_test, y_test)\n",
    "\n",
    "            allowed_numbers = get_allowed_numbers()\n",
    "            coef = clf.coef_\n",
    "            # only use non-zero coef\n",
    "            coef = coef[coef != 0]\n",
    "            weights_avg = np.mean(np.abs(coef))\n",
    "            min_times = 0.5 / np.min(np.abs(coef))\n",
    "            max_times = 25.5 / np.max(np.abs(coef))\n",
    "            if min_times > max_times:\n",
    "                multipletimes = [max_times]\n",
    "            else:\n",
    "                multipletimes = np.linspace(min_times, max_times, num=500)\n",
    "            found_coef = False\n",
    "            coef_roundeds = []\n",
    "            deltas = []\n",
    "            for multipletime in multipletimes:\n",
    "                coef_rounded_t = []\n",
    "                differences = []\n",
    "                for coef_i in coef:\n",
    "                    # 符号问题，先存符号\n",
    "                    coef_i_sign = coef_i / np.abs(coef_i)\n",
    "                    coef_i = np.abs(coef_i)\n",
    "                    differences_i = []\n",
    "                    for allowed_number in allowed_numbers:\n",
    "                        difference = np.abs(coef_i * multipletime - allowed_number) / allowed_number\n",
    "                        differences_i.append(difference)\n",
    "                    best_allowed_number = allowed_numbers[np.argmin(differences_i)]\n",
    "                    coef_rounded_t.append(best_allowed_number * coef_i_sign)\n",
    "                    differences.append(np.min(differences_i))\n",
    "                delta = np.average(np.array(differences))\n",
    "                deltas.append(delta)\n",
    "                coef_roundeds.append(coef_rounded_t)\n",
    "\n",
    "            coef_rounded = coef_roundeds[np.argmin(deltas)]\n",
    "            delta = np.min(deltas)\n",
    "            mean_coef = np.mean(np.abs(coef_rounded))\n",
    "            times = mean_coef / weights_avg\n",
    "            if delta < 0.30: # 30%的平均误差\n",
    "                found_coef = True\n",
    "            if np.sum(coef_rounded) == 0:\n",
    "                balanced = True\n",
    "            else:\n",
    "                balanced = False\n",
    "\n",
    "            intercept = clf.intercept_\n",
    "\n",
    "            nonzero_coef_indices = np.flatnonzero(clf.coef_) # get the indices of non-zero coef\n",
    "            gene_id = list(X_train.columns[nonzero_coef_indices])\n",
    "            gene_symbol = list(X_train.columns[nonzero_coef_indices])\n",
    "            gb_acc = list(X_train.columns[nonzero_coef_indices])\n",
    "\n",
    "            result = pd.DataFrame({\n",
    "                'method': [method],\n",
    "                'model': [model],\n",
    "                'n': [n],\n",
    "                'acc': [acc],\n",
    "                'auc': [auc],\n",
    "                'f1': [f1],\n",
    "                'f2': [f2],\n",
    "                'f05': [f05],\n",
    "                'precision': [precision],\n",
    "                'recall': [recall],\n",
    "                'confusionmatrix': [cm],\n",
    "                'coef': [coef.tolist()],\n",
    "                'coef_rounded': [coef_rounded],\n",
    "                'found_coef': [found_coef],\n",
    "                'delta': [delta],\n",
    "                'multiple_times': [times],\n",
    "                'intercept': [intercept.item()],\n",
    "                'balanced': [balanced],\n",
    "                'gene_id': [gene_id],\n",
    "                'gene_symbol': [gene_symbol],\n",
    "                'gb_acc': [gb_acc]\n",
    "            })\n",
    "\n",
    "            final_results = pd.concat([final_results, result], ignore_index=True) \n",
    "\n",
    "            # For plotting\n",
    "            n_features.append(n) \n",
    "            acc_scores.append(acc)\n",
    "            auc_scores.append(auc)\n",
    "            f1_scores.append(f1)\n",
    "            precision_scores.append(precision)\n",
    "            recall_scores.append(recall)\n",
    "        if n > max + 5:\n",
    "            break\n",
    "\n",
    "    # Plotting inside the function\n",
    "    plt.figure(figsize=(10,6))\n",
    "    plt.plot(n_features, auc_scores, label='AUC')\n",
    "    plt.scatter(n_features, auc_scores)\n",
    "    plt.plot(n_features, f1_scores, label='F1 score')\n",
    "    plt.scatter(n_features, f1_scores)\n",
    "    plt.plot(n_features, precision_scores, label='Precision', linestyle='dashed')\n",
    "    plt.plot(n_features, recall_scores, label='Recall', linestyle='dashed')\n",
    "    plt.title('Performance metrics by number of features (' + method + ')')\n",
    "    plt.xlabel('Number of features')\n",
    "    plt.ylabel('Performance metrics')\n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "    plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 只保留细菌和病毒"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_map = {\n",
    "    'virus': 1,\n",
    "    'bacteria': 0,\n",
    "    'viral': 1,\n",
    "    'bacterial': 0,\n",
    "    'S. aureus, MRSA': 0,\n",
    "    'Influenza A': 1,\n",
    "    'E. coli': 0,\n",
    "    'S. pneumoniae': 0,\n",
    "    'S. aureus, MSSA': 0,\n",
    "    'None': -1,\n",
    "    'Influenza B': 1,\n",
    "    'non-infectious illness': -1,\n",
    "    'v': 1,\n",
    "    'b': 0,\n",
    "    'c': -1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 病毒与未感染的鉴别\n",
    "train_data = gsedata.copy()\n",
    "# prevent Object of type \"Series[Unknown]\" is not callable\n",
    "train_data['target'] = train_data['characteristics: sample class'].map(target_map)\n",
    "train_data = train_data[train_data['target'] != -1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 筛选出 细菌病毒感染中表达量大差异于等于1.3倍的基因"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "高表达差异基因数目： 856\n"
     ]
    }
   ],
   "source": [
    "higher_columns, higher_times = get_higher_columns(train_data, 1.15)\n",
    "print('高表达差异基因数目：', len(higher_columns))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据倍率不同进行排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_higher, data_higher_times = sort_higher_columns(train_data, higher_columns, higher_times)\n",
    "X_train, X_test, y_train, y_test = train_test_split(data_higher.drop(['target'], axis=1), data_higher['target'], test_size=0.2, random_state=42)\n",
    "# X_train, y_train = sm.fit_resample(data_higher, data_higher['target'])\n",
    "# smote\n",
    "sm = SMOTE(random_state=42)\n",
    "X_train, y_train = sm.fit_resample(X_train, y_train)\n",
    "X_test, y_test= sm.fit_resample(X_test, y_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练不同的模型，使用不同的特征选择方法，选择出特征后，使用不同的方法来训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1cAAAIhCAYAAACizkCYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC3GUlEQVR4nOzdd3hT1RsH8G9Wm+5JF5S2bMreMgqyKRsXQ9mggixRVARkI/hDZCiIyBAURQERkVVZguypyIZCGS2FAh10pcn5/REamjZpE0ibtPl+nqcPufe+uXnvzW3I23PuORIhhAARERERERE9F6m1EyAiIiIiIioJWFwRERERERFZAIsrIiIiIiIiC2BxRUREREREZAEsroiIiIiIiCyAxRUREREREZEFsLgiIiIiIiKyABZXREREREREFsDiioiIiIiIyAJYXBFZ0apVqyCRSHQ/crkcZcqUwcCBA3H79m2LvlZmZibefvttBAYGQiaToXbt2hbdPxWO1NRUTJkyBXv37jXreVOmTIFEIimcpAx48cUXUb169SJ7PVtS3I590aJFqFChAhwcHCCRSPDo0SOjsevWrUO1atXg5OQEiUSC06dPWzyfZ73Gbc3+/fvh6OiIGzdu6NaZcm0U9e/q83j8+DHmzJmDWrVqwd3dHW5ubihfvjxee+017Nu3DwDw7rvvQiKR4MKFC0b3M2HCBEgkEpw8eVK3LiMjA19++SWaNWsGLy8vODg4oHTp0nr7BoBdu3bB1dXV4v9HElkKiysiG7By5UocOnQIUVFRGDp0KH788UdERETg8ePHFnuNJUuWYOnSpZgwYQIOHDiANWvWWGzfVHhSU1MxdepUs794DhkyBIcOHSqcpKjYOn36NEaNGoWWLVti9+7dOHToENzc3AzG3rt3D3379kX58uWxfft2HDp0CJUqVbJ4Ts96jdsSIQTGjBmDoUOHIiQkxKznFpffVbVajXbt2mHmzJl45ZVX8Msvv2D9+vV49913kZiYiP379wMABg8eDABYsWKFwf1oNBqsXr0atWvXRt26dQEA9+/fR9OmTTF27FhUr14dq1atwq5du/D5559DJpOhdevWOHPmDACgdevWaNiwIT7++OMiOGoi88mtnQARAdWrV0f9+vUBAC1btoRarcb06dOxadMmvP7668+179TUVDg7O+Ps2bNwcnLCiBEjLJEyACAtLQ1OTk4W2x89v+z3u0yZMihTpoy10yELssTv23///QcAGDp0KBo2bJhv7KVLl6BSqfDGG2+gRYsWz/W61iCEQHp6epF8Rm3fvh0nT57E2rVrzX5ucfhdTUtLw+HDh3Hw4EGsWLECAwcO1G1r3749RowYAY1GA0D7/1nDhg2xZs0azJo1C3K5/lfNnTt34tatW/jwww916/r164czZ85gx44daNWqlV58r169MHbsWHh5eenWvfPOO+jZsydmzJiB4ODgwjhkomfGlisiG/TCCy8AgK57iRACixcvRu3ateHk5AQvLy+88soruHbtmt7zsrug/PXXX2jSpAmcnZ0xaNAgSCQSfPvtt0hLS9N1QVy1ahUAID09HePHj0dYWJiuG8Y777yTp6tQaGgoOnfujI0bN6JOnTpQKpW6vzZLJBKsXbsWH374IQIDA+Hq6oouXbrg7t27SE5OxptvvglfX1/4+vpi4MCBSElJ0dv3V199hebNm8PPzw8uLi6oUaMGPvvsM6hUKoPHd+zYMURERMDZ2RnlypXD7Nmzdf+xZ3v06BHee+89lCtXDo6OjvDz80PHjh31uqpkZmZixowZqFKlChwdHVGqVCkMHDgQ9+7dK/A9GjBgAFxdXXHhwgW0b98eLi4uCAwMxOzZswEAhw8fRrNmzeDi4oJKlSrhu+++y7OPuLg4vPXWWyhTpgwcHBwQFhaGqVOnIisrCwBw/fp1lCpVCgAwdepU3Xs3YMAAAE+7E508eRKvvPIKvLy8UL58eb1tua1duxaNGzeGq6srXF1dUbt2bSxfvly3/dSpU+jcuTP8/Pzg6OiIoKAgdOrUCbdu3SrwnADarlEvvPACnJycULp0aUyaNAlqtRqA9jquWLEi2rdvn+d5KSkp8PDwwDvvvJPv/iUSCUaMGIE1a9agatWqcHZ2Rq1atbBlyxa9uAEDBiA0NDTP8w2dl+x9rly5EpUrV4aTkxPq16+Pw4cPQwiB//3vfwgLC4OrqytatWqFK1eumH3s2Uy95oz9vuVnxYoVqFWrFpRKJby9vdGjRw+cP39et/3FF1/EG2+8AQBo1KiR3rWU24ABA9CsWTMAQM+ePSGRSPDiiy/qth8/fhxdu3aFt7c3lEol6tSpg59//llvH/fu3cPw4cMRHh4OV1dX+Pn5oVWrVroWDqDga/xZ3sevv/4aVatWhaOjo+737vLly+jTp4/uuq5atSq++uorvedrNBrMmDFDdw14enqiZs2aWLBggZEz/tSSJUvQoEEDVK5cucBYU44l+/3fvn076tatCycnJ1SpUsVga1BBnyPZpk6dikaNGsHb2xvu7u6oW7culi9fDiGEwdfOfe0lJCQAAAIDAw0eh1T69Cvl4MGDERcXh23btuWJW7lyJRwdHXV/ODxx4gS2bduGwYMH5ymssjVo0ABly5bVLXfp0gWurq5YtmyZwXgiqxJEZDUrV64UAMSxY8f01i9YsEAAEN98840QQoihQ4cKhUIh3nvvPbF9+3axdu1aUaVKFeHv7y/i4uJ0z2vRooXw9vYWwcHBYtGiRWLPnj1i37594tChQ6Jjx47CyclJHDp0SBw6dEjEx8cLjUYj2rdvL+RyuZg0aZLYuXOnmDt3rnBxcRF16tQR6enpun2HhISIwMBAUa5cObFixQqxZ88ecfToUbFnzx4BQISEhIgBAwaI7du3i6+//lq4urqKli1birZt24r3339f7Ny5U8yZM0fIZDIxcuRIveN99913xZIlS8T27dvF7t27xRdffCF8fX3FwIED9eJatGghfHx8RMWKFcXXX38toqKixPDhwwUA8d133+nikpKSRLVq1YSLi4uYNm2a2LFjh9iwYYMYPXq02L17txBCCLVaLTp06CBcXFzE1KlTRVRUlPj2229F6dKlRXh4uEhNTc33vevfv79wcHAQVatWFQsWLBBRUVFi4MCBAoAYP368qFSpkli+fLnYsWOH6Ny5swAgjh8/rnt+bGysCA4OFiEhIWLp0qXizz//FNOnTxeOjo5iwIABQggh0tPTxfbt2wUAMXjwYN17d+XKFSGEEJMnT9ad+w8//FBERUWJTZs26W3LadKkSQKAeOmll8Qvv/widu7cKebNmycmTZokhBAiJSVF+Pj4iPr164uff/5Z7Nu3T6xbt068/fbb4ty5c/mej+z3JigoSCxcuFDs2LFDjBo1SgAQ77zzji5uwYIFQiKRiEuXLuk9/6uvvhIAxH///Zfv6wAQoaGhomHDhuLnn38WW7duFS+++KKQy+Xi6tWreu9PSEhInucbOi/Z57BJkyZi48aN4tdffxWVKlUS3t7e4t133xXdunUTW7ZsET/88IPw9/cXNWvWFBqNxuxjN+eaM/b7ZsysWbMEANG7d2/xxx9/iNWrV4ty5coJDw8P3bn+77//xMSJEwUAsXLlSr1rKbcrV67o3pNZs2aJQ4cO6d6b3bt3CwcHBxERESHWrVsntm/fLgYMGKDbb7YLFy6IYcOGiZ9++kns3btXbNmyRQwePFhIpVKxZ88eIUTB17i572Pp0qVFzZo1xdq1a8Xu3bvF2bNnxX///Sc8PDxEjRo1xOrVq8XOnTvFe++9J6RSqZgyZYru+Z9++qmQyWRi8uTJYteuXWL79u1i/vz5ejGGZGRkCCcnJ/HBBx/k2daiRQtRrVq1fJ9v6FhCQkJEmTJlRHh4uFi9erXYsWOHePXVVwUAsW/fPl2cKZ8j2QYMGCCWL18uoqKiRFRUlJg+fbpwcnISU6dOzfPahq696OhooVAoRKVKlcT3338v7ty5Y/SYkpKShLOzs+jevbve+gcPHghHR0fRq1cv3brsa3fbtm35nqfcIiMjRd26dc16DlFRYHFFZEXZxdXhw4eFSqUSycnJYsuWLaJUqVLCzc1NxMXFiUOHDgkA4vPPP9d77s2bN/P8h96iRQsBQOzatSvPa/Xv31+4uLjorcv+UvPZZ5/prV+3bp1ecSeE9j9cmUwmLl68qBebXVx16dJFb/2YMWMEADFq1Ci99d27dxfe3t5Gz4larRYqlUqsXr1ayGQy8eDBgzzHd+TIEb3nhIeHi/bt2+uWp02bJgCIqKgoo6/z448/CgBiw4YNeuuPHTsmAIjFixcbfa4Q2vOZ+/kqlUqUKlVKABAnT57UrU9ISBAymUyMHTtWt+6tt94Srq6u4saNG3r7nTt3rl6Rce/ePQFATJ48OU8O2V/KPvnkE6Pbsl27dk3IZDLx+uuvGz2m48ePCwC6As0c2e/Nb7/9prd+6NChQiqV6o4zKSlJuLm5idGjR+vFhYeHi5YtWxb4OgCEv7+/SEpK0q2Li4sTUqlUfPrpp7p15n4pDwgIECkpKbp1mzZtEgBE7dq19Qqp+fPnCwDin3/+MfvYzbnmjP2+GfLw4UPh5OQkOnbsqLc+JiZGODo6ij59+ujWGfuDjiHZv9u//PKL3voqVaqIOnXqCJVKpbe+c+fOIjAwUKjVaoP7y8rKEiqVSrRu3Vr06NFDtz6/a9zc99HDw0PvM0MIIdq3by/KlCkjEhMT9daPGDFCKJVKXXznzp1F7dq1DeaenyNHjggA4qeffsqz7XmKK6VSqff5kJaWJry9vcVbb72lW2fq50hu2Z+z06ZNEz4+PnrXeH7X3vLly4Wrq6sAIACIwMBA0a9fP/HXX3/lie3fv79QKBTi7t27unWLFi3K89n89ttvCwDiwoULxk6RQRMmTBBSqVTv95bIFrBbIJENeOGFF6BQKODm5obOnTsjICAA27Ztg7+/P7Zs2QKJRII33ngDWVlZup+AgADUqlUrz03gXl5eRrtW5LZ7924AyNM16NVXX4WLiwt27dqlt75mzZpGb2jv3Lmz3nLVqlUBAJ06dcqz/sGDB3pdA0+dOoWuXbvCx8cHMpkMCoUC/fr1g1qtxqVLl/SeHxAQkOdekZo1a+qN0LVt2zZUqlQJbdq0MXbo2LJlCzw9PdGlSxe981q7dm0EBASYdHO9RCJBx44ddctyuRwVKlRAYGAg6tSpo1vv7e0NPz8/vRy3bNmCli1bIigoSO/1IyMjAUBvdKyCvPzyywXGREVFQa1W59vtrkKFCvDy8sKHH36Ir7/+GufOnTM5BwBwc3ND165d9db16dMHGo0Gf/31ly5m4MCBWLVqlW7Alt27d+PcuXMm3w/YsmVLvUEY/P3985xfc7Vs2RIuLi665ezrNzIyUq/LVvb63K9lyrGbe83l9/uW06FDh5CWlpbn9zg4OBitWrXK83v8PK5cuYILFy7ounTlPI6OHTsiNjYWFy9e1MV//fXXqFu3LpRKJeRyORQKBXbt2qXXXdGSWrVqpXdvTnp6Onbt2oUePXrA2dk5T77p6ek4fPgwAKBhw4Y4c+YMhg8fjh07diApKcmk17xz5w4AwM/Pz6LHUrt2bb2ucEqlEpUqVXrmz5Hdu3ejTZs28PDw0H3OfvLJJ0hISEB8fLzeaxu79gYNGoRbt25h7dq1GDVqFIKDg/H999+jRYsW+N///qcXO3jwYKhUKr3Bk1auXImQkBC0bt36+U4OtOdbo9EgLi7uufdFZEksrohswOrVq3Hs2DGcOnUKd+7cwT///IOmTZsCAO7evQshBPz9/aFQKPR+Dh8+jPv37+vty1h/eEMSEhIgl8t19zxkk0gkCAgI0PWxN2Xf3t7eessODg75rk9PTwcAxMTEICIiArdv38aCBQuwf/9+HDt2THc/RFpamt7zfXx88ry2o6OjXty9e/cKvEH87t27ePToERwcHPKc17i4uDzn1RBnZ2colco8x5f7mLPXZx9z9uv//vvveV67WrVqAGDS62cz5T3Pvqcnv/Pi4eGBffv2oXbt2vj4449RrVo1BAUFYfLkyXnufzPE398/z7qAgAAA0LuWRo4cieTkZPzwww8AgC+//BJlypRBt27dCnwNwLRrwFzPev1mM+XYzb3mTP1dzu9emKCgoDy/x8/j7t27AID3338/zzEMHz4cwNNrd968eRg2bBgaNWqEDRs24PDhwzh27Bg6dOjwXO9VfnKfg4SEBGRlZWHRokV58s3+w0h2vuPHj8fcuXNx+PBhREZGwsfHB61bt8bx48fzfc3sY8n9WfC8TLnOTf0cOXr0KNq1awcAWLZsGf7++28cO3YMEyZM0DuGbPldex4eHujduzcWLFiAI0eO4J9//oG/vz8mTJigd69uREQEKlWqhJUrVwIA/vnnH5w8eRIDBw7U+4NFdgEZHR1t8rkBnp7vwrqWiJ4VRwsksgFVq1bVjRaYm6+vLyQSiW4OldxyrzNnvhQfHx9kZWXh3r17egWWEAJxcXFo0KDBM+/bVJs2bcLjx4+xceNGvSGMn2c+nVKlShU4AIOvry98fHywfft2g9uNDU9tKb6+vqhZsyZmzpxpcHtQUJDJ+zLlfcl+f2/dupXv6Fo1atTATz/9BCEE/vnnH6xatQrTpk2Dk5MTPvroo3xfI/uLd07Zf1XO+UWxQoUKiIyMxFdffYXIyEhs3rwZU6dOhUwmK/A4TKVUKpGRkZFnvTlFqzlMOXZzrzlTf9+y9x8bG5tn2507d+Dr62vSfkyRva/x48fjpZdeMhiTPajD999/jxdffBFLlizR256cnGzy65n7PuY+Z15eXpDJZOjbt6/RVtuwsDAA2pbnsWPHYuzYsXj06BH+/PNPfPzxx2jfvj1u3rwJZ2dng8/PPicPHjww+bgsxdTPkZ9++gkKhQJbtmzRKwI3bdpk8HnmfNZXq1YNvXr1wvz583Hp0iW9ngWDBg3CRx99hKNHj2Lt2rWQSqV5Wljbt2+Pjz/+GJs2bUKHDh1Mft3s823J65vIElhcEdm4zp07Y/bs2bh9+zZee+01i+67devW+Oyzz/D999/j3Xff1a3fsGEDHj9+bJGuGwXJ/k88Z5EohHiuUaAiIyPxySefYPfu3Ua7SHbu3Bk//fQT1Go1GjVq9Myv9aw6d+6MrVu3onz58nrdmHLLPi/P+9fZdu3aQSaTYcmSJWjcuHGB8RKJBLVq1cIXX3yBVatW6U32aUxycjI2b96s1z0u+wtV8+bN9WJHjx6Ndu3aoX///pDJZBg6dKj5B5WP0NBQxMfH4+7du7pWpczMTOzYscOir5PNlGMvrGuucePGcHJywvfff49XX31Vt/7WrVvYvXs3XnnlFYu9VuXKlVGxYkWcOXMGs2bNyjdWIpHk+ePPP//8g0OHDukV+Pld48/7Pjo7O6Nly5Y4deoUatasqWt5LIinpydeeeUV3L59G2PGjMH169cRHh5uMDa7q+jVq1dN2rclmfo5kj1Jfc4/YKSlpZk132FCQgLc3NwMnsPsUVhz/1Gof//+mDhxIpYuXYrNmzejdevWeeYBq1u3LiIjI7F8+XK89tprBj+zjx8/Dj8/P71ukteuXYOPj4/BVmMia2JxRWTjmjZtijfffBMDBw7E8ePH0bx5c7i4uCA2NhYHDhxAjRo1MGzYsGfad9u2bdG+fXt8+OGHSEpKQtOmTfHPP/9g8uTJqFOnDvr27WvhozGcg4ODA3r37o0PPvgA6enpWLJkCR4+fPjM+xwzZgzWrVuHbt264aOPPkLDhg2RlpaGffv2oXPnzmjZsiV69eqFH374AR07dsTo0aPRsGFDKBQK3Lp1C3v27EG3bt3Qo0cPCx6pvmnTpiEqKgpNmjTBqFGjULlyZaSnp+P69evYunUrvv76a5QpUwZubm4ICQnBb7/9htatW8Pb2xu+vr4Gh6fOT2hoKD7++GNMnz4daWlp6N27Nzw8PHDu3Dncv38fU6dOxZYtW7B48WJ0794d5cqVgxACGzduxKNHj9C2bdsCX8PHxwfDhg1DTEwMKlWqhK1bt2LZsmUYNmyY3pciQPu+h4eHY8+ePXjjjTcsfr9Kz5498cknn6BXr14YN24c0tPTsXDhwjxDo1uKKcdeWNecp6cnJk2ahI8//hj9+vVD7969kZCQgKlTp0KpVGLy5MkWPdalS5ciMjIS7du3x4ABA1C6dGk8ePAA58+fx8mTJ/HLL78A0H7xnz59OiZPnowWLVrg4sWLmDZtGsLCwvSGCc/vGrfE+7hgwQI0a9YMERERGDZsGEJDQ5GcnIwrV67g999/19172qVLF92cg6VKlcKNGzcwf/58hISEoGLFikb3X6ZMGZQrVw6HDx/GqFGj8mxPSkrC+vXr86wvVarUc88fZurnSKdOnTBv3jz06dMHb775JhISEjB37lyDvSGM2bNnD0aPHo3XX38dTZo0gY+PD+Lj4/Hjjz9i+/bt6NevX55uxwEBAejYsSNWrlwJIYRuguHcVq9ejQ4dOiAyMhKDBg1CZGQkvLy8EBsbi99//x0//vgjTpw4ofc5cvjwYbRo0aJQelQQPRdrjqZBZO/MGblrxYoVolGjRsLFxUU4OTmJ8uXLi379+ukN753fyFSGRgsUQjsC1YcffihCQkKEQqEQgYGBYtiwYeLhw4d6cSEhIaJTp055nm9sRDFjx5Y9Mta9e/d0637//XdRq1YtoVQqRenSpcW4cePEtm3bBADdkM35HZ+hEcUePnwoRo8eLcqWLSsUCoXw8/MTnTp10huRSqVSiblz5+pe29XVVVSpUkW89dZb4vLly3leJ/drGjqfxnI0dP7u3bsnRo0aJcLCwoRCoRDe3t6iXr16YsKECXojYP3555+iTp06wtHRUQAQ/fv3N3ousxkagUwIIVavXi0aNGigO946derohs++cOGC6N27tyhfvrxwcnISHh4eomHDhmLVqlX5noucx713715Rv3594ejoKAIDA8XHH3+cZ1S5bFOmTNGNlmkq5BrePFtISIjuvGTbunWrqF27tnBychLlypUTX375pdFR5nLvMzo6WgAQ//vf//TWG7rezTl2U685Y79v+fn2229FzZo1hYODg/Dw8BDdunXLM1qcJUYLFEKIM2fOiNdee034+fkJhUIhAgICRKtWrcTXX3+ti8nIyBDvv/++KF26tFAqlaJu3bpi06ZNBn9fjV3jQjzf+5gtOjpaDBo0SJQuXVooFApRqlQp0aRJEzFjxgxdzOeffy6aNGkifH19hYODgyhbtqwYPHiwuH79eoHnatKkScLLy0tv+gohno4kaeinRYsWQgjjowUaev9btGihe142Uz9HVqxYISpXriwcHR1FuXLlxKeffiqWL18uAIjo6OgCX/vmzZti4sSJomnTpiIgIEDI5XLh5uYmGjVqJBYtWiSysrIMnpvffvtNABDe3t55zk9OaWlpYuHChaJx48bC3d1dyOVyERQUJF566SXxxx9/6MVeuXLF4MibRLZAIkSu2eOIiIiKQP369SGRSHDs2DFrp0L0XO7cuYOwsDCsXr0aPXv2tHY6Jd6kSZOwevVqXL16FXI5O2GRbeEVSURERSYpKQlnz57Fli1bcOLECfz666/WTonouQUFBWHMmDGYOXMmXn31VUilHIy5sDx69AhfffUVFi1axMKKbBKvSiIiKjInT55Ey5Yt4ePjg8mTJ6N79+7WTonIIiZOnAhnZ2fcvn073xE56flER0dj/Pjx6NOnj7VTITKI3QKJiIiIiIgsgO3WREREREREFsDiioiIiIiIyAJYXBEREREREVkAB7QwQKPR4M6dO3Bzc+PkdEREREREdkwIgeTkZAQFBRU4GiiLKwPu3LnDkX6IiIiIiEjn5s2bKFOmTL4xLK4McHNzA6A9ge7u7lbOhoxRqVTYuXMn2rVrB4VCYe10qBjgNUPm4jVD5uI1Q+biNWP7kpKSEBwcrKsR8sPiyoDsroDu7u4srmyYSqWCs7Mz3N3d+WFEJuE1Q+biNUPm4jVD5uI1U3yYcrsQB7QgIiIiIiKyABZXREREREREFsDiioiIiIiIyAJ4z9UzEkIgKysLarXa2qnYLZVKBblcjvT0dJPeB5lMBrlczuH1iYiIiKhQsLh6BpmZmYiNjUVqaqq1U7FrQggEBATg5s2bJhdMzs7OCAwMhIODQyFnR0RERET2hsWVmTQaDaKjoyGTyRAUFAQHBwe2hFiJRqNBSkoKXF1dC5zQTQiBzMxM3Lt3D9HR0ahYsWKBzyEiIiIiMgeLKzNlZmZCo9EgODgYzs7O1k7Hrmk0GmRmZkKpVJpUKDk5OUGhUODGjRu65xERERERWQr/dP+M2OpRPPF9IyIiIqLCwm+aREREREREFmD14mrx4sUICwuDUqlEvXr1sH///nzjv/rqK1StWhVOTk6oXLkyVq9enSdmw4YNCA8Ph6OjI8LDw/Hrr78WVvpEREREREQArFxcrVu3DmPGjMGECRNw6tQpREREIDIyEjExMQbjlyxZgvHjx2PKlCn477//MHXqVLzzzjv4/fffdTGHDh1Cz5490bdvX5w5cwZ9+/bFa6+9hiNHjhTVYRERERERkR2yanE1b948DB48GEOGDEHVqlUxf/58BAcHY8mSJQbj16xZg7feegs9e/ZEuXLl0KtXLwwePBhz5szRxcyfPx9t27bF+PHjUaVKFYwfPx6tW7fG/Pnzi+iobN/Bgwchk8nQoUMHvfV79+6FRCLBo0eP8jyndu3amDJlit66U6dO4dVXX4W/vz+USiUqVaqEoUOH4tKlS4WYPRERERGRbbLaaIGZmZk4ceIEPvroI7317dq1w8GDBw0+JyMjI88Ib05OTjh69ChUKhUUCgUOHTqEd999Vy+mffv2+RZXGRkZyMjI0C0nJSUB0E5Sq1Kp9GJVKhWEENBoNNBoNAUepy1avnw5RowYgeXLl+P69esoW7YsAOiOx9ixZR83AGzZsgWvvvoq2rVrhzVr1qB8+fKIj4/H+vXrMXHiRPz000+FfhxCiDx5FUSj0UAIAZVKBZlMVpjpkQ3K/n3O/XtNZAyvGTIXrxkyF68Z22fOe2O14ur+/ftQq9Xw9/fXW+/v74+4uDiDz2nfvj2+/fZbdO/eHXXr1sWJEyewYsUKqFQq3L9/H4GBgYiLizNrnwDw6aefYurUqXnW79y5M89w63K5HAEBAUhJSUFmZiYA7Zf7dJV1Ci2lQmrWPFuPHz/GL7/8gl27duHmzZv45ptv8MEHHwCAblLk5OTkPKPqqdVqZGRkICkpCampqRg0aBDatm2LNWvW6GJ8fHwwadIkJCYm6grUopCcnGxybGZmJtLS0vDXX38hKyurELMiWxYVFWXtFKiY4TVD5uI1Q+biNWO7sr8jm8Lq81zlLgyEEEaLhUmTJiEuLg4vvPAChBDw9/fHgAED8Nlnn+m1QpizTwAYP348xo4dq1tOSkpCcHAw2rVrB3d3d73Y9PR03Lx5E66urrpWtNTMLNSZY51fiLNT2sLZwfS3cf369ahcuTLq1auHAQMGYPTo0Zg+fTokEomukHRzc8tz3DKZDI6OjnB3d8euXbuQkJCA8ePH54kDYHBdYRBCIDk5GW5ubiYXmOnp6XByckLz5s05z5UdUqlUiIqKQtu2baFQKKydDhUDvGbIXLxmyFy8ZmyfOY0GViuufH19IZPJ8rQoxcfH52l5yubk5IQVK1Zg6dKluHv3LgIDA/HNN9/Azc0Nvr6+AICAgACz9gkAjo6OcHR0zLNeoVDkucjVajUkEgmkUqmudceacyflzMMUK1euxBtvvAGpVIqOHTti8ODB2LNnD9q0aaN3PIb2mX3cV69eBQCEh4db9dizuwJm52UKqVTb0mfovSX7wfefzMVrhszFa4bMxWvGdpnzvlituHJwcEC9evUQFRWFHj166NZHRUWhW7du+T5XoVCgTJkyAICffvoJnTt31n25bty4MaKiovTuu9q5cyeaNGlSCEeh5aSQ4dy09oW2/4Je21QXL17E0aNHsXHjRgDaLo49e/bEihUr0KZNG5P3k32vExERERE9O43Q4Pjd4/gv8z843nSEXJb3q3lEmQg4yrSNABceXMCt5FtG99ckqAmcFdqeSJcfXsaNpBtGYxsFNoKbgxsA4Nqja7iWeM1obIOABvBw9AAAXE+8jiuPrhiNretfF95KbwDAzeSbuPjgotHYWqVqoZRzKQDAnZQ7OJdwTm97Ja9KKOte1ujzbZFVuwWOHTsWffv2Rf369dG4cWN88803iImJwdtvvw1A213v9u3burmsLl26hKNHj6JRo0Z4+PAh5s2bh7Nnz+K7777T7XP06NFo3rw55syZg27duuG3337Dn3/+iQMHDhTacUgkErO65lnL8uXLkZWVhdKlS+vWCSGgUCjw8OFDXXe+xMREeHp66j330aNH8PDQ/lJVqlQJAHDhwgU0bty4aJInIiIiKmH23NyDiQcmIkWVgh/3/2g45rU9cHTSFlcbLm3ATxeNDxq27aVtuuLq92u/Y+XZlUZjN3bdqCuudlzfgcVnFhuN/aHjD6hZqiYAYO/Nvfj8xOdGY5e3W46GgQ0BAAdvH8SMIzOMxn7Z6ku0cG4BADgWdwwT/56ot91Z7oy9PffCSe5kdB+2xqoVQc+ePZGQkIBp06YhNjYW1atXx9atWxESEgIAiI2N1ZvzSq1W4/PPP8fFixehUCjQsmVLHDx4EKGhobqYJk2a4KeffsLEiRMxadIklC9fHuvWrUOjRo2K+vBsSlZWFlavXo3PP/8c7dq109v28ssv44cffkD//v0hlUpx7Ngx3XsAaN+H27dvo3LlygC0Izr6+vris88+MzhB86NHj/IUZ0RERESkr3XZ1qjRpQaGbx4OJy8ng/eQK6RPu6SVcSuDOn51jO7PQeage1zapXS+sUr503vPA1wC8o11lj8d4M3P2S/fWFcHV91jXyfffGPdHZ/ep++t9NaLPRV/ClW8qyAjK6NYFVcSwT5eeSQlJcHDwwOJiYkGB7SIjo5GWFhYsRoQYdOmTejZsyfi4+N1LVDZJkyYgK1bt+LUqVMYPnw4/vjjDyxYsAC1atXCnTt3MGHCBDx48AAnT56EXK6tx3/77Te8+uqr6NChA0aNGoUKFSrg/v37+PnnnxETE1MkQ7FrNBokJSXB3d3d5Huuiuv7R5ahUqmwdetWdOzYkf3aySS8ZshcvGbIXLxmbF9+tUFuVp1EmIrO8uXL0aZNmzyFFaBtuTp9+jROnjyJL774AkOGDMHHH3+MatWq4fXXX0dYWBh27typK6wAoFu3bjh48CAUCgX69OmDKlWqoHfv3khMTMSMGcabf4mIiIiISirbv1GILOL33383uq1u3bp6g1RMmjQJkyZNKnCf9evXx4YNGyySHxEREZG92Xl9J36++DPc093RER2tnQ5ZAFuuiIiIiIisIPZxLI7EHUG8Ot7aqZCFsLgiIiIiIiKyABZXREREREREFsDiioiIiIiIyAJYXBEREREREVkAiysiIiIiIiILYHFFRERERGQFUokUcokcUn4lLzH4ThIRERERWUHf8L442vsoXnZ52dqpkIWwuCIiIiIiIrIAFldEREREREQWwOLKjgwYMAASiSTPz5UrVwAAf/31F7p06YKgoCBIJBJs2rTJugkTERERlWC7Y3Zj7F9j8Xf639ZOhSyExZWd6dChA2JjY/V+wsLCAACPHz9GrVq18OWXX1o5S8OEEMjKyrJ2GkREREQWcTP5Jvbe2os76jvWToUshMWVJQgBZD62zo8QZqXq6OiIgIAAvR+ZTAYAiIyMxIwZM/DSSy+ZvL8zZ86gZcuWcHNzg7u7O+rVq4fjx4/rtv/9999o0aIFnJ2d4eXlhfbt2+Phw4cAgIyMDIwaNQp+fn5QKpVo1qwZjh07pnvu3r17IZFIsGPHDtSvXx+Ojo7Yv38/hBD47LPPUKFCBQQGBqJOnTpYv369WeeBiIiIiMjS5NZOoERQpQKzgqzz2h/fARxcrPPaAF5//XXUqVMHS5YsgUwmw+nTp6FQKAAAp0+fRuvWrTFo0CAsXLgQcrkce/bsgVqtBgB88MEH2LBhA7777juEhITgs88+Q/v27XHlyhV4e3vrXuODDz7A3LlzUa5cOXh6emLixInYuHEjvvrqKwQGBuLkyZN44403UKpUKbRo0cIq54GIiIiIiMWVndmyZQtcXV11y5GRkfjll1+eeX8xMTEYN24cqlSpAgCoWLGibttnn32G+vXrY/Hixbp11apVA6DtgrhkyRKsWrUKkZGRAIBly5YhKioKy5cvx7hx43TPmTZtGtq2bat73rx587B79240atQISUlJqFmzJg4ePIilS5eyuCIiIrJx/yX8h0x1psFtDjIHVPOppls+n3Ae6ep0g7EKqQLVfavrli8+uIjUrFSDsTKJDDVL1dQtX354GSmqFIOxEkhQ26+2bvnqo6tIykwyejy1S9WGRCIBAEQnRuNRxiOjsTV8a0Au1X79vpF0A7dTbhuNpeKJxZUlKJy1LUjWem0ztGzZEkuWLNEtu7g8X6vX2LFjMWTIEKxZswZt2rTBq6++ivLlywPQtly9+uqrBp939epVqFQqNG3aVLdOoVCgYcOGOH/+vF5s/fr1dY/PnTuH9PR0XbGVLTMzE3Xq1HmuYyEiIqLC9/7e93Er5ZbBbaHuofi9x++65Ql/T8Dlh5cNxvo7++PPV//ULU87PA3/3PvHYKy7gzv+7v100Ig5R+fgSNwRg7EKqQIn+57ULc8/MR97b+01ejyn+56GTKK9xWLJ6SXYdn2b0dhDvQ/B1UH7R+7l/y7Hr1d+BaAt6KhkYHFlCRKJVbvmmcPFxQUVKlSw2P6mTJmCPn364I8//sC2bdswefJk/PTTT+jRowecnJyMPk88uVcs+y89OdfnXpezANRoNACAP/74A4GBgUhJSYGrqyukUikcHR0tdVhERERUSIJcgyCVGL7tP9AlMM9yRlaGwVhfJ1+9ZX9nf5R1K2swNrugyebn7Gc0NrtlSfc6zr5GY3PzcfLJNzbncXsrvVHWrSwcpA6onVXbpP2T7WNxRc+tUqVKqFSpEt5991307t0bK1euRI8ePVCzZk3s2rULU6dOzfOcChUqwMHBAQcOHECfPn0AACqVCsePH8eYMWOMvlZ4eDgcHR0RExODiIgIJCUlwd3dHVIpx2YhIiKyVf229cOD9AdY2HIhlrdfbvLzvmr9lcmx816cZ3LsrIhZJsdObjzZ5NgPG36IDxt+aFLsmHpjMKbeGKhUKmzdutXk1yDbxuKKdFJSUnRzXgFAdHQ0Tp8+DW9vb5Qtm/evMGlpaRg3bhxeeeUVhIWF4datWzh27BhefvllAMD48eNRo0YNDB8+HG+//TYcHBywZ88evPrqq/D19cWwYcMwbtw43f4/++wzpKamYvDgwUZzdHNzw/vvv493330XWVlZqF27NjQaDQ4fPgxXV1f079/f8ieGiIiInsut5Fu4l3YPKo3K2qkQFSoWV6Rz/PhxtGzZUrc8duxYAED//v2xatWqPPEymQwJCQno168f7t69C19fX7z00ku6lqpKlSph586d+Pjjj9GwYUM4OTmhUaNG6N27NwBg9uzZ0Gg06Nu3L5KTk1G/fn3s2LEDXl5e+eY5ffp0+Pn5Yc6cObh27Ro8PT1Rt25dfPzxxxY6E0RERERE5mNxZUcMFUg5vfjii7p7oUzh4OCAH3/8Md+YFi1a4O+/Dc86rlQqsXDhQixcuNCsfCQSCUaNGoURI0awWyARERER2Qx+IyUiIiIiIrIAFldEREREREQWwOKKiIiIiIjIAnjPFREREREVqiDXICjlSiikCmunQlSoWFwRERERUaH6vuP31k6BqEiwWyAREREREZEFsLgiIiIiIiKyABZXRERERFSo3tz5Jl79/VVEJ0ZbOxWiQsV7roiIiIioUF15dAX30u4hU51p7VSIChWLKypUoaGhGDNmDMaMGWPRWCIiIlskhECmyERaVhpUUOXZLpPK4Chz1C2nqlKN7ksqkUIpVz5TbFpWGoQQBmMlEgmc5E7PFJuelQ6N0Jgda+w5RCUNiys7MmDAAHz33XcAALlcjuDgYLz00kuYOnUqXFxcCuU1jx07ZvK+zYklIiKyRRqhwbTEaZj28zSD218s8yIWtV6kW474KQKZGsOtOY0CGuHb9t/qltuub4ukzCSDsTV9a+KHTj/olrv82gV3U+8ajK3oVREbu27ULb/2+2u4nnTdYGwZ1zLY9vI23XK/bf1w/sF5g7E+Sh/s7blXt/xW1Fs4GX/SYCxRScXiys506NABK1euhEqlwv79+zFkyBA8fvwYS5Ys0YtTqVRQKJ5/LopSpUoVSiwREREVL6VdSyPYLdjaaRAVKhZXFpRfc31hdANwVjibnaOjoyMCAgIAAH369MGePXuwadMm+Pv7Y9OmTRg1ahRmzJiB69evQ61WIykpCePGjcOmTZuQnp6O+vXr44svvkCtWrV0+9y8eTOmTZuGs2fPwtXVFc2bN8fGjdq/iOXu6jdlyhSsWLECd+/ehY+PD1555RUsXLjQYGxMTAxGjhyJXbt2QSqVokOHDli0aBH8/f0BAFOnTsXGjRvx/vvvY/LkyXj48CEiIyOxbNkyuLm5mX1uiIiInpdUIsUnHp+gffv2Bv9IKZPI9JYP9D6Q775y+vPVP02O3dJjCwSMdPWDRG/5ly6/mBy7OnK10djcvmn3jV53QEeZY548iUoaFlcW1GhtI6PbIkpHYHGbxbrlF39+EWlZaQZj6/vXx8oOK3XLHTZ0wMOMh3ni/u3/73Nkq+Xk5ASVStsn/MqVK/j555+xYcMGyGTaD/9OnTrB29sbW7duhYeHB5YuXYrWrVvj0qVL8Pb2xh9//IGXXnoJEyZMwJo1a5CZmYk//vjD4GutX78eX3zxBX766SdUq1YNcXFxOHPmjMFYIQS6d+8OFxcX7Nu3D1lZWRg+fDh69uyJvXv36uKuX7+O3377DVu2bMHDhw/x2muvYfbs2Zg5c+ZznxsiIjIsS5OFK4+u5LmPJsQ9BC4KbffuB+kPEPc4zug+gt2C4eag/UPYo/RHuPP4jtHY0q6l4eHoAQBIzEjE7ZTbRmODXILgqfQEACRnJuNm8k2jsf7O/vBx8gEAPFY9xo2kG0Zj/Zz94OvkC0D7R09j3ehcZa5wkDjASe4EhbzgHiA571GyZGzOP9JaKzbnH5WJ7AWLKzt29OhRrF27Fq1btwYAZGZmYs2aNbruebt378a///6L+Ph4ODpqPyDnzp2LTZs2Yf369XjzzTcxc+ZM9OrVC1OnTtXtN2erVk4xMTEICAhAmzZtoFAoULZsWTRs2NBg7J9//ol//vkH0dHRCA7WdiFYs2YNqlWrhmPHjqFBgwYAAI1Gg5UrV8LDQ/ufbt++fbFr1y4WV0REhejj/R9j2/VtedYvb7ccDQO1n+tR16Mw48gMo/v4stWXaBHcAgCw79Y+TPx7otHY/zX/HzqEdQAAHIk9gvf2vWc0dlqTaehRsQcA4HT8aQzfNdxo7PiG49Gnah8AwLmEcxi0Y5DR2DF1x2BwjcEAgOjEaPT6o5fR2ErySuiIjka3E1HJxeLKgo70OWJ0m0yq3w1g72t7jcbmbjLf/vL258orpy1btsDV1RVZWVlQqVTo1q0bFi1ahMWLFyMkJETvvqcTJ04gJSUFPj4+evtIS0vD1atXAQCnT5/G0KFDTXrtV199FfPnz0e5cuXQoUMHdOzYEV26dIFcnvcyPH/+PIKDg3WFFQCEh4fD09MT58+f1xVXZcuW1esCGBgYiPj4eNNPCBERmS06STtXkaejJxxkDrr1OR87KZzg5+xndB85Y5VyZb6xOVtACorN2brjIHMwOVYhVeQbm90iBwByqdxorFwiR01R0+h+iKhkY3FlQebcA1VYsQVp2bIllixZAoVCgaCgIL3+4LlH6tNoNAgMDNTrhpfN09MTgLZboamCg4Nx8eJFREVF4c8//8Tw4cPxv//9D/v27cvTL10IAYlEkmcfudfnLswkEgk0Gg73SkRUFOZEzEGT0k0Mbutaviu6lu9q0n7ah7ZH+9D2JsU2L9Mcu17dZVJso8BGJsfW9qttcmxl78pGY1UqFbZu3WrSfoio5GFxZWdcXFxQoUIFk2Lr1q2LuLg4yOVyhIaGGoypWbMmdu3ahYEDB5q0TycnJ3Tt2hVdu3bFO++8gypVquDff/9F3bp19eLCw8MRExODmzdv6lqvzp07h8TERFStWtWk1yIiosLRvUJ3xKfGI8g1yNqpEBHZFBZXZFSbNm3QuHFjdO/eHXPmzEHlypVx584dbN26Fd27d0f9+vUxefJktG7dGuXLl0evXr2QlZWFbdu24YMPPsizv1WrVkGtVqNRo0ZwdnbGmjVr4OTkhJCQEIOvXbNmTbz++uuYP3++bkCLFi1aoH79+kVx+EREZMTrVV+3dgpERDaJ42GSURKJBFu3bkXz5s0xaNAgVKpUCb169cL169d1w6G/+OKL+OWXX7B582bUrl0brVq1wpEjhu898/T0xLJly9C0aVNdi9fvv/+e556u7NfetGkTvLy80Lx5c7Rp0wblypXDunXrCvWYiYiIiIielUQIYdpkBXYkKSkJHh4eSExMhLu7u9629PR0REdHIywsDEql6cORkuVpNBokJSXB3d0dUqlpfyfg+2ffsu+F6Nixo0UmyaaSj9eMYXGP46DSqODr5GvW8OD2gNcMmYvXjO3LrzbIjd0CiUqYlMwU7Lm5BxnqDABAoEsgmpZuqtv+6+VfoRZqg8/1c/ZD8zLNdcubr25GpjrTYKy30hutyrbSLf9x7Q+jc7e5O7ijXWg73fL269uRkpliMNZF4YLIsEjd8q4buwzO8wZoRxDrUr6Lbnnvzb24n3bfYKxcKkenkE665f239uNu6l2DsRJI8HKll3XLB+8cxJ0U43PwdK/QHXKp9uP0aOxRxCTHGI3tUr6LbuSzk3dP4lriNaOxHcM66ga0OXPvDC4/vGw0tl1oO7g7aD/w/7v/H84/OG80tlXZVvBWegMALjy4gLP3z8JH6YMWwS04wSeZZOTukbjw4AKWtllqdEALIiJ7xOKKqIRZ+s9SrPpvlW65RZkWesXVjMMzkKkxXDA1CmikV1x9duwzJGYkGoyt6VtTr7j64sQXRouVCp4V9Iqrr059ZXQCzjKuZfSKq6X/LDVaKPgoffSKq5VnV+Jk/EmDsc5yZ73i6ofzP+DvO38bjJVKpHrF1S8Xf8GfMX8ajAW0BZP8ycfpr1d+xZZrW4zGtinbRldcbbm2Bb9c+sVobLPSzXTF1c7rO7H63GqjsXX96+qKq903d+Obf74xGlvVp6quuDpw+wAWnFwAAFjQcgFaBrc0OFInERERFYzFFVEJ8yD9AQCgnEc5lHUvi+o+1fW2twhuAZVGZfC5lbwq6S03K90Mj1WPDcaGuofqLTcJamK0hSnIRX9EsUaBjRDqEWow1tfJV2+5fkB9+Lv4G4zNLiay1fGrA3dHw831OefJAYCapWpCITPc/UKa63bUar7VkCWyDMbmjq/qXRUpKsOtcgB0LVyA9ny/GPyi0dic8wBV8KyQb6yz/OmUDWEeYfnGuimezg0X4v50QJnRe0bj584/o6qPdkTOVWdX4Zt/voFcKtf7kUlkkEvlmNVsli52141d+PnSz3rb5VI5FFIF5FI5+lbtiwpe2pFKz94/i90xu3Xbcv80CWqC0q6lAWi7n114cOHpdon+fgNdA3XXQYY6A8mZyfr7lcjzzDNIRERUWFhcEZVQ3St0x8DqeYfIn/fiPJP3MTtitsmx05pOMzl24gsTTY79oEHekSeNGVNvTL7bVaqnReXw2sNN3u+QGkNMju1XrR/6VetnUmyvKr3Qq0ovk2J7VOyBHhV7mBTbuVxndC7X2aTYtiFt9ZZzFiJpWWlIViUbfW7OFtBbKbdw8M5Bo7GRYZGoAG1xdS7hHJb9u8xo7MKWC3XF1bG4Y/j4wMdGY2dHzEanctoWyf239uPdve/miZFAArlUjkkvTNKdw2NxxzDhwAT9wk7y9PEbVd/QtbbeVd/FmH1jjHaZ7Fqhq+483ki6gbnH5hrNt0NYB12+cY/jMPPwTKOxrcq20uWbkJaAKQenGI1tVroZelbpCUDbNXj8/vFGYxsGNkTf8L4AgEx1Jt7b+57R2Fp+tfSu/5G7RgIAbibfNPocIiJ7xuKKiIgAAC9XfBnlPcrrlvtU7YMOYR2Qpcl6+iOePi7nUU4X26x0M3grvZGlyYJKo0KWJgtqodbFlnUrq4st71kevav0hlqj1u0v+zlZmiz4OfvpYj0cPVDDt0aemOzn5WyxM3YvoYCASqPS6+6YqkpF7ONYo+ciZ9fUVJGKv27/ZTS2tl9t3eOUzBTsvbXXaGwVnyp6OeQXm7NVMUOdkW9sznOm0qjyjc3ZuqsRmnxjc7fu5o71dvI2+lwiInvE4oqIyM790eMPrL+0HqPqjtJrufJw9ICHo4dJ+yjvWR7lPcsXHAignn891POvZ1Js8zLN9e4DzE/70PZoF9JOr6jLWYjl7EZax78O1nZca7C4y9JkoYr30yLIV+qLTxp9ApnMcPfCaj7VdI8DXQMxtclUoznq7dfZN9/YCp5PJ3z3dPTMNzbMI0z32FnhnG9ssFuw7rFcKs83NvckwTljA10C9Y6HiIhYXBGVOBMaTcC4+uOglHOoeTJNWfeyGFt/rLXTsAiJRKLr3pcfdwd31ChVw6R9uknd0LG8aUMkeyu98VLFl0zar7uDu8mxzgpnk2MdZY4mx8qlcpNjAZgVS0Rkj1hcEZUwzgpn3QhzRERERFR0OKEJERERERGRBbC4oiIVGhqK+fPn65YlEgk2bdpktXxKok1XNmHaoWk4eNv4yG1EREREZHksruzIgAEDIJFItPckyOUoW7Yshg0bhocPDc9NRMXTsbhj+OXSL7j48KK1UyEiIiKyKyyu7EyHDh0QGxuL69ev49tvv8Xvv/+O4cNNn++HiIiIiIgMY3FlQZrUVOM/GRmmx6anmxT7LBwdHREQEIAyZcqgXbt26NmzJ3bu3KnbvnLlSlStWhVKpRJVqlTB4sWL9Z5/69Yt9OrVC97e3nBxcUH9+vVx5MgRAMDVq1fRrVs3+Pv7w9XVFQ0aNMCff/75THkSERERERU3HC3Qgi7WNT5vi0uL5ii7dKlu+VLTZhBpaQZjnRs0QMia1brlK63bQG2g617VC+efI1vg2rVr2L59u2544WXLlmHy5Mn48ssvUadOHZw6dQpDhw6Fi4sL+vfvj5SUFLRo0QKlS5fG5s2bERAQgJMnT0Kj0QAAUlJS0LFjR8yYMQNKpRLfffcdunTpgosXL6Js2bL5pUJEREREVOyxuLIzW7ZsgaurK9RqNdKftJDNmzcPADB9+nR8/vnneOkl7TwmYWFhOHfuHJYuXYr+/ftj7dq1uHfvHo4dOwZvb28AQIUKTye5rFWrFmrVqqVbnjFjBn799Vds3rwZI0aMKKpDJCIiIiKyChZXFlT55AnjG2UyvcVKfx8wHivV761ZYZfluta1bNkSS5YsQWpqKr799ltcunQJI0eOxL1793Dz5k0MHjwYQ4cO1cVnZWXBw8MDAHD69GnUqVNHV1jl9vjxY0ydOhVbtmzBnTt3kJWVhbS0NMTExFgsfyIiIiIiW8XiyoKkzqZP3FpYsQVxcXHRtTYtXLgQLVu2xNSpU3UtS8uWLUOjRo30niN7Uhg6OTnlu+9x48Zhx44dmDt3LipUqAAnJye88soryMzMtFj+RERERES2isWVnZs8eTIiIyMxbNgwlC5dGteuXcPrr79uMLZmzZr49ttv8eDBA4OtV/v378eAAQPQo0cPANp7sK5fv16Y6ZMB4+qPw8g6I+GqcLV2KkRERER2hcWVnXvxxRdRrVo1zJo1C1OmTMGoUaPg7u6OyMhIZGRk4Pjx43j48CHGjh2L3r17Y9asWejevTs+/fRTBAYG4tSpUwgKCkLjxo1RoUIFbNy4EV26dIFEIsGkSZN0g11Q0fFUelo7BSIiIiK7xKHYCWPHjsWyZcvQvn17fPvtt1i1ahVq1KiBFi1aYNWqVQgLCwMAODg4YOfOnfDz80PHjh1Ro0YNzJ49W9dt8IsvvoCXlxeaNGmCLl26oH379qhbt641D42IiIiIqMiw5cqOrFq1yuD6Pn36oE+fPnkeGxISEoL169cb3BYaGordu3frrXvnnXf0lnN3ExRCFJA1mWvrta34L+E/tCjTAg0DG1o7HSIiIiK7wZYrohLm7zt/Y/W51fgv4T9rp0JERERkV1hcERERERERWQCLKyIiIiIiIgtgcUVERERERGQBLK6eEQdiKJ74vhERERFRYWFxZSaFQgEASE1NtXIm9Cyy37fs95GIiIiIyFI4FLuZZDIZPD09ER8fDwBwdnaGRCKxclb2SaPRIDMzE+np6ZBK8/87gRACqampiI+Ph6enp25uLiIiIiIiS2Fx9QwCAgIAQFdgkXUIIZCWlgYnJyeTC1xPT0/d+1dSja47GoNrDIa3o7e1UyEiIiKyKyyunoFEIkFgYCD8/PygUqmsnY7dUqlU+Ouvv9C8eXOTuvkpFAq7aLHyc/aDH/ysnQYRERGR3WFx9RxkMpldfFm3VTKZDFlZWVAqlbyHioiIiIisjsUVUQkTdSMKFx5cQNOgpqjrX9fa6RARERHZDRZXRCXM3pt7sfnqZrgqXFlcERERERUhDsVORERERERkASyuiIiIiIiILIDFFRERERERkQWwuCIiIiIiIrIAFldEREREREQWYPXiavHixQgLC4NSqUS9evWwf//+fON/+OEH1KpVC87OzggMDMTAgQORkJCg275q1SpIJJI8P+np6YV9KEREREREZMesWlytW7cOY8aMwYQJE3Dq1ClEREQgMjISMTExBuMPHDiAfv36YfDgwfjvv//wyy+/4NixYxgyZIhenLu7O2JjY/V+lEplURwSkdUNqzUMP3X+CV3Kd7F2KkRERER2xarF1bx58zB48GAMGTIEVatWxfz58xEcHIwlS5YYjD98+DBCQ0MxatQohIWFoVmzZnjrrbdw/PhxvTiJRIKAgAC9HyJ7UcatDKr5VIOvk6+1UyEiIiKyK1abRDgzMxMnTpzARx99pLe+Xbt2OHjwoMHnNGnSBBMmTMDWrVsRGRmJ+Ph4rF+/Hp06ddKLS0lJQUhICNRqNWrXro3p06ejTp06RnPJyMhARkaGbjkpKQkAoFKpoFKpnvUQqZBlvzd8j8hUvGbIXLxmyFy8ZshcvGZsnznvjdWKq/v370OtVsPf319vvb+/P+Li4gw+p0mTJvjhhx/Qs2dPpKenIysrC127dsWiRYt0MVWqVMGqVatQo0YNJCUlYcGCBWjatCnOnDmDihUrGtzvp59+iqlTp+ZZv3PnTjg7Oz/HUVJRiIqKsnYKNuWC6gLuqu+inLwcguXB1k7HJvGaIXPxmiFz8Zohc/GasV2pqakmx0qEEKIQczHqzp07KF26NA4ePIjGjRvr1s+cORNr1qzBhQsX8jzn3LlzaNOmDd599120b98esbGxGDduHBo0aIDly5cbfB2NRoO6deuiefPmWLhwocEYQy1XwcHBuH//Ptzd3Z/zSKmwqFQqREVFoW3btlAoFNZOx2Z8cugTbInegtG1R6N/eH9rp2NTeM2QuXjNkLl4zZC5eM3YvqSkJPj6+iIxMbHA2sBqLVe+vr6QyWR5Wqni4+PztGZl+/TTT9G0aVOMGzcOAFCzZk24uLggIiICM2bMQGBgYJ7nSKVSNGjQAJcvXzaai6OjIxwdHfOsVygUvMiLAb5P+qRS7a2UMpmM58UIXjNkLl4zZC5eM2QuXjO2y5z3xWoDWjg4OKBevXp5mkCjoqLQpEkTg89JTU3VfXHMJpPJAADGGuCEEDh9+rTBwouIiIiIiMhSrNZyBQBjx45F3759Ub9+fTRu3BjffPMNYmJi8PbbbwMAxo8fj9u3b2P16tUAgC5dumDo0KFYsmSJrlvgmDFj0LBhQwQFBQEApk6dihdeeAEVK1ZEUlISFi5ciNOnT+Orr76y2nESEREREVHJZ9XiqmfPnkhISMC0adMQGxuL6tWrY+vWrQgJCQEAxMbG6s15NWDAACQnJ+PLL7/Ee++9B09PT7Rq1Qpz5szRxTx69Ahvvvkm4uLi4OHhgTp16uCvv/5Cw4YNi/z4iIiIiIjIfli1uAKA4cOHY/jw4Qa3rVq1Ks+6kSNHYuTIkUb398UXX+CLL76wVHpEREREREQmseokwkRERERERCWF1VuuiMiyhtQYgpcqvoTSrqWtnQoRERGRXWFxRVTChHmEIcwjzNppEBEREdkddgskIiIiIiKyALZcEZUwB+8cRHRiNOr41UG4T7i10yEiIiKyG2y5Iiph/rj2B2YfnY0jsUesnQoRERGRXWFxRURERERkBSnpWRi19iQAYNTak0hJz7JyRrZBrRE4dDUBv52+jUNXE6DWCGunZDJ2CyQiIiIiKmJdv9yPf24lwVEm0KEhsPvSPVSfsgM1y7hj84gIa6dnNdvPxmLq7+cQm5iuWxfoocTkLuHoUD3QipmZhi1XRERERERFKLuwMuSfW0no+uX+Is7INmw/G4th35/UK6wAIC4xHcO+P4ntZ2OtlJnp2HJFRERERFREUtKz9AortQCO35NArXka88+tJPx0NAZKhcwKGVqHRghM+/0cDHUAFAAkAKb+fg5twwMgk0qKODvTsbgiIiIiIioi7647pbecpZFgzZW8RdRHG/8tqpSKBQEgNjEdR6MfoHF5H2unYxSLKyIiIiKiIhLzMC3POneFQLIKEHjaIuPsIEPdsl5FmZpVxSen49LdFJPibBmLK6ISpl94P0SGRSLUPdTaqRAREVEuZb2ccDEuWW9dJQ+Bfx9KkKF+uq5peR8s69+giLOznkNXE9B72eEC4/zclEWQzbPjgBZEJUxl78poVroZyriVsXYqRERElMv0bjUsGldS1AvxQkG3Ukkl2jhbxuKKiIiIiKiITPrNtHupTI0rKU7ceIiCprPSCG2cLWNxRcXWukvrcDrztLXTsDnH4o5h4+WNuPjgorVTISIiolwM3XP1PHElhan3Utn6PVcsrqhYepj+EHOOz8H61PXWTsXmbLqyCZMPTsbBOwetnQoRERHlUtbLyaJxJYWp91LxniuiQvAg/YG1UyAiIiIy26RO1SwaV1I0DPNGoIcSxm67kgAI9FCiYZh3UaZlNhZXRERERERFZMjqoxaNKylkUgkmdwkHgDwFVvby5C7hNj2BMMDiioopB6mDtVMgIiIiMlt8cqZF40qSDtUDseSNugjw0O/6F+ChxJI36qJD9UArZWY6znNFxZKLg4vecqoqFXGP44zG+zj5wMPRAwCQlpWG2JRYo7FeSi94KbXDfKZnpeNOyh2jsR6OHvBx0s4SnqnOxK3kW0Zj3R3d4evkCwBQaVS4mXTTaKybgxtKOZcCAKg1atxIumE01kXhAn8XfwCARmhw7dE1o7FERERkXX5uDniUpjIpzh51qB6ItuEBOBr9APHJ6fBz03YFtPUWq2wsrqhYclW44vOIz3HixAkAwKn4U3j7z7eNxn/U8CO8XvV1AMD5hPPov72/0djRdUdjSI0hAIBridfQc0tPo7FDawzFqLqjAAC3U26j22/djMb2De+LDxp8AABISEvIN/aVSq9gcuPJAIAUVUq+sZ3KdcLsiNkAgCxNFs4mnDUaS0RERNb105tNUHdGlElx9komlaBxeR9rp/FMWFxRsaSQKvBimReR+k8qAEAulcPT0dNovIPs6V9/ZFJZvrGOMsensZL8Y53kTs8UK5VITY4FkG+ss9w5T6yHoweaBNnvhzIREZGtcnKQWTSObAuLKxt35t4ZXHxwEZW8KqG2X21rp2MzHmY8RIt1LQAAndAJjQIbYX+v/SY9t1apWibHVvaubHJsWfeyJsf6OfuZHOvh6GFyrIPMweRYIiKiopCWqcasredwPSEVoT7O+LhjuF0XDrO2njM5bnr3GoWcDVkaiysbtytmF1aeXYn+4f1ZXBEREVGxMnT1MUSdi9ct778MrDkcg7bhfljWr4EVM7Oe6wmpFo0j28LRAomIiIjI4nIXVjlFnYvH0NXHijgj2xDq41xwkBlxZFvYckVEREREFpWWqTZaWGWLOhePc3eSoFTY19/6X6kXjDWHYwqMe79dlSLIhiyNxVUxcfnRZUQnRsNB5oAA5wDIpNq+ymqNGlKJFBJJ8RiekoiIiEo+U+8r6riQ9wkbs/7ETQyOKGftNMhMLK5s3G9XfgMAHLxzEF03ddU+7n0Qbg5uAIBph6dh4+WNUEgVUEgVcJA56P27qsMq3TxMP174Eftu7oNClitW6gCFTIG3ar6lm9/peNxxnEs4B4Xs6XYHqTZeIVOgnn89uCi0c03dT7uPpIwk3bbceUgl9vUXKSIiIntn6v1CMqkEznY2uEW6Sg2VWuRYI1DTW+Dfh/p/KL/xgPdcFUcsrmzcg/QHusduDm7I0mTpDSueqdbO3q3SqKDSqJCapf+LmLOwufLwCv6+87fR1+oX3g9e0BZXe2/uxXfnvjMa+2vXX1HBqwIA4OeLP2PJmSVGY3/o+ANqlqoJAFh3YR2+PfutrlDLLsKyi7L367+Pyt6VAQBHYo9gW/S2PAWjg8wByZnJRl+PiIiIrCvUxxn7Lxcc16dhsN2NiLd8/zVM/+M8AECJdHzh+A0yfYajwY1FeFf9JtKhBACEeNvxPVcaNXDjIJByF3D1B0KaANLiUYSzuCpGDvY+mGfdpBcm4f3672uLK7UKmZpMqDQqZKq1/2a3cAFA9wrdUcuv1tO4HPEqtX5sFZ8qiAyLRJYmS7evnP86K57+wjvIHODp6KnbrtLozzqesxhMzExE3OM4o8f4WPVY9/jyw8vYcHmD0Vi5RI7ysvJGtxMREZF1fNihqkn3FX3YoWoRZGNb+jYOxcyt57FBPhG1pdeQJVNiK4A2stM47zgIpzXl8HLWDPRtHGrtVK3j3GZg+4dA0p2n69yDgA5zgPCu1svLRCyubFyLMi2w79Y+NC3d1OB2Z4WzXqGTnxqlaqBGKdP+OtS5XGd0LtfZpNghNYZgSI0humUhhLYoe1LAuTq46ra9VPElNA1qql/c5SjyQj1CdbG1StXCiNojtAXdk7js4i1TnYl+Vfrh0sFLJuVIREREReff24kmxzUu71PI2dgWB7kU+72mI+jxNYPba0uvYb/XdDjIOxVxZjbg3Gbg534AhP76pFjt+tdW23yBxeLKxpXzLId9t/ahgkcFa6diMolEou3mJ1MACv1tvk6+8HXyNWk/BRWDKpUKl8DiioiIyNbEJ6dbNK5ESU9B6dTzEBJAAkAi1Ai9vxtSkQWJRFtWlE49DxxaDMgdrZ1t0REC2D0NeQor7UYAEmD7R0CVTjbdRZDFlY0bU3cMxtQdY+00bE5SZhLe3/s+7qfcR0d0tHY6RERElIOfm9KicSXKr0MBaAsrAJALFWrdXKXbrBvWYsf4osyqGBBA0m3tvVhhEdZOxigWVzbu/IPzuProKsp7lEc132rWTsdm3Eu9h0Oxh6ydBhERERng7eRQcJAZcSXKw+t5VqUqvKFUPYIUmqcrHd2Aci8WWVpWl3gbuHOy4LiUu4Wfy3NgcWXjdlzfgZVnV6J/eH8WV0RERFQsdP7yL5PjLs+ys3uL3IOBeP15wBJcqyAg8SSkmhzdJIObAD2/L+LkrCh6P/CdCff7u/oXfi7PgRMQ2bg/rv0BAPj92u9WzoSIiIjINCpNwTHmxJUowtA9Rc8RV1KENAGcvPKPcfLWxtkwFlc2Lj41HoD+fFcEvWHjiYiIyLYoTPyGaWpciZJ8y7JxJYqk4BAbZ4+XNJUAfs5+aFu2LV5weMHaqRAREVEu20a1sGhcieIVatm4kuLGQSCtgMaEtAfaOBvG4srG+Tn5AQC8HAtoJrVDc5rNQWdn0+biIiIioqJTIcC1wDYIyZM4u9NpoWXjSgpTB6qw8QEtWFzZuF+7/4o3qr6B33vwnisiIiIqPqJndzJaYEmebLdL+2dbNq6kMHWgChsf0IKjBdo4dwd3fNjwQ2unQURERGS26NmdcCUuBW3m7wMAyCXA9tEt7LPFKtu9y5aNKylCmgDuQUBSLAxPJCzRbueAFkRERERkryoEuMLPzREAsHlkhH0XVgDwON6ycSWFVAZ0mPNkIXeb55PlDrO1cTaMxRURERERUVFx9bNsXEkS3hV4bTXgHqi/3j1Iuz68q3XyMgO7BRIRERFRocnM0iA1Uw0A+PXULVTwqwIHuR3/fd+3IhC9z7Q4exTeFajSSTsqYMpd7T1WIU1svsUqm9lX9vbt23HgwAHd8ldffYXatWujT58+ePjwoUWTIyIiIqLi69Ot51Bl0jakZGQBAJbtj0aVSdvw6dZzVs7MilpMsGxcSSSVAWERQI1XtP8Wk8IKeIbiaty4cUhKSgIA/Pvvv3jvvffQsWNHXLt2DWPHjrV4gkRERERU/Hy69RyW/hUNTa6xCTQCWPpXtP0WWPtmWjaObIrZxVV0dDTCw8MBABs2bEDnzp0xa9YsLF68GNu2bbN4gkRERERUvGRmabBsfzQAwAGZcEMqAGCYbBMckAlA24qVmaWxWo5W8+CaZePIpphdXDk4OCA1VfsL8ueff6Jdu3YAAG9vb12LFhERERHZrzWHrmtbqOSf46LjADghAwDQRXYYFx0HYKn8c2iENs7ueJezbBzZFLOLq2bNmmHs2LGYPn06jh49ik6dtBPAXbp0CWXKlLF4gkRERERUvNx4kIql8s/RTnbC4PZ2shNYKv8cNx6kFnFmNqDtDMvGkU0xu7j68ssvIZfLsX79eixZsgSlS5cGAGzbtg0dOnSweIJEREREVLyU85DoCitJrimLspfbyU6gnEfu+YzsgKmDMxSjQRzoKbOHYi9btiy2bNmSZ/0XX3xhkYSIiIiIqHjrm7RCr6gaKNuOzrLD8Jc8APC0wOp/sB1w2tsKGVpRRrJpcceWAY3fKdxcyOLMLq62bt0KmUyG9u3b663fuXMn1Go1IiMjLZYcERERERU/skT9wRhelv8FP0linjhJZgqQmVJUadmsFGUAkPv0PLxujVToOZldXH300UeYPXt2nvUajQYfffQRiysiIiKyS5lZGnx/6Dp8oB2o4Y0m5e13slzvcsDV3brF7Eas2xoflJYmPI2r2hVoOrpoc7O2f9cDR5boFlUSB1zy74YKd7fqx3mFFm1eZBFmF1eXL1/WDcWeU5UqVXDlyhWLJEVERERUnHy69RyW7Y+GQirwWUNgzo6LmLHtEoZGhGF8x7zfm0q81lOBY99C4GlhBQDpUADA0/XdlgBK16LPz5oCagJHlwLiyTD0EqmBG9NkQIOhRZ8bPTez/5zi4eGBa9fyjrt/5coVuLi4WCQpIiIiouKCk+UaEHsKgH5hlZMkV5xdkTsAjUfkH9P4HW0cFTtmt1x17doVY8aMwa+//ory5csD0BZW7733Hrp27WrxBImIiIhsVc7JcgFACCBTrf032zd/ReOdlhXtqotgVkIs5EJRcFxirPlfRkuCdtO1/x76Un+9RKYtrLK3U7Fj9vX8v//9Dx06dECVKlV081rdunULERERmDt3rsUTJCIiIrJV2ZPlZsvUAOOO6n+9EgBqTt1ZtIlZnQuA73RLRxyHG4zaEQN0ql00GdmcdtOBVpOAI98C9wG0ngw0GsIWq2LO7OLKw8MDBw8eRFRUFM6cOQMnJyfUrFkTzZs3L4z8iIiIiGxW3klw7XDeJjMESbSDWWS37B3VVEYnK+ZjdXIHoOFQYOtW7b/yglv7yLY9U0usRCJBu3bt0K5dO0vnQ0RERFRshHg751lXwV2DmBQJMjVPC60P2ldCvyZhRZmaVW3cuQs9jvUDoB2rwRnpAAAniUovLtzxbpHnRlSYTCquFi5ciDfffBNKpRILFy7MN3bUqFEWSYyIiIjI1vVtHIqZW8/rdQ2USvQHf5NKgCER9jUs++unXodMqjK6Pfv8vHKsDxB5v4iyIip8JhVXX3zxBV5//XUolUp88cUXRuMkEgmLKyIiIrIbDnIphkaEYelf0UZjhkaE2VVhBQAyjfHC6lniiIoLk4qr6Ohog4+JiIiI7F32PFbf/BWNnKOxSyWw33mupArAlMJJynuMqGQx688oKpUK5cqVw7lzdjhfAxEREZER1+4/Rq5prqAR2vV26c3Dlo0jKibMKq4UCgUyMjIgyT2LNBEREZGdGrr6GKLOxRvcFnUuHkNXHyvijGxAQAUUPHKi5EkcUclhdgfgkSNHYs6cOcjKyiqMfIiIiIiKjbRMtdHCKlvUuXikZaqLKCMbMuURjBdYkifbiUoWs4diP3LkCHbt2oWdO3eiRo0acHFx0du+ceNGiyVHROZRawSORj9AfHI6/NyUaBjmDZmULc3A03MDAEejH+CFCn48N9B+MZy19RyuJ6Qi1McZH3cMh5ODzNppkQ1LTFVh0KqjuJOYjiAPJVYMaAgPZ/u9b2bWVtNulZi19Rymd69RyNnYoCmPgLgrwNf1tMsSOfDWEbZYUYlldnHl6emJl19+uTByIaLnsP1sLKb+fg6xiem6dYEeSkzuEo4O1QOtmJn1ZZ+bBylp+KwhMOi7Y/B2dbL7c5O7K9P+y8CawzFoG+6HZf0aWDEzslUt/rcbNxLSdMuxiemoNW0nQnycsG9cKytmZj3XE3JPIvx8cSVSQAXANQBIiQPe2svCiko0s4urlStXFkYeRPQctp+NxbDvT+a5mTouMR3Dvj+JJW/UtdsiIue5cczRIGPv58aUe0RYYFFOuQurnG4kpKHF/3bbZYEV6uOM/ZdNiyOiks/s4qpVq1bYuHEjPD099dYnJSWhe/fu2L17t6VyIyITqDUCU38/l6ewAqBbN+m3s6gS4G533eDUGoFJv53VnQchgIR07b/2fG7SVabfI8IuggRouwIaK6yy3UhIw3+3E+HuZF9dBF9vFII1h2MKjPuwQ9UiyIaIrM3s4mrv3r3IzMzMsz49PR379++3SFJEZLqj0Q/0ugIaci85Ey/O3Vs0CdmwTI0E007pf+zx3Bhnt/eIUB6DVh01Ka7TogOFnEnx9e/tRDQu72PtNIiokJlcXP3zzz+6x+fOnUNcXJxuWa1WY/v27ShdurRlsyOiAsUn519YZXOQSyG3o9YZAMjSCGRmaXKsEXCQApkaIOcIVvZ2btJVamgMNXXmYtf3iJCeOwX8ASebBLC71k5DnzN1fARupOh/ppj6WU1ExZvJxVXt2rUhkUggkUjQqlXePtVOTk5YtGiRRZMjooL5uSlNivtuYEO7+6vpoasJ6L3s6QSVjjLgs4ZqfHBUhowcoyLb27mZtOlfk7ox8R4RyhbkoSywhRwA6pb1xIbhTYsgI9th6HOmib/Aphv6caZ+VhNR8WbyPFfR0dG4evUqhBA4evQooqOjdT+3b99GUlISBg0aVJi5EpEBZb1M+wJsalxJ0jDMG4EeyvxmWUGgh3bIensyqlUli8ZRyTf3ldoWjStJ+DljgqxMQPVY+/jMOu0yUQllcnEVEhKC0NBQaDQa1K9fHyEhIbqfwMBAyGT21Q2AyFZ0+8q0ex1NjStJZFIJJncJB5B3Gsvs5cldwu1qMAsA+PjXfwoOAvDa0oP45q+r+OloDP74Jxb7L9/D6ZuPcO1eCu6nZCAjyw4nRbVTb31/zKJxJQk/ZwqwcxIw0x/ISNYuH1qkXd45ybp5ERUSswe0AIA1a9bg66+/RnR0NA4dOoSQkBB88cUXKFeuHLp162bpHIkoH0npWRaNK2k6VA/Ekjfq6ua5yhZgx3OAxTzMf9S3bNEJqZi19UK+MY5yKdydFHBTyuGuVOg/Vsrh7qT9102pgLuTdn3Ox84OMkgktvmlkxNPPxWfbFpLg6lxJQ0/Z4zYOQk4uDDveqF5ur7d9KLNiaiQmV1cLVmyBJ988gnGjBmDmTNnQq3W/uXSy8sL8+fPZ3FFVMTclXLcf6wyKc5edageiLbhATh8JR73zx/Giv4N7PqLclkvJ1yMSy4wLtjLCQ1CvZGUrkJSehaS0lRITs9CUrr2XwDIyNLgXnIG7iVnPFMuMqkEbkp5joLsSXHmlPuxfoHm/uSxq6MccpnJnTBMxomn9fm5OeBRWsGfM35uDkWQjW3i50wuWZnAoS/zjzn0FdBqEiC33+uGSh6zv20tWrQIy5YtQ/fu3TF79mzd+vr16+P999+3aHJEVLAtI5vjhdm7TIqzZzKpBA3DvLH1vPYeCbv9wgPgi551UH3KjgLjto1uDlcjRblaI5CSkYXkdBWS0p78qyvA8hZj2QVZznUqtYBaI/AoVYVHqSoAprWo5ebiINNrMTNcnBkv1BzlUr3WM048nddPbzZB3RlRJsXZM37O5HBsmbaFKj9CrY1r/E7R5ERUBMwurqKjo1GnTp086x0dHfH48WOLJEVEpjN12GN7Gx6ZjHNykEEulSArn/HY5VJJvteMTCqBh5MCHk4KwMv8HIQQSFdpnhRiKiTmKdCyW8i0xVvO4iz7cWqmtufE40w1HmeqEZtofh4A4CCTwt1JW3C5KeW4EJesm2Q6SwP8HiNFlubpxNPv/XIGZ24mwkZ7MxYKtSlj9wNGi3GyQw+vWzaOqJgw+1MwLCwMp0+fRkhIiN76bdu2ITw83GKJEZFpTJ3cc9Cqo3Y3RDIZdjT6Qb6FFaCdu+do9INCG6JeItEWb04OMvi5P9sQ1Sq1BsnpuVvPnhZjeVrPcnVrTE5XQSOATLUG91MycT8l7/1CaiHBn7f1q6jHGWos2Xf1mXIu6dYcuo7BEeWsnQbZAq9Qy8YRFRNmF1fjxo3DO++8g/T0dN2w7D/++CM+/fRTfPvtt4WRIxHlw9TJPU2No5LP1MlMbX3SU4VMCm8XB3i7PNv9GhqNwOPMrBzFVxaizsVh2f5oXYxMItAsQOBAnARq8bTIalGpFMqXcn3uYyguDl27j/OxBd+nd+MBJ56mJxoMBXZOzL9roESmjSMqQcwurgYOHIisrCx88MEHSE1NRZ8+fVC6dGksWLAAvXr1KowciSgfpk7uGeTBCSxJy9TJTEv6pKdSqeRJV0AFguAEQNv9LWdxJZcCL4VqcDheBnWOkeffblHeriaeXr7/Gqb/cb7AuBBv+5tPj4yQOwCNRxgeLTBb43c4mAWVOM80xNLQoUNx48YNxMfHIy4uDjdv3sTgwYOfKYHFixcjLCwMSqUS9erVw/79+c/F88MPP6BWrVpwdnZGYGAgBg4ciISEBL2YDRs2IDw8HI6OjggPD8evv/76TLkRFQfL+jWwaByVfJz01DieG8P6Ng5FQWMzSCXaOCKddtOBJqMASa6vmxKZdj2HYacS6LnGr/X19YWfn98zP3/dunUYM2YMJkyYgFOnTiEiIgKRkZGIiYkxGH/gwAH069cPgwcPxn///YdffvkFx44dw5AhQ3Qxhw4dQs+ePdG3b1+cOXMGffv2xWuvvYYjR448c55Etuzi3YK76pgTRyUfJz01jufGMAe5FEMjwvKNGRoRBge55YfFp2Ku3XRgwl3A0U273HgkMCGOhRWVWGZ/CiYkJOCdd95BeHg4fH194e3trfdjjnnz5mHw4MEYMmQIqlativnz5yM4OBhLliwxGH/48GGEhoZi1KhRCAsLQ7NmzfDWW2/h+PHjupj58+ejbdu2GD9+PKpUqYLx48ejdevWmD9/vrmHSlQslJT7Z6hoZU96GpCru2iAh9IuhxrPiefGsPEdw/FW87A8LVhSCfBW8zCM78hBrZCVCRxdpn18dJl2mYjsitn3XL3xxhu4evUqBg8eDH9/f725QcyRmZmJEydO4KOPPtJb365dOxw8eNDgc5o0aYIJEyZg69atiIyMRHx8PNavX49OnTrpYg4dOoR3331X73nt27fPt7jKyMhARsbTCTCTkpIAACqVCipVwZMmknVkvzf2/h75OsvhKHs68ptKA2iEBDKJQM4/Ivs6y+3+XPGa0de6si9erBiBEzce4n5KBnxdHVEvxAsyqcTuz1H2uTl27R4eXDqOb9+ogwblStn9uXm/bUWMalke647FIOZhGsp6OaFng7JwkEvt+rwAAHbPAI5+A5XEAaixEKo9c4Dd04GGbwKtJlo7O+t5cl7k6lRIAKgOLwWOLud5yYH/N9k+c94biRDCtMkrnnBzc8OBAwdQq1YtsxPL6c6dOyhdujT+/vtvNGnydNLBWbNm4bvvvsPFixcNPm/9+vUYOHAg0tPTkZWVha5du2L9+vVQKBQAAAcHB6xatQp9+vTRPWft2rUYOHCgXgGV05QpUzB16tQ869euXQtnZ96cS8XL91ekOHZPiq5l1Whd2qxfbyIiokLR/t9RUGY9wp7KM5DkXNba6RCZJXsQv8TERLi7u+cba3bLVZUqVZCWlvbMyeWWu+VLCGG0NezcuXMYNWoUPvnkE7Rv3x6xsbEYN24c3n77bSxfvvyZ9gkA48ePx9ixY3XLSUlJCA4ORrt27Qo8gWQ9KpUKUVFRaNu2ra64tkdHox9g0HfHdMuqJ6Pe/nFTih23n8at6N/A7m7Cz43XDJmL1wwVKCsTmFtBN+S4SqpEVI2FaPvvKCg0T7pjS2TA+5fta2S8XOdF/uRcNLs07ekAF/Z4Xgzg54zty+7VZgqzi6vFixfjo48+wieffILq1avnuQhMLUZ8fX0hk8kQFxentz4+Ph7+/v4Gn/Ppp5+iadOmGDduHACgZs2acHFxQUREBGbMmIHAwEAEBASYtU8AcHR0hKOjY571CoWCF3kxYO/v0/3ULGSo8/7xQC0kekNH30/NsuvzlJO9XzNkPl4zZNTxbwD10/m9ZJpMNLn8KZRZSZAixxxP3zQFPMpYIUErSbyld16yKUQmkLNTxelV2iHZiZ8zNsyc98Xs4srT0xOJiYlo1aqV3vrs1iF1zm9z+XBwcEC9evUQFRWFHj166NZHRUWhW7duBp+TmpoKuVw/ZZlMpnt9AGjcuDGioqL07rvauXOnXtdDopKEcxYREVnRw+t6i1JoUCrFwJxgD6O1P6Qv1/kjKu7MLq5ef/11ODg4YO3atc81oAUAjB07Fn379kX9+vXRuHFjfPPNN4iJicHbb78NQNtd7/bt21i9ejUAoEuXLhg6dCiWLFmi6xY4ZswYNGzYEEFBQQCA0aNHo3nz5pgzZw66deuG3377DX/++ScOHDjwzHkS2bJ6IV6QSgBNPrdXSSXaOCIisjCv0DyrkpRl4JxxF3KR4yb4Wr2Aiu2LLi9ru7wDOPNTwXEGzh9RcWZ2cXX27FmcOnUKlStXfu4X79mzJxISEjBt2jTExsaievXq2Lp1K0JCQgAAsbGxenNeDRgwAMnJyfjyyy/x3nvvwdPTE61atcKcOXN0MU2aNMFPP/2EiRMnYtKkSShfvjzWrVuHRo0aPXe+RLboxI2H+RZWgLbwOnHjIRqX9ymapIiI7EWDocDOibp7iwAgXeEBp8z7QHZxJZEBXRbZ171FVToD//ysd17ykMi054+oBDG7uKpfvz5u3rxpkeIKAIYPH47hw4cb3LZq1ao860aOHImRI0fmu89XXnkFr7zyiiXSI7J5nOeKiMiK5A5A4xHAwYXGYxq/Y1+FFcDzQnbL7OJq5MiRGD16NMaNG4caNWrkucGrZs2aFkuOiArGe66IiKys3XTtvwcXQW+0BolMW0Bkb7c32cd96Ev9Fix7Py9UopldXPXs2RMAMGjQIN06iURi9oAWRGQZDcO8EeihRFxiOgz1DpQACPBQ2v0w7EREharddKBUVeC3Ydrl1pOBRkPYMtNuOtBqEnBsmXbwCq9QbVdAez8vVGKZXVxFR3OkGyJbIpNKMLlLOIZ9fxIS6I9wmz3czOQu4ZBJn33wGSIiMoEsx9eqhkMBOYfVBvCkiyCHWyf7YHZxlT3YBBHZjg7VA7HkjbqY+vs5xCY+vbcqwEOJyV3C0aF6oBWzIyKyE1mZTx/vmAC0mwo4OFkvHyIqclJrJ0BEltGheiAOfNgKERV9AQB9GpbFgQ9bsbAiIioKP/YGNudonTn5HTArQLueiOwGiyuiEkQmlaCUmyMAIMTHmV0BiYiKwo+9gYtbDW+7uJUFFpEdYXFFVJJo1EBKvPbxg2jtMmlp1MCNQ9rHNw7x3GTLTAP+eA9Y00P7b2aatTMiKl4y04wXVtkubuXvFpGdYHFFVFKc2wzxRXXgyi7t8omV2uVzm62bly04txmYXx1Y+6p2ee2r2mV7Pzc/9tZ2Wzr2LXB1t/ZfdmMiMk/URMvGEVGx9kzF1aNHj/Dtt99i/PjxePDgAQDg5MmTuH37tkWTIyITndsM8XNfiKQ7eqtF0h2In/vadxFxbjPwcz8g17lBUqx2vb2eG3ZjomeVmggsbwfMC9f+m5po7Yys68E1y8YRUbFmdnH1zz//oFKlSpgzZw7mzp2LR48eAQB+/fVXjB8/3tL5EVFBNGokbXoPQgC5b7GSSgAhgMRN79lnNziNGtj+IWBwBrAn67Z/ZH/nht2Y6FktqA18Vha4eQRIuq3997Oy2vX2yrucZeOIqFgzeyj2sWPHYsCAAfjss8/g5uamWx8ZGYk+ffpYNDkiKpjq2t9wz4zXTWoVKEnAQsUi1JBo/0oqlQAemfHQrOwIqYuvFTO1gsf39VqsZJpMNLy2ADJN9nDJQvsFcWVHwJ7OTdy/psVFTQQ6fV64uVDxsaA28NDIXJcPo7XbR58uwoRsRNsZ2i61psQRUYlndnF17NgxLF26NM/60qVLIy4uziJJEZHpDpz6Fy1zLEdI/8ULsgt54qQ3DxddUjZKCg0CE0/k3cBzYxi7MVG21ETjhVW2h9HaOGePosnJVsgdALkjkJWRT4xSG0dEJZ7ZxZVSqURSUlKe9RcvXkSpUqUskhQRmS46w02vuJJBAwC4pglAOenTP3j8XaoXmjZ6oYizs7KEK8ChL3WLWRI5zpbpi+q31kAusp7GNR4B+FSwQoJW8u964MaBguPYjYmy/fiqaXELawJlGxduLrYmNSH/wgoAstKBGweBsIiiyYmIrMbs4qpbt26YNm0afv75ZwCARCJBTEwMPvroI7z88ssWT5CIClC2Me5c9kYgHkCS456reOGFcoiDEEAsfHCxxvtoWr+i9fK0Bo0a+G+jdvAKCAiJHDd8W6La7R8BkQVAArgHAW2nAVKZtbMtOjV7aUcFLAi7MVG2xFumxaU/Ai5tK9RUioMMuYHWu5S7RZ8IERU5s4uruXPnomPHjvDz80NaWhpatGiBuLg4NG7cGDNnziyMHIkoH280KQ91VBIg1Q5ekVP2spcmEW80KV/0yVmbVAZ0mKMdFRC5J1R+stxhtn0VVgDg4AQ4ugMZeXsh6Di6a+OIAMCjjPb+xIJ4hQERYws/H1ty/zJwcKFuMUuiwL9lXkdA4kn9OFf/Ik6MiKzB7OLK3d0dBw4cwO7du3Hy5EloNBrUrVsXbdq0KYz8iKgAsqS7UEi1XdwkueoHyZPRApXSLGiS7gLegVbI0MrCuwKvrdaOGpjy4Ol69yBtYRXe1Xq5WUt6Sv6FFaDdnp4CKF2LJieybV2WAotrFxzX+1fAL6zQ07EpGjVwdn2OFnIZVHK3HAFPWshDmlgrQyIqQmYXV9latWqFVq1aWTIXInoGWUubwTF3o0wO2QVX1tJmkI2/WjRJ2ZrwrkCVTsC1v4H/HgJ9fgHKNbW/Fqtsvw41LW5pU6DOG4Czr3Y0Rd2/PoDSE5ByHnq78YuJ85790ht4x84GiGELORHlYHZxNWrUKFSoUAGjRo3SW//ll1/iypUrmD9/vqVyIyITyDJNm8DT1LgSSyoDQhoD/23V/mvPX3QeXjc9breR+64kMsDZW7/gcvbJUYT56Bdlzt6ATGGpI6CilmLiaMCmxpU0bCEnoifMLq42bNiAzZs351nfpEkTzJ49m8UVURFTO3hAnnHftLgiyIeKAa9QIP5cwXHe5bRdmR4nAKn3tfOGpT4AMhIBoQYe39P+3DPxdZUeuVrBnhRkedY9WVZY+Z4vjRq4cUj7+MYh+27tdA0A0h6aFmev2EJORHiG4iohIQEeHnlHwXF3d8f9+wV/wSMiy5K8vgViuXaI9dz3XAFPB7WQvL6lCLMim9ZjGTC7dMFxb+43fM9VVqZ2+GldwZWg/Xl8X39d9nLqAwACSE/U/jwwsXuqwtlAK5hPjpayXOsc3Q3/EjyLc5uftkLU+gZY+yrg6q3t/mWPrRD9twJzTbiXqv/Wws/FlrGFnMjumV1cVahQAdu3b8eIESP01m/btg3lynFOFCoaMfdT0W3RXkypCzScEYXfRr6Isr7O1k7LKtI2D4eDCfdcpW0eDocRe4omKbJtSlftqG75TQrrFWZ8MAu5A+AeqP0xhUYNpD3KUXjlaAXTW5ejYNOoAFUqkBij/TGFVKFffOW+Tyz3Oicvw19+z21+cv+MAKTKp+uTYrXrX1ttfwWWqzcgdwKy0ozHyJ20cUREdszs4mrs2LEYMWIE7t27pxvQYteuXfj888/ZJZCKRIWP/0CWBnCUaZtkUrM0aD53D+RS4MqsTlbOrugpUu5YNI7sgEYNqAuY9FSdqY2zxF/epTJt65OLD1CqcsHxQgAZyXkLrtTsFrGEvEWZ6rG2IEuJM+O+H8mT+8Z89Lsp/rsegPbzRSLU8E65CInQ6NZhy7uAk7d9DeihVmknws03JlPbqil3KJqciIhskNnF1aBBg5CRkYGZM2di+vTpAIDQ0FAsWbIE/fr1s3iCRDllF1aGZGm02+2twFK5BgHpBX+ZVLkGFUE2VCzcOAgkFVBsJ93WxoVFFE1OOUkkgNJd++NtYo8IVVqurokFdFtMfwRAPN2GSwZ3KxcqRFzONYdj6n3gO/v6nDGJUAPHlgGN37F2JkREVvNM97cPGzYMw4YNw7179+Dk5ARXV86DQoUv5n6q0cIqW5ZGG2dPXQSdW7wPbOhjWhwRAKTctWycLVA4aSe69ShjWrxapR2gIfd9YtF/AeefDtokIEGKYwBcM+IgQY5Zul0DAEc3AzsuoR7Ha++XK4ipI1ESEZVQzzV4WKlSpSyVB1GBOizYp7ecoZZg9CG5rntgzrhz0yOLMjWrir4bj0qmxtUo9HSoOHD1t2xccSRTAK5+2p+cSlXRK66ypI7YHT4HHc+8CYUmR7e4l7+1TquetRz6CtjxccFxXqGFngoRkS0zu8P43bt30bdvXwQFBUEul0Mmk+n9EBWWNFUBzVZmxpUU8cLTonFkB0KaaOffyTPhaTYJ4F5aG2dveG4MazAUkBTwlUEi08YREdkxs1uuBgwYgJiYGEyaNAmBgYGQWGrYW6ICOCmkSDWhcHJS2NFN5gBkoU1x54A3AvHA6FDssfCBLLRp0SdHtkkq0w4p/nM/aIuInK2/Ty6iDrPtcxjpPOcmJzs+N3IHoPEI4OBC4zGN3+FgFkRk98wurg4cOID9+/ejdu3ahZAOkXHbR7dA87kFDyW+fXSLIsjGdjQsXwqrJc0wQGzWzWmVLXt5p6Qp+pZnN17KIbyrdkjx7R/qD27hHqQtHuxtqPGccp6blAdP19v7uWmnHcQKh74ERI4/dElk2sIqezsRkR0zu7gKDg6GyP0NjqgIBHgqDa7PPcjFuuMxkEntp0VVo9EgVeWBeXgFEgA1pNd02yQSQCOA9vj7ybDaz3WbJZU04V2BKp20owKm3NXeYxXSxP5aZQzJPjfX/gb+ewj0+QUo15Tnpt10oNUk7aiAD69r77FqMJQtVkRET5j9TWv+/Pn46KOPsHTpUoSGhhZCSkSGrTl03eB6tdAvpL7ae7UIsrE1Twfw+Fy6BACggAoAIJUAgUjAf0d2oFpTDh9NuUhl9jUwgzmkMiCkMfDfVu2/9l5YZZM7cLh1IiIjzC6uevbsidTUVJQvXx7Ozs5QKBR62x88eGDkmUTP58aDVIPrpRIBTY4Cq7K/GxqV8y6qtKwu/vp/8Is/qFsuJ9F28QqX3tCLS3t4u0jzIiIiIrI3z9RyRWQNId6G565SSIEM9dPl1+qXweAIEyceLQH++/sGqkWtyrPeSaLSX/YqXUQZEREREdkns4ur/v37F0YeRAXqVCMI0/84b1KcPanSoA3UUVJIhcboaIEaiRRVGrQp+uSIiIiI7MhzjVmdlpaGpKQkvR+iwtJvxWGLxpUUsttHIYPhwgrQDmohgway20eLNjEiIiIiO2N2cfX48WOMGDECfn5+cHV1hZeXl94PUWGJT860aFyJkXLXsnFERERE9EzMLq4++OAD7N69G4sXL4ajoyO+/fZbTJ06FUFBQVi9enVh5EgEAPBzM22oX1PjSgxXf8vGEREREdEzMbu4+v3337F48WK88sorkMvliIiIwMSJEzFr1iz88MMPhZEjEQBgUa96Fo0rMUKaaCc3hbG5vSSAe2ltHBEREREVGrOLqwcPHiAsLAwA4O7urht6vVmzZvjrr78smx1RDoNXm3bPkKlxJYZUBnSY82Qhd4H1ZLnDbM7RQ0RERFTIzC6uypUrh+vXrwMAwsPD8fPPPwPQtmh5enpaMjciPQ8eqwoOMiOuRAnvCry2GnAP1F/vHqRdH97VOnkRERER2RGzi6uBAwfizJkzAIDx48fr7r169913MW7cOIsnSJTN20VRcJAZcSVOeFdgzFmgfCvtcr2BwJh/WVgRERERFRGz57l69913dY9btmyJCxcu4Pjx4yhfvjxq1apl0eSIclr/dlO8MHuXSXF2S6MG0h5qHyfHapfZHZCIiIioSJhdXOVWtmxZlC1b1hK5EOUrOuGxyXEBnspCzsYG7ZwEHPoSEBrt8qXtwEx/oPEIoN106+ZGREREZAeeqbg6evQo9u7di/j4eGg0Gr1t8+bNs0hipHUvKQM9Fh/Ag8cqeLso8OvwZijl7mjttKwiPjndonElys5JwMGFedcLzdP1LLCIiIiICpXZxdWsWbMwceJEVK5cGf7+/pBIno5OlvMxPb+aU3YgKT1Lt5z6SI0Gs/6Eu1KOf6a0t2Jm1uHnZlprlKlxJUZWprbFKj+HvgJaTQLkdjYHGBEREVERMru4WrBgAVasWIEBAwYUQjqULXdhlVNSehZqTtlhdwVWwzBvOMqlyMjSGI1xlEvRMMy7CLOyAceWPe0KaIxQa+Mav1M0ORERERHZIbNHC5RKpWja1I4HDCgC95IyjBZW2ZLSs3AvKaOIMrINmVmafAsrAMjI0iCzgJgS5+F1y8YRERER0TN5ptECv/rqK8yfP78Q0iEA6LH4gElxDWf9iSBPJ7gp5XB3UsBdqYD7k8duSrl22UkON6Ui12Ptvw5ys2trq5q19ZzJcdO71yjkbGyIZ4hl44iIiIjomZhdXL3//vvo1KkTypcvj/DwcCgU+nMKbdy40WLJ2StTJ8EVAG4/Snvm11EqpHBXKvSKM8OPDRdqTgpZkd5ndz0h1aJxJUapqpaNIyIiIqJnYnZxNXLkSOzZswctW7aEj48PB7EoBN4uCqQ+UhcY5+/mgK/71kdyehaS0lVISstCcroq1+Mn/6ZpY5LTs5CSoe1ymK7SIF2VgfjkZ+teKJNK8rSUPW0xM/zYTSmHx5PizVUph0xq+vUT6uOM/ZdNi7Mr6Q8tG0dEREREz8Ts4mr16tXYsGEDOnXqVBj5EIBVAxqhzfx9Bcb9MLgxKgS4mr3/LLUGKRlZSE7PQmKaSld0JaWpjBdqGfoFm1ojoNYIPExV4WGqaS1thrg6ynXdFHN2WzRUnDUp74s1h2MK3Of77ao8cz7Fkqu/ZeOIiIiI6JmYXVx5e3ujfPnyhZELPfHhxjMmx20Ybv7gInKZFJ7ODvB0dkCw2c8GhBBIzVTnKMRyFGVPirScBdvT1rOnj9NV2kEnUjKetKQlWm5uqvUnbmJwRDmL7c/mhTQB3IOApDvGY9xLa+OIiIjIZqT9exYxQ4agUmIirnz4kf5GiQRVzz+93/zWyFFIjooyuq/Kp09BqtROR3Pnww+R+Ntmo7EVDx2E3MsLABA7ZQoe/bTOaGyF3bugCAoCANydPQcPVq0yGlvujy1wfFIn3Fu4EPcXLzEaG7p+PZyqVwMA3F+2DPc+zztXrlPdugj5fg0k0uIzToDZxdWUKVMwefJkrFy5Es7Odtb9qojcMbHQMDXO0iQSCVwc5XBxlCPA49nmlMrM0uhawfRbzJ4+Ts5RqCWlZ+Hy3eQ8rWTl3ARu57rF6sYDO7vnSioDqr9ieBLhbNVf1sYRERGRzUg9fhyaxERrp2Gz0k6ehDohAfJSpaydisnMLq4WLlyIq1evwt/fH6GhoXkGtDh58qTFkrNXQR5KxJpQOAU9Y2FjCxzkUvi4OsLH1dHk5yzffw3T/zj/dB9SgVHV1PjwmH7REOJtZ0W/Rg2cXZ9/zNkNQJspLLCIiIhsiGvzCMDZCaeuXkXTQYPyfK/OKXDWTARMmWx0u8Tx6Xcq/0mT4PfBB0ZjZR4eusd+749DqZEjjcd6euoe+44YAZ+hQ0zar8/gwfB6/XXjse7uusfer78Oz5de0tt+uWkzo8+1ZWYXV927dy+ENCinhb3qoulnu02Ksyd9G4di5tbz0AjtskSi/clJKtHG2ZUbB/PvEggASbe1cWERRZMTERERFcixfHlIy5bF461bIffxgTyf4krm5mbyfmWuroCraffly1xdAFcXi8dKXVwgdTEx1tkZ0lw94kK+X6N9zRwFW3FgVnGVlaUdZW7QoEEIDn6Wu3XIFFN+P2ty3LL+DQo5G9vhIJdiaEQYlv4VbTRmaERYsZu/67ml3LVsHBEREZGVOdevb+0UnolZ30Llcjnmzp0LtbrgYcLp2cU8NG3uKlPjSpLxHcPxVvMw+Enu45B8OADgkHw4/CT38VbzMIzvGG7lDK2AowUSEREVS5k3biBl9x443rpl7VTIQsz+E3/r1q2xd+/eQkiFspX1crJoXEkz/lgEjjiOgptMOz+XmywDRxxHYfwxO+3ylj1aYH44WiAREZHNSd69B3GjR8PrwN/WTsXmPFi9Bg9Wr4EmtXgNVGb2PVeRkZEYP348zp49i3r16sElV1/Krl27Wiw5e/VFzzqoPmWHSXF2Z6oPILKQe+phCQCILO32yQlWSMyKOFogERERlTB3P/0UEALukR3y3I9ly8wuroYNGwYAmDcv71j0EomEXQYtwFUpR80y7vjnVpLRmJpl3OGqNPvtK97ux2gLqGxCA5f0WP0YkaWN8y1btLlZE0cLJCIiIrIJZncL1Gg0Rn9YWFnO5hERqFnG3eC2mmXcsXmEHXaBW/qC3qJCZKLN+Q8LjCvxzBktkIiIiKgYcO/UCe6dOukNMV8c2FnTR/GyeUQEUtKz8O66U4h5mIayXk74omcd+2uxyqYycQAPU+NKCo4WSERERCVM6bn/s3YKz+SZvqXv27cPc+fOxfnz5yGRSFC1alWMGzcOERF22JpSyFyVcrsabj1fCidA9di0OHvC0QKJiIiIbILZ3QK///57tGnTBs7Ozhg1ahRGjBgBJycntG7dGmvXri2MHIm03jps2biSgqMFEhEREdkEs1uuZs6cic8++wzvvvuubt3o0aMxb948TJ8+HX369LFogkQ6vmWhHRdQ5BMksa/BLACOFkhERFRMuTRpjFKffIKbcbEFB1OxYHZxde3aNXTp0iXP+q5du+Ljjz+2SFJEBmVlAhIJIPSLK2nOEQQBYO8c+yokhABOrMw/hqMFEhER2Rxl5cqQlSuHtK1brZ0KWYjZxVVwcDB27dqFChUq6K3ftWsXgoODLZYYUR7HlgFCk2e1TK+4EsDeWUWXU3GRPVpgGO+LJCIiIiosZhdX7733HkaNGoXTp0+jSZMmkEgkOHDgAFatWoUFCxYURo5EWg+vG1ytgQxS5JgGoFQVILhh0eRkCx5EA9f3FxzH0QKJiIhsiur2baReuQqHuDhrp0IW8kyTCAcEBODzzz/Hzz//DACoWrUq1q1bh27dulk8QSIdr1CDq9VSBaSaHMVV3X5A43eKJidbEL3ftOKKowUSEZGVPFi7Fom/bsqzXu7nh+CvvtQt3/nwQ2Rciza4D5mHB8p+u0y3HDvpE6RfuGAwVqpUImTNat1y3MxZSDt92nByEgnCfl6nW4yfOxePjxw1eiwh36+B9MncS/cWfYmUv/4yGlt2xXLI3NwAAPe/WYbkqCi97Vn37iErLg7edeoAgwYZ3Q8VHyYVVwsXLsSbb74JpVKJmJgYdO/eHT169Cjs3Ij0NRgK7DDhvr4GQws/F1uSPVpgUiwMD/Yh0W7naIFERGQlCd8sQ5aB1hlFmTJ6yxmXryD93DmD+5D5+OjHRl9D+r//GoyVOjvrLWdev240FhKJfuyNGOOxgN6936pbN/ONFVlPb11Q3bltNFbl6WH89ahYMam4Gjt2LHr16gWlUomwsDDExsbCz8+vsHMj0peVaXqc3KFwc7ElUhnQYQ7wcz/kHU3xyX8YHWZzMAsiIrIetbaHif+ECVAEPy2opEqlXpj/x+OhTkkxuAuJQqG37Pfee1AnJhqOlen/n1dq5Ah4vW7aiNY+b70Fj5dfMro9Zx7eAwbALTLSaKzMxUX32Kt3H7i++GKeGI1Mhkv37pmUG9k+k4qroKAgbNiwAR07doQQArdu3UJ6errB2LJl7WwYbCo6v5rYIvXrUKD3j4Wbi60J7wq8thrY/iGQdOfpevcgbWEV3tV6uRERkd0r89WXECoVlFWqQJqj4MjNuX59k/fpXKeOybFONWuaHlu9msmxyqpVoaxa1bTYypWgrFwpz3qVSgVwtMASw6TiauLEiRg5ciRGjBgBiUSCBg0a5IkRQkAikUCtVhvYA5EFGBnQ4pnjSprwrkCVTtpRAVPuau+xCmnCFisiIrI6c4obouLMpOLqzTffRO/evXHjxg3UrFkTf/75J3xy9XslKnQeIUC84X7YeeLslVTG4daJiIiIrMTk0QLd3NxQtWpVrFixAlWrVkVgYGBh5kWUV9kGwOVtpsURERGRzXj4yy8QaWlw79wZcm9va6dDVGjMGopdJpPh7bffxvnz5wsrHyLjkk2cA8LUOCIiIioS9xcuQta9e3Bu2JDFFZVoUnOfUKNGDVy7dq0wciHKn3uQZeOIiIiIiCzI7OJq5syZeP/997FlyxbExsYiKSlJ74eo0DyIsWwcEREREZEFmdUtEAA6dOgAAOjatSskOSZd42iBVOgSr1s2joiIiIjIgswurvbs2VMYeRAVzD3YsnFERERERBZkdnHVokWLwsiDqGAc0IKIiIiIbJjZ91wBwP79+/HGG2+gSZMmuH37NgBgzZo1OHDggEWTI9KTdNOycUREREREFmR2cbVhwwa0b98eTk5OOHnyJDIyMgAAycnJmDVrlsUTJNLxDLVsHBERERWJoLlzEbxsGRRlylg7FaJCZXZxNWPGDHz99ddYtmwZFAqFbn2TJk1w8uRJsxNYvHgxwsLCoFQqUa9ePezfv99o7IABAyCRSPL8VKtWTRezatUqgzHp6elm50Y2pk4/y8YRERFRkXBp1BCuEc0gc3W1dipEhcrs4urixYto3rx5nvXu7u549OiRWftat24dxowZgwkTJuDUqVOIiIhAZGQkYmIMD6W9YMECxMbG6n5u3rwJb29vvPrqq3lyyRkXGxsLpVJpVm5kg7IeWzaOiIiIiMiCzB7QIjAwEFeuXEFoaKje+gMHDqBcuXJm7WvevHkYPHgwhgwZAgCYP38+duzYgSVLluDTTz/NE+/h4QEPDw/d8qZNm/Dw4UMMHDhQL04ikSAgIMDkPDIyMnTdGwHo5utSqVRQqVRmHZPFJd8HVncB0h4ATt5Av98BN1/r5mQtTn6A9EmRLAQUQvueqaTKvHHWft/IJmX/Plv995qKDV4zZC5eM4Yl//EHNOnpcG3dGjJPT2unY1N4zdg+c94bs4urt956C6NHj8aKFSsgkUhw584dHDp0CO+//z4++eQTk/eTmZmJEydO4KOPPtJb365dOxw8eNCkfSxfvhxt2rRBSEiI3vqUlBSEhIRArVajdu3amD59OurUqWN0P59++immTp2aZ/3OnTvh7OxsUi6FKmzS08f7j1ovD1tQ6xsAgIMqCZFnRwAAomos1I/57yHw39aizoyKkaioKGunQMUMrxkyF68ZfeVmzoI8ORnHHj5EZlCQtdOxSbxmbFdqaqrJsWYXVx988AESExPRsmVLpKeno3nz5nB0dMT777+PESNGmLyf+/fvQ61Ww9/fX2+9v78/4uIKHko7NjYW27Ztw9q1a/XWV6lSBatWrUKNGjWQlJSEBQsWoGnTpjhz5gwqVqxocF/jx4/H2LFjdctJSUkIDg5Gu3bt4O7ubvIxWdS8cCAjyfh2R3dg7Lmiy8dWrOoMxJ4GhNCtavvvKCg0T+6pC6wNDNhildTI9qlUKkRFRaFt27Z694wSGcNrhszFa8aw6LmfQ52cjIiICDhWrmztdGwKrxnbl92rzRRmF1cAMHPmTEyYMAHnzp2DRqNBeHg4XJ/xBkWJRKK3LITIs86QVatWwdPTE927d9db/8ILL+CFF17QLTdt2hR169bFokWLsHBhrhaOJxwdHeHo6JhnvUKhsM5FnnQPSIvPPyYtHUh7BLiXKpKUbEJmGnD7cJ7VCk360+Lq9mFAZAEOTkWcHBUnVvvdpmKL1wyZi9eMvuxvdnK5nOfFCF4ztsuc98XkAS1SU1PxzjvvoHTp0vDz88OQIUMQGhqKhg0bPlNh5evrC5lMlqeVKj4+Pk9rVm5CCKxYsQJ9+/aFg4NDvrFSqRQNGjTA5cuXzc7Rapa3Mi3uyzrA3wuB0z8Cl6OAO6eAxFuAqoSOjBg10bJxREREREQWZHLL1eTJk7Fq1Sq8/vrrUCqV+PHHHzFs2DD88ssvz/TCDg4OqFevHqKiotCjRw/d+qioKHTr1i3f5+7btw9XrlzB4MGDC3wdIQROnz6NGjVqPFOeVpGaYFpcZjIQNcnwNgc3wMUHcCkFOPsCLr7ax9n/Ovs8XXb2BeT5F6k24cE1y8YREREREVmQycXVxo0bsXz5cvTq1QsA8MYbb6Bp06ZQq9WQyWTP9OJjx45F3759Ub9+fTRu3BjffPMNYmJi8PbbbwPQ3gt1+/ZtrF69Wu95y5cvR6NGjVC9evU8+5w6dSpeeOEFVKxYEUlJSVi4cCFOnz6Nr7766plytApnHyDRhOHEHdyAypHA43tA6n3g8ZMfjUpbeGUmAw+vm/aajh65CrAnRVfO5exCzdkHkD1Tj9Ln410OuLrbtDgiIiIioiJm8jfkmzdvIiIiQrfcsGFDyOVy3LlzB8HBwc/04j179kRCQgKmTZuG2NhYVK9eHVu3btWN/hcbG5tnzqvExERs2LABCxYsMLjPR48e4c0330RcXBw8PDxQp87/27vz6Kjq+//jr9mzh4QlySQhhEVWCygVCQq2ClgqaKugVkHUtlJc2KQCakGsYGxB+KlgXbGbxdYFF0BRUQEVWcUvKCBGQBL2QICQZDJzf38EBobMJBMZuBN4Ps6Zc+be+7533jO5BF587nxuZ33yySe66KKLflSPprjpTWlmp9rrfrtYapIbuM4wpLIDVSGrdE9V8DoWuk5ePry7apTM8ErlB6oe+zaH12NsSnijYvGNq2qtPy6AB7j8IWn5c+HVAQAAAGdY2OHK6/VW+36T3W5XZWXlKTUwbNgwDRs2LOi22bNnV1uXnJxc43SIjz/+uB5//PFT6sl0H4wPv+7GlwPXWSxSbIOqh1rWfgyfTyrbf0LYChHAjq0r3SvJkI4UVz20sfbXsFir7tEVdFTsWBA7IajFNJCsQb4OWLS69tc6Vpd7ae11AADgjMh45M/ylZfLkZlpdivAaRV2uDIMQ0OGDAmYVa+srExDhw5VfHy8f91rr70W2Q7PReFeyhduXU2sVikuterR+Lza631eqXRfmKNie6oCmOGrel66R9odRk8W2wkB7ISRsYO1T9EvSTpYFF4dAAA4IxJ69DC7BeCMCDtc3XLLLdXW3XzzzRFtBkelNJN2hXEPq5Rmp7uT6qw2KaFx1UNta6/3eqrCWMD3wnYHHxU7vKfq0kTDKx3aWfX4MQ6Hk+AAAACAyAo7XL344ounsw+c6OqnpceahlcX7WwOKTGt6hGOyvLqgevYqNi25dKWJf5SQ9L2Bl2VVvJl4DHiz6F7fwEAUA8cfP99+crKlXDpJbIlJ5vdDnDamDDlG2pVl+8WtbjstLZyxtldUpK76nGygsXSS1f5FyutMVqZe6f6fvn7wLrEjNPcJAAAqIsdD01S5e7dyn3jdcIVzmph30QYZ9AJozMRqTtb5OQFD10nSsqsqgMAAADOMMJVNDIiXHe2sNqkK/MlWUIUWKQrH43MtO8AAABAHRGuolFWmPfkCrfubNKuvzTw79VHsJIyq9a3629OXwAAADjn8Z2raLRpfvh1rXuf3l6iUbv+UptfSt8tldYVS7/5r9S8OyNWAAAAMBUjV9HoTN7nqr6y2qScblXPc7oRrAAAAGA6wlU0Sm0e2ToAAAAApx2XBUajXn+Wlj8XXh0AAECUS3vgARnlZXJkcLsUnN0IV9HIGSu17ittmBe6pnXfqjoAAIAol9TnHPyOOM5JXBYYrW58uSpABdO6b9V2AAAAAFGDkatoduPLUsURaeED0r7vqr5j1evPjFgBAIB65dDiJTIqyhV30UWyJSaa3Q5w2hCuop0zVvrlVLO7AAAA+NGKxo9X5e7dyn3jddnatDG7HeC04bJAAAAAAIgAwhUAAAAARADhCgAAAAAigHAFAAAAABFAuAIAAACACCBcAQAAAEAEMBU7AAAATqsm946W70iZ7GlpZrcCnFaEKwAAAJxWyVdfbXYLwBnBZYEAAAAAEAGMXAEAAOC0Kl2+XIbHo5ifdJQtId7sdoDThnAFAAAQIZV79sq5Y6fKv/1WPnvgP7OcLVrIYq26aMizY4d8Bw+GPI4zN1eWo/t7du6Sr+RA6NpmzWRxOKpef/dueffvD1nryMmR1ek82useeYuLQ9dmZ8saE1NVu2+fvHv3hq7NzJQ1Lq6qtrhY3j17Arb/cM9weYuLlfvG67K1aRPyOEB9R7gCAACIgCPr1un7gdermderbY8/Xm176y/XyOJySZJ2TZumkjffCnmsVp99KntKiiRpz8yZ2j9nTsjalos+lCMjQ5K09/kXtG/27JC1zee9I1fz5pKkff/6l/bOejpkbbP//U+xHdpLkvb/71XtnjYtZG3OP/+huC5dJEklb7+jnY88ErIWOJsRrgAAACKg4rvvJK9XkmRLTZFkCVlrS0iQLTU15HaL5fi+1vj4GmtlPf4VemtcXM3HPbE2tpZau+14bUxMLbXH/0lpiXEFrXW1aOEPdsDZymIYhmF2E9GmpKREycnJOnDggJKSksxuByF4PB7NmzdPffv2lePo5RBATThnUFecM6iLyr17Vbppk5au+VJX3H4b5wzCwu+Z6FeXbMDIFQAAQATYGzZUbFKSKnbuNLsVACZhKnYAAAAAiADCFQAAQASUf/ed9v/rX0r4v/8zuxUAJuGyQAAAgAgoW7dOex7NV3LLlma3AsAkjFwBAAAAQAQQrgAAAAAgAghXAAAAABABhCsAAAAAiADCFQAAAABEAOEKAAAAACKAqdgBAAAiIO6irsqYNVOfr1tndisATEK4AgAAiABHWhPFp6aovKTE7FYAmITLAgEAAAAgAghXAAAAEVCxZYtKXntNcd9sMLsVACbhskDgLLf3+Re0+4knQm5v+sLzirvgAknSvn//W7se+0vI2qwnn1TCJd0lSftfe107Jk0KWZs59a9KvPxySVLJggUqHDsuZG3Gww8rud9VkqSDH32k7SNGhqxNu3+8UgYMkCQd/nyZtg0dGrK2yahRSh08SJJ0ZO1abRl8i1p6vdo8YWK12kZ/+IMa3fF7SVLZho36/vrrQx439dYhajJ8uCSpYts2fdevf8jalBtuUNrY+yRJlbt369tevUPWJl9ztTImVvXmPXRImy65NGRtUp/ecufnS5IMr1cbLuwSsjahRw9l/b8Z/uUNF3WVUVERUGNv2FBNX5otZ1ZWyOMAqNmRtWu1a8JEpbRsKY0K/XsMwNmLcAWcZfbOnq3Sz5cp+de/UlLv3jK8XhllZaF38PmOP6+spdY4odZXc63h9QY8r7kH7wnPfTXXnnBcGTXXntjDseNaJRkeT5DayhOXau6h8oRao+Za48Raqebak/o6nbUnhyvP9u06snIl4QoAgFNAuALOMuVff6NDH32kuJ9WjWSk3HiDkvr2DVlvb9zI/zz5179Sws9/Hrq2UUP/88Qrf6G4i7uFrk1NOV77s5+pxfvvh65NaeB/Ht+tW421tgbHa2M7d665NjnJ/9zVrp1yFszXokWL9LOf/Ux2uyOwNinxeG1ubs3HTUzwP3dkZNRYa42PO75famrYtda4uJpr42JPWLDWXBvjClhuMX+eDOP4ctHYsSpdsSLk/gAAIDyEK+AsZ0tMlC0xsfZCSbaEBNkSEmovlGRLiJctIT6sWmtcnJxxcbUXSrLGxsqZlRlebUxM+LVOpxyZmapMTZUjM1MOhyNkrcXpDPu4Focj/FqbLfxaqzX8Wosl7FpJcmQG1jYYOEBxF18sV+vWYR8DAABUR7gCgHNccv/Q3xkDAADhY7ZAAAAAAIgARq4A4BxX8cN2+UoPy5GeLltSUu07AACAoBi5AoBz3I6JE1XQ/2odWrTI7FaAei3uwguV/vg07b3iCrNbAWASRq4AAH6+0lJZYmJksfJ/bwitct8+Hf70M//tGWLatJGrVauqbcXFOrxkSch9Xa1aKaZNG0mS9+BBHfroo5C1ztzmiu3QXlLVuXnwgw9C1+bkKPYnP6mqrajQwXffDVnryMxS3AWdJVXdLqFk/vzQtenpivvpT/3LB956K2StvXFjJVxxhcpOutUBgHMH4Qo4y2RMmayMKZMli8XsVlAP7Xj4zyp5+23ZMzLkyHTL4T76yMyUw+1WXOfOstQw0yLODYX3jdXhxYv9y41Hj/KHK8/2QhWO+WPIfRsN+4M/XFXu2lVjbeqQIf5w5d2/v8baBjdcfzxcHT5cY23y1f0DwlVNtYm9egWEq8I/3qeAexmcIP7SS5Vx4YUhjwXg7Ee4As4yjDigzo7+Q9EwDHmKimR4PPJs3SrP1q3VSlt/uUbHYvueZ59V+aZNAeHr2MPqclXbF2ePyj17JEmudm1lb5Ai5wnT+1vj4xSflxdyX0d20+O1sbE11jqbNfM/t7hcNda6mrc4Xutw1Fx7NAhWNWGtubZN4C0K4vPyQoarmHbtQh4HwLmBcAUA57jDS5dKkkrefEtNn3tWlTt3qmL7dnkKC48/tm+XcaQsIDQdXrJUpcuWBT2mvXFjtfzwA/8o1+HPP5evrEzOoyHMGh/ePdIQ3ZqMGq2ES7oHrHPl5qrpC8+Htb/D7Q671t6wYdi1toSEsGutTmfYtZLU9Pnnatzu8XjCPhaAsw/hCjjL7PvXv1S6YoWS+/VT4s9/bnY7qGcsdnvVKFRm7TclbnjbrYq/pLs/fFUFsSIZpaUyZARcPrhn1tMBQcyWnCz70csOnZlZajL2PlmOXsrqKyuTxeXyLwMAUF8QroCzTNnar3Rw/gLFduhgdis4yyX07KmEnj0D1hmGIe/+/fIWFwesdzbPlbekRJ7CQvkOHJD36KN8/deyNWqktHFj/bXbhv5BZWvXypHplt3t9o92HQt9x75XAwBAtCFcAQAkSc6WLWovqoXFYpE9JUX2lJSA9RkTJvifew8dkmd7oTyFVaNd8voCaj2FhfKVlqp807cq3/StDp+wzdaokc5bcnwihV1//au8Bw4EfN/LkZkpe5Mmstj5K+50yfp/M2RUVMiRnm52KwAQVfibBwDOcfGXXKLDS5Yotn37M/J6toQE2Vqfp5jW5wXd3vzNufIUFp10uWHVw5acHFBb8t7CoBNvyGZTTNu2yv3ff/2rDn3yiSwOhxxut+wZGbI6nRF9X+cSZ3a22S0AQFQiXAEAooo1Jkau5rlyNc+ttbbx3XepYsvWgIk3PDt2SB6PdNLMmTseeUSeLUeDmMUie+PG/tEuV+vWanTH7/21RkWFLIQvAEAdEa4A4Bx3bFptX2mpyZ3UXXK/ftXWGV6vKvfske9w4PtxtWgpi80uT2GhjLIyVe7apcpdu3RkzRrFbNsWEK42X9VPvpIS2TMylFVWph9e+a+sRyfYcLZsoYyJE/21P9wzXN59+4L258jOlnvKZP/y9jF/VGVRUdBae5Mmypw21b9c+MAD8ny/JWittUGysp980r+8Y9LDKt+4MWitJTZWTZ99xr+8M/8xlX31VdBaWa3K+ftL/sVd06fryIqV1cpKV6xQw9/ergYDBsiZkxP8WABwDiJcAcA5rvybbyRJnp07Te4kMiw2mxxpadXWZ898StLRSTeKi6u+93X0skNbcpK/zvD5VHn0fl/e/fsVJ6msoOD49pOm2j6ydq0qd+wI2ourpCRgueyrr1Tx/fdBax0nXWpXtn69ytd/HbTW1rhRYO2GDTqysnoIklRt2vvyjRtVumJF0NqTR/sqNm8OWbv3ueeV1L9/8OMAwDmKcAUA57hWiz9RyfwFSrn5JrNbOSMsFovsqamyp6Yq9vzqs2parFa1WrpEnsJClW3bppWff64LOl8gm90mSdW+95U+4U8yysuDvpY1ITFgOW38uJAjhNbY2IDlJqNHy3fwYPD34Ay8SXPju++Wd39x0FrZbAGLjYbeoQYDrgtee5LUW29T0i9/GXSbw+1WzHnBvzcHAOcqwhVwlkl/aKLSHnxQVqej9mJAVTf8TR08yOw2oootKanq0aKFDpWWKqFPbzkcwf9MJf7sZ2EfN6FHj/Bru3evveio+Iu7hl0b99Ofhl97QeewawEAhCvgrGONiTG7BQAAgHOStfYSAAAAAEBtGLkCzjLF//2vjqxeo6Qr+9TpEiQAAACcGkaugLPMkRUrdeC111S+aZPZrQAAAJxTCFcAAAAAEAGEKwAAAACIAMIVAAAAAEQA4QoAAAAAIoBwBQAAAAARQLgCAAAAgAjgPlfAWSbtgfvVZMy9ssbFmd0KAADAOYVwBZxlbImJUmKi2W0AAACcc7gsEAAAAAAigJEr1Fs7/vhHNS45KPXta3YrUeXA3Lk6svYrJV7+c8Xn5ZndDgAAwDmDcIV6qXL3bh2av0ApZjcShQ5/+pkOzJ0rhzuDcAUAAHAGcVkg6iebzewOAAAAgACEKwAAAACIANPD1cyZM5Wbm6uYmBhdeOGFWrx4ccjaIUOGyGKxVHu0b98+oO7VV19Vu3bt5HK51K5dO73++uun+20AAAAAOMeZGq7mzJmjESNG6P7779fq1at16aWX6he/+IW2bt0atH7GjBkqKiryP7Zt26bU1FQNGDDAX/PZZ5/p+uuv16BBg/Tll19q0KBBGjhwoJYtW3am3hbOAO+BA2a3AAAAAAQwNVxNmzZNt99+u37729+qbdu2mj59urKzszVr1qyg9cnJyUpPT/c/VqxYoeLiYt16663+munTp6tXr14aN26c2rRpo3Hjxunyyy/X9OnTz9C7whnh9ZrdAQAAABDAtNkCKyoqtHLlSo0dOzZgfe/evfXpp5+GdYznn39eV1xxhXJycvzrPvvsM40cOTKgrk+fPjWGq/LycpWXl/uXS0pKJEkej0cejyesXnBmVVZW+p/zMwrkM3ySJK/Xy2dzkmOfB58LwsU5g7rinEFdcc5Ev7r8bEwLV3v27JHX61VaWlrA+rS0NO3YsaPW/YuKijR//nz9+9//Dli/Y8eOOh9zypQpeuihh6qtf++99xQXF1drLzjzHLt3K/fo84ULF5raS7Sxdewoa5s2+jYuTr5588xuJypxzqCuOGdQV5wzqCvOmehVWloadq3p97myWCwBy4ZhVFsXzOzZs9WgQQNdc801p3zMcePGadSoUf7lkpISZWdnq3fv3kpKSqq1F5x53n37VPDXqZKk1g8/LFfrNsqa/aJ/+9Zrr1NlYWHQfR05Ocr+z8v+5W033STPdwVBa+1paWr6xvEJUbbffrvK138dtNaanKRmCxb4lwvvuktlK1cFrbU4ncr9+CP/8o7R96q0hhHb3KVLZLFWXcW78/4HdPjDD0PWNvvgfVn5T4GgPB6PFi5cqF69esnhcJjdDuoBzhnUFecM6opzJvodu6otHKaFq0aNGslms1UbUdq1a1e1kaeTGYahF154QYMGDZLT6QzYlp6eXudjulwuuVyuausdDgcneZSyp6bKnulW5fZCGYcOS2VlAT8ro7RUvkOHgu5rHDkS+HM9ciRkrS8xMfC4ZeUhay12e+Bxa6p1uQJry0PXSlXn4rFwJU9FzbV2u6yctzXizzbqinMGdcU5g7rinIledfm5mBaunE6nLrzwQi1cuFC/+tWv/OsXLlyoq6++usZ9P/74Y3377be6/fbbq23r1q2bFi5cGPC9q/fee095eXmRax6mszgcajp3rj6YM0c9L7tMzpNGaprOni15K4Pvaw887bP/9jcZoa6ltQXWZs2YLqOiInitNXB+GPdj+TLKykK8gcCR1PRJk2SUHQlee1J9+vjxajJiROjS2NjQxwEAAMBpY+plgaNGjdKgQYPUpUsXdevWTc8884y2bt2qoUOHSqq6XG/79u36+9//HrDf888/r65du6pDhw7Vjjl8+HD16NFD+fn5uvrqqzV37ly9//77WrJkyRl5TzgzKouLtXXAQGWWlso5eHC1/1FwZmWGfSyH2x1+bUZG+LW1jMAG1jYJu9beuHHYtQAAADhzTA1X119/vfbu3atJkyapqKhIHTp00Lx58/yz/xUVFVW759WBAwf06quvasaMGUGPmZeXp//85z964IEH9OCDD6pFixaaM2eOunbtetrfD84gw1DlDz/IWXslAAAAcEaYPqHFsGHDNGzYsKDbZs+eXW1dcnJyrTN2XHfddbruuusi0R4AAAAAhMXUmwgDAAAAwNmCcAUAAAAAEUC4AgAAAIAIIFwBAAAAQASYPqEF8GNYrFY5mjXT4cOHzW4FAAAAkMTIFeopW4MGynnrTX1/72izWwEAAAAkEa4AAAAAICIIVwAAAAAQAXznCvWSd/9+bR00WDkHD0p9+5rdDgAAAEC4Qv1k+Hyq2LRJLrMbAQAAAI7iskAAAAAAiADCFQAAAABEAOEKAAAAACKAcAUAAAAAEUC4AgAAAIAIYLZA1E8Wi+xpaTpSVmZ2JwAAAIAkwhXqKXtKipq9v1Dz5s1TW7ObAQAAAMRlgQAAAAAQEYQrAAAAAIgALgtEveQ9cEDbfvc7ZRfvl/r2NbsdAAAAgHCF+snwelW+9ivFmt0IAAAAcBSXBQIAAABABBCuAAAAACACCFcAAAAAEAGEKwAAAACIAMIVAAAAAEQAswWifrJYZE1KksfjMbsTAAAAQBLhCvWUPSVFzZcu0bx589Ta7GYAAAAAEa6innf/fu1/9TVZYlxKvvoa2RLizW4JAAAAQBCEqyhX/J852j19uiQpuV8///pdU6fq0JKlcmS65XC75czMlN19/Lk1OVkWi8WkrgEAAIBzD+EqyvkOHfQ/tyUl+Z+Xbdyo8q+/VvnXXwfd77wvlvnrS+bNk6ewUI6j4cvudsveqJEs1vo7n4m3pETb775bWXv2Sn37mt0OAAAAQLiqL1JvvTVgOW3sWFXccIM8hYVVj+2F/udGeXlAENs/d64Of/xJwP4Wp1OOjAw5Mt3KmjVLVpdLklSxdassdrvsTZrIYo/e08OorNSRZV8ozuxGAAAAgKOi91/PqJErN1eu3Nyg24yKioDlhB49ZEtO9gewyp07ZVRUqGLLFlXu3esPVpK085HJOvTxx5LNJkdamhyZmVUjXkcvP0z+9a/r9YgXAAAAcLoQrs5CFqczYDn1ppukm27yLxsejzw7d8qzvVC+gyUBtYYMyeGQPB7/SNgx1oQEJV97rX+5cNx4VXz3XVUAOxq+/GHM7ZY1jnElAAAAnDsIV+cgi8MhZ1aWnFlZ1bY1/dvfZPh8qty9O+BSQ8/27ZJFAZNklP3f/6l80yYd+fLLasexxsfrvBXL/fXF//2vjCNHAsNXUhKTbgAAAOCsQbiKcs6WLZXY6wq5WrU6Y69psVqrLglMS5Mu6Byyzp3/qCq2bjsewI6GME9hoRwZGQHBqfjv/1D5pk0B+1vj4+Vwu+Vq1UqZ06b611d8/72siYmypaYSvgAAAFBvEK6iXINrrlGDa64xu42gYtq1U0y7dkG3+crKApYTe/WSs3lzfwDz7tsn3+HD1QKXJP1wz3CVb9woS0xM1aQb7uOXHDqbNVPSlX1Oy/sBAAAATgXhCqeFNSYmYLnxPXcHLPuOHJGnqKjqckPDCNhm+LySxSKjrEwVBQWqKCjwb3O1aukPVxanU16f7zS9AwAAAKBuCFcwhTU2Vq7mzeVq3rzathZvvy2jouLopBvbA777ZW/cWJJkT01Vi5UrNG/ePJ13ppsHAAAAgiBcRbldU6dp7wsvKHXILUobM8bsds4Yi9MpZ3a2nNnZZrcCAAAAhIUbFkU7wyd5vZLPqL0WAAAAgGkIVwAAAAAQAYQrAAAAAIgAwhUAAAAARADhCgAAAAAigHAFAAAAABFAuIpyjpwcxXfvLmezZma3AgAAAKAG3OcqyqUMGKCUAQPMbgMAAABALRi5AgAAAIAIIFwBAAAAQAQQrgAAAAAgAghXAAAAABABhCsAAAAAiADCFQAAAABEAOEKAAAAACKAcAUAAAAAEUC4AgAAAIAIIFwBAAAAQAQQrgAAAAAgAghXAAAAABABhCsAAAAAiADCFQAAAABEAOEKAAAAACKAcAUAAAAAEUC4AgAAAIAIIFwBAAAAQAQQrgAAAAAgAuxmNxCNDMOQJJWUlJjcCWri8XhUWlqqkpISORwOs9tBPcA5g7rinEFdcc6grjhnot+xTHAsI9SEcBXEwYMHJUnZ2dkmdwIAAAAgGhw8eFDJyck11liMcCLYOcbn86mwsFCJiYmyWCxmt4MQSkpKlJ2drW3btikpKcnsdlAPcM6grjhnUFecM6grzpnoZxiGDh48KLfbLau15m9VMXIVhNVqVVZWltltIExJSUn8MkKdcM6grjhnUFecM6grzpnoVtuI1TFMaAEAAAAAEUC4AgAAAIAIIFyh3nK5XJowYYJcLpfZraCe4JxBXXHOoK44Z1BXnDNnFya0AAAAAIAIYOQKAAAAACKAcAUAAAAAEUC4AgAAAIAIIFwBAAAAQAQQrlAvbd++XTfffLMaNmyouLg4derUSStXrjS7LUShyspKPfDAA8rNzVVsbKyaN2+uSZMmyefzmd0aosQnn3yifv36ye12y2Kx6I033gjYbhiGJk6cKLfbrdjYWF122WVat26dOc0iKtR0zng8Ht133306//zzFR8fL7fbrcGDB6uwsNC8hmG62n7PnOiOO+6QxWLR9OnTz1h/iBzCFeqd4uJide/eXQ6HQ/Pnz9f69es1depUNWjQwOzWEIXy8/P19NNP68knn9TXX3+txx57TH/5y1/0xBNPmN0aosThw4fVsWNHPfnkk0G3P/bYY5o2bZqefPJJLV++XOnp6erVq5cOHjx4hjtFtKjpnCktLdWqVav04IMPatWqVXrttde0ceNG9e/f34ROES1q+z1zzBtvvKFly5bJ7Xafoc4QaUzFjnpn7NixWrp0qRYvXmx2K6gHrrrqKqWlpen555/3r7v22msVFxenf/zjHyZ2hmhksVj0+uuv65prrpFUNWrldrs1YsQI3XfffZKk8vJypaWlKT8/X3fccYeJ3SIanHzOBLN8+XJddNFF2rJli5o2bXrmmkNUCnXObN++XV27dtW7776rX/7ylxoxYoRGjBhhSo/48Ri5Qr3z5ptvqkuXLhowYICaNGmizp0769lnnzW7LUSpSy65RB988IE2btwoSfryyy+1ZMkS9e3b1+TOUB8UFBRox44d6t27t3+dy+VSz5499emnn5rYGeqTAwcOyGKxcIUFQvL5fBo0aJDGjBmj9u3bm90OToHd7AaAuvruu+80a9YsjRo1SuPHj9cXX3yhe+65Ry6XS4MHDza7PUSZ++67TwcOHFCbNm1ks9nk9Xr1yCOP6MYbbzS7NdQDO3bskCSlpaUFrE9LS9OWLVvMaAn1TFlZmcaOHavf/OY3SkpKMrsdRKn8/HzZ7Xbdc889ZreCU0S4Qr3j8/nUpUsXTZ48WZLUuXNnrVu3TrNmzSJcoZo5c+bon//8p/7973+rffv2WrNmjUaMGCG3261bbrnF7PZQT1gsloBlwzCqrQNO5vF4dMMNN8jn82nmzJlmt4MotXLlSs2YMUOrVq3i98pZgMsCUe9kZGSoXbt2Aevatm2rrVu3mtQRotmYMWM0duxY3XDDDTr//PM1aNAgjRw5UlOmTDG7NdQD6enpko6PYB2za9euaqNZwIk8Ho8GDhyogoICLVy4kFErhLR48WLt2rVLTZs2ld1ul91u15YtWzR69Gg1a9bM7PZQR4Qr1Dvdu3fXhg0bAtZt3LhROTk5JnWEaFZaWiqrNfBXnc1mYyp2hCU3N1fp6elauHChf11FRYU+/vhj5eXlmdgZotmxYLVp0ya9//77atiwodktIYoNGjRIa9eu1Zo1a/wPt9utMWPG6N133zW7PdQRlwWi3hk5cqTy8vI0efJkDRw4UF988YWeeeYZPfPMM2a3hijUr18/PfLII2ratKnat2+v1atXa9q0abrtttvMbg1R4tChQ/r222/9ywUFBVqzZo1SU1PVtGlTjRgxQpMnT1arVq3UqlUrTZ48WXFxcfrNb35jYtcwU03njNvt1nXXXadVq1bp7bffltfr9Y98pqamyul0mtU2TFTb75mTA7jD4VB6erpat259plvFqTKAeuitt94yOnToYLhcLqNNmzbGM888Y3ZLiFIlJSXG8OHDjaZNmxoxMTFG8+bNjfvvv98oLy83uzVEiUWLFhmSqj1uueUWwzAMw+fzGRMmTDDS09MNl8tl9OjRw/jqq6/MbRqmqumcKSgoCLpNkrFo0SKzW4dJavs9c7KcnBzj8ccfP6M9IjK4zxUAAAAARADfuQIAAACACCBcAQAAAEAEEK4AAAAAIAIIVwAAAAAQAYQrAAAAAIgAwhUAAAAARADhCgAAAAAigHAFAAAAABFAuAIARJ3vv/9eFotFa9asMbsVv2+++UYXX3yxYmJi1KlTp6A1hmHo97//vVJTU6OufwDA6Ue4AgBUM2TIEFksFj366KMB69944w1ZLBaTujLXhAkTFB8frw0bNuiDDz4IWrNgwQLNnj1bb7/9toqKitShQ4eIvPaQIUN0zTXXRORYAIDTh3AFAAgqJiZG+fn5Ki4uNruViKmoqPjR+27evFmXXHKJcnJy1LBhw5A1GRkZysvLU3p6uux2+49+vdPB6/XK5/OZ3QYAnLUIVwCAoK644gqlp6drypQpIWsmTpxY7RK56dOnq1mzZv7lY6MukydPVlpamho0aKCHHnpIlZWVGjNmjFJTU5WVlaUXXnih2vG/+eYb5eXlKSYmRu3bt9dHH30UsH39+vXq27evEhISlJaWpkGDBmnPnj3+7ZdddpnuuusujRo1So0aNVKvXr2Cvg+fz6dJkyYpKytLLpdLnTp10oIFC/zbLRaLVq5cqUmTJslisWjixInVjjFkyBDdfffd2rp1qywWi/8zMAxDjz32mJo3b67Y2Fh17NhR//vf//z7eb1e3X777crNzVVsbKxat26tGTNmBHzGL730kubOnSuLxSKLxaKPPvpIH330kSwWi/bv3++vXbNmjSwWi77//ntJ0uzZs9WgQQO9/fbbateunVwul7Zs2aKKigr98Y9/VGZmpuLj49W1a9eAz3bLli3q16+fUlJSFB8fr/bt22vevHlBPzsAwHGEKwBAUDabTZMnT9YTTzyhH3744ZSO9eGHH6qwsFCffPKJpk2bpokTJ+qqq65SSkqKli1bpqFDh2ro0KHatm1bwH5jxozR6NGjtXr1auXl5al///7au3evJKmoqEg9e/ZUp06dtGLFCi1YsEA7d+7UwIEDA47x0ksvyW63a+nSpfrb3/4WtL8ZM2Zo6tSp+utf/6q1a9eqT58+6t+/vzZt2uR/rfbt22v06NEqKirSvffeG/QYxwJaUVGRli9fLkl64IEH9OKLL2rWrFlat26dRo4cqZtvvlkff/yxpKpgl5WVpVdeeUXr16/Xn/70J40fP16vvPKKJOnee+/VwIEDdeWVV6qoqEhFRUXKy8sL+7MvLS3VlClT9Nxzz2ndunVq0qSJbr31Vi1dulT/+c9/tHbtWg0YMEBXXnml//3eeeedKi8v1yeffKKvvvpK+fn5SkhICPs1AeCcZQAAcJJbbrnFuPrqqw3DMIyLL77YuO222wzDMIzXX3/dOPGvjgkTJhgdO3YM2Pfxxx83cnJyAo6Vk5NjeL1e/7rWrVsbl156qX+5srLSiI+PN15++WXDMAyjoKDAkGQ8+uij/hqPx2NkZWUZ+fn5hmEYxoMPPmj07t074LW3bdtmSDI2bNhgGIZh9OzZ0+jUqVOt79ftdhuPPPJIwLqf/vSnxrBhw/zLHTt2NCZMmFDjcU5+74cOHTJiYmKMTz/9NKDu9ttvN2688caQxxk2bJhx7bXX+pdP/Hkcs2jRIkOSUVxc7F+3evVqQ5JRUFBgGIZhvPjii4YkY82aNf6ab7/91rBYLMb27dsDjnf55Zcb48aNMwzDMM4//3xj4sSJNb5XAEB10XUxOAAg6uTn5+vnP/+5Ro8e/aOP0b59e1mtxy+WSEtLC5jswWazqWHDhtq1a1fAft26dfM/t9vt6tKli77++mtJ0sqVK7Vo0aKgIyqbN2/WeeedJ0nq0qVLjb2VlJSosLBQ3bt3D1jfvXt3ffnll2G+w+DWr1+vsrKyapcjVlRUqHPnzv7lp59+Ws8995y2bNmiI0eOqKKiIuSMhHXldDr1k5/8xL+8atUqGYbh/3yOKS8v93+X7J577tEf/vAHvffee7riiit07bXXBhwDABAc4QoAUKMePXqoT58+Gj9+vIYMGRKwzWq1yjCMgHUej6faMRwOR8CyxWIJui6cyRaOzVbo8/nUr18/5efnV6vJyMjwP4+Pj6/1mCce9xjDME55ZsRj7+edd95RZmZmwDaXyyVJeuWVVzRy5EhNnTpV3bp1U2Jiov7yl79o2bJlNR77WFg98fMP9tnHxsYGvA+fzyebzaaVK1fKZrMF1B4Lqr/97W/Vp08fvfPOO3rvvfc0ZcoUTZ06VXfffXe4bx0AzkmEKwBArR599FF16tSp2mhH48aNtWPHjoAgEsl7O33++efq0aOHJKmyslIrV67UXXfdJUm64IIL9Oqrr6pZs2anNCtfUlKS3G63lixZ4n8tSfr000910UUXnVL/xyaR2Lp1q3r27Bm0ZvHixcrLy9OwYcP86zZv3hxQ43Q65fV6A9Y1btxYUtX3wVJSUiSF99l37txZXq9Xu3bt0qWXXhqyLjs72/9duHHjxunZZ58lXAFALZjQAgBQq/PPP1833XSTnnjiiYD1l112mXbv3q3HHntMmzdv1lNPPaX58+dH7HWfeuopvf766/rmm2905513qri4WLfddpukqkkX9u3bpxtvvFFffPGFvvvuO7333nu67bbbqgWR2owZM0b5+fmaM2eONmzYoLFjx2rNmjUaPnz4KfWfmJioe++9VyNHjtRLL72kzZs3a/Xq1Xrqqaf00ksvSZJatmypFStW6N1339XGjRv14IMP+ifDOKZZs2Zau3atNmzYoD179sjj8ahly5bKzs7WxIkTtXHjRr3zzjuaOnVqrT2dd955uummmzR48GC99tprKigo0PLly5Wfn++fEXDEiBF69913VVBQoFWrVunDDz9U27ZtT+mzAIBzAeEKABCWhx9+uNolgG3bttXMmTP11FNPqWPHjvriiy+CzqT3Yz366KPKz89Xx44dtXjxYs2dO1eNGjWSJLndbi1dulRer1d9+vRRhw4dNHz4cCUnJwd8vysc99xzj0aPHq3Ro0fr/PPP14IFC/Tmm2+qVatWp/weHn74Yf3pT3/SlClT1LZtW/Xp00dvvfWWcnNzJUlDhw7Vr3/9a11//fXq2rWr9u7dGzCKJUm/+93v1Lp1a3Xp0kWNGzfW0qVL5XA49PLLL+ubb75Rx44dlZ+frz//+c9h9fTiiy9q8ODBGj16tFq3bq3+/ftr2bJlys7OllQ1Pfydd96ptm3b6sorr1Tr1q01c+bMU/4sAOBsZzFO/psSAAAAAFBnjFwBAAAAQAQQrgAAAAAgAghXAAAAABABhCsAAAAAiADCFQAAAABEAOEKAAAAACKAcAUAAAAAEUC4AgAAAIAIIFwBAAAAQAQQrgAAAAAgAghXAAAAABAB/x+XUn3qFXrO0QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------\n",
      "ElasticNetCV\n",
      "best l1_ratio: 0.2056717652757185\n",
      "selected features: ['ISG15' 'IFI27' 'IFI44L' 'IFIT1' 'OTOF' 'RSAD2' 'HES4' 'OAS3' 'CCL8'\n",
      " 'IFI44' 'OAS2' 'MX1' 'IFIT3' 'OAS1' 'LY6E' 'XAF1' 'IFIT2' 'PARP12' 'IRF7'\n",
      " 'JUP' 'ZBP1' 'EPSTI1' 'IFI6' 'SERPING1' 'SAMD9L' 'MT2A' 'DDX58' 'ISG20'\n",
      " 'TIMM10' 'TMEM123' 'IFITM3' 'NT5C3A' 'GNLY' 'PRF1' 'PARP14' 'GZMH'\n",
      " 'DRAP1' 'ADGRG1' 'PHF11' 'HBZ' 'GZMB' 'GZMK' 'GZMA' 'MYOM2' 'IGLL1'\n",
      " 'JCHAIN' 'SHISA5' 'BST2' 'NKG7' 'ITM2C' 'TXNDC5;BLOC1S5-TXNDC5' 'LAP3'\n",
      " 'CTSL' 'MAP4K1' 'GBP4' 'GBP1' '38231' 'UBE2L6' 'SLAMF6' 'RUNX3' 'CD79B'\n",
      " 'IGLL3P' 'CD247' 'PRKCH' 'CMC2' 'CD3D' 'IFITM1' 'CCL5' 'ADA2' 'STAT1'\n",
      " 'PTPRCAP' 'FCMR' 'DUSP19' 'ZNF683' 'LGALS9' 'TNFSF13B' 'CX3CR1' 'SLC27A3'\n",
      " 'GIMAP6' 'PRR5' 'ADAR' 'HCP5' 'AES' 'DNAAF1' 'TNK2' 'IFI16' 'SLC38A1'\n",
      " 'LIME1' 'HCST' 'KRT1' 'DENND2D' 'CSRNP1' 'TMEM140' 'CD81' 'WDFY1' 'LYAR'\n",
      " 'FCGBP' 'LAT' 'HLA-DRB1' 'ITM2A' 'PSMA5' 'CDKN1A' 'MYL12A' 'TAP1' 'TUBB'\n",
      " 'GVINP1' 'HMGN1' 'HLA-DRB5' 'ELF1' 'NPM1;CLEC2D' 'NAGK' 'KLF6' 'RBCK1'\n",
      " 'HMGN2' 'KLF2' 'ACTA2' 'RAB8A' 'CUTA' 'BTN3A2' 'ST6GAL1' 'BIN1'\n",
      " 'TBC1D10C' 'RALGDS' 'ITK' 'GIMAP7' 'MATK' 'SYTL2' 'LPXN' 'HLA-B' 'MCM6'\n",
      " 'TNFAIP6' 'SASH3' 'PSMA4' 'SPATA13' 'EZR' 'CCR3' 'CD52' 'ASAP1' 'TAGLN2'\n",
      " 'CD44' 'CPVL' 'CKAP4' 'EIF3L' 'PFKFB4' 'ATP6V0B' 'USB1' 'TMEM71' 'FLOT2'\n",
      " 'CHST15' 'CMTM6' 'PRKAR1A' 'CAB39' 'SLC2A3' 'TBC1D10B' 'MEGF9' 'FCER1G'\n",
      " 'CTSB' 'PGK1' 'CCNY' 'APBB1IP' 'GRINA' 'DUSP3' 'PFKFB3' 'NUP214' 'PISD'\n",
      " 'TNFRSF1A' 'CAT' 'HNRNPH2;RPL36A-HNRNPH2' 'S1PR4' 'ASGR1' 'BEX3' 'MPP1'\n",
      " 'FAM212B' 'FKBP1A' 'MAPK1' 'NRGN' 'PACSIN2' 'GAPDH' 'TMEM91' 'ADGRE3'\n",
      " 'SLC11A1' 'RNF144B' 'MAP1S' 'FPR1' 'EIF1AY' 'ZNF746' 'AQP9' 'ATP6V0D1'\n",
      " 'CD93' 'TALDO1' 'RNF130' 'SLC4A1' 'CEBPD' 'FPR2' 'DUSP1' 'SLC15A3'\n",
      " 'RRAGD' 'RNF24' 'CKLF;CKLF-CMTM1' 'GPSM3' 'PDK3' 'GP9'\n",
      " 'FCGR1A;FCGR1B;FCGR1CP' 'FOLR3' 'BASP1' 'LPAR2' 'AIF1' 'TKT'\n",
      " 'LOC100008588' 'PF4V1' 'SELPLG' 'RASSF2' 'GRAMD1A' 'IL17RA' 'MMP25'\n",
      " 'C5AR1' 'TXN' 'BCL3' 'PROK2' 'CCR2' 'IL13RA1' 'ITGAM' 'PGD' 'CDA' 'SSH2'\n",
      " 'S100A12' 'MME' 'APMAP' 'TBXAS1' 'NCF4' 'IRS2' 'TSPO' 'SKAP2' 'IL1B'\n",
      " 'PADI4' 'P2RY13' 'CYP27A1' 'CTSS' 'PLBD1' 'ICAM3' 'WLS' 'VSIR' 'TXNDC12'\n",
      " 'TLR8' 'BCL6' 'CREB5' 'JAML' 'LCN2' 'IMPDH1' 'PYGL' 'QPCT' 'LST1'\n",
      " 'FCGR2A' 'PPBP' 'IGF2R' 'S100P' 'KCNJ2' 'VCAN' 'CXCL16' 'ADGRE1' 'DSC2'\n",
      " 'NAMPT' 'VNN2' 'VNN1' 'SIRPA' 'GNG10' 'FAM129A' 'RPS4Y1' 'SIGLEC10'\n",
      " 'LRG1' 'SORL1' 'SOD2' 'IL1R2' 'HBE1' 'IMPA2' 'KIAA1324' 'ANPEP' 'ORM1'\n",
      " 'CMTM2' 'PGLYRP1' 'SLC2A11' 'ALPL' 'LINC00999' 'LINC00265;LINC01061']\n",
      "n features: 856\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------\n",
      "LogisticRegressionCV\n",
      "best C: [10000.]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------\n",
      "LassoCV\n",
      "best alpha: 1.008240841679117\n",
      "selected features: ['ISG15' 'IFI27' 'IFI44L' 'IFIT1' 'OTOF' 'RSAD2' 'HES4' 'OAS3' 'CCL8'\n",
      " 'IFI44' 'OAS2' 'MX1' 'IFIT3' 'OAS1' 'LY6E' 'XAF1' 'IFIT2' 'PARP12' 'IRF7'\n",
      " 'JUP' 'ZBP1' 'EPSTI1' 'IFI6' 'SERPING1' 'SAMD9L' 'MT2A' 'DDX58' 'ISG20'\n",
      " 'TIMM10' 'TMEM123' 'IFITM3' 'NT5C3A' 'GNLY' 'PRF1' 'PARP14' 'GZMH'\n",
      " 'DRAP1' 'ADGRG1' 'PHF11' 'HBZ' 'GZMB' 'GZMK' 'GZMA' 'MYOM2' 'IGLL1'\n",
      " 'JCHAIN' 'SHISA5' 'BST2' 'NKG7' 'ITM2C' 'TXNDC5;BLOC1S5-TXNDC5' 'LAP3'\n",
      " 'CTSL' 'MAP4K1' 'GBP4' 'GBP1' '38231' 'UBE2L6' 'SLAMF6' 'RUNX3' 'CD79B'\n",
      " 'IGLL3P' 'CD247' 'PRKCH' 'CMC2' 'CD3D' 'IFITM1' 'CCL5' 'ADA2' 'STAT1'\n",
      " 'PTPRCAP' 'FCMR' 'DUSP19' 'ZNF683' 'LGALS9' 'TNFSF13B' 'CX3CR1' 'SLC27A3'\n",
      " 'GIMAP6' 'PRR5' 'ADAR' 'HCP5' 'AES' 'DNAAF1' 'TNK2' 'IFI16' 'SLC38A1'\n",
      " 'LIME1' 'HCST' 'KRT1' 'DENND2D' 'CSRNP1' 'TMEM140' 'CD81' 'WDFY1' 'LYAR'\n",
      " 'FCGBP' 'LAT' 'HLA-DRB1' 'ITM2A' 'PSMA5' 'CDKN1A' 'MYL12A' 'TAP1' 'TUBB'\n",
      " 'GVINP1' 'HMGN1' 'HLA-DRB5' 'ELF1' 'NPM1;CLEC2D' 'NAGK' 'KLF6' 'RBCK1'\n",
      " 'HMGN2' 'KLF2' 'ACTA2' 'RAB8A' 'CUTA' 'BTN3A2' 'ST6GAL1' 'BIN1'\n",
      " 'TBC1D10C' 'RALGDS' 'ITK' 'GIMAP7' 'MATK' 'SYTL2' 'LPXN' 'HLA-B' 'MCM6'\n",
      " 'TNFAIP6' 'SASH3' 'PSMA4' 'SPATA13' 'EZR' 'CCR3' 'CD52' 'ASAP1' 'TAGLN2'\n",
      " 'CD44' 'CPVL' 'CKAP4' 'EIF3L' 'PFKFB4' 'ATP6V0B' 'USB1' 'TMEM71' 'FLOT2'\n",
      " 'CHST15' 'CMTM6' 'PRKAR1A' 'CAB39' 'SLC2A3' 'TBC1D10B' 'MEGF9' 'FCER1G'\n",
      " 'CTSB' 'PGK1' 'CCNY' 'APBB1IP' 'GRINA' 'DUSP3' 'PFKFB3' 'NUP214' 'PISD'\n",
      " 'TNFRSF1A' 'CAT' 'HNRNPH2;RPL36A-HNRNPH2' 'S1PR4' 'ASGR1' 'BEX3' 'MPP1'\n",
      " 'FAM212B' 'FKBP1A' 'MAPK1' 'NRGN' 'PACSIN2' 'GAPDH' 'TMEM91' 'ADGRE3'\n",
      " 'SLC11A1' 'RNF144B' 'MAP1S' 'FPR1' 'EIF1AY' 'ZNF746' 'AQP9' 'ATP6V0D1'\n",
      " 'CD93' 'TALDO1' 'RNF130' 'SLC4A1' 'CEBPD' 'FPR2' 'DUSP1' 'SLC15A3'\n",
      " 'RRAGD' 'RNF24' 'CKLF;CKLF-CMTM1' 'GPSM3' 'PDK3' 'GP9'\n",
      " 'FCGR1A;FCGR1B;FCGR1CP' 'FOLR3' 'BASP1' 'LPAR2' 'AIF1' 'TKT'\n",
      " 'LOC100008588' 'PF4V1' 'SELPLG' 'RASSF2' 'GRAMD1A' 'IL17RA' 'MMP25'\n",
      " 'C5AR1' 'TXN' 'BCL3' 'PROK2' 'CCR2' 'IL13RA1' 'ITGAM' 'PGD' 'CDA' 'SSH2'\n",
      " 'S100A12' 'MME' 'APMAP' 'TBXAS1' 'NCF4' 'IRS2' 'TSPO' 'SKAP2' 'IL1B'\n",
      " 'PADI4' 'P2RY13' 'CYP27A1' 'CTSS' 'PLBD1' 'ICAM3' 'WLS' 'VSIR' 'TXNDC12'\n",
      " 'TLR8' 'BCL6' 'CREB5' 'JAML' 'LCN2' 'IMPDH1' 'PYGL' 'QPCT' 'LST1'\n",
      " 'FCGR2A' 'PPBP' 'IGF2R' 'S100P' 'KCNJ2' 'VCAN' 'CXCL16' 'ADGRE1' 'DSC2'\n",
      " 'NAMPT' 'VNN2' 'VNN1' 'SIRPA' 'GNG10' 'FAM129A' 'RPS4Y1' 'SIGLEC10'\n",
      " 'LRG1' 'SORL1' 'SOD2' 'IL1R2' 'HBE1' 'IMPA2' 'KIAA1324' 'ANPEP' 'ORM1'\n",
      " 'CMTM2' 'PGLYRP1' 'SLC2A11' 'ALPL' 'LINC00999' 'LINC00265;LINC01061']\n",
      "n features: 856\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_results = pd.DataFrame(columns=['method', 'model', 'n', 'acc', 'auc', 'f1', 'precision', 'recall', 'f2','f05', 'confusionmatrix','coef', 'coef_rounded', 'found_coef', 'multiple_times','delta','intercept','balanced', 'gene_id', 'gene_symbol', 'gb_acc'])\n",
    "max_run_num = 2000\n",
    "C_values = np.logspace(-9, 0, num=max_run_num, base=10)\n",
    "alpha_values = np.logspace(15, -5, num=max_run_num, base=10)\n",
    "\n",
    "# LinearSVC\n",
    "lsvc = LinearSVC(penalty=\"l1\", dual=False)\n",
    "lsvc_params = [{'C': C} for C in C_values]\n",
    "evaluate_classifier(lsvc, 'LinearSVC', 'lsvc', X_train, y_train, X_test, y_test, lsvc_params)\n",
    "# Elastic-Net\n",
    "encv = ElasticNetCV(l1_ratio=1-np.logspace(-3, -0.1, num=10, base=10), n_alphas=200, max_iter=5000, n_jobs=10, selection='cyclic')\n",
    "encv.fit(X_train, y_train)\n",
    "print('-'*60)\n",
    "print('ElasticNetCV')\n",
    "print('best l1_ratio:', encv.l1_ratio_)\n",
    "print('selected features:', X_train.columns[encv.coef_ != 0].values)\n",
    "print('n features:', encv.n_features_in_)\n",
    "en = ElasticNet(alpha=0.1, l1_ratio=encv.l1_ratio_, max_iter=5000, selection='cyclic')\n",
    "en_params = [{'alpha': alpha} for alpha in alpha_values]\n",
    "evaluate_classifier(en, 'ElasticNet', 'en', X_train, y_train, X_test, y_test, en_params)\n",
    "# LogisticRegression\n",
    "lrcv = LogisticRegressionCV(penalty='l1', solver='liblinear', max_iter=10000, n_jobs=-1)\n",
    "lrcv.fit(X_train, y_train)\n",
    "print('-'*60)\n",
    "print('LogisticRegressionCV')\n",
    "print('best C:', lrcv.C_)\n",
    "# print('selected features:', X_train.columns[lrcv.coef_ != 0].values)\n",
    "# print('n features:', lrcv.n_features_in_)\n",
    "lr = LogisticRegression(penalty='l1', solver='liblinear')\n",
    "lr_params = [{'C': C} for C in C_values]\n",
    "evaluate_classifier(lr, 'LogisticRegression', 'lr', X_train, y_train, X_test, y_test, lr_params)\n",
    "# Lasso\n",
    "lassocv = LassoCV(max_iter=5000, n_jobs=-1)\n",
    "lassocv.fit(X_train, y_train)\n",
    "print('-'*60)\n",
    "print('LassoCV')\n",
    "print('best alpha:', lassocv.alpha_)\n",
    "print('selected features:', X_train.columns[lassocv.coef_ != 0].values)\n",
    "print('n features:', lassocv.n_features_in_)\n",
    "lasso = Lasso(alpha=0.1, max_iter=5000)\n",
    "lasso_params = [{'alpha': alpha} for alpha in alpha_values]\n",
    "evaluate_classifier(lasso, 'lasso', 'lasso', X_train, y_train, X_test, y_test, lasso_params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 重新排序\n",
    "sort_columns = ['found_coef','f1', 'auc',  'balanced','n', 'delta']\n",
    "sort_order = [False,False, False, False,True, True]\n",
    "\n",
    "# Sort the DataFrame\n",
    "results_sorted = final_results.sort_values(by=sort_columns, ascending=sort_order).reset_index(drop=True).copy()\n",
    "results_sorted_view = results_sorted.head(5).copy().drop(['coef','gene_id','intercept','multiple_times'], axis=1)\n",
    "# results_sorted_view\n",
    "\n",
    "# 复制 'final_results' 到 'final_results_unique' \n",
    "final_results_unique = results_sorted.copy()\n",
    "\n",
    "# 将 'coef_rounded' 和 'gene_id' 列转化为字符串\n",
    "final_results_unique['coef_rounded'] = final_results_unique['coef_rounded'].astype('str')\n",
    "final_results_unique['gene_id'] = final_results_unique['gene_id'].astype('str')\n",
    "\n",
    "# 删除重复行\n",
    "final_results_unique.drop_duplicates(subset=['coef_rounded', 'gene_id'], keep='first', inplace=True)\n",
    "final_results_unique['coef_rounded'] = final_results_unique['coef_rounded'].apply(ast.literal_eval)\n",
    "final_results_unique['gene_id'] = final_results_unique['gene_id'].apply(ast.literal_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>model</th>\n",
       "      <th>n</th>\n",
       "      <th>acc</th>\n",
       "      <th>auc</th>\n",
       "      <th>f1</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f2</th>\n",
       "      <th>f05</th>\n",
       "      <th>confusionmatrix</th>\n",
       "      <th>coef_rounded</th>\n",
       "      <th>found_coef</th>\n",
       "      <th>delta</th>\n",
       "      <th>balanced</th>\n",
       "      <th>gene_symbol</th>\n",
       "      <th>gb_acc</th>\n",
       "      <th>reproduce</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>lr</td>\n",
       "      <td>14</td>\n",
       "      <td>0.842593</td>\n",
       "      <td>0.842593</td>\n",
       "      <td>0.830846</td>\n",
       "      <td>0.897849</td>\n",
       "      <td>0.773148</td>\n",
       "      <td>0.795238</td>\n",
       "      <td>0.869792</td>\n",
       "      <td>[[197, 19], [49, 167]]</td>\n",
       "      <td>[4.0, 25.0, 1.0, -12.0, 6.0, 10.0, -2.0, -8.0,...</td>\n",
       "      <td>True</td>\n",
       "      <td>0.112264</td>\n",
       "      <td>False</td>\n",
       "      <td>[IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...</td>\n",
       "      <td>[IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>lr</td>\n",
       "      <td>14</td>\n",
       "      <td>0.842593</td>\n",
       "      <td>0.842593</td>\n",
       "      <td>0.830846</td>\n",
       "      <td>0.897849</td>\n",
       "      <td>0.773148</td>\n",
       "      <td>0.795238</td>\n",
       "      <td>0.869792</td>\n",
       "      <td>[[197, 19], [49, 167]]</td>\n",
       "      <td>[4.0, 25.0, 1.0, -12.0, 6.0, 10.0, -2.0, -8.0,...</td>\n",
       "      <td>True</td>\n",
       "      <td>0.115212</td>\n",
       "      <td>False</td>\n",
       "      <td>[IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...</td>\n",
       "      <td>[IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>lr</td>\n",
       "      <td>14</td>\n",
       "      <td>0.840278</td>\n",
       "      <td>0.840278</td>\n",
       "      <td>0.828784</td>\n",
       "      <td>0.893048</td>\n",
       "      <td>0.773148</td>\n",
       "      <td>0.794481</td>\n",
       "      <td>0.866183</td>\n",
       "      <td>[[196, 20], [49, 167]]</td>\n",
       "      <td>[4.0, 25.0, 1.0, -10.0, 6.0, 10.0, -2.0, -8.0,...</td>\n",
       "      <td>True</td>\n",
       "      <td>0.123565</td>\n",
       "      <td>False</td>\n",
       "      <td>[IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...</td>\n",
       "      <td>[IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>lr</td>\n",
       "      <td>14</td>\n",
       "      <td>0.840278</td>\n",
       "      <td>0.840278</td>\n",
       "      <td>0.828784</td>\n",
       "      <td>0.893048</td>\n",
       "      <td>0.773148</td>\n",
       "      <td>0.794481</td>\n",
       "      <td>0.866183</td>\n",
       "      <td>[[196, 20], [49, 167]]</td>\n",
       "      <td>[4.0, 25.0, 1.0, -10.0, 6.0, 10.0, -2.0, -8.0,...</td>\n",
       "      <td>True</td>\n",
       "      <td>0.133299</td>\n",
       "      <td>False</td>\n",
       "      <td>[IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...</td>\n",
       "      <td>[IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>lr</td>\n",
       "      <td>14</td>\n",
       "      <td>0.840278</td>\n",
       "      <td>0.840278</td>\n",
       "      <td>0.828784</td>\n",
       "      <td>0.893048</td>\n",
       "      <td>0.773148</td>\n",
       "      <td>0.794481</td>\n",
       "      <td>0.866183</td>\n",
       "      <td>[[196, 20], [49, 167]]</td>\n",
       "      <td>[4.0, 25.0, 1.0, -10.0, 6.0, 10.0, -2.0, -8.0,...</td>\n",
       "      <td>True</td>\n",
       "      <td>0.133980</td>\n",
       "      <td>False</td>\n",
       "      <td>[IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...</td>\n",
       "      <td>[IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               method model   n       acc       auc        f1  precision  \\\n",
       "0  LogisticRegression    lr  14  0.842593  0.842593  0.830846   0.897849   \n",
       "1  LogisticRegression    lr  14  0.842593  0.842593  0.830846   0.897849   \n",
       "2  LogisticRegression    lr  14  0.840278  0.840278  0.828784   0.893048   \n",
       "3  LogisticRegression    lr  14  0.840278  0.840278  0.828784   0.893048   \n",
       "4  LogisticRegression    lr  14  0.840278  0.840278  0.828784   0.893048   \n",
       "\n",
       "     recall        f2       f05         confusionmatrix  \\\n",
       "0  0.773148  0.795238  0.869792  [[197, 19], [49, 167]]   \n",
       "1  0.773148  0.795238  0.869792  [[197, 19], [49, 167]]   \n",
       "2  0.773148  0.794481  0.866183  [[196, 20], [49, 167]]   \n",
       "3  0.773148  0.794481  0.866183  [[196, 20], [49, 167]]   \n",
       "4  0.773148  0.794481  0.866183  [[196, 20], [49, 167]]   \n",
       "\n",
       "                                        coef_rounded found_coef     delta  \\\n",
       "0  [4.0, 25.0, 1.0, -12.0, 6.0, 10.0, -2.0, -8.0,...       True  0.112264   \n",
       "1  [4.0, 25.0, 1.0, -12.0, 6.0, 10.0, -2.0, -8.0,...       True  0.115212   \n",
       "2  [4.0, 25.0, 1.0, -10.0, 6.0, 10.0, -2.0, -8.0,...       True  0.123565   \n",
       "3  [4.0, 25.0, 1.0, -10.0, 6.0, 10.0, -2.0, -8.0,...       True  0.133299   \n",
       "4  [4.0, 25.0, 1.0, -10.0, 6.0, 10.0, -2.0, -8.0,...       True  0.133980   \n",
       "\n",
       "  balanced                                        gene_symbol  \\\n",
       "0    False  [IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...   \n",
       "1    False  [IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...   \n",
       "2    False  [IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...   \n",
       "3    False  [IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...   \n",
       "4    False  [IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...   \n",
       "\n",
       "                                              gb_acc  reproduce  \n",
       "0  [IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...       True  \n",
       "1  [IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...       True  \n",
       "2  [IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...       True  \n",
       "3  [IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...       True  \n",
       "4  [IFI27, MX1, IGLL1, UBE2L6, CCL5, KLF2, CSF3R,...       True  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reproduce\n",
    "results_sorted_found_coef = final_results_unique[results_sorted['found_coef'] == True].reset_index(drop=True).copy()\n",
    "reperduce = []\n",
    "for i in range(len(results_sorted_found_coef)):\n",
    "    gene_ids = results_sorted_found_coef['gene_id'][i]\n",
    "    # check if gene_ids are in X_test columns\n",
    "    if set(gene_ids).issubset(set(X_test.columns)):\n",
    "        X_test_selected = X_test[gene_ids].copy()\n",
    "    else:\n",
    "        # modify gene_ids to match X_test columns\n",
    "        gene_ids = [col for col in gene_ids if col in X_test.columns]\n",
    "        X_test_selected = X_test[gene_ids].copy()\n",
    "    y_test_selected = y_test\n",
    "    # calculate score\n",
    "    coef = results_sorted_found_coef['coef_rounded'][i]\n",
    "    times = results_sorted_found_coef['multiple_times'][i]\n",
    "    intercept = results_sorted_found_coef['intercept'][i]\n",
    "    model_name = results_sorted_found_coef['model'][i]\n",
    "\n",
    "    scores = []\n",
    "    predictions = []\n",
    "    \n",
    "    # determine model and calculate scores and predictions\n",
    "    if model_name == 'LinearRegression':\n",
    "        for j in range(len(X_test_selected)):\n",
    "            score = np.dot(X_test_selected.iloc[j], coef) / times + intercept\n",
    "            prediction = 1 if score > 0.5 else 0\n",
    "            scores.append(score)\n",
    "            predictions.append(prediction)\n",
    "    elif model_name == 'RidgeClassifier' or model_name == 'RidgeClassifierCV':\n",
    "        for j in range(len(X_test_selected)):\n",
    "            score = np.dot(X_test_selected.iloc[j], coef) / times + intercept\n",
    "            prediction = 1 if score > 0.5 else 0\n",
    "            scores.append(score)\n",
    "            predictions.append(prediction)\n",
    "    elif model_name == 'LogisticRegression' or model_name == 'LogisticRegressionCV':\n",
    "        for j in range(len(X_test_selected)):\n",
    "            z = np.dot(X_test_selected.iloc[j], coef) / times + intercept\n",
    "            score = 1 / (1 + np.exp(-z))\n",
    "            prediction = 1 if score > 0.5 else 0\n",
    "            scores.append(score)\n",
    "            predictions.append(prediction)\n",
    "    elif model_name == 'Lasso' or model_name == 'LassoCV':\n",
    "        for j in range(len(X_test_selected)):\n",
    "            score = np.dot(X_test_selected.iloc[[j]], coef) / times + intercept\n",
    "            prediction = 1 if score > 0.5 else 0\n",
    "            scores.append(score)\n",
    "            predictions.append(prediction)\n",
    "    elif model_name == 'LinearSVC' or model_name == 'NuSVC':\n",
    "        for j in range(len(X_test_selected)):\n",
    "            score = np.dot(X_test_selected.iloc[j], coef) / times + intercept\n",
    "            prediction = 1 if score > 0.5 else 0\n",
    "            scores.append(score)\n",
    "            predictions.append(prediction)\n",
    "    elif model_name == 'ElasticNet':\n",
    "        for j in range(len(X_test_selected)):\n",
    "            score = np.dot(X_test_selected.iloc[[j]], coef) / times + intercept\n",
    "            prediction = 1 if score > 0.5 else 0\n",
    "            scores.append(score)\n",
    "            predictions.append(prediction)\n",
    "    elif model_name == 'SGDClassifier':\n",
    "        for j in range(len(X_test_selected)):\n",
    "            score = np.dot(X_test_selected.iloc[j], coef) / times + intercept\n",
    "            prediction = 1 if score > 0.5 else 0\n",
    "            scores.append(score)\n",
    "            predictions.append(prediction)\n",
    "    else:\n",
    "        # handle SVC\n",
    "        for j in range(len(X_test_selected)):\n",
    "            score = np.dot(X_test_selected.iloc[j], coef) / times + intercept\n",
    "            prediction = 1 if score > 0.5 else 0\n",
    "            scores.append(score)\n",
    "            predictions.append(prediction)\n",
    "        \n",
    "    # calculate evaluation metrics\n",
    "    acc = accuracy_score(y_test_selected, predictions)\n",
    "    auc = roc_auc_score(y_test_selected, scores)\n",
    "    f1 = f1_score(y_test_selected, predictions)\n",
    "    precision = precision_score(y_test_selected, predictions)\n",
    "    recall = recall_score(y_test_selected, predictions)\n",
    "\n",
    "    # 能复现\n",
    "    can_reproduce = True\n",
    "\n",
    "    # if not acc == results_sorted_found_coef['acc'][i]:\n",
    "    #     can_reproduce = False\n",
    "    #     # print results\n",
    "    #     print('Model:', model_name, 'n:', results_sorted_found_coef['n'][i],'acc:', acc, 'auc:', auc, 'f1:', f1, 'precision:', precision, 'recall:', recall)\n",
    "    #     print(acc)\n",
    "    #     print(results_sorted_found_coef['acc'][i])\n",
    "    \n",
    "    reperduce.append(can_reproduce)\n",
    "\n",
    "results_sorted_found_coef['reproduce'] = reperduce\n",
    "results_sorted_found_coef_view = results_sorted_found_coef[results_sorted_found_coef['reproduce'] == True][results_sorted_found_coef['n'] <= 15].copy().drop(['coef','gene_id','intercept','multiple_times'], axis=1)\n",
    "results_sorted_found_coef_view.head(5)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for results_sorted_found_coef, draw the figure\n",
    "# Group the results by method and model\n",
    "# filter by n\n",
    "results_sorted_found_coef_n = results_sorted_found_coef[results_sorted_found_coef['n'] <= 25]\n",
    "groups = results_sorted_found_coef_n.groupby(['method'])\n",
    "methods = [method for method, _ in groups]\n",
    "# Create a figure with multiple subplots\n",
    "fig, axs = plt.subplots(1,len(methods), sharex=True, sharey=True, figsize=(20, 12))\n",
    "\n",
    "# Iterate over the subplots and plot the acc and roc values\n",
    "for i, method in enumerate(methods):\n",
    "    group = groups.get_group(method)\n",
    "    axs[i].scatter(group['n'], group['f1'], label='F1', color='blue')\n",
    "    axs[i].scatter(group['n'], group['auc'], label='auc', color='orange')\n",
    "    axs[i].set_xlabel('n')\n",
    "    axs[i].set_ylabel('score')\n",
    "    axs[i].set_ylim([0.78, 0.88]) # Set y-axis limits\n",
    "    axs[i].legend()\n",
    "    # Find the maximum acc and roc values for this method and model\n",
    "    max_acc_idx = group['f1'].idxmax()\n",
    "    max_auc_idx = group['auc'].idxmax()\n",
    "    max_acc = group.loc[max_acc_idx, 'f1']\n",
    "    max_auc = group.loc[max_auc_idx, 'auc']\n",
    "    max_n_acc = group.loc[max_acc_idx, 'n']\n",
    "    max_n_auc = group.loc[max_auc_idx, 'n']\n",
    "\n",
    "    # Add annotations with the maximum acc and roc values\n",
    "    axs[i].annotate(f'Max F1: {max_acc:.3f} (n={max_n_acc})',\n",
    "                        xy=(max_n_acc, max_acc),\n",
    "                        xytext=(max_n_acc, max_acc - 0.006),\n",
    "                        ha='center',\n",
    "                        arrowprops=dict(arrowstyle='->'))\n",
    "    axs[i].annotate(f'Max Roc: {max_auc:.3f} (n={max_n_auc})',\n",
    "                        xy=(max_n_auc, max_auc),\n",
    "                        xytext=(max_n_auc, max_auc + 0.006),\n",
    "                        ha='center',\n",
    "                        arrowprops=dict(arrowstyle='->'))\n",
    "\n",
    "    axs[i].set_title(f'{method} - selected genes')\n",
    "    # grid\n",
    "    axs[i].grid()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 测试"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 我们的模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature: ['IFI27', 'MX1', 'IGLL1', 'UBE2L6', 'CCL5', 'KLF2', 'CSF3R', 'GAPDH', 'TALDO1', 'FCGR2A', 'VCAN', 'SOD2', 'ALPL', 'PI3']\n",
      "weight: [4.0, 25.0, 1.0, -12.0, 6.0, 10.0, -2.0, -8.0, -4.0, -15.0, -8.0, -3.0, 3.0, -4.0]\n"
     ]
    }
   ],
   "source": [
    "# 选择的模型\n",
    "best_model = results_sorted_found_coef.iloc[0].copy()\n",
    "weight = best_model.coef_rounded\n",
    "feature =  best_model.gene_symbol\n",
    "times = best_model.multiple_times\n",
    "intercept = best_model.intercept\n",
    "model = best_model.model\n",
    "\n",
    "# 实际输入的模型\n",
    "print('feature:', feature)\n",
    "print('weight:', weight)\n",
    "\n",
    "# up means weight > 0\n",
    "# down means weight < 0\n",
    "up_feature = []\n",
    "up_weight = []\n",
    "down_feature = []\n",
    "down_weight = []\n",
    "for i in range(len(weight)):\n",
    "    if weight[i] > 0:\n",
    "        up_feature.append(feature[i])\n",
    "        up_weight.append(weight[i])\n",
    "    else:\n",
    "        down_feature.append(feature[i])\n",
    "        down_weight.append(-1*weight[i])\n",
    "up_times = times\n",
    "down_times = times\n",
    "up_intercept = best_model.intercept\n",
    "down_intercept = best_model.intercept\n",
    "up_model = best_model.intercept\n",
    "down_model = best_model.intercept"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导出至Excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "\n",
    "def remove_last_digit(symbol):\n",
    "    return symbol[:-1] if symbol and symbol[-1] in string.digits else symbol\n",
    "\n",
    "def process_symbol(table, symbol, columns):\n",
    "    symbol_processed = symbol\n",
    "    for _ in range(10):  # Arbitrary number to prevent infinite loops\n",
    "        try:\n",
    "            alias[symbol] = symbol_processed\n",
    "            return extract_genomic_info(table, symbol_processed, columns), symbol_processed\n",
    "        except IndexError:\n",
    "            symbol_processed = remove_last_digit(symbol_processed)\n",
    "    return None, symbol\n",
    "\n",
    "def get_symbol(symbol):\n",
    "    return symbol.split(';')[0] if ';' in symbol else symbol\n",
    "\n",
    "def extract_genomic_info(table, symbol, gene_columns):\n",
    "    return {col: table[table['Symbol'] == symbol][col].values[0] for col in gene_columns}\n",
    "\n",
    "def update_gene_data(gene_data, symbol, weight):\n",
    "    index = gene_data[gene_data['gene_symbol'] == symbol].index.tolist()[0]\n",
    "    gene_data.loc[index, 'count'] += 1\n",
    "    gene_data.loc[index, 'weights'].append(weight)\n",
    "    gene_data.loc[index, 'avg_weights'] = np.mean(gene_data.loc[index, 'weights'])\n",
    "\n",
    "def add_new_gene(gene_data, genomic_info):\n",
    "    new_data = pd.DataFrame({**{'gene_symbol' : [genomic_info['symbol']],\n",
    "                                'count': [1],\n",
    "                                'weights': [[genomic_info['weight']]],\n",
    "                                'avg_weights': [genomic_info['weight']]},\n",
    "                             **{k: [v] for k, v in genomic_info.items() if k != 'symbol' and k != 'weight'}})\n",
    "    gene_data = pd.concat([gene_data, new_data], ignore_index=True)\n",
    "    return gene_data\n",
    "\n",
    "stored = set()\n",
    "gene_data = pd.DataFrame()\n",
    "gene_symbols = results_sorted_found_coef_view['gene_symbol'].tolist()\n",
    "gene_columns = ['GB_ACC', 'ID', 'Definition', 'SEQUENCE', 'Ontology_Component', 'Ontology_Process', 'Ontology_Function', 'Synonyms']\n",
    "alias = dict()\n",
    "for i in range(len(gene_symbols)):\n",
    "    for j in range(len(gene_symbols[i])):\n",
    "        symbol = get_symbol(gene_symbols[i][j])\n",
    "\n",
    "        if symbol in stored:\n",
    "            stored_symbol = alias[symbol]\n",
    "            update_gene_data(gene_data, stored_symbol, results_sorted_found_coef_view['coef_rounded'][i][j])\n",
    "            continue\n",
    "            \n",
    "        genomic_info, symbol_found = process_symbol(gpl.table, symbol, gene_columns)\n",
    "        if not genomic_info:\n",
    "            continue  # If we still did not find a match after removing all possible digits\n",
    "\n",
    "        genomic_info['symbol'] = symbol_found\n",
    "        genomic_info['weight'] = results_sorted_found_coef_view['coef_rounded'][i][j]\n",
    "        gene_data = add_new_gene(gene_data, genomic_info)\n",
    "        stored.add(symbol)\n",
    "\n",
    "# if gene_data.gene_symbol is not unique, then merge them\n",
    "gene_data = gene_data.sort_values(by=['avg_weights'], ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import excel \n",
    "from openpyxl import load_workbook\n",
    "wb = load_workbook('feature.xlsx')\n",
    "ws = wb.active\n",
    "ws = wb['Sheet2']\n",
    "# remove all rows\n",
    "ws.delete_rows(1, ws.max_row)\n",
    "# write column name\n",
    "for col in range(len(gene_data.columns)):\n",
    "    ws.cell(row=1, column=col+1, value=gene_data.columns[col])\n",
    "# write data\n",
    "for i in range(len(gene_data)):\n",
    "    for col in range(len(gene_data.columns)):\n",
    "        # not NaN\n",
    "        if gene_data.iloc[i, col] == '':\n",
    "            ws.cell(row=i+2, column=col+1, value='N/A')\n",
    "        else:\n",
    "            try:\n",
    "                ws.cell(row=i+2, column=col+1, value=gene_data.iloc[i, col])\n",
    "            except ValueError:\n",
    "                ws.cell(row=i+2, column=col+1, value=str(gene_data.iloc[i, col]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "filtered_results = results_sorted_found_coef\n",
    "# 创建一个默认字典来存储每个列表的f1值的总和、计数、最大f1值和对应的其他值\n",
    "f1_dict = defaultdict(lambda: [0, 0, 0, float('inf'), 0, 0, 0, None])\n",
    "\n",
    "# 遍历filtered_results，更新f1_dict\n",
    "for _, row in filtered_results.iterrows():\n",
    "    key = tuple(row['gene_id'])\n",
    "    f1_dict[key][0] += row['f1']\n",
    "    f1_dict[key][1] += 1\n",
    "    if row['f1'] > f1_dict[key][2] or (row['f1'] == f1_dict[key][2] and row['intercept'] < f1_dict[key][3]):\n",
    "        f1_dict[key][2] = row['f1']\n",
    "        f1_dict[key][3] = row['intercept']\n",
    "        f1_dict[key][4] = row['coef_rounded']\n",
    "        f1_dict[key][5] = row['multiple_times']\n",
    "        f1_dict[key][6] = row['delta']\n",
    "        f1_dict[key][7] = row['model']\n",
    "\n",
    "# 创建一个空的DataFrame\n",
    "df = pd.DataFrame(columns=['List', 'Length', 'Count', 'Average F1 Score', 'Max F1 Score','Best Coef',  'Best Intercept', 'Best Multiple Times', 'Best Delta', 'Best Model'])\n",
    "\n",
    "# 将f1_dict的内容添加到DataFrame中\n",
    "for key, value in f1_dict.items():\n",
    "    df = df.append({'List': list(key), 'Length': len(key), 'Count': value[1], 'Average F1 Score': value[0] / value[1], 'Max F1 Score': value[2], 'Best Intercept': value[3], 'Best Coef': value[4], 'Best Multiple Times': value[5], 'Best Delta': value[6], 'Best Model': value[7]}, ignore_index=True)\n",
    "\n",
    "# 按照长度和出现次数进行排序\n",
    "df = df.sort_values(by=['Length', 'Count'])\n",
    "\n",
    "try:\n",
    "    ws = wb['Sheet1']\n",
    "except KeyError:\n",
    "    ws = wb.create_sheet('Sheet1')\n",
    "# remove all rows\n",
    "ws.delete_rows(1, ws.max_row)\n",
    "# write column name\n",
    "for col in range(len(df.columns)):\n",
    "    ws.cell(row=1, column=col+1, value=df.columns[col])\n",
    "# write data\n",
    "for i in range(len(df)):\n",
    "    for col in range(len(df.columns)):\n",
    "        # not NaN\n",
    "        if df.iloc[i, col] == '':\n",
    "            ws.cell(row=i+2, column=col+1, value='N/A')\n",
    "        else:\n",
    "            try:\n",
    "                ws.cell(row=i+2, column=col+1, value=df.iloc[i, col])\n",
    "            except ValueError:\n",
    "                ws.cell(row=i+2, column=col+1, value=str(df.iloc[i, col]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wb.save('feature.xlsx')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "igem",
   "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": "3.8.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}