ml-homework/exper.ipynb

{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-02T05:55:44.467787Z",
     "start_time": "2025-06-02T05:55:42.040134Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm  # 进度条\n",
    "import time  # 计时\n",
    "from collections import Counter\n",
    "from sklearn.metrics import accuracy_score, roc_curve, auc, precision_score, recall_score, f1_score, confusion_matrix, \\\n",
    "    classification_report\n",
    "from sklearn.model_selection import train_test_split # 划分训练集和测试集\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder # 标准化和独热编码\n",
    "from sklearn.compose import ColumnTransformer # 列转换器，用于预处理\n",
    "from sklearn.datasets import load_breast_cancer # 加载乳腺癌数据集（最后的任务用于对比）\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['MiSans']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# --- 加载原始数据集 ---\n",
    "try:\n",
    "    df = pd.read_csv(\"hf://datasets/schooly/online-shoppers-intention/online_shoppers_intention.csv\")\n",
    "    print(\"成功从 Hugging Face🤗 加载 'online_shoppers_intention' 数据集\")\n",
    "except Exception as e:\n",
    "    print(f\"错误: 数据集联网加载失败: {e}\")\n",
    "    # 如果联网失败，尝试加载本地副本\n",
    "    try:\n",
    "        df = pd.read_csv(\"online_shoppers_intention.csv\")\n",
    "        print(\"成功加载本地数据集 'online_shoppers_intention.csv'\")\n",
    "    except FileNotFoundError:\n",
    "        print(\"错误: 本地也未找到 'online_shoppers_intention.csv'。请确保文件存在或网络连接正常。程序将退出。\")\n",
    "        exit()\n",
    "\n",
    "# --- 初步数据探索 (先康康原始数据集) ---\n",
    "print(\"\\n--- 数据集概览 ---\")\n",
    "display(df.head())\n",
    "print(\"\\n--- 数据信息 ---\")\n",
    "print(df.info())\n",
    "print(\"\\n--- 缺失值检查 ---\")\n",
    "print(df.isnull().sum())\n",
    "\n",
    "# 目标变量分布\n",
    "print(\"\\n--- 目标变量 'Revenue' 分布 ---\")\n",
    "print(df['Revenue'].value_counts(normalize=True))  # 归一化的类别计数\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.countplot(x='Revenue', data=df)\n",
    "plt.title('目标变量 Revenue 分布')\n",
    "plt.xlabel('是否产生收益 (Revenue)')\n",
    "plt.ylabel('访客数量')\n",
    "plt.xticks([0, 1], ['未产生 (False)', '产生 (True)'])\n",
    "plt.show()"
   ],
   "id": "aa755d8a43b0e60b",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/grtsinry43/.conda/envs/ml/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "成功从 Hugging Face🤗 加载 'online_shoppers_intention' 数据集\n",
      "\n",
      "--- 数据集概览 ---\n"
     ]
    },
    {
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       "   Administrative  Administrative_Duration  Informational  \\\n",
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       "\n",
       "   Informational_Duration  ProductRelated  ProductRelated_Duration  \\\n",
       "0                     0.0               1                 0.000000   \n",
       "1                     0.0               2                64.000000   \n",
       "2                     0.0               1                 0.000000   \n",
       "3                     0.0               2                 2.666667   \n",
       "4                     0.0              10               627.500000   \n",
       "\n",
       "   BounceRates  ExitRates  PageValues  SpecialDay Month  OperatingSystems  \\\n",
       "0         0.20       0.20         0.0         0.0   Feb                 1   \n",
       "1         0.00       0.10         0.0         0.0   Feb                 2   \n",
       "2         0.20       0.20         0.0         0.0   Feb                 4   \n",
       "3         0.05       0.14         0.0         0.0   Feb                 3   \n",
       "4         0.02       0.05         0.0         0.0   Feb                 3   \n",
       "\n",
       "   Browser  Region  TrafficType        VisitorType  Weekend  Revenue  \n",
       "0        1       1            1  Returning_Visitor    False    False  \n",
       "1        2       1            2  Returning_Visitor    False    False  \n",
       "2        1       9            3  Returning_Visitor    False    False  \n",
       "3        2       2            4  Returning_Visitor    False    False  \n",
       "4        3       1            4  Returning_Visitor     True    False  "
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     "text": [
      "\n",
      "--- 数据信息 ---\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12330 entries, 0 to 12329\n",
      "Data columns (total 18 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Administrative           12330 non-null  int64  \n",
      " 1   Administrative_Duration  12330 non-null  float64\n",
      " 2   Informational            12330 non-null  int64  \n",
      " 3   Informational_Duration   12330 non-null  float64\n",
      " 4   ProductRelated           12330 non-null  int64  \n",
      " 5   ProductRelated_Duration  12330 non-null  float64\n",
      " 6   BounceRates              12330 non-null  float64\n",
      " 7   ExitRates                12330 non-null  float64\n",
      " 8   PageValues               12330 non-null  float64\n",
      " 9   SpecialDay               12330 non-null  float64\n",
      " 10  Month                    12330 non-null  object \n",
      " 11  OperatingSystems         12330 non-null  int64  \n",
      " 12  Browser                  12330 non-null  int64  \n",
      " 13  Region                   12330 non-null  int64  \n",
      " 14  TrafficType              12330 non-null  int64  \n",
      " 15  VisitorType              12330 non-null  object \n",
      " 16  Weekend                  12330 non-null  bool   \n",
      " 17  Revenue                  12330 non-null  bool   \n",
      "dtypes: bool(2), float64(7), int64(7), object(2)\n",
      "memory usage: 1.5+ MB\n",
      "None\n",
      "\n",
      "--- 缺失值检查 ---\n",
      "Administrative             0\n",
      "Administrative_Duration    0\n",
      "Informational              0\n",
      "Informational_Duration     0\n",
      "ProductRelated             0\n",
      "ProductRelated_Duration    0\n",
      "BounceRates                0\n",
      "ExitRates                  0\n",
      "PageValues                 0\n",
      "SpecialDay                 0\n",
      "Month                      0\n",
      "OperatingSystems           0\n",
      "Browser                    0\n",
      "Region                     0\n",
      "TrafficType                0\n",
      "VisitorType                0\n",
      "Weekend                    0\n",
      "Revenue                    0\n",
      "dtype: int64\n",
      "\n",
      "--- 目标变量 'Revenue' 分布 ---\n",
      "Revenue\n",
      "False    0.845255\n",
      "True     0.154745\n",
      "Name: proportion, dtype: float64\n"
     ]
    },
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       "<Figure size 600x400 with 1 Axes>"
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-02T05:55:44.565802Z",
     "start_time": "2025-06-02T05:55:44.528749Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# --- 数据预处理 (原始数据集) ---\n",
    "print(\"\\n--- 开始数据预处理 ---\")\n",
    "# 将布尔值转换为整数\n",
    "df['Weekend'] = df['Weekend'].astype(int)\n",
    "df['Revenue'] = df['Revenue'].astype(int)  # 目标变量\n",
    "\n",
    "# 识别类别特征和数值特征\n",
    "# 'OperatingSystems', 'Browser', 'Region', 'TrafficType' 虽然是数值类型，但它们代表类别，所以也当类别处理\n",
    "categorical_features = ['Month', 'VisitorType']\n",
    "numerical_features = ['Administrative', 'Administrative_Duration', 'Informational',\n",
    "                      'Informational_Duration', 'ProductRelated', 'ProductRelated_Duration',\n",
    "                      'BounceRates', 'ExitRates', 'PageValues', 'SpecialDay']\n",
    "# 数值型的类别特征\n",
    "numerical_categorical_features = ['OperatingSystems', 'Browser', 'Region', 'TrafficType']\n",
    "\n",
    "# 确保将这些数值型类别特征转换为字符串类型，以便OneHotEncoder正确处理\n",
    "for col in numerical_categorical_features:\n",
    "    df[col] = df[col].astype(str)\n",
    "\n",
    "all_categorical_features = categorical_features + numerical_categorical_features\n",
    "print(f\"所有识别出的类别特征: {all_categorical_features}\")\n",
    "print(f\"所有识别出的数值特征: {numerical_features}\")\n",
    "\n",
    "# 创建预处理器\n",
    "# 对于数值特征：进行标准化\n",
    "# 对于类别特征：进行独热编码 (One-Hot Encoding)，drop='first'避免多重共线性\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', StandardScaler(), numerical_features),\n",
    "        ('cat', OneHotEncoder(handle_unknown='ignore', drop='first'), all_categorical_features)\n",
    "    ],\n",
    "    remainder='passthrough'  # 保留其他未指定列 (如 'Weekend')\n",
    ")\n",
    "\n",
    "# 分离特征和目标变量\n",
    "X = df.drop('Revenue', axis=1)\n",
    "y = df['Revenue']\n",
    "\n",
    "# 应用预处理\n",
    "X_processed = preprocessor.fit_transform(X)\n",
    "\n",
    "# 如果 X_processed 是稀疏矩阵，转换为密集数组\n",
    "if hasattr(X_processed, \"toarray\"):\n",
    "    X_processed = X_processed.toarray()\n",
    "\n",
    "print(f\"\\n--- 处理后的特征维度 ---\")\n",
    "print(f\"X_processed.shape: {X_processed.shape}\")\n",
    "\n",
    "# 划分训练集和测试集\n",
    "# 这里的 random_state 是为了结果可复现, stratify=y 确保类别比例在训练集和测试集中保持一致\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_processed, y.values, test_size=0.2, random_state=42, stratify=y)\n",
    "\n",
    "print(f\"训练集大小: X_train: {X_train.shape}, y_train: {y_train.shape}\")\n",
    "print(f\"测试集大小: X_test: {X_test.shape}, y_test: {y_test.shape}\")\n",
    "print(\"--- 数据预处理完成 ---\")"
   ],
   "id": "3d840330aa069496",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- 开始数据预处理 ---\n",
      "所有识别出的类别特征: ['Month', 'VisitorType', 'OperatingSystems', 'Browser', 'Region', 'TrafficType']\n",
      "所有识别出的数值特征: ['Administrative', 'Administrative_Duration', 'Informational', 'Informational_Duration', 'ProductRelated', 'ProductRelated_Duration', 'BounceRates', 'ExitRates', 'PageValues', 'SpecialDay']\n",
      "\n",
      "--- 处理后的特征维度 ---\n",
      "X_processed.shape: (12330, 68)\n",
      "训练集大小: X_train: (9864, 68), y_train: (9864,)\n",
      "测试集大小: X_test: (2466, 68), y_test: (2466,)\n",
      "--- 数据预处理完成 ---\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-02T05:55:44.964206Z",
     "start_time": "2025-06-02T05:55:44.583484Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# --- 从零实现逻辑回归 ---\n",
    "class MyLogisticRegression:\n",
    "    \"\"\"\n",
    "    自定义逻辑回归分类器。\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, learning_rate=0.01, n_iterations=1000, verbose=False, tol=1e-4, early_stopping_rounds=None):\n",
    "        \"\"\"\n",
    "        初始化函数。\n",
    "        参数:\n",
    "            learning_rate (float): 学习率。\n",
    "            n_iterations (int): 最大迭代次数。\n",
    "            verbose (bool): 是否打印训练过程中的损失。\n",
    "            tol (float): 收敛的容忍度。如果两次迭代之间的损失变化小于此值，则认为收敛。\n",
    "            early_stopping_rounds (int, optional): 早停轮数。如果在指定轮数内损失没有改善，则停止训练。\n",
    "        \"\"\"\n",
    "        self.learning_rate = learning_rate\n",
    "        self.n_iterations = n_iterations\n",
    "        self.weights = None  # 权重\n",
    "        self.bias = None  # 偏置\n",
    "        self.verbose = verbose\n",
    "        self.costs = []  # 记录每次迭代的损失\n",
    "        self.tol = tol\n",
    "        self.early_stopping_rounds = early_stopping_rounds\n",
    "\n",
    "    def _sigmoid(self, z):\n",
    "        \"\"\"Sigmoid激活函数，并进行数值裁剪防止溢出。\"\"\"\n",
    "        z = np.clip(z, -500, 500)  # 防止np.exp()溢出\n",
    "        return 1 / (1 + np.exp(-z))\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        \"\"\"\n",
    "        训练模型。\n",
    "        参数:\n",
    "            X (numpy.ndarray): 特征数据。\n",
    "            y (numpy.ndarray): 目标标签。\n",
    "        \"\"\"\n",
    "        n_samples, n_features = X.shape\n",
    "        # 初始化权重和偏置\n",
    "        self.weights = np.zeros(n_features)\n",
    "        self.bias = 0\n",
    "        self.costs = []\n",
    "\n",
    "        no_improvement_count = 0\n",
    "        min_cost_for_early_stopping = float('inf')\n",
    "\n",
    "        if self.verbose:\n",
    "            print(f\"开始逻辑回归训练，学习率={self.learning_rate}, 最大迭代次数={self.n_iterations}\")\n",
    "\n",
    "        for i in range(self.n_iterations):\n",
    "            # 线性模型: z = X.w + b\n",
    "            linear_model = np.dot(X, self.weights) + self.bias\n",
    "            # 应用sigmoid函数得到预测概率\n",
    "            y_predicted_proba = self._sigmoid(linear_model)\n",
    "\n",
    "            # 计算梯度\n",
    "            dw = (1 / n_samples) * np.dot(X.T, (y_predicted_proba - y))  # 权重梯度\n",
    "            db = (1 / n_samples) * np.sum(y_predicted_proba - y)  # 偏置梯度\n",
    "\n",
    "            # 更新权重和偏置\n",
    "            self.weights -= self.learning_rate * dw\n",
    "            self.bias -= self.learning_rate * db\n",
    "\n",
    "            # 计算并记录损失 (二元交叉熵损失)\n",
    "            # 添加一个小的epsilon防止log(0)\n",
    "            epsilon = 1e-9\n",
    "            cost = - (1 / n_samples) * np.sum(\n",
    "                y * np.log(y_predicted_proba + epsilon) + (1 - y) * np.log(1 - y_predicted_proba + epsilon))\n",
    "\n",
    "            # 检查收敛性 (基于tol)\n",
    "            if i > 0 and abs(self.costs[-1] - cost) < self.tol:\n",
    "                if self.verbose:\n",
    "                    print(f\"迭代 {i}: 损失变化小于容忍度 {self.tol}，模型已收敛。当前损失: {cost:.4f}\")\n",
    "                self.costs.append(cost)\n",
    "                break\n",
    "            self.costs.append(cost)\n",
    "\n",
    "            if self.verbose and (i % (self.n_iterations // 10) == 0 or i == self.n_iterations - 1 or i == 0):\n",
    "                print(f\"迭代 {i}, 损失: {cost:.4f}\")\n",
    "\n",
    "            # 早停逻辑\n",
    "            if self.early_stopping_rounds:\n",
    "                if cost < min_cost_for_early_stopping - self.tol:  # 必须有显著改善\n",
    "                    min_cost_for_early_stopping = cost\n",
    "                    no_improvement_count = 0\n",
    "                else:\n",
    "                    no_improvement_count += 1\n",
    "\n",
    "                if no_improvement_count >= self.early_stopping_rounds:\n",
    "                    if self.verbose:\n",
    "                        print(f\"早停: 在迭代 {i} 次时，连续 {self.early_stopping_rounds} 轮损失未显著改善。\")\n",
    "                    break\n",
    "        if self.verbose:\n",
    "            print(\"逻辑回归训练完成。\")\n",
    "\n",
    "    def predict_proba(self, X):\n",
    "        \"\"\"\n",
    "        预测每个样本属于正类的概率。\n",
    "        参数:\n",
    "            X (numpy.ndarray): 特征数据。\n",
    "        返回:\n",
    "            numpy.ndarray: 每个样本属于正类的概率。\n",
    "        \"\"\"\n",
    "        linear_model = np.dot(X, self.weights) + self.bias\n",
    "        return self._sigmoid(linear_model)\n",
    "\n",
    "    def predict(self, X, threshold=0.5):\n",
    "        \"\"\"\n",
    "        根据概率预测类别标签。\n",
    "        参数:\n",
    "            X (numpy.ndarray): 特征数据。\n",
    "            threshold (float): 概率阈值，大于此阈值的判为正类。\n",
    "        返回:\n",
    "            numpy.ndarray: 预测的类别标签 (0 或 1)。\n",
    "        \"\"\"\n",
    "        y_predicted_proba = self.predict_proba(X)\n",
    "        return np.array([1 if i > threshold else 0 for i in y_predicted_proba])\n",
    "\n",
    "\n",
    "# --- 训练自定义逻辑回归模型 ---\n",
    "print(\"\\n--- 训练自定义逻辑回归模型 ---\")\n",
    "log_reg_model = MyLogisticRegression(learning_rate=0.1, n_iterations=2000, verbose=True, tol=1e-5,\n",
    "                                     early_stopping_rounds=50)\n",
    "log_reg_model.fit(X_train, y_train)\n",
    "\n",
    "# 绘制损失曲线\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(range(len(log_reg_model.costs)), log_reg_model.costs)\n",
    "plt.xlabel(\"迭代次数 (Iteration)\")\n",
    "plt.ylabel(\"损失值 (Cost)\")\n",
    "plt.title(\"逻辑回归训练损失曲线\")\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# --- 进行预测 ---\n",
    "y_pred_proba_lr = log_reg_model.predict_proba(X_test)  # 获取概率用于ROC曲线\n",
    "y_pred_labels_lr = log_reg_model.predict(X_test)  # 获取类别标签\n",
    "\n",
    "# --- 模型评估 (Logistic Regression) ---\n",
    "print(\"\\n--- 逻辑回归模型评估 ---\")\n",
    "accuracy_lr = accuracy_score(y_test, y_pred_labels_lr)\n",
    "print(f\"准确率 (Accuracy): {accuracy_lr:.4f}\")\n",
    "\n",
    "print(\"\\n分类报告:\")\n",
    "print(classification_report(y_test, y_pred_labels_lr, target_names=['不会购买 (0)', '会购买 (1)']))\n",
    "\n",
    "print(\"\\n混淆矩阵:\")\n",
    "cm_lr = confusion_matrix(y_test, y_pred_labels_lr)\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(cm_lr, annot=True, fmt='d', cmap='Blues', xticklabels=['预测:不会购买', '预测:会购买'],\n",
    "            yticklabels=['实际:不会购买', '实际:会购买'])\n",
    "plt.xlabel('预测标签')\n",
    "plt.ylabel('实际标签')\n",
    "plt.title('混淆矩阵 (逻辑回归)')\n",
    "plt.show()\n",
    "\n",
    "# ROC曲线和AUC\n",
    "fpr_lr, tpr_lr, _ = roc_curve(y_test, y_pred_proba_lr)\n",
    "roc_auc_lr = auc(fpr_lr, tpr_lr)\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(fpr_lr, tpr_lr, color='darkorange', lw=2, label=f'ROC 曲线 (面积 = {roc_auc_lr:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')  # 对角线\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('假正例率 (False Positive Rate)')\n",
    "plt.ylabel('真正例率 (True Positive Rate)')\n",
    "plt.title('ROC 曲线 (逻辑回归)')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "print(f\"AUC值: {roc_auc_lr:.4f}\")"
   ],
   "id": "1d8d2fe502f8a2f7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- 训练自定义逻辑回归模型 ---\n",
      "开始逻辑回归训练，学习率=0.1, 最大迭代次数=2000\n",
      "迭代 0, 损失: 0.6931\n",
      "迭代 200, 损失: 0.2997\n",
      "迭代 400, 损失: 0.2924\n",
      "迭代 531: 损失变化小于容忍度 1e-05，模型已收敛。当前损失: 0.2906\n",
      "逻辑回归训练完成。\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- 逻辑回归模型评估 ---\n",
      "准确率 (Accuracy): 0.8832\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    不会购买 (0)       0.89      0.98      0.93      2084\n",
      "     会购买 (1)       0.75      0.37      0.50       382\n",
      "\n",
      "    accuracy                           0.88      2466\n",
      "   macro avg       0.82      0.67      0.72      2466\n",
      "weighted avg       0.87      0.88      0.87      2466\n",
      "\n",
      "\n",
      "混淆矩阵:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC值: 0.8854\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-02T05:55:53.782706Z",
     "start_time": "2025-06-02T05:55:44.980688Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# --- 从零实现SVM (SMO) ---\n",
    "def linear_kernel(X1, X2):\n",
    "    \"\"\"线性核函数\"\"\"\n",
    "    return np.dot(X1, X2.T)\n",
    "\n",
    "\n",
    "def rbf_kernel(X1, X2, gamma=0.1):\n",
    "    \"\"\"\n",
    "    径向基核函数 (RBF Kernel)。\n",
    "    参数:\n",
    "        X1, X2 (numpy.ndarray): 输入数据。\n",
    "        gamma (float): RBF核的参数。\n",
    "    返回:\n",
    "        numpy.ndarray: 核矩阵。\n",
    "    \"\"\"\n",
    "    if X1.ndim == 1: X1 = X1[np.newaxis, :]  # 确保是二维\n",
    "    if X2.ndim == 1: X2 = X2[np.newaxis, :]\n",
    "    # 使用广播计算平方欧氏距离 (a-b)^2 = a^2 - 2ab + b^2\n",
    "    X1_norm_sq = np.sum(X1 ** 2, axis=1)[:, np.newaxis]\n",
    "    X2_norm_sq = np.sum(X2 ** 2, axis=1)[np.newaxis, :]\n",
    "    K_dot = np.dot(X1, X2.T)\n",
    "    sq_dists = X1_norm_sq + X2_norm_sq - 2 * K_dot\n",
    "    # 确保 sq_dists 中的值非负，避免因浮点数精度问题导致 np.exp 中出现复数\n",
    "    sq_dists = np.maximum(sq_dists, 0)\n",
    "    return np.exp(-gamma * sq_dists)\n",
    "\n",
    "class SMO_SVM:\n",
    "    \"\"\"\n",
    "    使用简化SMO算法实现的SVM分类器。\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, C=1.0, kernel='rbf', gamma='scale', tol=1e-3, max_outer_passes=5):\n",
    "        \"\"\"\n",
    "        初始化函数。\n",
    "        参数:\n",
    "            C (float): 惩罚参数。\n",
    "            kernel (str): 核函数类型 ('rbf' 或 'linear')。\n",
    "            gamma (float or str): RBF核的参数。可以是浮点数，或 'scale', 'auto'。\n",
    "            tol (float): KKT条件检查的容忍度。\n",
    "            max_outer_passes (int): 最大外部迭代轮数。在这些轮数内，如果算法未通过“无alpha改变”收敛，则强制停止。\n",
    "                                    这也是tqdm进度条的总轮数。\n",
    "        \"\"\"\n",
    "        self.C = C\n",
    "        self.tol = tol\n",
    "        self.max_outer_passes = max_outer_passes  # 控制外部循环的最大次数\n",
    "        self._kernel_str = kernel\n",
    "        self.gamma_val = gamma\n",
    "        self.alphas = None  # 拉格朗日乘子\n",
    "        self.b = 0  # 偏置项\n",
    "        self.X = None  # 训练数据特征\n",
    "        self.y_svm = None  # 训练数据标签 (-1, 1)\n",
    "        self.K = None  # 预计算的核矩阵\n",
    "        self.actual_gamma = None  # 实际使用的gamma值\n",
    "\n",
    "    def _get_gamma(self, X):\n",
    "        \"\"\"根据输入和初始化参数计算实际的gamma值。\"\"\"\n",
    "        if isinstance(self.gamma_val, (int, float)):\n",
    "            return self.gamma_val\n",
    "        # 如果X的样本量为0或特征数为0，或者X的方差为0，则返回一个默认值 (如1.0)，避免除零错误\n",
    "        if X.shape[0] * X.shape[1] == 0 or X.var() == 0:\n",
    "            return 1.0\n",
    "        if self.gamma_val == 'scale':\n",
    "            return 1.0 / (X.shape[1] * X.var())\n",
    "        elif self.gamma_val == 'auto':\n",
    "            return 1.0 / X.shape[1]\n",
    "        else:  # 默认情况\n",
    "            print(f\"警告: 未识别的gamma值 '{self.gamma_val}', 将使用默认值 0.1。\")\n",
    "            return 0.1\n",
    "\n",
    "    def _select_kernel(self, X1, X2, gamma_val_runtime):\n",
    "        \"\"\"根据选择的核函数类型计算核矩阵。\"\"\"\n",
    "        if self._kernel_str == 'rbf':\n",
    "            return rbf_kernel(X1, X2, gamma=gamma_val_runtime)\n",
    "        elif self._kernel_str == 'linear':\n",
    "            return linear_kernel(X1, X2)\n",
    "        else:\n",
    "            raise ValueError(f\"不支持的核函数类型: {self._kernel_str}\")\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        \"\"\"\n",
    "        使用SMO算法训练SVM模型。\n",
    "        \"\"\"\n",
    "        self.y_svm = np.where(y <= 0, -1, 1)  # 将标签转换为-1, 1\n",
    "        n_samples, n_features = X.shape\n",
    "        self.X = X\n",
    "\n",
    "        self.actual_gamma = self._get_gamma(X)\n",
    "        print(f\"SVM训练: 使用的gamma值 = {self.actual_gamma:.4f} (基于 '{self.gamma_val}')\")\n",
    "\n",
    "        self.alphas = np.zeros(n_samples)\n",
    "        self.b = 0\n",
    "\n",
    "        print(\"SVM训练: 开始预计算核矩阵...\")\n",
    "        start_kernel_time = time.time()\n",
    "        self.K = self._select_kernel(self.X, self.X, self.actual_gamma)\n",
    "        print(f\"SVM训练: 核矩阵预计算完成，耗时 {time.time() - start_kernel_time:.2f} 秒。\")\n",
    "\n",
    "        # outer_pass_count 控制外部循环，对应tqdm的进度\n",
    "        # converged_passes_count 计连续多少轮没有alpha改变\n",
    "        outer_pass_count = 0\n",
    "\n",
    "        print(f\"SVM训练: 开始SMO迭代，最大外部轮数 = {self.max_outer_passes}\")\n",
    "        with tqdm(total=self.max_outer_passes, desc=\"SVM训练进度\") as pbar:\n",
    "            while outer_pass_count < self.max_outer_passes:\n",
    "                num_changed_alphas = 0  # 当前轮中改变的alpha数量\n",
    "                iter_start_time = time.time()\n",
    "\n",
    "                for i in range(n_samples):  # 内层循环遍历所有样本\n",
    "                    # 计算样本i的误差Ei = f(xi) - yi\n",
    "                    Ei = self._decision_function_single(i) - self.y_svm[i]\n",
    "\n",
    "                    # 检查样本i是否违反KKT条件\n",
    "                    if ((self.y_svm[i] * Ei < -self.tol and self.alphas[i] < self.C) or\n",
    "                            (self.y_svm[i] * Ei > self.tol and self.alphas[i] > 0)):\n",
    "\n",
    "                        # 启发式选择第二个样本j (简化：随机选择一个不为i的样本)\n",
    "                        j_candidates = [idx for idx in range(n_samples) if idx != i]\n",
    "                        if not j_candidates: continue  # 如果只有一个样本，无法选择j\n",
    "                        j = np.random.choice(j_candidates)\n",
    "\n",
    "                        Ej = self._decision_function_single(j) - self.y_svm[j]\n",
    "\n",
    "                        alpha_i_old, alpha_j_old = self.alphas[i].copy(), self.alphas[j].copy()\n",
    "\n",
    "                        # 计算alpha_j的边界L和H\n",
    "                        if self.y_svm[i] != self.y_svm[j]:\n",
    "                            L = max(0, self.alphas[j] - self.alphas[i])\n",
    "                            H = min(self.C, self.C + self.alphas[j] - self.alphas[i])\n",
    "                        else:\n",
    "                            L = max(0, self.alphas[i] + self.alphas[j] - self.C)\n",
    "                            H = min(self.C, self.alphas[i] + self.alphas[j])\n",
    "\n",
    "                        if L == H: continue  # 如果L和H相等，无法优化\n",
    "\n",
    "                        # 计算eta = Kii + Kjj - 2Kij\n",
    "                        eta = self.K[i, i] + self.K[j, j] - 2 * self.K[i, j]\n",
    "                        if eta <= 0: continue  # 如果eta非正，无法优化 (通常eta > 0)\n",
    "\n",
    "                        # 更新alpha_j\n",
    "                        self.alphas[j] += self.y_svm[j] * (Ei - Ej) / eta\n",
    "                        self.alphas[j] = np.clip(self.alphas[j], L, H)  # 裁剪到边界内\n",
    "\n",
    "                        # 如果alpha_j的变化很小，则忽略此次更新\n",
    "                        if abs(self.alphas[j] - alpha_j_old) < 1e-5:\n",
    "                            continue\n",
    "\n",
    "                        # 更新alpha_i\n",
    "                        self.alphas[i] += self.y_svm[i] * self.y_svm[j] * (alpha_j_old - self.alphas[j])\n",
    "\n",
    "                        # 更新偏置b\n",
    "                        b1 = self.b - Ei - self.y_svm[i] * (self.alphas[i] - alpha_i_old) * self.K[i, i] \\\n",
    "                             - self.y_svm[j] * (self.alphas[j] - alpha_j_old) * self.K[i, j]\n",
    "                        b2 = self.b - Ej - self.y_svm[i] * (self.alphas[i] - alpha_i_old) * self.K[i, j] \\\n",
    "                             - self.y_svm[j] * (self.alphas[j] - alpha_j_old) * self.K[j, j]\n",
    "\n",
    "                        if 0 < self.alphas[i] < self.C:\n",
    "                            self.b = b1\n",
    "                        elif 0 < self.alphas[j] < self.C:\n",
    "                            self.b = b2\n",
    "                        else:\n",
    "                            self.b = (b1 + b2) / 2\n",
    "\n",
    "                        num_changed_alphas += 1\n",
    "\n",
    "                pbar.update(1)  # 更新tqdm进度条\n",
    "                outer_pass_count += 1  # 外部迭代轮数增加\n",
    "\n",
    "                iter_time = time.time() - iter_start_time\n",
    "                pbar.set_postfix_str(f\"本轮alpha改变数: {num_changed_alphas}, 耗时: {iter_time:.2f}s\")\n",
    "\n",
    "                if num_changed_alphas == 0:\n",
    "                    print(f\"\\nSVM训练: 在第 {outer_pass_count} 轮外部迭代后，没有alpha发生改变，模型已收敛。\")\n",
    "                    break  # 收敛，提前退出\n",
    "\n",
    "            if outer_pass_count >= self.max_outer_passes and num_changed_alphas > 0:\n",
    "                print(\n",
    "                    f\"\\nSVM训练: 已达到最大外部迭代轮数 ({self.max_outer_passes})，最后一轮仍有 {num_changed_alphas} 个alpha改变。\")\n",
    "\n",
    "        sv_indices = np.where(self.alphas > 1e-5)[0]\n",
    "        print(f\"SVM训练完成。共找到 {len(sv_indices)} 个支持向量。\")\n",
    "\n",
    "    def _decision_function_single(self, i):\n",
    "        \"\"\"计算训练样本i的决策函数值 f(xi)，使用预计算的核矩阵。\"\"\"\n",
    "        return np.sum(self.alphas * self.y_svm * self.K[:, i]) + self.b\n",
    "\n",
    "    def project(self, X_test):\n",
    "        \"\"\"\n",
    "        计算新数据点的决策函数值 (即wx+b的值，或在核空间中的对应值)。\n",
    "        参数:\n",
    "            X_test (numpy.ndarray): 测试数据特征。\n",
    "        返回:\n",
    "            numpy.ndarray: 测试数据的决策函数值。\n",
    "        \"\"\"\n",
    "        if self.X is None: raise ValueError(\"SVM模型尚未训练 (X is None)。\")\n",
    "        # 计算测试数据与所有训练支持向量之间的核函数\n",
    "        K_test = self._select_kernel(self.X, X_test, self.actual_gamma)\n",
    "        # 决策函数: sum(alpha_i * y_i * K(x_i, x_test)) + b\n",
    "        return np.dot(self.alphas * self.y_svm, K_test) + self.b\n",
    "\n",
    "    def predict(self, X_test):\n",
    "        \"\"\"\n",
    "        预测测试数据的类别标签。\n",
    "        参数:\n",
    "            X_test (numpy.ndarray): 测试数据特征。\n",
    "        返回:\n",
    "            numpy.ndarray: 预测的类别标签 (0 或 1)。\n",
    "        \"\"\"\n",
    "        # 决策函数值大于等于0判为正类(1)，否则为负类(0，对应原始的-1)\n",
    "        return np.where(self.project(X_test) >= 0, 1, 0)\n",
    "\n",
    "\n",
    "# --- 训练自定义SVM模型 ---\n",
    "print(\"\\n--- 训练自定义SVM模型 ---\")\n",
    "# emm从零实现的SMO会比较慢，尤其这个数据集好大噢。为了演示，max_outer_passes设置得较小。\n",
    "svm_model = SMO_SVM(C=1.0, kernel='rbf', gamma='scale', tol=1e-3, max_outer_passes=3)  # max_outer_passes 较小以加速\n",
    "svm_model.fit(X_train, y_train)  # y_train 是 0/1\n",
    "\n",
    "# --- 进行预测 ---\n",
    "y_project_svm = svm_model.project(X_test)  # 获取决策函数值用于ROC\n",
    "y_pred_labels_svm = svm_model.predict(X_test)  # 获取类别标签\n",
    "\n",
    "print(\"\\n--- SVM模型评估 ---\")\n",
    "accuracy_svm = accuracy_score(y_test, y_pred_labels_svm)\n",
    "print(f\"准确率 (Accuracy): {accuracy_svm:.4f}\")\n",
    "print(\"\\n分类报告:\")\n",
    "# zero_division=0 避免在某个类别没有预测样本时产生警告\n",
    "print(classification_report(y_test, y_pred_labels_svm, target_names=['不会购买 (0)', '会购买 (1)'], zero_division=0))\n",
    "\n",
    "print(\"\\n混淆矩阵:\")\n",
    "cm_svm = confusion_matrix(y_test, y_pred_labels_svm)\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(cm_svm, annot=True, fmt='d', cmap='Blues', xticklabels=['预测:不会购买', '预测:会购买'],\n",
    "            yticklabels=['实际:不会购买', '实际:会购买'])\n",
    "plt.xlabel('预测标签')\n",
    "plt.ylabel('实际标签')\n",
    "plt.title('混淆矩阵 (SVM)')\n",
    "plt.show()\n",
    "\n",
    "# ROC曲线和AUC (使用决策函数值 y_project_svm)\n",
    "fpr_svm, tpr_svm, _ = roc_curve(y_test, y_project_svm)\n",
    "roc_auc_svm = auc(fpr_svm, tpr_svm)\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(fpr_svm, tpr_svm, color='darkorange', lw=2, label=f'ROC 曲线 (面积 = {roc_auc_svm:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('假正例率 (False Positive Rate)')\n",
    "plt.ylabel('真正例率 (True Positive Rate)')\n",
    "plt.title('ROC 曲线 (SVM)')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "print(f\"AUC值: {roc_auc_svm:.4f}\")"
   ],
   "id": "3c81d82685181328",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- 训练自定义SVM模型 ---\n",
      "SVM训练: 使用的gamma值 = 0.0678 (基于 'scale')\n",
      "SVM训练: 开始预计算核矩阵...\n",
      "SVM训练: 核矩阵预计算完成，耗时 0.63 秒。\n",
      "SVM训练: 开始SMO迭代，最大外部轮数 = 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "SVM训练进度: 100%|██████████| 3/3 [00:07<00:00,  2.51s/it, 本轮alpha改变数: 989, 耗时: 2.42s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SVM训练: 已达到最大外部迭代轮数 (3)，最后一轮仍有 989 个alpha改变。\n",
      "SVM训练完成。共找到 2479 个支持向量。\n",
      "\n",
      "--- SVM模型评估 ---\n",
      "准确率 (Accuracy): 0.8500\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    不会购买 (0)       0.85      1.00      0.92      2084\n",
      "     会购买 (1)       0.93      0.03      0.07       382\n",
      "\n",
      "    accuracy                           0.85      2466\n",
      "   macro avg       0.89      0.52      0.49      2466\n",
      "weighted avg       0.86      0.85      0.79      2466\n",
      "\n",
      "\n",
      "混淆矩阵:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC值: 0.8823\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-02T05:55:55.281873Z",
     "start_time": "2025-06-02T05:55:53.806272Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# --- 从零实现决策树 ---\n",
    "class MyDecisionTreeClassifier:\n",
    "    \"\"\"\n",
    "    自定义决策树分类器。\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, max_depth=None, min_samples_split=2, criterion='gini', max_features=None):\n",
    "        \"\"\"\n",
    "        初始化函数。\n",
    "        参数:\n",
    "            max_depth (int, optional): 树的最大深度。\n",
    "            min_samples_split (int): 节点分裂所需的最小样本数。\n",
    "            criterion (str): 不纯度度量标准 ('gini' 或 'entropy')。\n",
    "            max_features (int, float, str, optional): 寻找最佳分裂时考虑的特征数量。\n",
    "                                                      用于随机森林中的特征子抽样。\n",
    "        \"\"\"\n",
    "        self.max_depth = max_depth\n",
    "        self.min_samples_split = min_samples_split\n",
    "        self.criterion = criterion\n",
    "        self.tree = None  # 训练好的决策树结构\n",
    "        self.max_features = max_features  # 用于随机森林的特征子集大小\n",
    "\n",
    "    def _calculate_impurity(self, y):\n",
    "        \"\"\"计算给定目标标签的不纯度（基尼指数或熵）。\"\"\"\n",
    "        if len(y) == 0: return 0\n",
    "        class_counts = Counter(y)  # 统计各类别数量\n",
    "        total_samples = len(y)\n",
    "        impurity = 0\n",
    "        if self.criterion == 'gini':  # 基尼指数 = sum(pk * (1-pk))\n",
    "            for cls_count in class_counts.values():\n",
    "                p_k = cls_count / total_samples\n",
    "                impurity += p_k * (1 - p_k)\n",
    "            return impurity\n",
    "        elif self.criterion == 'entropy':  # 信息熵 = -sum(pk * log2(pk))\n",
    "            for cls_count in class_counts.values():\n",
    "                p_k = cls_count / total_samples\n",
    "                if p_k > 0: impurity -= p_k * np.log2(p_k)\n",
    "            return impurity\n",
    "        else:\n",
    "            raise ValueError(f\"未知的评价标准 (criterion): {self.criterion}\")\n",
    "\n",
    "    def _calculate_information_gain(self, X_column, y, threshold):\n",
    "        \"\"\"计算给定特征列和阈值下的信息增益。\"\"\"\n",
    "        parent_impurity = self._calculate_impurity(y)  # 父节点不纯度\n",
    "\n",
    "        # 根据阈值划分数据\n",
    "        left_indices = X_column <= threshold\n",
    "        right_indices = X_column > threshold\n",
    "        y_left, y_right = y[left_indices], y[right_indices]\n",
    "\n",
    "        if len(y_left) == 0 or len(y_right) == 0: return 0  # 如果划分导致一个子集为空，则增益为0\n",
    "\n",
    "        n, n_left, n_right = len(y), len(y_left), len(y_right)\n",
    "        # 子节点加权不纯度\n",
    "        child_impurity = (n_left / n) * self._calculate_impurity(y_left) + \\\n",
    "                         (n_right / n) * self._calculate_impurity(y_right)\n",
    "\n",
    "        information_gain = parent_impurity - child_impurity  # 信息增益\n",
    "        return information_gain\n",
    "\n",
    "    def _find_best_split(self, X, y):\n",
    "        \"\"\"寻找最佳分裂特征和阈值。\"\"\"\n",
    "        best_gain = -1.0  # 初始化为负值，确保任何正增益都更好\n",
    "        best_feature_idx, best_threshold = None, None\n",
    "        n_samples, n_features_total = X.shape\n",
    "\n",
    "        # 特征子抽样 (用于随机森林)\n",
    "        feature_indices_to_consider = np.arange(n_features_total)\n",
    "        if self.max_features is not None and self.max_features < n_features_total:\n",
    "            # 根据 self.max_features (可能是整数、浮点数比例或字符串如 'sqrt') 确定抽样数量\n",
    "            if isinstance(self.max_features, float) and 0 < self.max_features < 1:\n",
    "                num_feat_to_sample = int(self.max_features * n_features_total)\n",
    "            elif isinstance(self.max_features, int):\n",
    "                num_feat_to_sample = self.max_features\n",
    "            # (可以添加 'sqrt', 'log2' 等逻辑，但这里简化为需要预先计算好的整数值)\n",
    "            else:  # 默认或无法解析时，使用所有特征\n",
    "                num_feat_to_sample = n_features_total\n",
    "\n",
    "            num_feat_to_sample = max(1, min(num_feat_to_sample, n_features_total))  # 保证在[1, n_features_total]\n",
    "            feature_indices_to_consider = np.random.choice(n_features_total, num_feat_to_sample, replace=False)\n",
    "\n",
    "        for feature_idx in feature_indices_to_consider:\n",
    "            X_column = X[:, feature_idx]\n",
    "            # 对于数值特征，可能的阈值是排序后唯一值的中间点\n",
    "            # 简化做法: 尝试每个唯一值作为阈值 (或它们之间的中点)\n",
    "            thresholds = np.unique(X_column)\n",
    "            if len(thresholds) > 10:  # 如果唯一值过多，抽样一部分阈值避免计算量过大\n",
    "                # 使用百分位数生成阈值候选项\n",
    "                percentiles = np.arange(10, 100, 10)  # 10%, 20%, ..., 90%\n",
    "                thresholds = np.percentile(X_column, percentiles)\n",
    "                thresholds = np.unique(thresholds)  # 去除重复值\n",
    "\n",
    "            for threshold in thresholds:\n",
    "                gain = self._calculate_information_gain(X_column, y, threshold)\n",
    "                if gain > best_gain:\n",
    "                    best_gain = gain\n",
    "                    best_feature_idx = feature_idx\n",
    "                    best_threshold = threshold\n",
    "        return best_feature_idx, best_threshold, best_gain\n",
    "\n",
    "    def _build_tree(self, X, y, depth=0):\n",
    "        \"\"\"递归构建决策树。\"\"\"\n",
    "        n_samples, n_features = X.shape\n",
    "        n_labels = len(np.unique(y))  # 当前节点中类别的数量\n",
    "\n",
    "        # 停止条件\n",
    "        if (self.max_depth is not None and depth >= self.max_depth) or \\\n",
    "                n_labels == 1 or \\\n",
    "                n_samples < self.min_samples_split:\n",
    "            # 叶节点：值为多数类，并存储类别概率\n",
    "            leaf_value = Counter(y).most_common(1)[0][0]\n",
    "            leaf_proba = {cls: count / n_samples for cls, count in Counter(y).items()}\n",
    "            return {'value': leaf_value, 'proba': leaf_proba, 'is_leaf': True, 'samples': n_samples, 'depth': depth}\n",
    "\n",
    "        best_feature_idx, best_threshold, best_gain = self._find_best_split(X, y)\n",
    "\n",
    "        # 如果信息增益很小 (例如，小于0.001)，也停止分裂 (预剪枝，避免过拟合)\n",
    "        current_proba = {cls: count / n_samples for cls, count in Counter(y).items()}  # 当前节点的类别概率\n",
    "        if best_gain <= 1e-4:  # 可调参数\n",
    "            leaf_value = Counter(y).most_common(1)[0][0]\n",
    "            return {'value': leaf_value, 'proba': current_proba, 'is_leaf': True, 'samples': n_samples, 'depth': depth}\n",
    "\n",
    "        # 划分数据集\n",
    "        left_indices = X[:, best_feature_idx] <= best_threshold\n",
    "        right_indices = X[:, best_feature_idx] > best_threshold\n",
    "        X_left, y_left = X[left_indices], y[left_indices]\n",
    "        X_right, y_right = X[right_indices], y[right_indices]\n",
    "\n",
    "        # 确保子集非空，如果一个子集为空，则无法继续分裂，当前节点成为叶节点\n",
    "        if len(y_left) == 0 or len(y_right) == 0:\n",
    "            leaf_value = Counter(y).most_common(1)[0][0]\n",
    "            return {'value': leaf_value, 'proba': current_proba, 'is_leaf': True, 'samples': n_samples, 'depth': depth}\n",
    "\n",
    "        # 递归构建左右子树\n",
    "        left_subtree = self._build_tree(X_left, y_left, depth + 1)\n",
    "        right_subtree = self._build_tree(X_right, y_right, depth + 1)\n",
    "\n",
    "        # 返回内部节点信息\n",
    "        return {\n",
    "            'feature_index': best_feature_idx, 'threshold': best_threshold,\n",
    "            'left': left_subtree, 'right': right_subtree, 'is_leaf': False,\n",
    "            'info_gain': best_gain, 'samples': n_samples, 'proba': current_proba, 'depth': depth\n",
    "        }\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        \"\"\"训练决策树模型。\"\"\"\n",
    "        if self.verbose: print(f\"开始决策树训练，最大深度={self.max_depth}, 最小分裂样本数={self.min_samples_split}\")\n",
    "        self.tree = self._build_tree(X, y)\n",
    "        if self.verbose: print(\"决策树训练完成。\")\n",
    "\n",
    "    def _traverse_tree(self, x, node, get_proba=False):\n",
    "        \"\"\"遍历决策树进行预测。\"\"\"\n",
    "        if node['is_leaf']:  # 如果是叶节点\n",
    "            if get_proba:\n",
    "                # 返回正类（假设类别1为正类）的概率\n",
    "                return node['proba'].get(1, 0.0)  # 如果叶节点中没有类别1，则概率为0\n",
    "            return node['value']  # 返回叶节点的预测值\n",
    "\n",
    "        # 如果是内部节点，根据特征值选择分支\n",
    "        if x[node['feature_index']] <= node['threshold']:\n",
    "            return self._traverse_tree(x, node['left'], get_proba)\n",
    "        else:\n",
    "            return self._traverse_tree(x, node['right'], get_proba)\n",
    "\n",
    "    def predict(self, X):\n",
    "        \"\"\"预测类别标签。\"\"\"\n",
    "        return np.array([self._traverse_tree(x, self.tree, get_proba=False) for x in X])\n",
    "\n",
    "    def predict_proba(self, X):\n",
    "        \"\"\"预测每个样本属于正类（类别1）的概率。\"\"\"\n",
    "        return np.array([self._traverse_tree(x, self.tree, get_proba=True) for x in X])\n",
    "\n",
    "    # 添加 verbose 属性，虽然在 fit 中没有直接使用，但可以被外部调用者设置\n",
    "    verbose = False\n",
    "\n",
    "\n",
    "# --- 训练自定义决策树模型 ---\n",
    "print(\"\\n--- 训练自定义决策树模型 ---\")\n",
    "tree_model = MyDecisionTreeClassifier(max_depth=7, min_samples_split=10, criterion='gini')\n",
    "tree_model.verbose = True  # 开启详细输出\n",
    "tree_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred_proba_tree = tree_model.predict_proba(X_test)  # 获取概率用于ROC曲线\n",
    "y_pred_labels_tree = tree_model.predict(X_test)  # 获取类别标签\n",
    "\n",
    "print(\"\\n--- 决策树模型评估 ---\")\n",
    "accuracy_tree = accuracy_score(y_test, y_pred_labels_tree)\n",
    "print(f\"准确率 (Accuracy): {accuracy_tree:.4f}\")\n",
    "print(\"\\n分类报告:\")\n",
    "print(classification_report(y_test, y_pred_labels_tree, target_names=['不会购买 (0)', '会购买 (1)']))\n",
    "\n",
    "print(\"\\n混淆矩阵:\")\n",
    "cm_tree = confusion_matrix(y_test, y_pred_labels_tree)\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(cm_tree, annot=True, fmt='d', cmap='Blues', xticklabels=['预测:不会购买', '预测:会购买'],\n",
    "            yticklabels=['实际:不会购买', '实际:会购买'])\n",
    "plt.xlabel('预测标签')\n",
    "plt.ylabel('实际标签')\n",
    "plt.title('混淆矩阵 (决策树)')\n",
    "plt.show()\n",
    "\n",
    "# ROC曲线和AUC\n",
    "fpr_tree, tpr_tree, _ = roc_curve(y_test, y_pred_proba_tree)  # 使用概率\n",
    "roc_auc_tree = auc(fpr_tree, tpr_tree)\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(fpr_tree, tpr_tree, color='darkorange', lw=2, label=f'ROC 曲线 (面积 = {roc_auc_tree:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('假正例率 (False Positive Rate)')\n",
    "plt.ylabel('真正例率 (True Positive Rate)')\n",
    "plt.title('ROC 曲线 (决策树)')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "print(f\"AUC值: {roc_auc_tree:.4f}\")"
   ],
   "id": "5c6a5dd68e0a1e3f",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- 训练自定义决策树模型 ---\n",
      "开始决策树训练，最大深度=7, 最小分裂样本数=10\n",
      "决策树训练完成。\n",
      "\n",
      "--- 决策树模型评估 ---\n",
      "准确率 (Accuracy): 0.8954\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    不会购买 (0)       0.92      0.96      0.94      2084\n",
      "     会购买 (1)       0.70      0.57      0.63       382\n",
      "\n",
      "    accuracy                           0.90      2466\n",
      "   macro avg       0.81      0.76      0.78      2466\n",
      "weighted avg       0.89      0.90      0.89      2466\n",
      "\n",
      "\n",
      "混淆矩阵:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC值: 0.9028\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-02T05:55:55.516619Z",
     "start_time": "2025-06-02T05:55:55.299878Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# --- 从零实现高斯朴素贝叶斯 ---\n",
    "class MyGaussianNaiveBayes:\n",
    "    \"\"\"\n",
    "    自定义高斯朴素贝叶斯分类器。\n",
    "    假设特征服从高斯分布。\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, verbose=False):\n",
    "        self.class_priors_ = None  # 各类别的先验概率 P(Y=c)\n",
    "        self.class_means_ = None  # 各类别下各特征的均值 E[Xj | Y=c]\n",
    "        self.class_vars_ = None  # 各类别下各特征的方差 Var(Xj | Y=c)\n",
    "        self.classes_ = None  # 所有类别标签\n",
    "        self.epsilon = 1e-9  # 防止除以零或log(0)的小常数\n",
    "        self.verbose = verbose\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        \"\"\"\n",
    "        训练高斯朴素贝叶斯模型。\n",
    "        计算先验概率、各类别下特征的均值和方差。\n",
    "        \"\"\"\n",
    "        if self.verbose: print(\"开始高斯朴素贝叶斯训练...\")\n",
    "        n_samples, n_features = X.shape\n",
    "        self.classes_ = np.unique(y)  # 获取所有唯一类别\n",
    "        n_classes = len(self.classes_)\n",
    "\n",
    "        # 初始化存储参数的数组\n",
    "        self.class_priors_ = np.zeros(n_classes)\n",
    "        self.class_means_ = np.zeros((n_classes, n_features))\n",
    "        self.class_vars_ = np.zeros((n_classes, n_features))\n",
    "\n",
    "        for idx, c in enumerate(self.classes_):\n",
    "            X_c = X[y == c]  # 取出类别c的所有样本\n",
    "            self.class_priors_[idx] = X_c.shape[0] / n_samples  # P(Y=c) = Nc / N\n",
    "            self.class_means_[idx, :] = X_c.mean(axis=0)  # E[Xj | Y=c]\n",
    "            self.class_vars_[idx, :] = X_c.var(axis=0) + self.epsilon  # Var(Xj | Y=c)，加epsilon防方差为0\n",
    "\n",
    "        if self.verbose: print(\"高斯朴素贝叶斯训练完成。\")\n",
    "\n",
    "    def _calculate_log_likelihood(self, class_idx, x_row):\n",
    "        \"\"\"计算给定样本x_row在类别class_idx下的对数似然 log(P(X=x_row | Y=c))。\"\"\"\n",
    "        mean = self.class_means_[class_idx]\n",
    "        var = self.class_vars_[class_idx]\n",
    "        # 高斯分布的对数概率密度函数 log(P(xj | Y=c))\n",
    "        # log(P) = -0.5 * log(2*pi*var) - 0.5 * ( (x - mean)^2 / var )\n",
    "        # 对所有特征求和 (因为朴素贝叶斯假设特征条件独立)\n",
    "        log_likelihood_terms = -0.5 * np.log(2 * np.pi * var) - 0.5 * ((x_row - mean) ** 2) / var\n",
    "        return np.sum(log_likelihood_terms)\n",
    "\n",
    "    def _calculate_log_posterior(self, x_row):\n",
    "        \"\"\"计算给定样本x_row属于每个类别的对数后验概率 log(P(Y=c | X=x_row))。\"\"\"\n",
    "        log_posteriors = []\n",
    "        for idx, c in enumerate(self.classes_):\n",
    "            log_prior = np.log(self.class_priors_[idx] + self.epsilon)  # log(P(Y=c))\n",
    "            log_likelihood = self._calculate_log_likelihood(idx, x_row)  # log(P(X=x_row | Y=c))\n",
    "            log_posteriors.append(log_prior + log_likelihood)  # log(P(Y=c|X)) propto log(P(Y=c)) + log(P(X|Y=c))\n",
    "        return np.array(log_posteriors)\n",
    "\n",
    "    def predict_proba(self, X):\n",
    "        \"\"\"\n",
    "        预测每个样本属于正类（类别1）的概率。\n",
    "        使用log-sum-exp技巧进行归一化，避免数值下溢/上溢。\n",
    "        \"\"\"\n",
    "        # 获取所有样本对于所有类别的对数后验概率（未归一化）\n",
    "        log_posteriors_all = np.array([self._calculate_log_posterior(x_row) for x_row in X])\n",
    "\n",
    "        # 使用log-sum-exp技巧进行归一化得到概率\n",
    "        max_log = np.max(log_posteriors_all, axis=1, keepdims=True)  # 每行的最大值\n",
    "        log_posteriors_shifted = log_posteriors_all - max_log  # 平移，使得最大值为0\n",
    "        exp_log_posteriors_shifted = np.exp(log_posteriors_shifted)  # 计算指数\n",
    "        sum_exp = np.sum(exp_log_posteriors_shifted, axis=1, keepdims=True)  # 每行求和\n",
    "        probabilities = exp_log_posteriors_shifted / sum_exp  # 归一化得到概率\n",
    "\n",
    "        # 假设是二分类，并且类别1是正类，通常在self.classes_的索引1处\n",
    "        # 如果self.classes_是 [0, 1]，那么probabilities[:, 1] 是类别1的概率\n",
    "        # 需要找到类别1在self.classes_中的索引\n",
    "        idx_of_class_1 = np.where(self.classes_ == 1)[0]\n",
    "        if len(idx_of_class_1) > 0:\n",
    "            return probabilities[:, idx_of_class_1[0]]\n",
    "        elif probabilities.shape[1] == 2:  # 如果只有两列且没找到1，默认第二列是正类\n",
    "            return probabilities[:, 1]\n",
    "        else:  # 多分类或单分类的特殊情况 (对于ROC通常只关心二分类的正类概率)\n",
    "            print(\"警告: 无法明确识别正类(1)的概率，将返回所有类别的概率。ROC曲线可能不准确。\")\n",
    "            return probabilities  # 或者只返回第一列作为示例\n",
    "\n",
    "    def predict(self, X):\n",
    "        \"\"\"根据最大的对数后验概率预测类别标签。\"\"\"\n",
    "        predictions = []\n",
    "        for x_row in X:\n",
    "            log_posteriors = self._calculate_log_posterior(x_row)\n",
    "            # 选择具有最大对数后验概率的类别\n",
    "            predictions.append(self.classes_[np.argmax(log_posteriors)])\n",
    "        return np.array(predictions)\n",
    "\n",
    "\n",
    "# --- 训练自定义朴素贝叶斯模型 ---\n",
    "print(\"\\n--- 训练自定义高斯朴素贝叶斯模型 ---\")\n",
    "nb_model = MyGaussianNaiveBayes(verbose=True)\n",
    "nb_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred_proba_nb = nb_model.predict_proba(X_test)  # 获取概率用于ROC曲线\n",
    "y_pred_labels_nb = nb_model.predict(X_test)  # 获取类别标签\n",
    "\n",
    "print(\"\\n--- 高斯朴素贝叶斯模型评估 ---\")\n",
    "accuracy_nb = accuracy_score(y_test, y_pred_labels_nb)\n",
    "print(f\"准确率 (Accuracy): {accuracy_nb:.4f}\")\n",
    "print(\"\\n分类报告:\")\n",
    "print(classification_report(y_test, y_pred_labels_nb, target_names=['不会购买 (0)', '会购买 (1)']))\n",
    "\n",
    "print(\"\\n混淆矩阵:\")\n",
    "cm_nb = confusion_matrix(y_test, y_pred_labels_nb)\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(cm_nb, annot=True, fmt='d', cmap='Blues', xticklabels=['预测:不会购买', '预测:会购买'],\n",
    "            yticklabels=['实际:不会购买', '实际:会购买'])\n",
    "plt.xlabel('预测标签')\n",
    "plt.ylabel('实际标签')\n",
    "plt.title('混淆矩阵 (高斯朴素贝叶斯)')\n",
    "plt.show()\n",
    "\n",
    "# ROC曲线和AUC\n",
    "fpr_nb, tpr_nb, _ = roc_curve(y_test, y_pred_proba_nb)  # 使用概率\n",
    "roc_auc_nb = auc(fpr_nb, tpr_nb)\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(fpr_nb, tpr_nb, color='darkorange', lw=2, label=f'ROC 曲线 (面积 = {roc_auc_nb:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('假正例率 (False Positive Rate)')\n",
    "plt.ylabel('真正例率 (True Positive Rate)')\n",
    "plt.title('ROC 曲线 (高斯朴素贝叶斯)')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "print(f\"AUC值: {roc_auc_nb:.4f}\")"
   ],
   "id": "861f61020e0a034",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- 训练自定义高斯朴素贝叶斯模型 ---\n",
      "开始高斯朴素贝叶斯训练...\n",
      "高斯朴素贝叶斯训练完成。\n",
      "\n",
      "--- 高斯朴素贝叶斯模型评估 ---\n",
      "准确率 (Accuracy): 0.2733\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    不会购买 (0)       0.97      0.14      0.25      2084\n",
      "     会购买 (1)       0.17      0.97      0.29       382\n",
      "\n",
      "    accuracy                           0.27      2466\n",
      "   macro avg       0.57      0.56      0.27      2466\n",
      "weighted avg       0.84      0.27      0.26      2466\n",
      "\n",
      "\n",
      "混淆矩阵:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC值: 0.7597\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-02T05:58:06.636636Z",
     "start_time": "2025-06-02T05:55:55.548908Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# --- 从零实现K-Nearest Neighbors ---\n",
    "class MyKNearestNeighbors:\n",
    "    \"\"\"\n",
    "    自定义K近邻分类器。\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, k=3, verbose=False):\n",
    "        \"\"\"\n",
    "        初始化函数。\n",
    "        参数:\n",
    "            k (int): 邻居的数量。\n",
    "            verbose (bool): 是否在预测时显示进度条。\n",
    "        \"\"\"\n",
    "        self.k = k\n",
    "        self.X_train = None  # 训练数据特征\n",
    "        self.y_train = None  # 训练数据标签\n",
    "        self.verbose = verbose\n",
    "\n",
    "    def fit(self, X_train, y_train):\n",
    "        \"\"\"存储训练数据。KNN是懒惰学习算法，fit阶段只存储数据。\"\"\"\n",
    "        if self.verbose: print(f\"KNN fit: 存储训练数据，k={self.k}\")\n",
    "        self.X_train = X_train\n",
    "        self.y_train = y_train\n",
    "        if self.verbose: print(\"KNN fit 完成。\")\n",
    "\n",
    "    def _euclidean_distance(self, x1, x2):\n",
    "        \"\"\"计算两个样本点之间的欧氏距离。\"\"\"\n",
    "        return np.sqrt(np.sum((x1 - x2) ** 2))\n",
    "\n",
    "    def _get_k_neighbors_labels(self, x_test_sample):\n",
    "        \"\"\"获取单个测试样本的k个最近邻的标签。\"\"\"\n",
    "        # 计算测试样本与所有训练样本的距离\n",
    "        distances = [self._euclidean_distance(x_test_sample, x_train_sample) for x_train_sample in self.X_train]\n",
    "        # 获取距离最近的k个训练样本的索引\n",
    "        k_indices = np.argsort(distances)[:self.k]\n",
    "        # 获取这些最近邻的标签\n",
    "        k_nearest_labels = [self.y_train[i] for i in k_indices]\n",
    "        return k_nearest_labels\n",
    "\n",
    "    def _predict_single(self, x_test_sample):\n",
    "        \"\"\"预测单个测试样本的类别（通过多数投票）。\"\"\"\n",
    "        k_nearest_labels = self._get_k_neighbors_labels(x_test_sample)\n",
    "        # 多数投票决定类别\n",
    "        most_common = Counter(k_nearest_labels).most_common(1)\n",
    "        return most_common[0][0]\n",
    "\n",
    "    def _predict_proba_single(self, x_test_sample):\n",
    "        \"\"\"预测单个测试样本属于正类（类别1）的概率。\"\"\"\n",
    "        k_nearest_labels = self._get_k_neighbors_labels(x_test_sample)\n",
    "        # 计算k个近邻中类别1的比例作为概率\n",
    "        prob_positive = sum(1 for label in k_nearest_labels if label == 1) / self.k\n",
    "        return prob_positive\n",
    "\n",
    "    def predict(self, X_test):\n",
    "        \"\"\"预测测试数据集的类别标签。\"\"\"\n",
    "        if self.verbose:\n",
    "            print(\"KNN predict: 开始预测...\")\n",
    "            predictions = [self._predict_single(x_test_sample) for x_test_sample in tqdm(X_test, desc=\"KNN 预测中\")]\n",
    "        else:\n",
    "            predictions = [self._predict_single(x_test_sample) for x_test_sample in X_test]\n",
    "        if self.verbose: print(\"KNN predict 完成。\")\n",
    "        return np.array(predictions)\n",
    "\n",
    "    def predict_proba(self, X_test):\n",
    "        \"\"\"预测测试数据集每个样本属于正类（类别1）的概率。\"\"\"\n",
    "        if self.verbose:\n",
    "            print(\"KNN predict_proba: 开始预测概率...\")\n",
    "            probabilities = [self._predict_proba_single(x_test_sample) for x_test_sample in\n",
    "                             tqdm(X_test, desc=\"KNN 预测概率中\")]\n",
    "        else:\n",
    "            probabilities = [self._predict_proba_single(x_test_sample) for x_test_sample in X_test]\n",
    "        if self.verbose: print(\"KNN predict_proba 完成。\")\n",
    "        return np.array(probabilities)\n",
    "\n",
    "\n",
    "# --- 训练自定义KNN模型 ---\n",
    "print(\"\\n--- 训练自定义KNN模型 ---\")\n",
    "knn_model = MyKNearestNeighbors(k=15, verbose=True)  # k值可以调整，增加verbose\n",
    "knn_model.fit(X_train, y_train)\n",
    "\n",
    "y_pred_proba_knn = knn_model.predict_proba(X_test)  # 获取概率用于ROC曲线\n",
    "y_pred_labels_knn = knn_model.predict(X_test)  # 获取类别标签\n",
    "\n",
    "print(\"\\n--- KNN模型评估 ---\")\n",
    "accuracy_knn = accuracy_score(y_test, y_pred_labels_knn)\n",
    "print(f\"准确率 (Accuracy): {accuracy_knn:.4f}\")\n",
    "print(\"\\n分类报告:\")\n",
    "print(classification_report(y_test, y_pred_labels_knn, target_names=['不会购买 (0)', '会购买 (1)']))\n",
    "\n",
    "print(\"\\n混淆矩阵:\")\n",
    "cm_knn = confusion_matrix(y_test, y_pred_labels_knn)\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(cm_knn, annot=True, fmt='d', cmap='Blues', xticklabels=['预测:不会购买', '预测:会购买'],\n",
    "            yticklabels=['实际:不会购买', '实际:会购买'])\n",
    "plt.xlabel('预测标签')\n",
    "plt.ylabel('实际标签')\n",
    "plt.title('混淆矩阵 (KNN)')\n",
    "plt.show()\n",
    "\n",
    "# ROC曲线和AUC\n",
    "fpr_knn, tpr_knn, _ = roc_curve(y_test, y_pred_proba_knn)  # 使用概率\n",
    "roc_auc_knn = auc(fpr_knn, tpr_knn)\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(fpr_knn, tpr_knn, color='darkorange', lw=2, label=f'ROC 曲线 (面积 = {roc_auc_knn:.2f})')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('假正例率 (False Positive Rate)')\n",
    "plt.ylabel('真正例率 (True Positive Rate)')\n",
    "plt.title('ROC 曲线 (KNN)')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "print(f\"AUC值: {roc_auc_knn:.4f}\")"
   ],
   "id": "e0c9e9a2f77f4f7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- 训练自定义KNN模型 ---\n",
      "KNN fit: 存储训练数据，k=15\n",
      "KNN fit 完成。\n",
      "KNN predict_proba: 开始预测概率...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KNN 预测概率中: 100%|██████████| 2466/2466 [01:04<00:00, 37.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN predict_proba 完成。\n",
      "KNN predict: 开始预测...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "KNN 预测中: 100%|██████████| 2466/2466 [01:05<00:00, 37.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN predict 完成。\n",
      "\n",
      "--- KNN模型评估 ---\n",
      "准确率 (Accuracy): 0.8755\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    不会购买 (0)       0.89      0.97      0.93      2084\n",
      "     会购买 (1)       0.70      0.34      0.46       382\n",
      "\n",
      "    accuracy                           0.88      2466\n",
      "   macro avg       0.80      0.66      0.69      2466\n",
      "weighted avg       0.86      0.88      0.86      2466\n",
      "\n",
      "\n",
      "混淆矩阵:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC值: 0.8300\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-02T05:58:21.769217Z",
     "start_time": "2025-06-02T05:58:06.662662Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# --- Part 2: 实现改进算法 (随机森林 MyRandomForestClassifier) ---\n",
    "# 随机森林是决策树的一种集成学习改进方法。\n",
    "# 它依赖于我们之前实现的 MyDecisionTreeClassifier，并确保其支持特征子抽样 (`max_features`)。\n",
    "\n",
    "class MyRandomForestClassifier:\n",
    "    \"\"\"\n",
    "    自定义随机森林分类器。\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, n_estimators=100, max_depth=None, min_samples_split=2,\n",
    "                 criterion='gini', max_features='sqrt', random_state=None, verbose=False):\n",
    "        \"\"\"\n",
    "        初始化函数。\n",
    "        参数:\n",
    "            n_estimators (int): 森林中树的数量。\n",
    "            max_depth (int, optional): 每棵树的最大深度。\n",
    "            min_samples_split (int): 每棵树节点分裂所需的最小样本数。\n",
    "            criterion (str): 每棵树的不纯度度量标准。\n",
    "            max_features (str, int, float): 每棵树寻找最佳分裂时考虑的特征数量。\n",
    "                                           'sqrt': sqrt(n_features)\n",
    "                                           'log2': log2(n_features)\n",
    "                                           int: 具体数量\n",
    "                                           float: n_features * float (比例)\n",
    "            random_state (int, optional): 随机种子，用于结果可复现。\n",
    "            verbose (bool): 是否打印训练过程信息。\n",
    "        \"\"\"\n",
    "        self.n_estimators = n_estimators\n",
    "        self.max_depth = max_depth\n",
    "        self.min_samples_split = min_samples_split\n",
    "        self.criterion = criterion\n",
    "        self.max_features_mode = max_features  # 保存原始的max_features设置\n",
    "        self.random_state = random_state\n",
    "        self.trees = []  # 存储森林中的所有决策树\n",
    "        self.feature_importances_ = None  # 特征重要性\n",
    "        self.verbose = verbose\n",
    "        self.actual_max_features_val = None  # 实际计算出的 max_features 整数值\n",
    "\n",
    "    def _get_actual_max_features(self, n_total_features):\n",
    "        \"\"\"根据输入模式计算实际使用的特征数量。\"\"\"\n",
    "        if self.max_features_mode is None:\n",
    "            return n_total_features\n",
    "        if isinstance(self.max_features_mode, str):\n",
    "            if self.max_features_mode == 'sqrt':\n",
    "                val = int(np.sqrt(n_total_features))\n",
    "            elif self.max_features_mode == 'log2':\n",
    "                val = int(np.log2(n_total_features)) if np.log2(n_total_features) >= 1 else 1\n",
    "            else:  # 未识别的字符串，使用所有特征\n",
    "                print(f\"警告: 未识别的 max_features 字符串 '{self.max_features_mode}', 将使用所有特征。\")\n",
    "                val = n_total_features\n",
    "        elif isinstance(self.max_features_mode, float) and 0 < self.max_features_mode <= 1.0:  # 比例\n",
    "            val = int(self.max_features_mode * n_total_features)\n",
    "        elif isinstance(self.max_features_mode, int):  # 具体数量\n",
    "            val = self.max_features_mode\n",
    "        else:  # 其他无效输入，使用所有特征\n",
    "            print(f\"警告: 无效的 max_features 输入 '{self.max_features_mode}', 将使用所有特征。\")\n",
    "            val = n_total_features\n",
    "\n",
    "        return max(1, min(val, n_total_features))  # 确保在 [1, n_total_features] 范围内\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        \"\"\"训练随机森林模型。\"\"\"\n",
    "        self.trees = []\n",
    "        n_samples, n_features = X.shape\n",
    "\n",
    "        if self.random_state is not None:\n",
    "            np.random.seed(self.random_state)  # 设置随机种子\n",
    "\n",
    "        # 计算实际的 max_features 值 (整数)\n",
    "        self.actual_max_features_val = self._get_actual_max_features(n_features)\n",
    "        if self.verbose:\n",
    "            print(\n",
    "                f\"开始随机森林训练，树的数量={self.n_estimators}, 每棵树最大特征数={self.actual_max_features_val} (基于 '{self.max_features_mode}')\")\n",
    "\n",
    "        self.feature_importances_ = np.zeros(n_features)  # 初始化特征重要性\n",
    "\n",
    "        desc = \"随机森林训练中\" if self.verbose else None\n",
    "        for i in tqdm(range(self.n_estimators), desc=desc, disable=not self.verbose):\n",
    "            # 1. Bootstrap自助采样 (有放回抽样)\n",
    "            #    为每棵树生成一个与原始训练集大小相同的自助样本集\n",
    "            indices = np.random.choice(n_samples, n_samples, replace=True)\n",
    "            X_sample, y_sample = X[indices], y[indices]\n",
    "\n",
    "            # 2. 为每棵树训练一个决策树模型\n",
    "            tree = MyDecisionTreeClassifier(\n",
    "                max_depth=self.max_depth,\n",
    "                min_samples_split=self.min_samples_split,\n",
    "                criterion=self.criterion,\n",
    "                max_features=self.actual_max_features_val  # 传递计算好的特征数量\n",
    "            )\n",
    "            # tree.verbose = False # 单个树的训练过程通常不打印\n",
    "            tree.fit(X_sample, y_sample)\n",
    "            self.trees.append(tree)\n",
    "\n",
    "            # 3. 累积特征重要性 (简化版：基于基尼增益或信息增益)\n",
    "            #    更复杂的方法会考虑分裂带来的不纯度减少量，并进行归一化\n",
    "            self._accumulate_feature_importances(tree.tree, n_features, X_sample.shape[0])\n",
    "\n",
    "        # 归一化特征重要性\n",
    "        if np.sum(self.feature_importances_) > 0:\n",
    "            self.feature_importances_ /= np.sum(self.feature_importances_)\n",
    "\n",
    "        if self.verbose: print(\"随机森林训练完成。\")\n",
    "\n",
    "    def _accumulate_feature_importances(self, node, n_total_features, n_tree_root_samples):\n",
    "        \"\"\"递归累积单个树的特征重要性。\"\"\"\n",
    "        if node['is_leaf']:  # 如果是叶节点，则终止\n",
    "            return\n",
    "\n",
    "        # 重要性计算方式: (该分裂带来的不纯度减少 * 该节点样本数) / 树的根节点样本数\n",
    "        # node['info_gain'] 是 (父节点不纯度 - 子节点加权不纯度)\n",
    "        # node['samples'] 是该节点的样本数\n",
    "        # 这里的 'info_gain' 实际上是 impurity_decrease\n",
    "        if 'info_gain' in node and node['info_gain'] > 0 and 'feature_index' in node:\n",
    "            # 在这里，我们使用 (info_gain * node_samples) 作为未标准化的重要性贡献\n",
    "            # 最后会在所有树累积完后，除以所有贡献的总和来归一化\n",
    "            # 或者，更标准的做法是除以当前树的根节点样本数，然后再在所有树之间平均\n",
    "            # 这里我们用一个简化版本：直接累加 (info_gain * node_samples / n_tree_root_samples)\n",
    "            # 保证 feature_index 在范围内\n",
    "            if 0 <= node['feature_index'] < len(self.feature_importances_):\n",
    "                self.feature_importances_[node['feature_index']] += (node['info_gain'] * node[\n",
    "                    'samples']) / n_tree_root_samples\n",
    "            else:\n",
    "                print(f\"警告: 特征索引 {node['feature_index']} 超出范围 [0, {len(self.feature_importances_) - 1}]。\")\n",
    "\n",
    "        # 递归到子节点\n",
    "        if 'left' in node and isinstance(node['left'], dict):\n",
    "            self._accumulate_feature_importances(node['left'], n_total_features, n_tree_root_samples)\n",
    "        if 'right' in node and isinstance(node['right'], dict):\n",
    "            self._accumulate_feature_importances(node['right'], n_total_features, n_tree_root_samples)\n",
    "\n",
    "    def predict_proba(self, X):\n",
    "        \"\"\"\n",
    "        预测每个样本属于正类（类别1）的概率。\n",
    "        通过平均森林中所有树的概率预测得到。\n",
    "        \"\"\"\n",
    "        # 收集所有树对每个样本的概率预测 (每棵树预测的是类别1的概率)\n",
    "        # tree_probas 的形状将是 (n_estimators, n_samples)\n",
    "        tree_probas = np.array([tree.predict_proba(X) for tree in self.trees])\n",
    "        # 对所有树的概率进行平均，得到最终概率\n",
    "        # axis=0 表示沿着树的维度（第一个维度）进行平均\n",
    "        return np.mean(tree_probas, axis=0)  # 返回形状为 (n_samples,) 的数组\n",
    "\n",
    "    def predict(self, X):\n",
    "        \"\"\"\n",
    "        预测类别标签。\n",
    "        通过森林中所有树的多数投票得到。\n",
    "        \"\"\"\n",
    "        # 收集所有树对每个样本的类别预测\n",
    "        # tree_preds 的形状将是 (n_estimators, n_samples)\n",
    "        tree_preds = np.array([tree.predict(X) for tree in self.trees])\n",
    "\n",
    "        # 进行多数投票\n",
    "        # 将 tree_preds 转置为 (n_samples, n_estimators)，方便对每个样本进行投票\n",
    "        predictions = []\n",
    "        for sample_preds_from_all_trees in tree_preds.T:\n",
    "            # Counter统计每个类别出现的次数，most_common(1)返回出现次数最多的类别及其计数\n",
    "            most_common_class = Counter(sample_preds_from_all_trees).most_common(1)[0][0]\n",
    "            predictions.append(most_common_class)\n",
    "        return np.array(predictions)\n",
    "\n",
    "\n",
    "# --- 辅助评估函数 ---\n",
    "def evaluate_model(model, X_test_data, y_test_data, model_name_cn, dataset_name_cn):\n",
    "    \"\"\"\n",
    "    评估已训练模型的性能。\n",
    "    参数:\n",
    "        model: 已训练的模型对象。\n",
    "        X_test_data (numpy.ndarray): 测试集特征。\n",
    "        y_test_data (numpy.ndarray): 测试集标签。\n",
    "        model_name_cn (str): 模型名称 (中文)。\n",
    "        dataset_name_cn (str): 数据集名称 (中文)。\n",
    "    返回:\n",
    "        tuple: (accuracy, roc_auc)\n",
    "    \"\"\"\n",
    "    print(f\"\\n--- {model_name_cn} 模型评估 ({dataset_name_cn}) ---\")\n",
    "\n",
    "    # 获取概率预测 (用于ROC曲线)\n",
    "    if hasattr(model, 'predict_proba'):\n",
    "        y_pred_proba_model = model.predict_proba(X_test_data)\n",
    "    else:  # 备用方案：如果模型没有predict_proba方法 (理论上我们都实现了)\n",
    "        print(f\"警告: 模型 {model_name_cn} 没有 predict_proba 方法，ROC曲线可能不准确。\")\n",
    "        y_pred_proba_model = model.predict(X_test_data)  # 这会导致阶梯状ROC\n",
    "\n",
    "    # 获取类别标签预测\n",
    "    y_pred_labels_model = model.predict(X_test_data)\n",
    "\n",
    "    accuracy_model = accuracy_score(y_test_data, y_pred_labels_model)\n",
    "    print(f\"准确率 (Accuracy): {accuracy_model:.4f}\")\n",
    "    print(\"\\n分类报告:\")\n",
    "    # 使用 '类别 0' 和 '类别 1' 作为通用名称\n",
    "    print(classification_report(y_test_data, y_pred_labels_model, target_names=['类别 0', '类别 1'], zero_division=0))\n",
    "\n",
    "    print(\"\\n混淆矩阵:\")\n",
    "    cm_model = confusion_matrix(y_test_data, y_pred_labels_model)\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    sns.heatmap(cm_model, annot=True, fmt='d', cmap='Blues', xticklabels=['预测:类别0', '预测:类别1'],\n",
    "                yticklabels=['实际:类别0', '实际:类别1'])\n",
    "    plt.xlabel('预测标签')\n",
    "    plt.ylabel('实际标签')\n",
    "    plt.title(f'混淆矩阵 ({model_name_cn} - {dataset_name_cn})')\n",
    "    plt.show()\n",
    "\n",
    "    # ROC曲线和AUC\n",
    "    fpr_model, tpr_model, _ = roc_curve(y_test_data, y_pred_proba_model)\n",
    "    roc_auc_model = auc(fpr_model, tpr_model)\n",
    "    plt.figure(figsize=(8, 5))\n",
    "    plt.plot(fpr_model, tpr_model, color='darkorange', lw=2, label=f'ROC 曲线 (面积 = {roc_auc_model:.2f})')\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('假正例率 (False Positive Rate)')\n",
    "    plt.ylabel('真正例率 (True Positive Rate)')\n",
    "    plt.title(f'ROC 曲线 ({model_name_cn} - {dataset_name_cn})')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "    print(f\"AUC值: {roc_auc_model:.4f}\")\n",
    "    return accuracy_model, roc_auc_model\n",
    "\n",
    "\n",
    "# --- 实验 1: Online Shoppers Intention 数据集 ---\n",
    "print(\"\\n\" + \"=\" * 60)\n",
    "print(\"实验 1: Online Shoppers Intention 数据集上的模型比较\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "# X_train, X_test, y_train, y_test 已经为这个数据集定义和处理好了\n",
    "\n",
    "print(\"\\n重新评估决策树 (基准模型) 在 Online Shoppers 数据集上的表现...\")\n",
    "# 使用之前训练好的 tree_model\n",
    "evaluate_model(tree_model, X_test, y_test, \"决策树 (基准)\", \"Online Shoppers\")\n",
    "\n",
    "print(\"\\n训练并评估随机森林 (改进模型) 在 Online Shoppers 数据集上的表现...\")\n",
    "rf_model_shoppers = MyRandomForestClassifier(\n",
    "    n_estimators=50,  # 为加速示例，实际应用中100-200更常见\n",
    "    max_depth=10,  # 随机森林中的树可以更深一些\n",
    "    min_samples_split=5,\n",
    "    max_features='sqrt',  # 常用默认值: sqrt(总特征数)\n",
    "    random_state=42,  # 保证结果可复现\n",
    "    verbose=True  # 开启详细输出\n",
    ")\n",
    "rf_model_shoppers.fit(X_train, y_train)\n",
    "evaluate_model(rf_model_shoppers, X_test, y_test, \"随机森林 (改进)\", \"Online Shoppers\")\n",
    "\n",
    "# 显示随机森林的特征重要性\n",
    "if hasattr(rf_model_shoppers, 'feature_importances_') and rf_model_shoppers.feature_importances_ is not None:\n",
    "    print(\"\\n--- 随机森林特征重要性 ---\")\n",
    "    try:\n",
    "        # 尝试获取预处理后的特征名称 (这部分比较复杂)\n",
    "        # 数值特征名称\n",
    "        num_feat_names = list(preprocessor.transformers_[0][2])  # numerical_features\n",
    "        # 类别特征名称 (来自OneHotEncoder)\n",
    "        cat_transformer = preprocessor.named_transformers_['cat']\n",
    "        if hasattr(cat_transformer, 'get_feature_names_out'):\n",
    "            cat_feat_names = list(cat_transformer.get_feature_names_out(all_categorical_features))\n",
    "        else:  # 兼容旧版sklearn或不同配置的OneHotEncoder\n",
    "            # 这是一个简化的回退逻辑，实际名称可能更复杂，特别是当 drop='first' 时\n",
    "            cat_feat_names = []\n",
    "            for i, col_name in enumerate(all_categorical_features):\n",
    "                unique_vals = sorted(df[col_name].astype(str).unique())  # 确保是字符串并排序\n",
    "                # 如果 drop='first'，第一个类别会被丢弃\n",
    "                start_idx = 1 if cat_transformer.drop == 'first' and len(unique_vals) > 1 else 0\n",
    "                for val_idx in range(start_idx, len(unique_vals)):\n",
    "                    cat_feat_names.append(f\"{col_name}_{unique_vals[val_idx]}\")\n",
    "\n",
    "        # 'remainder' 中保留的特征名称 (例如 'Weekend')\n",
    "        remainder_cols = []\n",
    "        if preprocessor.remainder == 'passthrough' and len(preprocessor.transformers_) > 1 and len(\n",
    "                preprocessor.transformers_[-1][2]) > 0:\n",
    "            # preprocessor.transformers_[-1] 应该是 ('remainder', 'passthrough', [col_indices])\n",
    "            # X.columns[i] for i in preprocessor.transformers_[-1][2]\n",
    "            # 这里需要原始X的列名\n",
    "            original_X_cols = X.columns\n",
    "            remainder_indices = preprocessor.transformers_[-1][2]\n",
    "            remainder_cols = [original_X_cols[i] for i in remainder_indices]\n",
    "\n",
    "        all_feature_names_processed = num_feat_names + cat_feat_names + remainder_cols\n",
    "\n",
    "        if len(all_feature_names_processed) == X_train.shape[1]:\n",
    "            importances = pd.Series(rf_model_shoppers.feature_importances_, index=all_feature_names_processed)\n",
    "            top_importances = importances.sort_values(ascending=False).head(15)  # 取最重要的15个\n",
    "\n",
    "            plt.figure(figsize=(12, 9))  # 增大图像尺寸以便容纳更多特征名\n",
    "            sns.barplot(x=top_importances.values, y=top_importances.index)\n",
    "            plt.title('最重要的15个特征 (随机森林)')\n",
    "            plt.xlabel('重要性值')\n",
    "            plt.ylabel('特征名称')\n",
    "            plt.tight_layout()  # 自动调整布局\n",
    "            plt.show()\n",
    "        else:\n",
    "            print(\n",
    "                f\"警告: 无法准确匹配所有特征名称 (预期 {X_train.shape[1]} 个, 得到 {len(all_feature_names_processed)} 个)。\")\n",
    "            print(f\"  数值特征数: {len(num_feat_names)}\")\n",
    "            print(f\"  OneHot编码后类别特征数: {len(cat_feat_names)}\")\n",
    "            print(f\"  保留特征数: {len(remainder_cols)}\")\n",
    "            print(\"  将不绘制特征重要性图。请检查 OneHotEncoder 的 get_feature_names_out 行为。\")\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"绘制特征重要性图时发生错误: {e}\")\n",
    "        print(\"特征重要性图已跳过。\")\n",
    "\n",
    "# --- 实验 2: 乳腺癌数据集 (来自 sklearn.datasets) ---\n",
    "print(\"\\n\" + \"=\" * 60)\n",
    "print(\"实验 2: 乳腺癌数据集上的模型比较\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "# 加载数据\n",
    "cancer = load_breast_cancer()\n",
    "X_cancer_raw = pd.DataFrame(cancer.data, columns=cancer.feature_names)\n",
    "y_cancer = pd.Series(cancer.target)  # 0: malignant (恶性), 1: benign (良性)\n",
    "\n",
    "print(\"乳腺癌数据集特征 (前5行):\")\n",
    "print(X_cancer_raw.head())\n",
    "print(\"\\n乳腺癌数据集目标变量分布:\")\n",
    "print(y_cancer.value_counts(normalize=True))  # 0 和 1 的比例\n",
    "\n",
    "# 数据预处理: 此数据集特征均为数值型，只需进行标准化\n",
    "scaler_cancer = StandardScaler()\n",
    "X_cancer_scaled = scaler_cancer.fit_transform(X_cancer_raw)\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train_bc, X_test_bc, y_train_bc, y_test_bc = train_test_split(\n",
    "    X_cancer_scaled, y_cancer.values, test_size=0.2, random_state=42, stratify=y_cancer\n",
    ")\n",
    "\n",
    "print(f\"\\n训练集大小 (乳腺癌): X_train: {X_train_bc.shape}, y_train: {y_train_bc.shape}\")\n",
    "print(f\"测试集大小 (乳腺癌): X_test: {X_test_bc.shape}, y_test: {y_test_bc.shape}\")\n",
    "\n",
    "# 训练并评估决策树 (基准模型)\n",
    "print(\"\\n训练并评估决策树 (基准模型) 在乳腺癌数据集上的表现...\")\n",
    "tree_model_bc = MyDecisionTreeClassifier(max_depth=5, min_samples_split=5, criterion='gini')\n",
    "tree_model_bc.verbose = True  # 可以设为 False 以减少输出\n",
    "tree_model_bc.fit(X_train_bc, y_train_bc)\n",
    "evaluate_model(tree_model_bc, X_test_bc, y_test_bc, \"决策树 (基准)\", \"乳腺癌\")\n",
    "\n",
    "# 训练并评估随机森林 (改进模型)\n",
    "print(\"\\n训练并评估随机森林 (改进模型) 在乳腺癌数据集上的表现...\")\n",
    "rf_model_bc = MyRandomForestClassifier(\n",
    "    n_estimators=50,  # 为加速示例\n",
    "    max_depth=7,\n",
    "    min_samples_split=5,\n",
    "    max_features='sqrt',\n",
    "    random_state=42,\n",
    "    verbose=True\n",
    ")\n",
    "rf_model_bc.fit(X_train_bc, y_train_bc)\n",
    "evaluate_model(rf_model_bc, X_test_bc, y_test_bc, \"随机森林 (改进)\", \"乳腺癌\")\n",
    "\n",
    "# 显示随机森林的特征重要性 (乳腺癌数据集)\n",
    "if hasattr(rf_model_bc, 'feature_importances_') and rf_model_bc.feature_importances_ is not None:\n",
    "    print(\"\\n--- 随机森林特征重要性 (乳腺癌) ---\")\n",
    "    importances_bc = pd.Series(rf_model_bc.feature_importances_, index=cancer.feature_names)\n",
    "    top_importances_bc = importances_bc.sort_values(ascending=False).head(15)\n",
    "\n",
    "    plt.figure(figsize=(12, 9))\n",
    "    sns.barplot(x=top_importances_bc.values, y=top_importances_bc.index)\n",
    "    plt.title('最重要的15个特征 (随机森林 - 乳腺癌)')\n",
    "    plt.xlabel('重要性值')\n",
    "    plt.ylabel('特征名称')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ],
   "id": "4355dfd07fd464d8",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "实验 1: Online Shoppers Intention 数据集上的模型比较\n",
      "============================================================\n",
      "\n",
      "重新评估决策树 (基准模型) 在 Online Shoppers 数据集上的表现...\n",
      "\n",
      "--- 决策树 (基准) 模型评估 (Online Shoppers) ---\n",
      "准确率 (Accuracy): 0.8954\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        类别 0       0.92      0.96      0.94      2084\n",
      "        类别 1       0.70      0.57      0.63       382\n",
      "\n",
      "    accuracy                           0.90      2466\n",
      "   macro avg       0.81      0.76      0.78      2466\n",
      "weighted avg       0.89      0.90      0.89      2466\n",
      "\n",
      "\n",
      "混淆矩阵:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC值: 0.9028\n",
      "\n",
      "训练并评估随机森林 (改进模型) 在 Online Shoppers 数据集上的表现...\n",
      "开始随机森林训练，树的数量=50, 每棵树最大特征数=8 (基于 'sqrt')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "随机森林训练中: 100%|██████████| 50/50 [00:12<00:00,  3.91it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "随机森林训练完成。\n",
      "\n",
      "--- 随机森林 (改进) 模型评估 (Online Shoppers) ---\n",
      "准确率 (Accuracy): 0.8938\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        类别 0       0.90      0.98      0.94      2084\n",
      "        类别 1       0.80      0.42      0.55       382\n",
      "\n",
      "    accuracy                           0.89      2466\n",
      "   macro avg       0.85      0.70      0.74      2466\n",
      "weighted avg       0.89      0.89      0.88      2466\n",
      "\n",
      "\n",
      "混淆矩阵:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ],
      "image/png": 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GBhs3bsTw4cNTPO+gQYPg7+8PV1dXVKpUCXv37kXv3r1TPO+OHTtQvXp1DBgwAHny5MHMmTMVfk5A4lCYbt26ISAgAF26dEHevHnxzz//YNmyZShcuDAGDx6MMmXKYNmyZRg7dqx4XtKbzaTXAVFm4jAGou/I5XKxB08mk+H9+/dYu3YtQkJC0LJlSwCJa6oCgK2t7Q8fq1ixYmLY/fjxo9j2q6KiosReu8DAQMTFxYm3o6Ki8OrVKxw4cCDVdXKNjIwQGBiI6dOno2fPnqhWrRrevn2LXr16wcDAANra2pDL5XB2doaTkxOsra0RFRUFZ2fnH9YUGRmZ5ooNbdq0wZIlS1C+fHksW7YMf/zxBwoXLozRo0dj7ty5qFevnnjs9z3ejx8/xr179wAAfn5+6NixI1asWAG5XA5t7fT/CWvSpAmaNGkC4NuELADQ0tJSWO4sLCwMMTExyJs3L4YOHZricf7++28UKVIE7969g5mZGbp3744lS5agUKFCOH78uPhzfP36Nby9vRXGGqcmaUm3tH5mScM6AKBy5crw8PCAj48PunXrhsePH6NgwYK4du0aJk2apHBejx490KNHD4wZMwYfPnzAjRs30Lt3b/H+OXPmYM6cOQrnJPWy7d69W5xEldTrGhERkeb3kPRz/d7mzZtRs2bNFO3KvG7OnTuHkJAQcfhMcHCwwvCAqlWrYvTo0Th+/DhGjBjxw8f7nouLCyZMmAAAGDhwIJo2bYrDhw+Lk8X++usvGBgYYM+ePeIb0x49eqBFixb4559/xF727/Xq1QufP3/Gpk2bcPz4cZQvXx716tWDi4uLwrCGJIIgYN++feKbPAsLC2zbtg33799H9erVcfDgQTx69AhjxowRl7UbNWoUhgwZgq1bt6J3797ixDuZTIbWrVuL39fIkSMxbtw4HDp0CFeuXFEYF165cmUsXrxYfLO4cOFCrF69Gvv370fnzp1x+PBhPH78GO7u7uLKH0kheu3atejXr584tv3NmzdYsGABWrduLT7+3bt3YWBggHXr1kEqTexTO3DggMK/fUm/X8osbUj0sxh2ib7z5cuXFH/EdXV10b9/f3HmvzKS/qBoaWkBSAzTvyokJEQhDAEQAyGQOInt3LlzqZ4bFxeHESNGwMDAAE2bNsWhQ4dgaWmJq1evwtLSEtu2bcO8efPg5uYGqVQKd3d3nD9/Pt2JSkZGRmkGI0tLS7Ro0QK3bt0SVxZwdXXFjRs3YGlpicOHDwNI/Ah+8uTJCucuX74cjRo1wpkzZ+Dm5oYLFy5g165diIuLy9BY1+vXryuEPeDbWNrVq1dj2LBhABLDwvPnz/H3338DAIYNGyaGqKNHj+LKlStwd3eHu7s75s2bh/Xr10NfXx+zZ89Gw4YNYW5ujqCgIHTu3Blz5szB7t27Ux3SkfznBaQdJvv375+irXLlyihatKj4cbSOjo5CyEhNiRIlsHDhQsTGxmLSpElwdXUVVzvYuXMnAgMDxd6/5CsWhIeHA0CKoQbJpTZREkCKj/F/VvKeezs7O4XA2LBhQwDf3kQqI+lNK5D4/VWrVg2nTp0CkPj6uHXrFhwdHRETEyO+8QUSfz7JV0L5nlQqxbhx49C/f3+cOXMGly5dwsaNG7F8+XLUq1cPCxYsUBj73qhRI4VPMxo2bIht27bB398fAHD16lVIJBK4urqKx2hpacHV1RUXL17ElStXxGsmlUoV1nmWSCQYNmwYDh06BG9vb4WwO3ToUIWf7aBBg7B+/Xp4e3ujc+fOuHz5MrS1tcU3cEkqVKiAq1ev4tWrV+KY9uLFi6f4HWzYsCHOnj2LP/74Q1xd4/sJqEm/XxlZ0pDoVzHsEn3H3NwcHh4eABJ7AletWoX+/ftj9OjR4jFJs7uT/yFIzZs3b8SeoYIFC0IikfzUx67f+5UJag8fPsTdu3cBAD179oSuri4GDx4shtDSpUsjOjoaL168wMaNG3Hq1Cls2LAh3WCZL1++H34kWb58edy6dUu8nfTHNigoSKEXNHnP7o0bN3D27Fls27YNZ86cgYGBAZYsWYKwsDB069YtzUlznz9/Fj+Gr1ChAo4ePYpPnz5h0KBB0NPTw9atW6Grq4sCBQpgz549OHnyJFauXAltbW0sXrwYhQoVUhheMGfOHLRq1UrsmdLS0sKcOXPw33//4dq1azh48CDWr18PIHEy2+bNm1N8bJvazwtQ7mNciUSC33//HWPGjMHly5fRv3//dGeyW1paomXLluJSa23atBGDytWrVyEIgkL4S5JUV9LHzalRdtkoZV43+vr6Cr8L34fupPV/f2YHuKShK8kfK+lxQkJCIJfL03zDmNryg9/LkycPOnXqhE6dOiE2Nha7d++Gl5cXJk+erDAM4PvvKam3NHktZmZmKdY6LliwIIDE3u7k31PSxMgkhQsXBpD4qUVq7cnrMDc3F49LWqUjrTdSyd+wf/9JDAB06dIFhoaGWL9+Pbp27Yo8efKgXbt2GD58uPg9Jw2PSXodEGUmhl2i7+jp6Yl/xJs1a4YHDx5g3bp1aN26tfiHrmzZsrCwsMDx48cxduzYVIPgkydP8OLFC7FXxsLCApUrV8a1a9fw/v37FH9wskrp0qUxffp02NrawsbGBtbW1uJHjUDi92ZgYIC+fftCIpFgw4YNCAkJwePHj1NdaSFJmTJlcPTo0VR7XG/duoXLly+nuqyXlZUVrly5AgA4deqUwtq7MpkMLVu2VFgZQSqVwtTUFAEBAShUqFCqtQwdOhQdO3ZEt27dYGBggOLFi2PWrFnQ0dFBREQExo0bhwULFsDIyAj6+vrYtm0bDAwMIJFIsHLlSmzfvh16enri49na2mLixIkKb1Tu37+PmTNnYtCgQQoByNTUFOPGjcO0adNgZ2eX6pJqQGIgKliwYLqTnnx9ffH27Vs4OjoCSFxRQU9PD7GxsWjTps0Pz01uy5YtsLS0zNCEOCBx+EjevHl/GHaVZWVlhVKlSuHSpUsIDg5Oddzu58+fcfXqVbXN0LewsIBEIkG9evXEzVGSSy3cJRcdHa0QTvX09NCjRw9cuXIF3t7eStcSGhqa4jGTerOT//zCw8MRHh6uEOTfvXsHIGWg9Pf3h729vXg7IiICX79+FY+zsLCArq4u5s+fn2pdGQn8rVu3RuvWrfHp0yccP34cCxcuxOvXr7F69WoAEH/v01tXnEgVOEGNKB0zZ86EtrY23NzcxB4NbW1t9OnTBx8/fsSUKVMUZhkDiX+Mxo0bBwMDA4XdrkaMGIGEhASMHj0aQUFBCud8/vwZXbt2zdBmCr/C0NAQ3bp1Q506dWBlZaUwJlYul4uTmuLj47Fz505UqFABe/fuFdd4TUvVqlURHx8v9iAmt3r1arx58wadOnXCmzdvkJCQII7fTNKwYUOMGDFC4Y9w+fLlMW7cuBSP9/LlS0RFRaW56UN8fLzCsJEpU6bg1atX6N+/PywsLFCrVi2MGTMGPj4+6N+/Pzp16gRbW1v07NkTWlpaGD58uMIyUYsWLYKlpaXCc2zZsgVly5YVh0Ek16VLF7Ru3TrdIFu1alU8fvw4xUoAQOL41mnTpqFly5Y4ePAggMQez6Sx1Xnz5sWwYcMUJuwliYmJwYsXL+Dv74/o6GicOnUKhw4dwqBBg8Sfy4/ExcXh0aNHqFq1arrHKmvAgAGIjo7GH3/8kWK8ZmhoKMaOHStuD60Ourq6qF69Ovz8/FC/fn00a9ZM/E9HR0dcyzg1f/75J3r06JHitf3161c8e/YszQmcaalTpw4EQcCmTZvENplMhk2bNkEikSgMTZDL5Qr/dgiCIPYif7/W89q1axVur1mzBvHx8eJxdevWRVxcHIyMjBS+/0KFCqFMmTLpDj24cOGCWHP+/Pnh6uqKOnXqKGzYc+fOHejo6PzUBiJEymLPLlE6ChcujD/++AMzZ87Exo0bxaXBBg4ciOfPn+PAgQO4desWnJ2dYWZmhrdv3+L06dOQyWSYO3euwmYOdevWxaRJkzB37ly4uLigadOmKFSoED58+ICTJ08iJiYGXbt2Tbem+Ph4cVZ4dHQ0IiMjxdvx8fEIDQ2Fj49PihAOAPv27cP58+fx/Plz+Pn5QSaTwdnZGc2bN8eaNWvg4+OD+vXr4/z58/j48SMKFy6Mu3fvKowHTI2LiwtmzJiBEydOpFjbNOmPa0BAAEaMGAG5XI7Zs2dj5syZ4hjZnTt3IiEhQWEDAGNjYxgbG6cIg7t27YK9vX2qk36AxC2Ck8bLLl68GEeOHME///yDZ8+eQUtLC1OmTMGZM2fQvXt3VK1aFWPGjMGlS5egq6uL5cuXw9XVFe3bt8eOHTtgYWGRakgZPXo09PT00hyXO2PGjBQfH3+vefPmOHz4MC5evChu6JAUlJo0aQIdHR2MGDECrq6u2Lt3Lzw9PWFsbIzNmzdDW1sbvXr1QqdOnfDXX38pDAWZOnWquCvctGnT0KtXL3Tu3DnF2OUmTZqkWuOVK1cQGRmZKbtbtW7dGk+ePMH69evRpEkTuLi4wMrKCgEBATh16hTCw8MxceJEpdfYTfrU4NSpU6hSpYrYE/4zxo8fj+7du6NVq1Zo2rQpDAwMcPnyZTx48ADz5s1Ls7feyckJx48fR/PmzeHs7IxChQohKCgIJ06cQHBwsLgSSEa1adMG27Ztw6JFi/D48WMUL14c3t7euH//Pnr16pViV7ht27bh1atXsLe3x7Vr13D79m20adMmxScyFy5cQJ8+fVC1alU8evQI58+fR7Vq1cTfwdatW2Pnzp0YOnQomjVrhqJFi+Lt27c4duwYqlevrrAsXGoOHz6MgwcP4uLFi6hcuTICAgJw4cIFMUzL5XKcPn0aderUSTGkhCgzMOwSZUCPHj1w7NgxLFmyBM7OzrC1tYWWlhYWLVqExo0bY/fu3Thy5Ii4XXCrVq3g6uqa6nbBrq6uqFy5MrZs2YJr166J2wU7OjpiwIABGfpY7/PnzykmjF24cEH8+r///sN///2X6rkPHjzAly9fUKdOHfTt2xcODg4wMzND69atUbNmTcybNw+lS5dGv379MGLECFSuXBmfP38WJwSlJU+ePHB2dsbRo0cxbtw4hWEASSZPnoy8efNi1apVGDx4MCZPnozOnTvj5cuX4lAKX19f6OrqprmW7sOHD7Fjxw6sWrVKoV1XVxf+/v7YtWsXPn/+LIafpk2bonbt2qhWrZq4dBiQ+JG6i4sLpk+frtDbmT9/fuzcuRNr1qz54UYa6Q1DMTQ0THMptSQNGjRAvnz5sHfvXjFoPHnyBHp6eujevbu4k9/QoUNx7do11KpVCwsWLBCHFmzevBnDhw9H3759MWPGDHTu3Bk7d+7EwYMHMXr0aFStWhUDBgzAkydP4OTkJIbk7yWtn9q8eXNYWlpi3759sLS0/OHub79iwoQJqFOnDrZt24bTp0+L2wXXq1cPvXr1+qltmq2srNChQwccOXIEJ0+e/KWwW758eezYsQPLli3D3r17ERsbi5IlS2Lp0qUpdghMrkWLFrCzs8OGDRvg7e2NwMBAGBkZoWLFiujbt2+6K3R8T0dHB5s2bcKSJUtw4sQJnD9/HoULFxYnGn5vw4YNmDdvHtatWwdTU1MMGTIk1ZUqlixZgq1bt2L9+vXQ09NDly5dMH78eHEcvY6ODjZs2IBly5bhxIkTOH78OKysrNC7d2+MHDky3brnzJmDkiVLYs+ePbhx4wYsLCzQuXNncQz7xYsXERQUhA4dOij18yD6aQIR5SgrVqwQ6tatKzx48CDd/27fvi3Y29sLe/bsSfdx379/r3A7ODhYcHV1FSpVqiSsWLEiQ7U9ffpUcHBwELZu3SoIgiBcuHBBqFOnjiAIghAeHi64uroKHz58EARBEF68eCEMHDhQqF69uuDg4CDY29uL//Xq1UvhcWNiYgR7e3vhwIEDQu/evYUFCxakeO6zZ88KVapUEezt7YXGjRsLX758SXHMli1bxHq+16ZNG2Hp0qU//P6uX78ulC5dWvDx8Ulxn5ubm9C0adMfnp+azZs3Cw4ODsKLFy8EQRCEV69eCQEBAeL9crlc+P3334VNmzYJMpksxfnh4eHCuHHjhI8fPwqCIAi3bt0SRo0aJd5///59YeDAgUKNGjWE0qVLK/yck/9XunRpITw8XHj16pVQqlQpYcOGDUp/L6QeEyZMEOzt7YX4+PgfHrd06VLB3t5e8PX1zaLKUtelSxehTZs2glwuV2sdpDkkgpBsGxYiIsrxZDJZhsbmUu4wceJE7Nu3D48fP/7h2tPLli3D8uXLcfLkyXTXOibKTThBjYgol2HQJSL6hj27RERERJRrsWeXiIiIiHIthl0iIiIiyrUYdomIiIgo12LYJSIiIqJci2GXiIiIiHItjd1BLTg4HHK5uqugzCaRAJaWJggKCgfXHcn9eL01C6+3ZuH11ixSKZAnj2q2k9bYsCsI4ItFg/B6axZeb83C661ZeL01gyqvMYcxEBEREVGuxbBLRERERLkWwy4RERER5VoMu0RERESUazHsEhEREVGuxbBLRERERLkWwy4RERER5VoMu0RERESUazHsEhEREVGuxbBLRERERLkWwy4RERER5VrZJuz6+PhgyJAhcHBwQEBAQKrHfPr0CcOGDUPlypVRq1YteHl5IS4uLosrJSIiIqKcQlvdBQDAxYsXMXToUJiZmaV5jEwmw4ABAxATE4PJkycjJCQEy5cvh0wmw5QpU7KwWiIiIiLKKbJF2BUEAdOnT0fhwoXRp0+fVI85d+4cXrx4gQMHDqBUqVIAAC0tLSxcuBAjRoyAubl51hVMRERERDlCtgi7jo6OAIDr16+neYy3tzdKlCghBl0AaNWqFebOnYvbt2/D2dlZqeeUSBL/o9wt6RrzWmsGXm/NwuutWb6/3rq++2B4zxOS+Aj1FUWZR8cYGPBcJQ+VLcJuRnz48AG2trYKbfny5YOBgQHev3+v9OPlyWOiqtIoB7C05PXWJLzemoXXOxt5vgu4Og2IC8+0p7BM+iLiQ6Y9B2W9T+FGiInXhm2e0MQGXdW9rnNM2I2OjkaBAgVStJuYmCA2NlbpxwsODodcrorKKDuTSBL/EAYFhUMQ1F0NZTZeb82izuvNXsXUaUX5q+V5ZYbWanle+nXxMglWnC+HGUeqomaxTzg64lhi772OMbRU9Bw5JuwaGBggPDzlO8Xw8HDo6+sr/XiCAP4x1CC83pqF11uzZPb11vXdB6P7isFWXaEuJ8mMAKollUAm/3axBR1jRFaagjjbdip/Lsp8ly75wc3tHJ4/DwIAnHpaBP8aHUaLFiUglSbrxf9FOSbsFi5cGN7e3gptnz9/RnR0NAoVKqSmqoiIKKdKLcSmJr1gy15FRZkVQCUSwMrKBCGB/OQmp3v/Pgzu7hdw6NBLsU0iAXr0KIcaNVT/esoxYbdmzZrYsmULnj17Jk5SO3z4MHR0dFCtWjU1V0dEROqW0fCa5Gd6Z5MHW/YqEiknJiYBK1fewpIlNxAdnSC2V6lSAF5eTqhcOeVwVVXItmE3ODgYd+7cQZkyZWBtbQ0nJyfY29tj5MiRGDx4MEJCQrBixQp07dr1h+vzEhFRzqFsYIVUAov/f6z9K0ML0uudZbAl+jWXLvlh7NhTePs2VGyzsjLA1Kn10aVLWUilmbesSrYNuy9fvsTw4cPh5eWF3377DVpaWli7di08PDwwa9Ys6OnpoXPnzvjzzz/VXSoREaVDVUMGUj0nlbaMDi1giCXKGnK5IAZdLS0J+vevhHHjasPMTPl5V8qSCIJmjnwJCuJqDJogaYxXIMd4aQRe75+jdG/qT/jVIQNpPi4nLGkMvr5zvn79DiEkJBqzZzuhdGmrHx4rlapuWcFs27NLRESZJ3nAzeqVBVQ1ZIATloiyH0EQsH//cxw/7oNVq1pAkmzXl2XLmsHQUFuhLSsw7BIRaYiMBNzMXFmAva5Eudvjx18wefI5XL2auNlXkybF0aFDafF+IyMdtdTFsEtElEOpavUBmaE1gygR/bSvX2Mwb95VrF9/H/Jkw4ouX36nEHbVhWGXiCgbUSbA/urqAwy4RPQr5HIB27Y9gqfnZQQFRYvtRYuaYdasRmjSpLgaq/uGYZeIKBP87KSvnw2wXH2AiLLSnTsfMWnSWdy9+0lsMzTUxujRNTFkSFXo62efiJl9KiEiysG+D7eqmPSVkQDL8EpEWe3Nm69o3ny7wsTQtm3tMX26IwoVUs0KCqrEsEtElAZVDSlQdtIXAywRZWfFipmjQ4fS2L37KUqVssTs2Y1Qr56NustKE8MuEWmczNzgAPgWbhlaiSg3uH37IypXLqCwy5m7e31UqpQffftWhI5Oalu7ZB8Mu0SUa6UVajNrgwOGWyLKTfz9wzF9+kXs3/8cixa5oEeP8uJ9+fMbY9CgKmqsLuMYdoko2/qpSV5SCSz+v/RNRkKtqjY4ICLKLWJjE7Bq1R0sWnQNUVEJAIBZsy6jVauSWbK9r6ox7BJRtmV03xPaoS+UPi+1D9S+D7UMsUREKZ0+/RqTJ5/HmzdfxTZLSwNMnlwPJiZ66ivsFzDsElG28X1PrjQ6AAAgSKSQGxTI0GNoSSWQJVvUnKGWiCh9b958xdSp53Hy5GuxTSqVoG/fipgwoQ7MzXNej24Shl0iyjbS6smVmZZASNtb6Z4vkQBWViYICQxXWBKHiIhSJwgC5s69iuXLbyEuTia216pVCLNnO6FcubxqrE41GHaJKFvQ9d0nBt3kPblJPbNERKR6EokEX75EiUG3QAEjTJ/uiPbtHSCRSNI5O2dg2CUitUoaupC8RzejPblERPTr3Nzq4fhxH3TtWhZjxtSEsbGuuktSKYZdIspSGdlpjD25RESqFxYWi/nzvVG8uAX69q0otltaGuDmzf4wNNRRY3WZh2GXiH6JssuD/Wg5sAQze04mIyJSMblcwM6djzFz5mUEBkbBzEwPrVuXhJWVoXhMbg26AMMuEf2E5AH3Z3cZA7jTGBFRZrt3LwCTJp3F7dsBYltcnAx37gSgSZPiaqws6zDsEpHoV7fRzcguYwDDLRFRZgsMjMLs2Zfx77+PFFanadWqJDw8HFGkiKn6istiDLtEBCAx6JpddFX6PJmhNcMrEVE2kZAgx6ZN9zFnzlWEhsaK7fb2eeDp2QiOjrZqrE49GHaJNNiPhiNwG10iopxn5cpbmDXrsnjb2FgX48bVxoABlaCjk9r+krkfwy5RLvejoQlpDUcIddzMEEtElAO5ulbAqlW3ERgYjc6dy2Dq1PrIn99I3WWpFcMuUQ6i7MoHwI9XP0iOwxGIiHKWuDgZHj36jCpVCoptZmb6WLSoCSwsDFCjRsbmUeR2DLtEavAzoRXIeHBNS2pDExhwiYhynrNnfTF58jkEBETA27svChQwFu9r2tROjZVlPwy7RFlEVct1JcnoygcAAy0RUW7x9m0opk49j+PHfcS2GTMuYeXK5mqsKntj2CXKBKn13P7qcl1JGFyJiDRPVFQ8li27ieXLbyI2Via216hhjaFDq6qxsuyPYZdIxTKyhBfHxxIRUUYIgoAjR15h2rTzeP8+XGzPl88I7u710bFjaUgkEjVWmP0x7BKpUGpBN3nPLQMuERFl1MeP4Rg58gQuXvQT27S1pRg0qDL++KMWTEz01FhdzsGwS5QOZSaTfT9UgUt4ERHRzzIx0cPLl8HibUdHW8ye3QglS+ZRY1U5D8MuURqSQq526IufOp9Bl4iIfoWxsS7c3RvA0/MyZsxwRIsWJThk4Scw7JLGS6vnNrUJZRmZTMahCkREpKwHDz7Bw+MSFi1ygY2Nmdjevr0Dmje3g4GBjhqry9kYdkljKdNzm2BmzwBLREQqFxwcDS+vK9i8+QEEAXB3v4ANG9qI90skEgbdX8SwSxrnRyH3+55b9tISEVFmkMnk2LLlIby8riAkJEZsf/48CKGhMTAz01djdbkLwy5plLSWBWPPLRERZZXr1z/Aze0cHj78LLYZGengzz9rY+DAytDV1VJjdbkPwy7lSgrjcKUSWMgFACnH4TLkEhFRVvn0KQIzZlzCrl1PFdo7dCgFd/cGClv+kuow7FKO9aMlwb4Ptam9R+ZqCURElFUEQUDXrvvw+PEXsa1s2bzw8nJCrVqF1FhZ7sewSzmO0kuCGReC7P89uwDH4RIRUdaTSCQYN642+vQ5CHNzPUycWBeurhWgpSVVd2m5HsMu5TipBd3UlgQTdIwRVWkKTKv1QkhgOAQhxSFERESZ4t27MAiCoLCMWPPmdpg1qyE6dCgNS0sDNVanWRh2KUfR9d0nBl1BIoXMtMQPe2m59jYREWWl6Oh4rFhxC0uX3kCdOkWwfXt7cSMIiUSCQYOqqLlCzcOwSznG9yspyExLIKTtLTVWRERElEgQBBw/7oOpU8/Dzy8MAHD2rC9OnnyNpk3t1FydZmPYpRzD6L6nwu3ISlPUVAkREdE3r14FY/Lkczh37q3YpqUlwYABlVG7dmE1VkYAwy7lEMmHLwBcSYGIiNQvIiIOCxdew+rVdxAfLxfb69e3wezZjeDgYKnG6igJwy7lCMl7dRPM7Bl0iYhIrU6efI0//jiFT58ixbZChUwwY4YjWrUqKY7TJfVj2KVsLWmZMa2wV2Ibhy8QEZG66ehIxaCrp6eF4cOrYdSoGjA01FFzZfQ9hl3KdpJvFpHajmfs1SUiInVr1Kgomje3gyAAM2Y4omhRc3WXRGlg2KVsJ60NI5K29iUiIsoqMpkc//77CBcuvMU//7RSGJ6wenVL6OszSmV3vEKUbXw/ZEGQSCE3KMAdz4iISC1u3fLHpEnncP/+JwDAgQMv0K6dg3g/g27OwKtE2cL3a+gCXEeXiIjU4/PnSMyadRk7djxWaL99+6NC2KWcgWGX1C61oMshC0RElNXi42VYt+4e5s/3Rnh4nNheurQVvLwaoU6dImqsjn4Wwy6p3febRXANXSIiymqXLvnBze0cnj8PEtvMzPQwYUId9OlTEdraUjVWR7+CYZfUThIfIX7NoEtERFnt+fMgdOiwW7wtkQA9epSDm1s9WFkZqrEyUgW+TSG10vXdJy4vJjO0ZtAlIqIs5+Bgid9+SxyLW6VKARw/3h0LFzZh0M0l2LNLapG08kLyJcYEHWM1VkRERJpAEARcufIOdeoUgVT6bRkxd/cGaNDAFl27llVop5yPYZcyVfINIpL7frMIgDujERFR5nr9OgSTJ5/DmTO+WLq0Kbp2LSveV7CgCbp3L6fG6iizMOxSpkprg4jkklZe4BAGIiLKDBERcVi8+DpWrbqDuDgZAGDmzEto1aokjI111VwdZTaGXco0ur77xKCbtEFEctwsgoiIMpMgCNi//zmmT7+Ijx+/fcJobW0MDw9HGBnpqLE6yioMu6Qy3w9ZSD5UgRtEEBFRVnry5Avc3M7h6tX3YpuurhaGDauK33+vyaCrQRh2SWV+NGSB43GJiCgrCIKAqVPPY926e5DJBLHdxaUYZs5siOLFLdRYHakDwy79lNQmnkmjAwAoDlngUAUiIspKEokEkZHxYtAtWtQMs2Y1QpMmxdVcGakLwy79lB/14nLIAhERqZObWz2cOfMG/fpVwpAhVaGvz7ijyXj1SWk/mniW1JNLRESU2b58iYKn5yVUrlwQrq4VxPa8eQ1x82Z/6Okx5lA2CLunT5/GokWL4OfnB1tbW4wZMwbOzs6pHvvq1SvMnz8fN27cAABUqVIF48ePh4ODQ1aWrNF0fffB7KKreJu9uERElNUSEuTYsOEe5s71RlhYLI4d80Hr1iWRJ4+BeAyDLiVR63bBd+/exahRo2Bvb485c+bAzs4Oo0aNwoMHD1IcGxUVBVdXV3z8+BFubm6YPHkygoKC0KdPH0RGRqqhes2g67sPFgeqIc/uUsizu5RC0AU48YyIiLLWlSvv4Oy8BZMnn0dYWCwAQCYT8PjxFzVXRtmVWt/2rF27FqVKlcLChQshkUjQvHlztGvXDmvXrsWyZcsUjvX19UVgYCDWrl2LMmXKAADKlSuHtm3bwsfHBxUqVEjtKegXfN+L+71Qx82ceEZERFnC3z8cI0acwM6djxXau3cvi8mT6yNvXkM1VUbZnVrD7rVr1zBs2DBIJIl7UEulUrRs2RLr1q1LcaydnR0KFSqELVu2YNiwYQCALVu2IH/+/ChZsqTSzy2RJP5HKen67oPhvZQT0GSG1gASx+VGVZqC+KLtkN1/hEnXmNdaM/B6axZeb80QG5uAv/++g0WLriEqKkFsr1QpP+bMcULVqgXVWB1lFlW+rtUWdkNCQhAZGYmiRYsqtNvY2CA0NBTh4eEwMTER2/X09LB27Vp07doVe/fuBQAYGxtj+/btMDAwgLLy5DFJ/yBN9HwXcCGV3tzWu6Bl31G8aZqFJamCpSWvtybh9dYsvN65m7v7OXh6XhZvW1kZwsvLGf36VYZUync6lD61hd2YmBgAiYE1OVNTU/H+5GE3Li4OEyZMgK2tLfr16wcAWL9+PaZOnYotW7ZAV1e5va2Dg8Mhl//Kd5A7mV+aovBLkWBmj6hKUxCXpykQGK62un6WRJL4hzAoKByCkP7xlLPxemsWXm/N0LNnWaxYcRMhITEYNqwaRo+uDjMzfQQHR6R/MuVYUqnqOibVFnb19fUBABERir+sYWFhAJCit/bAgQPw8/PDmTNnxBBcr149NG7cGIcOHUKHDh2Uen5BAP9x/E7yJcWA78bk5vCfFa+3ZuH11iy83rlHZGQ8Hj36jJo1C4lt5uYGWLq0KaytTdCwYXEEBvLNjSZQ5TVW22oMFhYWMDIygq+vr0K7n58fzMzMUvT4Pn78GMWKFVPo7TU1NUWxYsXw8OHDrCg51zO67yl+nWBmz8lnRESUJQRBwMGDL1Cv3kZ067YPnz4prrLk4lIc5crlVVN1lNOpdemxmjVr4ujRoxD+H9/lcjmOHDmCWrVqpTg2b968eP36Nb5+/Sq2ff36FT4+PsiXL19WlZxrfd+ryyXFiIgoKzx7FogOHXZjwIDD+PAhHBERcZg9+3L6JxJlkFpXYxg0aBB69OiBsWPHonHjxjh58iR8fHwwa9YsREdH48qVKyhWrBjs7OzQsWNHbN68Gb1790bPnj0BAFu3boWWlpbSQxgoMdwa3feEJD5xGIlWlL94H3t1iYgos4WFxWL+fG/8889dyGTfPrN2ciqKUaNqqLEyym0kgqDekS+nTp0Sd1CzsbERg+/79+/h7OyMESNGYOTIkQAAHx8fzJs3Dzdv3gQAVK9eHePHj4ednZ3SzxsUpHkT1JIH3OTh9nu5af1ciQSwsjLhGC8NweutWXi9cya5XMDOnY8xc+ZlBAZGie02NmaYNashmjYtLi5Jmhyvt2aRSlW30oraw666aGLYtThQLcXauYDi+rmRlabkmqAL8B9HTcPrrVl4vXOet29DMWTIEdy+HSC2GRhoY9SoGhg+vBr09dP+wJnXW7OoMuxy42gNkjRkQZBIITcokCvDLRERZV+WlgZ4//7bMpatWpWEh4cjihTJaau3U07CsKshdH33iUMX5AYFENzxmZorIiIiTWNsrAt39wZYvPg6PD0bwdHRVt0lkQZQKuwKgoBXr17h48ePiIyMhJGREaytrWFnZ5fq+BpSv6RxusmHLwg6xj84g4iI6Nd5e7/HzJmXsGZNSxQu/K3ntkOHUmjb1h46OlpqrI40SbphVy6X49ixYzhy5Ai8vb0RExOD5MN8JRIJ9PX1Ubt2bbRq1QrNmjWDVKrWFc0ome+DLsBlxYiIKPN8/BgOD49L2Ls38RPE6dMv4p9/Won3SyQSBl3KUumG3SZNmiA4OBiOjo6YOnUqypQpA2traxgZGSEyMhL+/v548uQJLl26hClTpmDRokU4depUVtROGZB8nK7MtATH6BIRUaaIi5Nh9eo7+Ouva4iKihfbfX2/IiIiDsbGumqsjjRZumG3e/fu6NKlC4yMjFLcZ2pqClNTU5QqVQq//fYbIiIi8N9//2VKofRr5AYFENL2lrrLICKiXOjsWV9MnnwOPj4hYpuFhT4mT66HHj3KQUuLn/iS+qQbdvv165dqu7+/PwIDA+Hg4AA9PT0AgLGxcZrHExERUe7y9m0opk49j+PHfcQ2qVQCV9cKmDixDiwsDNRYHVEipd9qBQUFoVevXnByckKXLl3w4MEDvHnzBq1bt8a7d+8yo0b6SclXYCAiIlIluVxAt257FYJujRrWOHWqB+bOdWbQpWxD6bA7e/ZshIWFYePGjeJEtbx580JHRwczZ85UeYH084zue4pfcwUGIiJSJalUAje3egCAfPmMsGJFMxw61AXly+dTc2VEipQOu1evXoWrqyvKli0rthkbG6NXr17iNr6kfrq++xRWYeAKDERE9CtevAiCn1+oQlvLliUwb54zvL37oFOnMlyGlLIlpcOuXC5HajsMh4aGwtDQUCVF0a9L3qubYGbPFRiIiOinhIfHwt39Aho23AI3t3MK90kkEvTpUxEmJnpqqo4ofUqH3UaNGmH9+vV48+YNACA+Ph5XrlzBmjVr4OLiovICSXns1SUiol8llwvYufMJatfeiL//vo2EBDlOnnyNs2d91V0akVKU3i54ypQp+P3339G5c2cAQP/+/SEIAho1aoTx48ervEDKuNR2S2OvLhERKevBg0+YNOkcbt78NslZX18LI0ZUR+3ahdRYGZHylA67xsbGWLduHV6/fo2XL18CABwcHFC0aFFV10ZK4m5pRET0K4KDozF79hVs2fIAyUcstmhRAjNmOMLGxkx9xRH9JKXDbu/evTF69GhUqVIFxYsXF9vv3LmDq1evYsSIESotkDIm+dAF7pZGRETKOnDgOcaPP4OQkBixrUQJC3h6NkKjRkXVVxjRL1I67N64cQMhISEp2l+/fo1169Yx7Gax1IYuyExLcLc0IiJSirGxrhh0jYx08OeftTFwYGXo6mqpuTKiX5OhsHv69GmcOXNGvL1lyxacPn1avJ2QkICLFy+iTJkyqq+QfohDF4iI6GcIgqCwVJizczE0a2YHY2NdTJtWHwUKcH12yh0yFHYjIiIQEBAg3g4JCVF4gUilUrRq1QoDBw5UfYX0Q5L4CAAcukBERBkTFyfD2rV3cfOmPzZsaK3w93zdulbQ0WFPLuUuGQq77dq1Q7t27QAATk5OmDBhAurUqZOZdZGS5AYFOHSBiIh+6Pz5t5g8+RxevgwGABw+/BKtW9uL9zPoUm6k9Jjds2fPZkYd9BN0ffdBK8o//QOJiEij+fmFwt39Ao4ceSW2SSTA06eBCmGXKDdSOuyGhYXBw8MDly9fRlhYWIr7nz59qpLCKH3Jd0kTdDi2ioiIFEVHx2PFiltYuvQGYmJkYnu1agUxZ44TKlTIr8bqiLKG0mF31qxZuHv3LoYNGwYvLy8MHjwYCQkJ2Lt3L0aOHJkZNVIaksbrApyURkRE3wiCgOPHfTB16nn4+X3rmMqb1xDTpjVAp06lIZVKfvAIRLmH0tsFX7x4EUOGDEGnTp0AALVq1cK4cePQsmVL3LrFMaNZJfkQBpmhNSelERGR6NGjL3B1PSgGXS0tCYYMqQpv777o0qUMgy5pFKXDrlwuh0QigaGhIYyNjfHu3TsAQKVKlXDu3DmVF0jf6Prug8WBasizuxTMLrqK7RzCQEREyZUvnw9t2yaOxa1f3wbnz/fGjBmOMDXVU3NlRFlP6bDr4uKCbdu2AQAaNmyIdevW4fz589izZw8KFCig8gLpm6Q1db+flMYhDEREmksQBJw69RpC8v19AUyf7oh//mmF3bs7wMHBUk3VEamf0mHXzc0NAwYMAABMmDABFhYWGDJkCB49eoTJkyervED6RmFNXUNrJJjZI9RxM4cwEBFpqEePvqBt2//Qo8d+7N6tOEG8UCETtGljr7COLpEmkgjfvxX8CeHh4TA0NISWVs5Zny8oKBxyubqryDhd333i0AWZoTWCOz5Tc0U5g0QCWFmZIDAwHL/+m07ZHa+3ZtHk6x0SEo05c65i06YHkMsTv/l8+Yxw40Y/GBrqqLm6zKHJ11sTSaWApaWJah4rowcGBwdj37592LhxIx4/fqxwn4mJCXx9fTF06FCVFEUpcZkxIiKSyeTYvPkBatfegA0b7otBt3hxcyxZ0iTXBl2iX5GhpceePXuGvn37IjIyEgYGBpg7dy4mTJiAPn364NOnT1iyZAn279+PokWLZnK5mknXdx+0Q1+ItzlGl4hI89y86Q83t3O4f/+T2GZoqIOxY2ti8OAq0NNTejVRIo2QoVfGggULYG1tjU2bNsHIyAgzZ87EkiVL8OnTJ2zbtg0WFhbw8PBAhw4dMrtejZN8+AIAJJjZc4wuEZEGkcsFjBlzEtu3K36q+ttvDnB3b4CCBVXzUS9RbpWhYQwPHz5E165dYWxsDIlEgr59+yI6Ohr79+/H6NGjcfLkSXTq1AlSqdLz3SgdyYcvAOzVJSLSNFKpRGGMaunSVti/vxNWrWrJoEuUARnq2Q0NDUWePHnE2+bm5gCAxYsXo2bNmplSGKUcvsCVF4iINIMgCAqrKEyZUg9XrrzDsGFV4epaEdra7FwiyqgMD/BJbemSnLT6Qk7D4QtERJrn/fswuLtfgKOjLXr3riC258tnhOvX+zHkEv2EDIfdpUuXYuvWrQCAhIQEAICXlxfMzMwUjlu/fr0Ky9NM3wddgMMXiIhys5iYBKxceQtLltxAdHQCrlx5h9atS8LCwkA8hkGX6OdkKOy2b98+Q22kGt+P0+XwBSKi3EkQBJw8+RpTppzH27ehYrtUKsXLlyGoUcPgB2cTUUZkKOx6eXlldh2UTNJOaQCDLhFRbvX6dQgmTz6HM2d8xTYtLQkGDKiMP/+sBTMzffUVR5SLcFG+bExmaM2gS0SUy0RExGHx4utYteoO4uJkYnu9ekXg6dkIpUtbqbE6otyHYZeIiCgLLVx4DcuX3xJvW1sbw8PDEW3a2Kc6GZyIfg1HuxMREWWhESOqw8JCH7q6Whg9ugauXOmLtm0dGHSJMgl7domIiDJJaGgMHj36grp1i4htefIYYOXK5ihWzBzFi1uosToizfBTYff169c4evQogoKC0L9/fxQuXBgPHjxAhQoV0j+ZiIgol5PLBWzb9gienpcRFyeDt3df5MtnJN7v7FxMjdURaRalhzFcvXoV7dq1w5EjR7Bjxw58/PhR3E74yJEjmVEjERFRjnHnzkc0b74NY8eeQlBQNMLD4zB37lV1l0WksZQOu3/99Rfatm2L//77D8L/N+suX748OnXqhBUrVqi8QE2j67sPWlH+6i6DiIiU9OVLFEaPPoFmzbbj7t1PYnvbtvYYO7aWGisj0mxKD2N4/fo1+vfvn6K9Ro0a2L9/vypq0ki6vvtgdN8T2qEvxDZBx1iNFRERUUYkJMixfv09zJvnjbCwWLG9VClLzJ7dCPXq2aixOiJSOuzmy5cPr1+/Rv369RXar1+/DhsbvqB/1vdBF+AWwURE2d3Ll8EYMOAQnj4NEttMTHQxYUId9O1bETo6WmqsjoiAnwi7/fv3x8yZMxEaGgqJRIIzZ85gx44dOHr0KObOnZsZNeZ6ur77xKArSKSQmZZAZKUp3FCCiCiby5/fCIGB0eLtbt3KYvLkegqT0YhIvZQOu507d4aRkRE2b94MfX197Ny5E6VKlcLSpUvh4uKSGTXmarq++2B20VW8LTMtgZC2t35wBhERqYsgCArr4Zqa6mHq1PrYsOEeZs92QtWqBdVYHRGlRiIkzTLLoKCgIFhaWmZWPVkmKCgccrm6qwAsDlRTGL4Q6riZPboqJJEAVlYmCAwMh3K/6ZQT8Xprlqy+3qdOvYaX1xVs3doO1tYmYrtcnvjkUik3hchMfH1rFqkUsLQ0Sf/AjDyWsic0aNAAgwYNwpEjRxAbG5v+CfRDkvgI8WsGXSKi7Of16xD07LkfPXrsx6NHX+DhcVHhfqlUwqBLlI0pHXbnz58PPT09uLm5oU6dOpg0aRK8vb0zozaNIjO0ZtAlIspGIiPj4eV1BQ0abMbJk6/F9k+fIhETk6DGyohIGUqP2W3RogVatGiByMhInDlzBseOHcOgQYNgaWmJ1q1b448//siMOnOdpKXGpNEB6i6FiIiSEQQBhw69hLv7BXz4EC62FyhghOnTHdG+vYPCuF0iyt6UHrP7vbCwMOzZswdLlixBbGwsnj59qqraMpU6xuwmBVxJfESKjSMSzOw5MS0TcIyXZuH11iyZcb2fPQuEm9s5XL78TmzT0ZFi6NCqGD26JoyNdVXzRKQ0vr41iyrH7CrdswsAEREROH36NI4dO4arV6/C2NgY7du3R5s2bVRSVG6V2lq6QGLQ5Zq6RETqJZPJ0bPnAfj5hYptTk5F4enZCHZ2FmqsjIh+hdJhd8iQIbh69SokEgkaNWqEJUuWoEGDBtDW/qncrFGSJqMJEinkBgUg6BhzPV0iomxCS0uKqVPrYeDAI7CxMcOsWQ3RtGlxDlkgyuGUTqjR0dFwd3dH06ZNYWzM7WwzStd3nzh0QW5QAMEdn6m5IiIizXbvXgDy5DGAjY2Z2NamjT2ioxPQrp0D9PXZiUOUG2TolRwVFQVDQ0MAwKZNmzK1oNzK6L6n+LWgwzcJRETqEhgYhdmzL+Pffx+haVM7bN7cVrxPIpGga9eyaqyOiFQtQ2G3WbNmWL58OSpUqICyZcv+8COdR48eqay43CT5erocn0tElPUSEuTYtOk+5sy5itDQxHXijx/3waVLfqhf30bN1RFRZslQ2O3Tpw+KFCkCAJg1a1amFpTbcT1dIqKs5+39HpMmncWTJ4Fim7GxLsaNq41atQqpsTIiymwZCrv9+vUTvy5cuDBKliwJc3NzhWPi4uIQFxen0uJyi+TjdYmIKOt8/BgOD4+L2Lv3uUJ7585lMHVqfeTPb6Smyogoqyi9g1rv3r1x+/btFO3Hjh2Di4uLSorKTXR998Hsoqt4m+N1iYiyxrZtj1C79kaFoFuhQj4cPtwVy5c3Y9Al0hAZ6tmNiIhAWFgYgMSdZYKDg+Hv/62nMiEhARcvXuTyLKlIPjEN4HhdIqKsYmlpgKioeABAnjz6cHOrhx49ykFLS+l+HiLKwTIUdjdu3Ijly5dDIpFAIpFg2rRpKY6RSqUYP378TxVx+vRpLFq0CH5+frC1tcWYMWPg7Oz8w3MmTpyIs2fP4vjx48iTJ89PPW9WSD4xLdRxM8frEhFlEkEQFDpdmjQpjqZNi8Pa2gQTJ9aBhYWBGqsjInXJUNjt0KEDateuDUEQ0LNnT4wdOxZVq1YV79fS0kKRIkVgaWmpdAF3797FqFGj0LRpUwwbNgwnT57EqFGjsH37dlSoUCHVcx49eoT9+/dj2rRp2TroJseJaUREmSMqKh5LltzAkyeB2LSpjRh4JRIJNm5sw55cIg0nEQTldpjet28fateujQIFCqikgGHDhiEgIAB79uyBRCKBXC5Hu3btYGtri2XLlqV6Ts+ePREVFYXdu3dDKv25f8SCgsIhl/9K5elLPl5XZmjNjSTUgHupaxZeb00j4OLF9xg9+jjevw8HAGzY0BotW5ZUc12UGfj61ixSKWBpaaKSx8pQz+6hQ4fg7OwMQ0NDaGtr4+bNm2ke27p1a6UKuHbtGoYNGya+E5dKpWjZsiXWrVuX6vEnT57ErVu3sH37dshksp8Ou1mBG0kQEWWO58+DMHnyOVy86Ce2aWtL4esbqsaqiCg7ylDY9fT0RMGCBVGtWjWMGzcuzeMkEolSYTckJASRkZEoWrSoQruNjQ1CQ0MRHh4OE5NvqT4hIQF//fUXdHV10a9fP8TExKB27dqYNWsWrK2tM/y8ibUm/peZko/Xjao0JdOfj1JK+pnzZ68ZeL1zv/DwWCxYcA1r1txFQsK3j+caNrTF7NmNULJkzhjaRsrj61uzqPI6Zyjs7tmzRwyTz56p7qP4mJgYAICxsWKvp6mpqXh/8rB77Ngx+Pr6olGjRujQoQNCQ0OxbNkyDBkyBPv371eqlzdPHtV0jf+Q9P9XyrgQTKv1yvznozSp6qMQyhl4vXMfuVzA1q0PMGHCaQQEfOtIsLU1w6JFTdGuXSmuCKQh+PomZWUo7BYqpLi7zJ07d1ClShUAQHBwMI4cOQJbW1s0aNBAqSfX19cHkLi0WXJJy5wZGCjOnD1//jzs7OywYsUKaGlpAQCKFi2KHj164N69e2JNGREcnLljdnV998E04gMAQCYXEBIYnnlPRmmSSBL/YQwK4hgvTcDrnXvdvOkPV9f94m19fS2MHFkdHh5OiIqKQVBQRNonU67A17dmkUpV1zGZobCb3N69ezF16lQcPHgQhQsXRrdu3fD161dERERgzJgxGDBgQIYfy8LCAkZGRvD19VVo9/Pzg5mZWYoe369fv6JUqVJi0AWAMmXKAAC+fPmi1PchCMjUF4vhPcXxunxhqldmX2/KXni9c59q1azRqlVJHD78Ei1alMCMGY6wtTWDgYEOIiNjeL01CF/fmkGV11jp2V2rVq1C7969YWdnh5MnT0IqleLSpUsYMWIEtm3bpnQBNWvWxNGjR5G0KIRcLseRI0dQq1atFMfa2tri/v37CtsS37p1CwBSjPtVJ13ffdAOfSHe5kYSREQZJ5PJcejQC3y/WJCHhyN27vwNGze2gY2NmZqqI6KcRumw+/nzZ3H921OnTqF58+bQ1dWFjY0NgoODlS5g0KBBePbsGcaOHYsjR45gzJgx8PHxwYABAxAdHY3Tp0/Dx8cHANCjRw8EBQWhb9++2LdvH9atW4dx48ahUaNGcHBwUPq5M0vyVRgSzOy5vi4RUQZdv/4BLi7/on//w9i7V3GOSJEipmjUqKh6CiOiHEvpsFu8eHHs3bsXBw8exNmzZ9G8eXMAiZPHSpUqpXQBlStXxpIlS/D8+XNMmDABL1++xJIlS1ChQgUEBQVh+PDhOHr0KADAzs4OGzduBABMnz4d69evR+vWrbFw4UKlnzczJV+Fgb26RETp+/QpAsOGHUPr1jvx6FHisLQZMy4hNjZBzZURUU6n9KYS3t7eGDlyJCIjIzFo0CCMGTMGN27cwIABA7B+/XpUq1Yts2pVqczaVIIbSWQvXIRcs/B65zxxcTKsXXsXCxZ4IzIyXmwvVy4vZs92Qq1ahdI8l9dbs/B6a5Ys31Qiudq1a+P69euIjo4WJ5DVqFEDJ0+eVNmuajkZN5IgIsqYc+d8MXnyObx6FSK2mZvrYdKkeujduzy3+SUilVA67AJAdHQ0jh07hufPnwMAHBwcxOEMmo5DGIiIfiwhQY6BAw/jyJFXYptEAvTqVQGTJtWFpaXBD84mIlKO0mH3zZs36N27N0JDQ1GyZOL+4//99x+WL1+OTZs2ZatVEdRJZmjNiWlERKnQ1pbCwEBHvF2tWkHMmeOEChXyq7EqIsqtlA67s2bNQp48ebB3717kzZsXQOIKDQMHDsSsWbPwzz//qLxIIiLKuZKmhiTf4czdvT7u3PmIMWNqoVOn0pBKufsZEWUOpQdE3blzB66urmLQBYB8+fKhd+/euHPnjkqLIyKinO3Vq2B06bIX27Y9UmjPn98YV6/2RZcuZRh0iShTKd2za2RkhM+fP6do//TpEwwNDVVSVE6l67sPWlH+6i6DiEjtIiLi8Ndf17BmzR3Ex8vx6NFntGxZEubm+uIxDLlElBWUDrtt27bFqlWrYGhoiDp16gAArly5gjVr1qBnz54qLzAn4UoMRKTpBEHAnj3P4OFxEZ8+RYrtenra8PMLVQi7RERZQemwO2bMGERFRWH+/PlISEhc7FtbWxudOnXCmDFjVF5gTqDruw9G9z2hFfZtZjFXYiAiTfPw4We4uZ3D9esfxDY9PS0MH14No0bVgKGhzg/OJiLKHEpvKpEkIiICvr6+AIBixYrByMhIlXVlOlVuKmFxoBq0Q1+ItxPM7BHS9pZqHpx+CRch1yy83uoREhKNOXOuYtOmB5DLv/3gmzWzw4wZjiha1DxTnpfXW7PwemsWtWwqERwcjIMHD+Ldu3ewtLSEk5MTypUrp5IicrqktXUFiRQy0xLs1SUijfLXX9ewYcN98Xbx4ubw9GwEZ+diaqyKiChRhsLu8+fP0bt3b8jlctjZ2cHf3x8rVqyAu7s7OnfunNk1ZmvJJ6XJDQqwR5eINM6YMbXw339PEBcnx9ixNTF4cBXo6f3UnkVERCqXoX+N5s2bB2tra2zatAmmpqaQyWSYOnUqvLy80LZtW+jp6WV2ndmSru8+mF10FW9zUhoR5XafPkXi2bNAODraim2WlgZYs6YVHBzyoGBB1XzsSESkKhlaZ/fRo0fo3r07TE1NAQBaWloYOHAgoqOj8ebNm0wtMDtLvvoCwElpRJR7xcfLsGrVbdSpswEDBhxGYGCUwv0NG9oy6BJRtpShsBsaGoo8efIotFlZWQEAwsPDVV9VDpE0VhcAQh03c3tgIsqVLl70g5PTVkybdgHh4XEIDY3FggXe6i6LiChDMjyoKvk2jxlp1yQyQ2sGXSLKdd6/D4O7+wUcOvRSbJNIgB49yuHPP2ursTIioozLcNhdunQptm7dKt5OWmPXy8sLZmZmYvv69etVWF72xd3SiCi3iolJwMqVt7BkyQ1ERyeI7VWrFsDs2U6oXLmAGqsjIlJOhsJu+/btlWrXBNwtjYhyo8ePv6BPn4N4+zZUbLOyMsTUqfXQpUtZbvFLRDlOhsKul5dXZteR4yQfr8uJaUSUWxQubILIyDgAgJaWBAMGVMaff9aCmRm3+SWinIkLIf4ijtclopxMEASFuRdmZvqYMqU+du9+Ck/PRihd2kqN1RER/boMrcZARES5iyAI2LfvGerX34SAgAiF+7p1K4s9ezoy6BJRrsCwS0SkYZ48+YL27Xdh8OCjePEiGB4eFxXul0gkXGmHiHINDmMgItIQoaExmDv3KjZsuA+ZTBDbw8JiERcng66ulhqrIyLKHD8Vdl+/fo2jR48iKCgI/fv3R+HChfHgwQNUqFBB1fUREdEvkssFbN/+CJ6elxEYGC22Fy1qhlmzGqFJk+JqrI6IKHMpHXavXr2KIUOGoFChQvD19UWLFi0QEhKCrl27Yv78+WjZsmVm1JmtcI1dIsop7tz5iEmTzuLu3U9im6GhNkaProkhQ6pCX58f8BFR7qb0mN2//voLbdu2xX///QdBSPwYrHz58ujUqRNWrFih8gKzG13ffTC76Cre5hq7RJRdxcfL0L//YYWg27atPa5c6YvRo2sy6BKRRlA67L5+/Rq1a6fcJrJGjRr48OGDSorKzpJvJgFwjV0iyr50dLQwdWp9AECpUpbYu7cj1q5thUKFTNRcGRFR1lH6bX2+fPnw+vVr1K9fX6H9+vXrsLGxUVlh2VXyzSRCHTdzjV0iyjauXHkHGxszFCliKra1b+8AiQRo1aokdHQ4AY2INI/SYbd///6YOXMmQkNDIZFIcObMGezYsQNHjx7F3LlzM6PGbImbSRBRduHvH47p0y9i//7naNmyBDZsaCPeJ5FI0L59KTVWR0SkXkqH3c6dO8PIyAibN2+Gvr4+du7ciVKlSmHp0qVwcXHJjBqzDU5MI6LsJDY2AatW3cGiRdcQFZUAADhy5BWuXXuPWrUKq7k6IqLs4admJ7Rs2VIjVl1IjhPTiCg7OXXqNaZMOY83b76KbZaWBpg8uR5q1CikvsKIiLIZpcPuoUOHfnh/69atf7qY7IwT04goO3j9OgTTpl3AyZOvxTapVIJ+/Spi/Pg6MDfXV2N1RETZj9Jhd9y4cam2J20tmVvDLiemEZG6rV17Bx4elxAXJxPbatcuhNmznVC2bF41VkZElH0pHXafPXumcFsul+PBgweYPn06PDw8VFZYdsWJaUSkLoULm4pBt0ABI0yf7vj/1RYkaq6MiCj7+uUVxaVSKSpVqoRu3brBw8MDe/fuVUVdREQaTy4XIJV+C7LNmtmhWTM72NvnwejRNWFsrKvG6oiIcgaVbZ8TGRmJN2/eqOrhiIg0VlhYLObP94av71ds3txW7LmVSCTYtKkNe3KJiJTwyxPUBEHAmzdvsHHjRlSpUkVlhRERaRq5XMB//z3BjBmXEBgYBQA4ceI1mjWzE49h0CUiUo5KJqiZmpqiTp06mDp1qkqKIiLSNPfuBWDSpHO4ffuj2Kavr4WAgIgfnEVEROn55QlqmoCbSRBRZgkKisbs2ZexdetDCMK39latSsLDw1Fh618iIlKeVNkTHB0dcfr06cyoJdtKvsYuN5MgIlVISJBj3bp7qFVrPbZs+RZ07e3zYNeuDli/vjWDLhGRCigddkuXLo3bt29nRi3ZVvI1drmZBBGpwo0bHzBp0lmEhsYCAIyNdeHh4Yhz53rB0dFWzdUREeUeSoddd3d3eHt74/z585lQTvbGNXaJSFXq1CmC5s0TJ5517lwG3t59MXRoVejoaKm5MiKi3EXpMbuNGzeGIAgYOnQopFJpipnBjx49UllxRES5QVycDAcOPEfHjqUV/s2cObMhhg+vjho1rNVYHRFR7qZ02J05cyaXviEiyqCzZ99g8uTz8PEJgY6OFtq1cxDvs7Exg42NmRqrIyLK/TIUdm/evImSJUvC3Nwcv/32W2bXRESU4/n6fsW0aRdw/LiP2DZjxkW0alUS2tpKjyAjIqKflKF/cXv37q1xk9KIiH5GVFQ85sy5gvr1NykE3Ro1rLFpU1sGXSKiLJahnl0h+eKPRESUgiAIOHz4JdzdL+D9+3CxPV8+I7i7108xXpeIiLJGhsfsauo/0txQgojSExubgJ49D+DChbdim7a2FIMGVcYff9SCiYmeGqsjItJsGQ67I0eOzFDgzW2rMXBDCSJKj56eNiwt9cXbjo62mD27EUqWzKPGqoiICFAi7Hbr1g1ly5bNzFqyHV3ffdAOfSHe5oYSRAQkDlkQBEAq/dYB4O7eAE+eBGLChDpo0aKExn4aRkSU3WQ47NauXRvOzs6ZWUu2k7xXN8HMnhtKEBEePvyMiRPPolev8uja9VsHQMGCJrhwoTdDLhFRNqP0OruahNsEE1GS4OBoeHldwebNDyAIwJs3X9GiRQmYmn4bj8ugS0SU/WQo7LZv3x7W1pq1w0/yiWncJphIc8lkcmzZ8hBeXlcQEhIjtpuZ6cHfP1wh7BIRUfaTbtg9e/YsvLy8MvyAp0+fRuPGjX+pqOyAE9OI6Pr1D3BzO4eHDz+LbUZGOvjjj1oYNKgKdHW11FgdERFlRLphd/z48bCyskLz5s1Rv359lC5dGgYGBuL9UVFRePr0KS5evIhjx44hJCQkV4RdDmEg0lyfPkVgxoxL2LXrqUJ7hw6l4O7eAAUK8A0wEVFOkW7YPXfuHP79918cPnwYf//9N6RSKczMzGBoaIjIyEiEhYVBLpejRIkS6NChA7p165YVdWcZDmEg0jx//XVdIeiWLZsXXl5OqFWrkBqrIiKin5Fu2DUxMcGQIUMwZMgQfPz4EY8fP4a/vz8iIyNhZGQEa2trlC1bFgULFsyKeomIMt24cbWxd+8zSKXAxIl14epaAVpa3OaXiCgnUmo1hoIFCzLUElGu4ucXilevQuDkVFRsy5vXEBs2tEaZMnlhaWmQ9slERJTtcekxItJI0dHxWL78FpYtuwF9fW14e/dTCLb169uosToiIlIVfi5HRBpFEAQcPfoK9etvwvz53oiJkeHr11gsWXJD3aUREVEmYM8uEWmMV6+C4eZ2DufPvxXbtLQkGDiwCv78s5YaKyMioszCsEtEuV5ERBz++usa1qy5g/h4udhev74NZs9uBAcHSzVWR0REmSlDwxji4uKwefNmyOVyREZGiu1Xr15FSEgIBg4cqNBORJRd3LnzEbVrb8CKFbfEoFuokAnWrWuF3bs7MOgSEeVyGQq7kZGR8PLywq5duzBx4kQAwOfPnzFmzBh8+PABly9fRnx8/E8Xcfr0abRs2RLly5dHq1atcObMmXTPefPmDcqVKwcHBwe8f//+p5+biHK34sUtkJCQGHL19LQwdmxNXLnSB61b20Mikai5OiIiymxKTVDr0KED/P39sXfvXnh5eaFTp04oV64cBEH46QLu3r2LUaNGwd7eHnPmzIGdnR1GjRqFBw8e/PC8WbNmwcjI6Kefl4hyJ5lMrnDb3Fwfbm510ayZHS5dcsXEiXVhaKijpuqIiCirKRV2ZTIZPD09oa+vj0qVKkFLSwsnTpz4pd6RtWvXolSpUli4cCFatmyJRYsWwc7ODmvXrk3znFOnTsHb2xujRo366eclotxFJpNjy5YHsLdfjoCACIX7evYsj82b26JoUXP1FEdERGqT4QlqgiBg+/btePjwIaZMmYKRI0fi3r17uHr16i/17F67dg3Dhg0TA7NUKkXLli2xbt26VI+PiYmBl5cXunTpAgcHh59+Xokk8b+MHks5U9K14zXM3W7d8sfEiedw//4nAMCsWZexfHkz8X4OV8id+PrWLLzemkWV1zndsBsXF4dTp05BIpGgT58+GDVqFObPn49+/fph6dKl+PLlCwDg+fPnMDY2BgCULVs2Q08eEhKCyMhIFC1aVKHdxsYGoaGhCA8Ph4mJicJ9q1evRmRkJH7//Xe8evUqQ8+Tmjx5TH58gDTxp6wllcDKKp1jKduztOQ1zI0+fYrAxIlnsHHjPYV2qVQKCwsjbvGrIfj61iy83qSsdMPurl27MH/+fADA8uXLUbNmTcydOxeFCxeGmZkZFi9eDADo168fgMQelEePHmXoyWNiYgBADMlJTE1NxfuTh913795h3bp1mDRpEszNzTP0HGkJDg6HXJ72/RZyAVoAZHIBIYHhv/RcpD4SSeI/jEFB4fiFDyAom4mPl2HdunuYN88b4eFxYnuZMlZYubIlypWzREgIV4jJ7fj61iy83ppFKs1Ax2QGpRt2mzVrhgYNGsDFxQXXr19HdHQ0OnfujH379iE6Ohrr16/HqVOncOXKFaUDqL6+PgAgIkJxfF1YWBgAwMBAcU96T09PFC5cGM2bN0dYWBiioqLE8+Pi4qCrq5vh5xYEZPjFwhdVzqfM9abs7dIlP7i5ncPz50Fim5mZHiZMqIO+fSuiQAEzBAbyj6Em4etbs/B6awZVXuN0P+OztLSEsbExJBIJ1q5dC7lcDkEQ4OnpieDgYISFhf30eDgLCwsYGRnB19dXod3Pzw9mZmYKPb4BAQE4d+4cfHx8ULNmTVSvXh0DBw4EALRt2xaHDx/+qRqIKOeIiUnAsGHHxKArkQA9e5aDt3dfDBhQGdraHLZARESKlNpBTV9fH9OmTUNkZCSmT5+O+fPno0KFCr80Qa1mzZo4evQo+vfvD4lEArlcjiNHjqBWLcWtOy0sLLB582aFthcvXmDWrFlYuHAhatSo8dM1EFHOoK+vDXf3Bhg27BiqVCkALy8nVK5cQN1lERFRNqZU2I2Li8P06dPRrFkzdOrUCYcOHULLli1/qYBBgwahR48eGDt2LBo3boyTJ0/Cx8cHs2bNQnR0NK5cuYJixYrBzs4ONWvWVDhXS0sLAFCxYkXkzZv3l+ogouxFEAScPPkaZcvmReHCpmJ7hw6lYGSkg6ZN7SCVclo2ERH9WIY/8xMEARcvXkSpUqUwZMgQ9OzZEz4+Prhx48YvFVC5cmUsWbIEz58/x4QJE/Dy5UssWbIEFSpUQFBQEIYPH46jR4/+0nMQUc7y+nUIunffh169DsDd/YLCfRKJBM2bl2DQJSKiDJEIGRiDIJPJcO/ePVStWlWh3cfHB0WKFMGaNWswcOBA6OnpZVqhqhYUlPZqDLq++2B20RUAIDO0RnDHZ1lYGamSRAJYWZlwwlIOERERh8WLr2PVqjuIi5OJ7cePd0OVKgXTPZ/XW7PwemsWXm/NIpWqbpm5DA1j0NLSShF0AcDOzg4AMGLECJUUk10Y3fcUvxZ0jH9wJBGpgiAI2L//OaZPv4iPH7+tzmJtbQwPD0eOyyUiop+Wbtg9c+ZMhh/MxMQEVatWFcfS5lSS+G9/bCMrTVFjJUS53+PHXzB58jlcvfpebNPV1cKwYVXx++81YWSko8bqiIgop0s37G7cuDHDD/bixQu4uLhg1qxZv1JTtiEztEacbTt1l0GUay1efB1z5lyFXP7tM0kXl2KYObMhihe3UGNlRESUW6Qbdrds2QIgcfeyHylSpAh27dqFbdu2qaYyIsr1SpTIIwbdokXNMGtWIzRpUlzNVRERUW6S4aXHXF1dFTaPSD6vTUdHBydOnICRkZFqqyOiXCUhQa6w8UPLliXQrJkdqlQpgCFDqkJfX6nVEImIiNKVob8sv//+Ow4cOAATExN4e3vDwMAApUqVErf7TdK4cWM0b948Uwolopzry5cozJ59GYGBUdiypZ3YLpFIsGlTm5/ehZGIiCg9GQq7J0+ehLu7OwBg+PDhsLS0xJcvX+Dg4ABnZ2d06NABlpaW0NXVzdRiiShnSUiQY8OGe5g71xthYbEAgFOnXsPF5dtQBQZdIiLKTBnaVCJpQ4mPHz9CEAQsXrwYN27cgK6uLv799180btwYixYtQnx8fGbXS0Q5xNWr7+DsvBWTJ58Xg66JiS5CQmLUXBkREWmSDO+g9u+//6JFixaIjo7Gxo0bsXTpUrx//x67du3Cjh07cPPmTXTp0gVfvnzJzHqJKJvz9w/HoEFH0K7dLjx9Gii2d+tWFt7efdG5cxk1VkdERJomQ2HX2toaa9aswc2bN7F582bkyZMH+/fvh66uLh4/fgwHBwds2rQJxYsXx9ChQzO7ZiLKhmJjE7BkyQ3UqbMB+/c/F9srVcqPo0e7YsmSpsiXj5NYiYgoa/1wzK5MJkOXLl3QrFkzvHjxAlWrVkWNGjVQo0YNjB8/HgcOHBBXZdDR0cGCBQvYs0ukoa5d+wBPz8vibUtLA0yeXA/du5eDVMpxuUREpB4/DLuCIKBnz544ffo0Bg8ejNjY2BT3AyknmDx9+lTFZRJRdufoaIumTYvj1Kk36Nu3IiZMqANzc/30TyQiIspEEiH5grk/EBMTg3PnzmHnzp24du0anJyc8Pvvv8Pa2jrFsSYmJiovVNWCgsIhl6d+X57dpaAV5Q+ZoTWCOz7L2sJIpSQSwMrKBIGB4cjYbzplRGRkPPbte4YePcopvNn18wtFWFgcypXLq5a6eL01C6+3ZuH11ixSKWBpqZo8meEV3PX19dG8eXM0b94cjx8/xpUrVxAXF5cjgi0RqYYgCDh06CWmTTsPf/8ImJnpoXVre/F+GxszNVZHRESU0k9tV1S2bFmULVtW1bUQUTb27Fkg3NzO4fLlb1uHz5p1GS1bluSYXCIiyrYytBrDhg0bEBYWJt6WyWTw9/dPcZyzszPevXuXop2Icq6wsFhMnXoejRptUQi6Tk5FsW1bewZdIiLK1jIUdufNm4egoCDx9tu3b+Hs7JziuA8fPiAhIUF11amBru8+aEWlDPJEmkYuF7Bjx2PUqrUBq1ffgUyWOEjOxsYMmze3xfbt7WFnZ6HmKomIiH4sQ8MYMjiHLVcwuu8pfi3oGKuxEiL1iYqKR4cOu3H79kexzcBAG6NG1cDw4dWgr/9TI6CIiIiyHP9ifUcSHyF+HVlpihorIVIfQ0MdFC5sIobdVq1KwsPDEUWKmKq5MiIiIuVkOOx+/fpVHMoQEhICAAgODhZ7fXNb76/M0Bpxtu3UXQZRlkhIkENLS6KwjNj06Y54+zYUkyfXg6OjrRqrIyIi+nkZDrvdu3cXv04KtnXr1lVo+35zCSLK/q5de4+JE89i+PBq6NSpjNheqJAJTpzoztc1ERHlaBkOuytXrkThwoXTvF8QBLRt21YlRakLJ6eRJvn4MRweHpewd2/ixikeHpfQrJkdTEz0xGMYdImIKKfLcNgtWrQoihUr9sNjcvpQBk5OI00QFyfD6tV38Ndf1xAVFS+2589vhC9fohTCLhERUU7HCWr/p+u7D9qhL8TbnJxGudHZs28wefJ5+PiEiG0WFvpwc6uHnj3LQUsrQ6sREhER5RhKh92BAwfi/v37qd6Xkz/yTN6rm2Bmz8lplKu8fRuKqVPP4/hxH7FNKpXA1bUCJk6sAwsLAzVWR0RElHmUDrsjR45EaGhoinZBEDB48GCVFKUOXHKMcrMlS64rBN0aNazh5eWE8uXzqbEqIiKizJehsNujRw+YmZkBACpUqJDmcTl9zC7AJccod5o4sS4OHHgBAwMduLvXR8eOpXP0JzFEREQZlaGwO3Xq1Aw92M6dO1GoUKFfKoiIfs2LF0Hw8wtF48bFxbZ8+YywdWs7lCuXlxPQiIhIo6hkglpYWBhMTU1RsWJFVTwcEf2E8PBYLFhwDWvX3oWpqS68vfsqjMWtXTvtpQOJiIhyqwxNvR42bBjevXuX5v1NmzYFAKxfvx7nz59XSWFElDFyuYCdO5+gdu2N+Pvv20hIkCM4OAZ//31b3aURERGpXYZ6dl+9eoXY2FjcuHED0dHRKe5PSEgAAGzcuBGLFi1SbYVElKaHDz9j4sSzuHnz22Yo+vpaGDmyBkaMqKbGyoiIiLKHDIXdpIksU6ZMQVRUFOLj4xEVFQVTU1PIZDJERUXhxo0bMDAwQNWqVTO1YCICgoOj4eV1BZs3P0DyeaEtWpTAjBmOsLExU19xRERE2UiGx+wmrbQwf/58PHz4EEePHsWIESMQHh6OuXPn4vDhw2jXrl1m1UlE/3ft2nu4uh5ESEiM2FaihAU8PRuhUaOi6iuMiIgoG8pw2P1+mSKJRKLQ9vXrVwwYMEB1lRFRquztLcWvjYx08McftTBoUBXo6mqpsSoiIqLsKd2wO3v2bISEhGDt2rX4+vVrqsdIJBIsXbpU1bUREYD4eBl0dL4F2Tx5DODmVg/Xrr2Hu3sDFChgrMbqiIiIsrd0V2OIiYmBIAiIi4vLFZtGEOUUcXEyrFhxCzVqrMeXL1EK97m6VsDff7dg0CUiIkpHumF3xowZyJMnD4YNGwYLC4tUjxEEARMmTEBQUJDKCyTSROfPv0WjRlvg4XERHz6Ew9PzkrpLIiIiypEytM4ukHLM7vfi4+OxZcuWXy6ISJP5+YWib9+D6Nx5D16+DAYASCSAjo4W5HJ+skJERKQspXdQmzhxImJjYxEZGYnp06dDLpcjPDwcffr0wdChQzFy5EhoaXGiDJEyoqPjsXz5LSxbdgMxMTKxvVq1gvDyckLFivnVWB0REVHOlaGwmzRW18PDA5GRkSnunzhxIipUqABjY2NcunQJDRs2VGmRmU3Xdx+0ovzTP5BIxQRBwLFjPpg27Tz8/MLE9rx5DTFtWgN06lQaUumPP1UhIiKitGUo7BYtWhR6enqoXbt26g+infgwLVu2xJs3b3JU2NX13Qezi67ibUGHE34o60RFJWD8+DP4/DnxTaS2thQDBlTGn3/WgqmpnpqrIyIiyvkkggqWWAgICECBAgVUUU+WCQoKh1wOWByoBu3QF2J7qONmxNm2U19hpFISCWBlZYLAwHBk18VEdu58gpEjj6N+fRvMnt0IDg6W6Z9EqcoJ15tUh9dbs/B6axapFLC0NFHJYyk9Zjc1OS3oJieJjxC/ZtClzCQIAvbseYY6dQrD2vrbC7hTp9LIl88QDRvapjsRlIiIiJST4dUYcjuZoTWDLmWahw8/o02b/zBs2DF4eFxUuE8qlaBRo6IMukRERJlAo8MuJ6ZRZgsJicaECWfg4vIvrl//AADYt+85Hj/+oubKiIiININKhjHkVEb3PcWvOTGNVEkmk2Pbtkfw9LyM4OAYsb14cXN4ejZC2bJ51VgdERGR5tDosJt8vG5kpSlqrIRyk1u3/DFp0jncv/9JbDM01MHYsTUxeHAV6Olp9MuOiIgoS/GvLjhel1TH0/Myliy5odD2228OcHdvgIIFVTOrlIiIiDJOY8OuztvDHK9LKle+fD7x69KlreDl1Qh16hRRY0VERESaTWPDruHD+eLXHK9LPysuTgZd3W/bY7duXRKtWpVEnTqF0adPRWhra/QcUCIiIrXT2LArif+27THH65Ky3r8Pg7v7BcTHy7F5c1uxXSKRYP361mqsjIiIiJLT2LCbhON1SRkxMQlYufIWliy5gejoBADA2bNv4ORUTM2VERERUWo0PuwSZYQgCDh58jWmTDmPt29DxXYrKwMx9BIREVH2w7BLlI7Xr0MwefI5nDnjK7ZpaUnQv38ljBtXG2Zm+uorjoiIiH6IYZcoDRERcVi8+DpWrbqDuDiZ2F63bmHMnu2E0qWt1FgdERERZQTDLlEarl//gKVLb4q3ra2N4eHhiDZt7CGRSNRYGREREWUU10UiSoOzczG4uBSDrq4WRo+ugStX+qJtWwcGXSIiohyEPbtEAL5+jcGePc/Qr19FhTDr5eWEhAQ5ihe3UGN1RERE9LMYdkmjyeUCtm9/hFmzLiMoKBoFChihZcuS4v02NmZqrI6IiIh+FYcxkMa6c+cjmjffhjFjTiEoKBoAMHfuVQiCoObKiIiISFXYs0sa58uXKHh6XsK2bY8V2tu1c4C7ewOOySUiIspFskXP7unTp9GyZUuUL18erVq1wpkzZ9I8duPGjXB2dkblypXRuXNn3Lp1KwsrpZwsIUGOtWvvoHbtDQpBt1QpS+zd2xFr1rREoUImaqyQiIiIVE3tYffu3bsYNWoU7O3tMWfOHNjZ2WHUqFF48OBBimP//vtvzJs3D02bNoWHhwcMDAwwaNAgBAQEqKFyykkiIuLg7LwVkyefR1hYLADAxEQXs2Y1xJkzPVGvno2aKyQiIqLMoPawu3btWpQqVQoLFy5Ey5YtsWjRItjZ2WHt2rUpjs2bNy8mTpyI8ePHo02bNlixYgXi4+Nx+fJlNVROOYmxsS5KlPi2okK3bmXh7d0XgwZVgY6OlhorIyIiosyk9jG7165dw7Bhw8RxklKpFC1btsS6detSHNuxY0eF20ZGRjAwMEBkZOQv1cAhmrlPbGwCdHW1xN8riQSYMcMRgYFRmDatPqpVs1ZzhZQZkl7LfE1rBl5vzcLrrVlUeZ3VGnZDQkIQGRmJokWLKrTb2NggNDQU4eHhMDFJewzlqVOnEBYWhtq1ayv93FpSifh/KyuO08xNjhx5gdGjT8DDoyG6dy8PALC0NIGlpQmuXh2g3uIoS1ha8jWtSXi9NQuvNylLrWE3JiYGAGBsbKzQbmpqKt6fVtj19/eHu7s7OnToAHt7e6WfWyYXoPX//4cEhit9PmU/r1+HYOrUCzh58jUA4I8/TqBuXWvY2loiKCgcXFEs95NIEv8Q8nprBl5vzcLrrVmkUiBPHtW8sVFr2NXX1wcAREREKLSHhYUBAAwMDFI9Lzw8HIMHD0aBAgUwderUX66DL5qcLTIyHkuWXMfKlbcRFycT24sWNUdISAxsbROvMa+z5uD11iy83pqF11szqPIaqzXsWlhYwMjICL6+vgrtfn5+MDMzS9HjCwBxcXEYPnw4IiIisHPnTjEwk+YRBAEHD76Au/sF+Pt/e8NUoIARpk93RPv2DpBKObiLiIhIk6l9NYaaNWvi6NGj4q5VcrkcR44cQa1atVIcKwgCJk2ahKdPn2LNmjXIly9fVpdL2cTTp4Ho0GE3Bg48IgZdHR0pRo6sjqtX++K330pxcwgiIiJS/2oMgwYNQo8ePTB27Fg0btwYJ0+ehI+PD2bNmoXo6GhcuXIFxYoVg52dHRYtWoTDhw/D1dUVb9++xdu3bwEA5ubmqFatmpq/E8pKK1bcwuXL78TbTk5F4enZCHZ2Fj84i4iIiDSNRBDUP/Ll1KlTWLRoEfz8/GBjYyMG3/fv38PZ2RkjRozAyJEj4eTkhA8fPqQ4v0aNGtiyZYtSzyn7xwFaoS8gM7RGcMdnqvpWKIt8+hSBWrU2wMrKEDNnNkTTpsVT7cmVSAArKxMEBnJCgybg9dYsvN6ahddbs0ilqlt5I1uEXXVg2M057t0LwOfPUWjSpLhC+82b/ihfPh/09dP+gIL/OGoWXm/NwuutWXi9NYsqw67ahzEQpSUwMAqzZ1/Gv/8+gqWlAa5e7Qtz828TEqtX58YQRERE9GNqn6CmLlrRH9VdAqUhIUGOdevuonbtDdi69REEAQgMjMa6dffUXRoRERHlMBrfsyvopFzejNTn2rX3mDjxLJ48CRTbjI11MX58bfTvX0l9hREREVGOpPFhN7LSFHWXQAA+fgyHh8cl7N2rOH66S5cymDKlPvLnN1JTZURERJSTaXTYlRlaI862nbrL0Hhnz/qiX79DiIqKF9sqVMgHLy8njsslIiKiX6LRYZeyhwoV8kFHJ3H4uIWFPtzc6qFnz3LQ0tLYIeVERESkIgy7lOViYhIUlguzsjKEm1s9PH0aiIkT6yBPHgM1VkdERES5CcMuZZmoqHgsXXoD//77COfO9YKVlaF4X9++FdVYGREREeVW/JyYMp0gCDh06AXq1duIhQuv49OnSMyefVndZREREZEGYM8uZarnz4Pg5nYOly75iW3a2lKYm+tDEIRUt/glIiIiUhWGXcoU4eGxmD//Gv755y4SEuRiu6OjLWbPboSSJfOosToiIiLSFAy7pFJyuYBdu55ixoyL+PIlSmy3sTGFh4cjWrQowd5cIiIiyjIMu6RSERFx8PC4gMDAaACAvr4WRo6sgREjqsHAQEfN1REREZGm4QQ1UilTUz1MmVIfANCiRQlcvtwH48bVZtAlIiIitWDPLv00mUyOLVseolkzOxQoYCy2d+1aFsWLW6BWrUJqrI6IiIiIYZd+0vXrHzBp0lk8evQF169/wN9/txDvk0olDLpERESULXAYAynl06cIDBt2DK1b78SjR18AAHv2PMOrV8FqroyIiIgoJfbsUobExcmwdu1dLFjgjcjIeLG9XLm8mD3bCSVKcCkxIiIiyn4Ydild58+/xeTJ5/Dy5bfeW3NzPUyaVA+9e5eHlhY/ICAiIqLsiWGXfmjixDNYv/6+eFsiAXr1qoBJk+rC0tJAjZURERERpY9dcvRDNWp8m2hWrVpBnDzZAwsWNGbQJSIiohyBPbskEgQB0dEJMDT8tiZu+/YOOHbsFRo3Lo5OnUpDKuXuZ0RERJRzMOwSAODVq2C4uZ2DkZEONmxoI7ZLJBKsXdtKjZURERER/TyGXQ0XERGHv/66hjVr7iA+Xg4gcUJaw4a2aq6MiIiI6NdpdNgVdIzTPyiXEgQBe/Y8g4fHRXz6FCm2Fy5sArlcUGNlRERERKqj0WE3stIUdZegFg8ffoab2zlcv/5BbNPT08Lw4dUwalQNhTG7RERERDmZxoZdmUFBxNm2U3cZWSokJBpz5lzFpk0PFHpvmzWzw4wZjiha1Fx9xRERERFlAo0Nu5ro+nV/bNjwbc3c4sXNMXt2Izg5FVNjVURERESZh2FXgzRtWhyNGtni+nV/jB1bE4MHV4GeHn8FiIiIKPdi0smlPn2KxN69zzBkSBVIJIlr40okEsyf3xg6OlIULGii5gqJiIiIMh/Dbi4THy/DunX3MH++N8LD41CsmDmaNbMT77exMVNjdURERERZi9sF5yIXL/rByWkrpk27gPDwOADAggXeEAQuJUZERESaiT27ucD792Fwd7+AQ4deim0SCdCjRzm4udUThzEQERERaRqG3RwsJiYBK1fewpIlNxAdnSC2V6lSAF5eTqhcuYAaqyMiou8JggC5XAa5XK7uUnIciQSIiYlBfHwc+IFlzieVSiGVamVJhxzDbg4VEhKNJk224e3bULHNysoAU6fWR5cuZSGVsjeXiCg7SUiIR2hoMOLjY9RdSo4VHCzlG4VcRFdXH6ameaCtnbmbWTHs5lAWFgYoU8YKb9+GQktLgv79K2HcuNowM9NXd2lERPQdQRAQFBQAqVQKMzMraGlpc4jZT9DSkkAmY7duTicIAmSyBEREfEVQUADy5Sucqa8Hht0cIioqHgYGiv84zpjRELGxMri7N0Dp0lZqrI6IiH4kISEegiCHmVle6OqyU+JnaWtLkZDAnt3cQQ9aWloIDv6EhIR46OjoZtozcTWGbE4QBOzb9wy1a2/AgQMvFO6ztTXDjh2/MegSEeUQEgn/7BIlyarXA1912djjx1/Qvv0uDB58FB8/RmD69AuIiIhTd1lEREQ5Smwsx0lrMobdbOjr1xi4uZ2Fs/NWXL36XmwvWzYvIiPj1VgZERGReh09egj9+/cCALx964t69aohISEhzeOfPn2Mnj27iBPb/PzeokGDGoiJicH48WPw/PmzdJ8zISEBnTq1xX//bVfJ9xAfH49nz57i9WsflTxediMIAnr37oJ//lml7lIAcMxutiKXC9i27RE8PS8jKChabC9a1Ayeno3g4lJcjdUREZGm6dixNQICPgJI3HLeyiovatSohSFDRsLCwkI87uvXr9i0aR0uXTqP4OAgWFpaoX59R7i69oeZmbnCY0ZFRWLr1k04e/Y0Pn/+BHNzczRo0Aj9+g2Cqampyr+HDRvWwsWlKaTSxP49AwMDyOVyREdHo1Wrtvjjj5FYtWo9ChcukuZjnD17GuHhoWjduh0AwMWlPqKjo9M8/nuXL9+CTCbDpEl/4t27t/D3/wAjI2P07z8Y8+fPxsOH91Ocs3TpKlSpUk25b/YHwsPDsXTpX7h06TwAoH79hvj99z9hbGyc6vExMTFYvXoFzpw5iaioSJQuXRbDho1C6dJlxWN27vwXu3f/h69fg1GsmB2GD/8dFStWhkQiQdeuPbF06V/o2bMP9PXVO06dYTebuHPnIyZNOou7dz+JbYaG2hg9uiaGDKkKfX1eKiIiynouLs3QqFFjCIIcHz68x44d/+LdOz+sWLEWEokEISEhGDSoD+Lj49ChQxdYW1vDz+8t9u7dhStXLmH16o0wNzcHAERGRmDo0P4ICAhA+/YdUaJESXz48B67d+/EtWtXsGbNRpiaptzWfs2aldi8eb1CW71634Jgw4a1xK83b96B4sVLAAAeP36EBw/uYcoUD7x754ciRWxgZGQEAPD3/4CCBQuiZcs2uHz5AmrUqCWe971Tp46hTp36MDAwAADs3n1IYa3fpJ7MXr36okmT5qk+hpaWFurVa4CwsFAcOXIQ27fvBQCcPn0Cw4b9jkaNnMVjhwzpm+pj/IoZM6bg6dMnGDRoOARBwIYNaxAeHoY5cxamevyUKePx5Mlj9OzpCjMzcxw8uA+jRg3B+vX/okgRG2zatA7r169Bly7dUaKEPY4cOYhx40Zj69b/kC9ffjRo0Ajz58/G9etX4ejopPLvRxlMUNnEmjV3FYJuu3YOcHdvgEKFTNRYFRERaTpb26Jo0KCheNvOriT++GMkPn70h7V1IaxevRxRURHYtGknrKy+TZhu3rw1+vfvgdWrl2PChCkAgFWrVuD9+3dYvXoDSpZ0SHZsKxw7dhiGhkZp1lG/fkMMHz4S586dxcmTxzBr1jz4+3/AuHG/Y/PmndDS0kKPHh3F42UyGRYvnoeePfsgKioKfft2R79+g3H06CEAwODBfWBqaoYCBQqiQIGCCj2WySUkJOD27Zvi9wAARkbGCj27L18+x9evIahVqza0tLQUzk8KyADQpk173LlzS6whibm5OQoWtBZvS6WKj/GrXr58Dm/vK/DyWoD69RsCACwtLTFlygT4+LyCnV2J745/gWvXrmL+/CWoXbsuAMDZuQm6dfsNe/fuwu+//wFLSyuMGDEGnTp1BQDUq9cArVq54MaNa2jVqi2MjY1Rtmx5XLvmzbBLidzd6+P4cR/Y2prC07MR6tWzUXdJREREKeTJYwkg8WPxuLg4nD17Gr169VEIugBQoEABdO3aC1u2bMCYMeMhkUhw8uRRtGjRWiHoAkD+/AXQp8+AHz6vsbExihYtBktLK+jq6sHWtqh4n42NLbS1FSPN/ft38fTpE0RERGDLlg1o1qwl6tVrgGLFimHOnJn4889JYvD7kdevXyEuLg729qXEtgcP7mHUqCEpju3evWOKtpkz56BRo8Z4//4dunZtL7bXq1cNzs4u6T5/apL3aifn5uaOFi1ap2i/desmjI2NUbt2PbGtbt0GMDAwxJ07t1KE3bdv3wAAqlatLrbp6+ujbNny+PDhHQCgVau2CucYGBhCT08fUVFRYpu9vQPu3bur5Henegy7anDlyjuEh8ehWTM7sa1gQRMcONAZZcpYQUdHte/oiIgo+9H13Qej+56QxEdk2XMKOsaIrDQFcbbtMnxOXFwcwsPDIQgCPn70x/Lli2BuboGiRYvC3/8DoqIiUaFC5VTPrVatOlavXo6PH/0hCAIiIyNRubLqxqH+SMmSDpg4cQo+f/6MHTv+Rf/+g2FhkQcmJiYwN7fA1auX8fq1Dz5+9MfHj/4oVswOo0f/meJxPn0KAJAYyJMrUKAgdu8+hEOH9uPq1Uvw8voLQOLH/2XKlEf37r3Qp093heN37TqIgwf34cyZk1iy5G/o6xvAzS3lc6Zn9uwFqbYnD+TJBQT4o0ABa4U3BDo6OsifvwA+fvyQ4ngzs8ShJB8/+iu8qfjw4R3s7Eqm+hwXL55DREQ4qlX7FpDz5y+Az58/pXp8VmLYzUL+/uGYPv0i9u9/DisrQ9SpUximpnri/RUr5ldjdURElJUMHy+BduiL9A/MhOdVJuxu3rxeYbyslVVeTJ/uCT09fYSHhwFI/Eg8NUnjb8PCQpO1/dzwvOjoKPj7++Pr169ISIjHx4/++PLlM4DEQJo0AS2JiYkJXFyawdW1O4YNGwULizwYMWIQ7t27A6lUitDQUJQrVwHW1oVgbV0INWrUTvV5k3oqkw9H+N7lyxfh6FgTACCXy1GmTPkUx2hra6NgQWtcvXoZWlpaCsMWwsPDFEKhXC774c+icuWqqbanNREsJiYm1YloJibGiI2NTdFesWIVWFpawctrBv74YwKMjIyxa9cOvHz5Aj17phxPHBAQgPnzvdCyZRuFcc+GhkYKPb3qwrCbBWJjE7Bq1R0sWnQNUVGJy6MEBkZh8+YHGDGiejpnExFRbhRVbjSM7s3K8p7dqLK/K3VOy5Zt0LRpC3z6FABPz+n488+JqFatBgDAxCRx9YSgoKBUVzNICsOmpmbiDqBfv379qdrPnz+L8+fPirc7dWojft2lS7tUz1mwYA4MDPRha1sUhw8fwKRJ02BsbIzdu3fCz88XHh5euHz5IhYvno/u3Xun+hiGhoYAEsN2WmOK69VroNCzm5YnTx7Bx+clAGDx4vkYOXIsAGDZskVYtmxRmud9r3nzRqm2pzWMQV9fHxERKX/PwsMjUg3Iurq6mDFjDqZOnYC+fXuI7TY2tmjYUHH8bUREBMaP/x358uXDmDHjFO6LiooUf37qxLCbyU6deo0pU87jzZuvYpulpQEmT66H7t3Lqa8wIiJSqzjbdkr1sKpLwYLW4hJY586dwdq1f6NOnfqQSqUoVKgwjIyM8ODBXVSsWCnFubdu3YCRkRGsrQtBIpHAzMwMt27dgItLM6XraN68FdzdZ+DgwQPYs+c/rFu3BW/f+qJHj444f/4atLW1FcayJiQk4NSp40hISMDUqRNRtGgxcZxpiRL2OHv2FM6ePY25c2di9uwFKcb8JkkavhAQEIDixe1SPSajNm9ejzJlyuHz50949Ogh5s+fjb//XofAwEBxzPOHD+9RqFDhHz7O0qWpr1+bfMhBcgULWuPEiaNISEgQv8/4+Hh8+hSAAgWsUz2nYsVK2L37EF6/foUvXz7D3d0NQ4eOVJiAFx8fDze3PxEZGYnVqzdCT08xOH/6FJBi+Ic6cFOJTPLmzVf07LkfPXrsF4OuVCpB//6V4O3dFz17lodUKlFvkUREREoYPHg43rx5jRMnjgJIHPfp5NQEO3ZsRVBQoMKxAQEB2LFjK5ydm0BHRwfa2tpo0aINTpw4iidPHikc6+//AevXr0F8vOo2TtLW1sa6dVtx/Ph5HD58CsuXrxHvK1u2HPz83mLePE94es7H168huHDhbKqPU7x4Cejq6uLFi7Q3n7h06QLq1auGevWqKfQ+J/f6tQ8eP36Enj37wNDQEMuXr0GXLj0QHByEXr064/p1b0RHR2Pw4D44der4D7+3KlWqpfqfpaVVmsdHRETA2/uy2HblykVER0ehatW0x1Dr6urCwaE0du7chnLlKihM6BMEAbNne+DFi+eYP39JigmKAPDixXM4OKQ+jjgrsWc3Exw9+gqDBh1BXNy3MTe1ahXC7NlOKFcurxorIyIi+nl2diXQuHFT/PPPKjg7N4Guri6GDBmOO3duon//Xv9fZ7cQ3r1LXGfX2NgEgwcPF8/v128Qbt++id9/H4b27TugZEkHfPjwHrt2bYepqRk6duwKHR0dldVraGiIe/du49Wrl3j16iWmTvXAy5cv8ddfXhAEAR07dkG1ajUwZ85MFC1aLNXH0NbWRtWq1XHt2lU0a9Yy1WPq13dMdxiDrW1RzJiR+LxA4tCCYsWKw81tHOzsSqBGjVqQSCQYPnw0Fi2aj6pVq4srX/wqe/tSqFWrDubO9cSXL1/EdXbr1WsgjrF9/vwZPn0KUFhmDgAOHdqPBw/uYd26LQrta9asxKlTx9G5cze8f/8O798nrtJgZmaOihUrISIiAo8fPxSXJlMnht1MUL26NfT1tREXJ0OBAkaYPt0R7ds7iOOViIiIcqoBA4age/cO2LPnP3Tr1hNmZuZYvXojNm78BwcO7EFwcBDy5LGEs3MT9OmjuIOagYEBVqxYi3//3YTTp09i9+6dMDMzh4tLM/TrN/iHO6gdO3YYx44dFm+ntalEksOH92POnFkwN7dA8eJ2KF7cDnPmzMKFC2fRsWMXdO/eGwsXzkPZsuVw48Y1tG/fKc3ndnFpjgULvBAVFSWOQQ0I+KhQQ/Kvz58/i5Urlyg8hpaWFipXroo7d26JbZs3r8ft2zewYcM2MSM0b94K586dxvz5XvDySn3VhZ/h7u6JJUsWYPXq5QAS1y0ePfrbGNvdu3fg2LHDuHz5W33BwUFYuXJpqsvFJfU+//ffdoVtlCtVqoLly9fg4sVz0NPTQ82adVT2PfwsiSAk3wNEc8j+cUBw21vpH5gBERFxMDbWVWjbsOE+3r8Pw5gxNVPcR1lHIgGsrEwQGBgOzfxN1yy83polJ13v+Pg4BAV9hKVlQejo8G+CstasWQk/P1+MGjUWMpk8zeM6dWoj7qAWERGB+Ph4hW2NHz68j4IFC4kfuW/ZshGbN69H/fqOmDZtZpqPm5CQgG7dOqBjx87o0qUH7ty5BU/P6di2bc8P6x40qA/69OmPRo0ai2137tzCggVe2Lx5J/74YyR++60zHB0VJ5wFBHyEh8dkzJ27OFO2UM5sgiCgT59uqF+/IQYMSLkecZIfvS6kUsDSUjUbazHs/oKwsFjMn++NPXue4dIlV1hapr0sCalHTvpjSL+O11uz5KTrzbCrGtraUiQkpB12KWfJqrDLCWo/QS4XsH37I9SqtQGrV99BYGAUvLyuqLssIiIiIvoOx+wq6d69AEyadBa3bweIbQYG2ihc2ASCIHBcLhEREVE2wrCbQYGBUZg9+zL+/feRwsdlrVuXxPTpjihSJOeNqSEiIiLK7Rh205GQIMemTfcxZ85VhIZ+21LP3j4PZs92QoMGNmqsjoiIchINnSZDlKqsej0w7KYjIiIOCxZcE4OusbEuxo+vjf79K0FHRyuds4mIiCDuOhX3v/buPS7nu3/g+KtSal3OEdUkzRVliJSZ5pQliiFssnSjnHO8u1kOc3YzbEtzHsMO2G0HwpRbDhPjdtjtTDZSZs1a50R9f3+4+/661hVdSdHez8fDQ32uz/X5vr+9u3q8r8/1+X6+ufcwM6tawdEI8WzIzX1YW5mYPN1yVIrdx6hZ05zw8A5MmhTNwIHOTJ/uibW1/ntjCyGEEPoYG5tgYaEhIyMFADOzqnKNRynk5xuRlyez4887RVHIzb1HRkYKFhYajI2f7n4JUuwWkpubx9q1p/H3b4q1tUZtHzSoOS1a1KNFC+sKjE4IIcTzrHr12gBqwSsMZ2xsTH6+bD1WWVhYaNTXxdMkxe7//PvfPxMefoD4+BQuXvyNFSu6q48ZGxtJoSuEEOKJGBkZUaNGHapVq0Ve3oOKDue5Y2QEtWpZkpKS+czvqywez8SkylOf0S3wTBS7MTExLF++nJs3b2Jvb8/EiRPp2rWr3r5paWksWLCAmJgYALy8vAgPD6datdJtPHzjRiozZsSyd2+82vavf13k739/BXv7GqUaUwghhCiOsbExxsZyYwlDGRmBubk5pqb3pdgVBqnwm0qcPn2a0NBQtFotixYtwtHRkdDQUH788Ue9/adMmcLBgweZOHEiEydOJDY2lrCwMIOPm3XfhEWLvqdDh406ha67uw379gVIoSuEEEIIUQlU+Mzu2rVradq0KcuWLcPIyAgfHx/eeOMN1q5dS0REhE7fixcvcvDgQSIjI/HyenifaSsrK0JDQ7l8+TJOTk4lPm67f/bhwo3j6vf16lkya5Yn/v7N5KIBIYQQQohKosJndo8dO0aPHj3UAtPY2JiePXty/PjxIn3j4uKoVq0anTp1Uts6d+7MCy+8oLf/oyT8/vACtCpVjBk9ug1xcUH07+8sha4QQgghRCVSoTO7KSkpZGZm0qhRI532hg0bkpqaSnp6us5a3MTERGxtbalS5f/DNjMzw8bGhlu3bhl07GrVzPD0bMisWZ44OtZ6ovMQz66C9y7Gxsgar78Ayfdfi+T7r0Xy/ddSlnOPFVrs5uTkAKDRaHTaq1evrj5euNjNzs7WeyFatWrV1LFKKjFxsqHhiudY7dqlu4BRPJ8k338tku+/Fsm3MFSFLmMwNzcHICMjQ6c9LS0NAAsLC512CwsL0tPTi4yTnp5epK8QQgghhBAVWuzWqlULS0tLfv75Z532mzdvUqNGjSIzvnZ2diQmJvLgwf/vT5ibm0tSUhK2trblEbIQQgghhHiOVPgFah4eHuzevRvlfwtw8vPziYqKol27dnr7pqenExsbq7YdOHCArKwsvf2FEEIIIcRfm5GiVOwy79OnTxMQEIC3tzdeXl7s27ePmJgYPv/8c5o0acL333+Pg4MDjo6OAAQHB3Pu3DnGjh0LQEREBK6urqxcubIiT0MIIYQQQjyDKrzYBYiOjlbvoNawYUMmTZqEl5cXt27domvXrowdO5Zx48YBD9fzzp8/X+cOatOnTy/1HdSEEEIIIUTl9UwUu0IIIYQQQjwNFb5mVwghhBBCiKdFil0hhBBCCFFpSbErhBBCCCEqrUpZ7MbExNCzZ09efvllfH192b9/f7F909LSmDp1Km5ubri5uTF16lS9N64Qzy5D8r1x40a6du2Kq6srAwYM4OTJk+UYqSgLhuS7wE8//UTz5s1xcnIy+NbiomKVJt9Tp07F3d2d33//vRwiFGXJkHxfu3aNESNG4OrqiqurK8OGDePy5cvlGK14UvHx8YwcORInJyd++eUXvX3u3LnD6NGjcXV1pV27dixcuJDc3FyDjlPpit3Tp08TGhqKVqtl0aJFODo6Ehoayo8//qi3/5QpUzh48CATJ05k4sSJxMbGEhYWVs5Ri9IyJN8rV65k8eLFeHt7M3v2bCwsLAgJCSn2BSaePYa+vgvMmzcPS0vLcopSlJXS5PvcuXN8/fXXTJgwgdq1a5djtOJJGZLvrKwshgwZwu3bt3nnnXcIDw/n7t27BAUFkZmZWQHRC0MdOnSIXr16PfL1nJeXx/Dhw7l69Srh4eEMGzaML774gsWLFxt2MKWSGTVqlNKnTx8lPz9fURRFycvLU/z8/JSxY8cW6XvhwgVFq9Uq0dHRatvevXsVrVarXLp0qdxiFqVnSL63b9+ufPLJJ+r36enpSvPmzZXt27eXW7ziyRiS7wL79u1TmjVrpmzZskXRarVKQkJCeYUrnlBp8h0QEKD06dNHycvLK68wRRkxJN/nz59XtFqtcv78ebXt4sWLilarVc6ePVtuMYvSi42NVbZt26YcPXpU0Wq1yu3bt4v0iY6OVrRarXLx4kW1bf369YqLi4uSkpJS4mNVupndY8eO0aNHD4yMjAAwNjamZ8+eHD9+vEjfuLg4qlWrRqdOndS2zp0788ILL+jtL549huTb39+fwMBA9XtLS0ssLCxkFuA5Yki+AXJycli4cCEDBw7EycmpPEMVZcDQfO/bt4+TJ08yY8YM8vLyyjNUUQYMybejoyO2trZs3ryZhIQEEhIS2Lx5M9bW1jRp0qS8Qxel0LFjR/r374+xcfGlaFxcHC+99BJNmzZV23x9fbl//z7/+c9/SnysSlXspqSkkJmZSaNGjXTaGzZsSGpqapG1uImJidja2lKlShW1zczMDBsbG1nX9xwwNN9/Fh0dTVpaGq+88spTjFKUldLke/Xq1WRmZjJ+/PhyilKUFUPz/eDBA5YuXYqZmRlDhw6lRYsWDB06lKSkpHKMWpSWofmuWrUqa9euJSYmBi8vL7y8vNi7dy/r1q3DwsKiHCMXT1NiYiL29vY6bfXq1cPCwsKgOq1SFbs5OTkAaDQanfbq1avrPF4gOztb753XqlWrVqSvePYYmu/CkpKSmDVrFv369UOr1T69IEWZMTTfCQkJrF+/ngkTJlCzZs1yiVGUHUPzvWfPHn7++Wfat2/P4sWLmTt3rnrxS35+fvkELUrN0Hzn5ubyj3/8A3t7e5YvX87y5ctxcHBgxowZBl+8JJ5dj6rT7t27V+Jxqjy+y/PD3NwcgIyMDJ32tLQ0gCLv9iwsLPTOBqWnp8s7w+eAofkukJ6ezogRI6hfvz4zZsx4ukGKMmNovufPn4+dnR0+Pj6kpaWRlZWlPj83NxczM7NyiFqUlqH5jo2NxdHRkcjISExMTABo1KgRAQEBnDlzhtatW5dD1KK0DM33N998w82bN9m/f79aDHXo0AEvLy927txJv379yiFq8bQ9qk4r+J0piUo1s1urVi0sLS35+eefddpv3rxJjRo1irxjtLOzIzExkQcPHqhtubm5JCUlYWtrWx4hiydgaL7hYX7HjBlDRkYGq1evNujFIiqWIfn+5ZdfOHDgAPHx8Xh4eNC2bVuCg4MB6N27N7t27SrP0EUpGPr6/uOPP2jatKla6AI4OzsDkJyc/NTjFU/G0HyfP38eBwcHnVm/6tWr4+DgwH//+9/yCFmUAzs7O27cuKHT9uuvv5KdnW1QnVapil0ADw8Pdu/ejaIoAOTn5xMVFUW7du309k1PTyc2NlZtO3DgAFlZWXr7i2ePIflWFIVp06Zx8eJF1qxZQ7169co7XPGESprvWrVqsWnTJp1/06dPB2DZsmV4enqWe+zCcIa8vu3t7Tl79qzOR9gF+2j/eR2oeDYZku+6dety/fp1/vjjD7Xtjz/+ID4+Xv62VyIeHh5cu3aNS5cuqW27du3C1NQUNze3Eo9jpBT8VlUSp0+fJiAgAG9vb7y8vNi3bx8xMTF8/vnnNGnShO+//x4HBwccHR0BCA4O5ty5c4wdOxaAiIgIXF1dWblyZUWehighQ/K9bNkyVq9ezZAhQ3B3d1fHqFmzpkEvGlFxDH19F3by5EkCAgLYv38/dnZ2FRC9MJQh+Y6Pj6dfv364uLjg7+/P77//zpo1a3B1dWXVqlUVfSqiBAzJ9507d+jVqxfW1tYMHjwYgC1btnDnzh2+/fZbrK2tK/hsREkdP36cwMBADh48iJmZGadOncLZ2RkbGxvy8vJ44403yMnJYcSIEaSkpBAZGYm/v786gVEiT7JH2rNq3759io+Pj+Li4qL4+Pio++gmJCQoWq1W+fDDD9W+qampSlhYmNK6dWuldevWSlhYmJKWllZRoYtSKGm+O3furGi12iL/Bg8eXJHhCwMZ8vou7MSJE7LP7nPIkHyfPn1aGTRokNKiRQulffv2yty5c5XMzMyKCl2UgiH5vnbtmhISEqK4uroqrq6uSkhIiHLt2rWKCl2U0rFjx9R9dgu+/te//qU+fvv2bWXkyJFKy5YtFXd3d2X+/PnKvXv3DDpGpZvZFUIIIYQQokClW7MrhBBCCCFEASl2hRBCCCFEpSXFrhBCCCGEqLSk2BVCCCGEEJWWFLtCCCGEEKLSkmJXCCGEEEJUWlLsCiGea/fv39e55bcQQghRmBS7QogylZGRQURERLkVoLt27WLgwIGP7PP2228TExNT7OO//fYbW7ZsIScnx6Bjp6Wl0aZNm0eOLSoXRVHw8/Pjgw8+qOhQhBAlVKWiAxBCVC7R0dFERUUBsGLFCrV93rx5Ord3XLhwIX379gVgzpw5fPrppyUaPywsjGHDhqnfx8TE4O7uTpcuXUhMTFTbvb29+fDDD4s8/8GDB6Slpem0ZWdns2TJEkxNTenWrZvOY5aWllStWlVvLJ999hlWVlZ07doVoEgMBcaOHcu4ceMeeV6Fb5lZv379R/Y1lJOTk/q1sbExNjY2dOvWjdDQUF544YUnGnv9+vVs2LCBvXv3otFo2L9/P6mpqWpuAXr16kXr1q159913n+hY+vz53Bo0aIC3tzdjx47F0tLysc/PyMhgw4YN9OnTp0S3kTYyMuJvf/sbCxYsICQkBAsLiyeKXwjx9EmxK4QoU7t27aJLly4ADBw4kAkTJtC3b19cXV05cuQIADNmzNB5zujRowkICNBpu3HjBqNGjeLTTz+lVq1aanudOnXUr7Ozszly5AirVq0iODiYkJAQ3nrrLV577TWqVq1Kly5d2LBhg864Z86cKXKsAjNnzmTmzJk6bdOmTSMoKEhv/507d9K9e3eMjIzUNl9fX3x8fHT6OTg46H1+eRoyZAju7u7k5eVx+fJlNmzYQHJyMkuXLn2icfv06UObNm3QaDQA/Pvf/+bmzZs6xe6iRYuoXbv2Ex3nUQqf29WrV/n4449JTk7mvffee+xzMzIyWLFiBe7u7iUqdgG6devGzJkzOXz4MK+//vqThi+EeMqk2BVClJk7d+4QFxfHqFGjiIuLQ1EU8vLy1P+zsrIAiixxsLKywsrKSqctLy8PAHt7e+rWrav3eLGxseTk5NCmTRuMjY2Jj4+nXbt2xfYHcHNzY8WKFXh6emJubk52djbBwcG4u7szduxYNm7cyJtvvompqSlffvkl/fv31ztOUlIS165dY/bs2Trtjo6OeHl5PfoHVQFcXFzUuLy9vbGxsSE8PJzp06frvJkwVO3atR9byDo7O5d6/JL487lZWVkxd+5cFixYgJmZWZkfr1q1arRq1UqKXSGeE7JmVwhRZtatW0deXp768fG2bdvo0KEDv/zyC2fPnsXX1xdfX1+OHz+OsbHun5+zZ88yfPhwg463detWAMzMzDh16hRZWVn4+vri6urK0aNH9T5HURRiYmIYMGAAp0+fJiAggKysLOrUqcP48eM5d+4cN2/eZODAgRw5cqTYdbznz5/HyMiIZs2alTje5ORkQkNDcXV1pUOHDsybN4979+7p7XvhwgUGDRpEq1ateO2111izZg2KogCQnp7O1KlTcXd3p127dkyaNInffvutxHEAtGnTBoCEhAQArl+/ztChQ3F1deXVV19lyZIl3L9/H3j4M1u+fDmenp60atWKoKAgrl+/DsCOHTvUYvbtt9/myy+/5IcffsDJyUmd4X/77bcJDw8nMTGRpk2b8t133+nEMnz4cEJCQsrs3KysrHjw4IH65urs2bMMGDCAFi1a0K1bNz7//HPg4dKRjh07AhAYGIiTkxM7duwA4OTJkwwYMICWLVvy+uuvs3HjRp1jODs7c/78eYPiEkJUDJnZFUKUiRs3brBt2zbMzc3VtsDAQMLDwwG4desWn332mfrYn9elpqamcubMGb3jFl5j27BhQ0xNTTl37hxxcXFqe0xMDL6+vgQFBTFhwgSdOAozMjLin//8Jx999BGZmZm0bNkSExMTFi1aRM+ePQkLCyMuLo5Bgwbh7+9f7PkmJSVRo0aNIutC7927V2RNsEajwdjYmG+//Zbk5GTCw8PJyMggMjISCwsLJk+eXGT8KVOmYGFhwdy5c7lx4wYrVqzA2tqa3r17M2HCBJKSkvj73/+OkZERn3zyCRMmTGDLli3FxvtnN2/eBB4Whunp6QQGBlK7dm3Cw8O5c+cOq1evJisri1mzZrFnzx7WrFnDmDFjePHFF/niiy8YNWoUu3fv1hlz/PjxREZGkpycrDcHtra2tGnThj179uDt7Q1ASkoKcXFxLF68GKBU55aTk0NaWhp5eXlcv36dDz74gCZNmlCzZk0AVq5cib29PYMHD+bSpUvMnj0bOzs7XFxc1LXk48ePR6vV4uzsTEJCAsOGDcPb25vAwEBu3LjBsmXL0Gg06u+EjY0Nt2/fLvHPWwhRcaTYFUKUCXNzc4YNG8bXX3+ttm3atIlNmzbh4OBA69atOXHiBPXr1+enn35iyJAhBAcHP3bcP6+v3b9/P3Z2dnz//fd07tyZAwcO8ODBA6KioujduzdarZbk5GSaNm1a7Ji5ubmYm5szb948Zs+eTXh4OCNGjKBZs2aMGzeObt26cebMGTw9PbG2ttY7RmZmpt4LoFatWsWqVat02vbt24e9vT3Dhg1TL67LyMjg9u3bHD58WG+xe/fuXQIDA/Hz8wOge/fuODo6Eh8fz5EjR9i6dSuNGzcGHhaRQUFB3Lp1q9h1p4ULwsuXL7NgwQJcXFxo0KABW7duJTU1lR07dlCvXj3g4YV57733HpMnT+bu3btoNBqCgoLQaDR4e3uTnJyMiYmJzjHc3NywsbHhwYMHxS7l6NWrFwsXLiQ7OxsLCwuio6OpWrUqXbt2LfW5/XmtdaNGjXR2SyjIR35+Pp07d+aHH37g8OHDeHp64unpCTyc6fbw8ADgvffeo3HjxjoXVCYlJfHNN9+oxa6lpSWZmZl64xFCPFuk2BVClAlra2vGjRunU+wWXKBWpUoV9uzZA8CCBQvw9/enZcuWJRr3yJEjetfgvvLKK3Ts2JEDBw7wzTffoNFoiIqK4scff8TJyanYXQauXbvGyJEjcXBwYMyYMQQFBVG7dm1++OEHoqOjuXr1Kq1bt8bGxgY/Pz+WLVtGhw4dioxTXLHTr18/evfuXeRnAw+XCixevJjjx4+rH7Hb2trqjXP8+PEsWLCAU6dO4e3trV4Id/XqVQC9263dvXu3xAWhVqtl8eLFGBkZceXKFZo0aaIWugDt27fn/v37/PTTT/j5+bFt2zZ8fHzo0aMHPj4+tGrVSu9xHqd79+7MmzePAwcO0KNHD3WW19zcvNTnNnLkSNq3b8/Zs2dZunQpH3zwgc6bnUOHDvH+++9z5coVdWnGSy+9VGyMV69e5cKFC7Rt21anvaAAh+Lf7Aghnj1S7AohykzhXQkAqlatql681KlTJyIiIrh48SJJSUnqmtHSatGihbpcoEaNGoSFhdG8eXP69+/PoEGDin3eiy++yPz58/Hw8CA3N5f69etTvXp1IiIimDlzJnXr1iUuLo7u3bvj4+NTbDFqY2NDamoqGRkZ6k4EAHZ2duoMYWG5ubkMHz6cmjVrMmPGDBo0aMDOnTs5duyY3vEHDRqEp6cnu3fv5osvvuD9999n8+bN6uPr1q0rcvGVVqst9rwLCkIjIyPq16+PnZ1dkXXTxalZsyZfffUVsbGx7N+/n6CgIPr161dkV42SqFGjBh07dmTPnj14eHhw/PhxPv74Y50+hp5b48aN8fDwwN3dnV27drFs2TLWrFkDwJUrVxg9ejRdunQhJCSEmjVrqksmHqXggsXCCuc5KSmJBg0aPHYcIUTFk2JXCPHUFCxjADhx4gRubm4EBwcTFBRU5CPwJ1Hwkfnly5dJS0tTt70yMTEpUoCvWbNGZ//fwqKjo4u0dejQgfXr1xdpd3FxQVEULl26hJub22NjjI+PJzExkZUrV6p7wxZcYKfPqVOnaN68OSNGjCAkJARfX1+2bt3KW2+9pfYpKKozMjL49ddfH7nna0FBqI9Wq+XLL7/k119/VWd3jx49iqmpKQ4ODmRlZXHt2jW8vLzw8vLC09OTiRMnMn78+CJjmZiYqLOnxenVqxdTpkzhq6++ol69empchffMNeTcChgZGTFp0iRGjBjBiRMnaNu2LUePHsXS0lLdczk3N5e7d++qzyko+AvvENK0aVN27tyJq6urWnRfunRJZ2b3woULNG/e/LExCSEqnhS7QoinZsCAAYSGhgIP1/SamJiQnJyss1duWbl//z7vvvsugwcPVgu2guK1YBcDeHjlv759dguK2sIFF1Ds1lU2Nja89NJLHDp0qETFbp06dahSpQqrVq2ic+fOHDp0iLi4OPWj8CpVHv45joqK4q233mLMmDE4Ojri7+9PWloaiYmJ2Nvb4+DgQOfOnQkLC1NnKjdv3sy9e/fYuXNniWdrC+vZsycrVqxg+PDhBAYGqheo9e/fH41Gw7p169THGzduzFdffUWdOnV0ZjoL2Nvbs2PHDjZv3kzjxo159dVXi/Tp1KkTVatWJSIigsDAQPUNSVmcW6dOnWjdujVLlixh27Zt1K9fn9TUVD788ENsbW3Zvn072dnZan8rKys0Gg1r167l7t27vPrqqwwYMIBNmzYxfPhw+vbty507d1i1ahWjRo0iJCSE9PR0zpw5Q2BgoME/ayFE+ZOtx4QQT425uTl169YlJSWFgIAALl26xMKFC5k7dy7h4eHk5ORw5swZnJycCA4OJj09HScnJ5ycnNQLszp06KC2Fd7OqjBFUZg2bRo5OTlqcZ2bm0tUVBTbt2/nypUramFmYWGh7g1b+B88/Jj6z+36CroCfn5+7Nmzh/z8/Mf+LOrVq8f8+fM5c+YMs2bNQqPR6Cy3aNasGS+//DJLlizBxMSEjRs3YmJiwrvvvsvq1asZOHAgb775JgBLly6lW7durFmzhtmzZ1OrVi1WrlxZqkIXHu4bu2nTJqysrJg/fz6fffYZgwcP5p133gFg6NChhISEsH37dqZNm0ZqaiqRkZF6jzdw4EDat2/P0qVL2bVrl97jmZmZ0b17d3Jycoqsby6Lc5s8eTJnz57lu+++w9vbmyFDhrBlyxaWL1+On5+fzr6/xsbGzJkzh+vXrzNnzhx+//13bG1t+eSTT8jLy2PWrFls2rSJwMBAhg4dCjx8E2Vubq5e3CaEeLYZKYWnPIQQ4gl16dKFyMhIDh8+THp6OpMnTyYqKoqjR48ybdo0NBoN58+f56OPPiIiIoL79+9z69atEo9vampKw4YNAUhLS6Nt27YcOnSIUaNGsXLlSvVisPz8fPr27YuiKDg7OzNnzhxMTU2LHdfZ2ZmtW7fy8ssvlziW1NRUunTpwqJFi4rcZlhUToqi0KtXL7y8vPQu4xBCPHuk2BVCVAqKohRZnyuEEELIMgYhRKUgha4QQgh9pNgVQgghhBCVlhS7QgghhBCi0pJiVwghhBBCVFpS7AohhBBCiEpLil0hhBBCCFFpSbErhBBCCCEqLSl2hRBCCCFEpSXFrhBCCCGEqLSk2BVCCCGEEJXW/wHzUR974zoIzwAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC值: 0.9167\n",
      "\n",
      "--- 随机森林特征重要性 ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/grtsinry43/.conda/envs/ml/lib/python3.11/site-packages/sklearn/compose/_column_transformer.py:1667: FutureWarning: \n",
      "The format of the columns of the 'remainder' transformer in ColumnTransformer.transformers_ will change in version 1.7 to match the format of the other transformers.\n",
      "At the moment the remainder columns are stored as indices (of type int). With the same ColumnTransformer configuration, in the future they will be stored as column names (of type str).\n",
      "To use the new behavior now and suppress this warning, use ColumnTransformer(force_int_remainder_cols=False).\n",
      "\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "实验 2: 乳腺癌数据集上的模型比较\n",
      "============================================================\n",
      "乳腺癌数据集特征 (前5行):\n",
      "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
      "0        17.99         10.38          122.80     1001.0          0.11840   \n",
      "1        20.57         17.77          132.90     1326.0          0.08474   \n",
      "2        19.69         21.25          130.00     1203.0          0.10960   \n",
      "3        11.42         20.38           77.58      386.1          0.14250   \n",
      "4        20.29         14.34          135.10     1297.0          0.10030   \n",
      "\n",
      "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
      "0           0.27760          0.3001              0.14710         0.2419   \n",
      "1           0.07864          0.0869              0.07017         0.1812   \n",
      "2           0.15990          0.1974              0.12790         0.2069   \n",
      "3           0.28390          0.2414              0.10520         0.2597   \n",
      "4           0.13280          0.1980              0.10430         0.1809   \n",
      "\n",
      "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
      "0                 0.07871  ...         25.38          17.33           184.60   \n",
      "1                 0.05667  ...         24.99          23.41           158.80   \n",
      "2                 0.05999  ...         23.57          25.53           152.50   \n",
      "3                 0.09744  ...         14.91          26.50            98.87   \n",
      "4                 0.05883  ...         22.54          16.67           152.20   \n",
      "\n",
      "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
      "0      2019.0            0.1622             0.6656           0.7119   \n",
      "1      1956.0            0.1238             0.1866           0.2416   \n",
      "2      1709.0            0.1444             0.4245           0.4504   \n",
      "3       567.7            0.2098             0.8663           0.6869   \n",
      "4      1575.0            0.1374             0.2050           0.4000   \n",
      "\n",
      "   worst concave points  worst symmetry  worst fractal dimension  \n",
      "0                0.2654          0.4601                  0.11890  \n",
      "1                0.1860          0.2750                  0.08902  \n",
      "2                0.2430          0.3613                  0.08758  \n",
      "3                0.2575          0.6638                  0.17300  \n",
      "4                0.1625          0.2364                  0.07678  \n",
      "\n",
      "[5 rows x 30 columns]\n",
      "\n",
      "乳腺癌数据集目标变量分布:\n",
      "1    0.627417\n",
      "0    0.372583\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "训练集大小 (乳腺癌): X_train: (455, 30), y_train: (455,)\n",
      "测试集大小 (乳腺癌): X_test: (114, 30), y_test: (114,)\n",
      "\n",
      "训练并评估决策树 (基准模型) 在乳腺癌数据集上的表现...\n",
      "开始决策树训练，最大深度=5, 最小分裂样本数=5\n",
      "决策树训练完成。\n",
      "\n",
      "--- 决策树 (基准) 模型评估 (乳腺癌) ---\n",
      "准确率 (Accuracy): 0.9123\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        类别 0       0.86      0.90      0.88        42\n",
      "        类别 1       0.94      0.92      0.93        72\n",
      "\n",
      "    accuracy                           0.91       114\n",
      "   macro avg       0.90      0.91      0.91       114\n",
      "weighted avg       0.91      0.91      0.91       114\n",
      "\n",
      "\n",
      "混淆矩阵:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC值: 0.9276\n",
      "\n",
      "训练并评估随机森林 (改进模型) 在乳腺癌数据集上的表现...\n",
      "开始随机森林训练，树的数量=50, 每棵树最大特征数=5 (基于 'sqrt')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "随机森林训练中: 100%|██████████| 50/50 [00:00<00:00, 52.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "随机森林训练完成。\n",
      "\n",
      "--- 随机森林 (改进) 模型评估 (乳腺癌) ---\n",
      "准确率 (Accuracy): 0.9561\n",
      "\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        类别 0       0.93      0.95      0.94        42\n",
      "        类别 1       0.97      0.96      0.97        72\n",
      "\n",
      "    accuracy                           0.96       114\n",
      "   macro avg       0.95      0.96      0.95       114\n",
      "weighted avg       0.96      0.96      0.96       114\n",
      "\n",
      "\n",
      "混淆矩阵:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC值: 0.9954\n",
      "\n",
      "--- 随机森林特征重要性 (乳腺癌) ---\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 8
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}