06_decision_trees.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6장 – 결정 트리**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_이 노트북에는 6장의 연습 문제에 대한 모든 샘플 코드와 솔루션이 포함되어 있습니다._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/rickiepark/handson-ml3/blob/main/06_decision_trees.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 설정"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 프로젝트에는 파이썬 3.7 이상이 필요합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "assert sys.version_info >= (3, 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "또한 사이킷런 ≥ 1.0.1이 필요합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from packaging import version\n",
    "import sklearn\n",
    "\n",
    "assert version.parse(sklearn.__version__) >= version.parse(\"1.0.1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이전 챕터에서와 마찬가지로 기본 글꼴 크기를 정의하여 그림을 더 예쁘게 만들어 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rc('font', size=14)\n",
    "plt.rc('axes', labelsize=14, titlesize=14)\n",
    "plt.rc('legend', fontsize=14)\n",
    "plt.rc('xtick', labelsize=10)\n",
    "plt.rc('ytick', labelsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그리고 `images/decision_trees` 폴더를 만들고(아직 존재하지 않는 경우), 이 노트북을 통해 책에 사용할 그림을 고해상도로 저장하는 데 사용되는 `save_fig()` 함수를 정의해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "IMAGES_PATH = Path() / \"images\" / \"decision_trees\"\n",
    "IMAGES_PATH.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = IMAGES_PATH / f\"{fig_id}.{fig_extension}\"\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 결정 트리 훈련과 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=2, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "iris = load_iris(as_frame=True)\n",
    "X_iris = iris.data[[\"petal length (cm)\", \"petal width (cm)\"]].values\n",
    "y_iris = iris.target\n",
    "\n",
    "tree_clf = DecisionTreeClassifier(max_depth=2, random_state=42)\n",
    "tree_clf.fit(X_iris, y_iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**이 코드 예제는 그림 6-1 붓꽃 결정 트리를 생성합니다:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "export_graphviz(\n",
    "        tree_clf,\n",
    "        out_file=str(IMAGES_PATH / \"iris_tree.dot\"),  # 경로가 책과 다릅니다.\n",
    "        feature_names=[\"petal length (cm)\", \"petal width (cm)\"],\n",
    "        class_names=iris.target_names,\n",
    "        rounded=True,\n",
    "        filled=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 7.1.0 (0)\n",
       " -->\n",
       "<!-- Title: Tree Pages: 1 -->\n",
       "<svg width=\"351pt\" height=\"314pt\"\n",
       " viewBox=\"0.00 0.00 351.00 314.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 310)\">\n",
       "<title>Tree</title>\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-310 347,-310 347,4 -4,4\"/>\n",
       "<!-- 0 -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>0</title>\n",
       "<path fill=\"#ffffff\" stroke=\"black\" d=\"M209.5,-306C209.5,-306 65.5,-306 65.5,-306 59.5,-306 53.5,-300 53.5,-294 53.5,-294 53.5,-235 53.5,-235 53.5,-229 59.5,-223 65.5,-223 65.5,-223 209.5,-223 209.5,-223 215.5,-223 221.5,-229 221.5,-235 221.5,-235 221.5,-294 221.5,-294 221.5,-300 215.5,-306 209.5,-306\"/>\n",
       "<text text-anchor=\"middle\" x=\"137.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal length (cm) &lt;= 2.45</text>\n",
       "<text text-anchor=\"middle\" x=\"137.5\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.667</text>\n",
       "<text text-anchor=\"middle\" x=\"137.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 150</text>\n",
       "<text text-anchor=\"middle\" x=\"137.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [50, 50, 50]</text>\n",
       "<text text-anchor=\"middle\" x=\"137.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = setosa</text>\n",
       "</g>\n",
       "<!-- 1 -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>1</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M105,-179.5C105,-179.5 12,-179.5 12,-179.5 6,-179.5 0,-173.5 0,-167.5 0,-167.5 0,-123.5 0,-123.5 0,-117.5 6,-111.5 12,-111.5 12,-111.5 105,-111.5 105,-111.5 111,-111.5 117,-117.5 117,-123.5 117,-123.5 117,-167.5 117,-167.5 117,-173.5 111,-179.5 105,-179.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"58.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"58.5\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 50</text>\n",
       "<text text-anchor=\"middle\" x=\"58.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [50, 0, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"58.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = setosa</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;1 -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>0&#45;&gt;1</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M109.87,-222.58C102.57,-211.77 94.68,-200.09 87.33,-189.19\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"90.37,-187.44 81.87,-181.12 84.57,-191.36 90.37,-187.44\"/>\n",
       "<text text-anchor=\"middle\" x=\"76.27\" y=\"-200.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">True</text>\n",
       "</g>\n",
       "<!-- 2 -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>2</title>\n",
       "<path fill=\"#ffffff\" stroke=\"black\" d=\"M286,-187C286,-187 147,-187 147,-187 141,-187 135,-181 135,-175 135,-175 135,-116 135,-116 135,-110 141,-104 147,-104 147,-104 286,-104 286,-104 292,-104 298,-110 298,-116 298,-116 298,-175 298,-175 298,-181 292,-187 286,-187\"/>\n",
       "<text text-anchor=\"middle\" x=\"216.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal width (cm) &lt;= 1.75</text>\n",
       "<text text-anchor=\"middle\" x=\"216.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.5</text>\n",
       "<text text-anchor=\"middle\" x=\"216.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 100</text>\n",
       "<text text-anchor=\"middle\" x=\"216.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 50, 50]</text>\n",
       "<text text-anchor=\"middle\" x=\"216.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;2 -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>0&#45;&gt;2</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M165.13,-222.58C170.82,-214.16 176.86,-205.2 182.74,-196.5\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"185.48,-198.7 188.17,-188.45 179.67,-194.78 185.48,-198.7\"/>\n",
       "<text text-anchor=\"middle\" x=\"193.78\" y=\"-208.04\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\n",
       "</g>\n",
       "<!-- 3 -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>3</title>\n",
       "<path fill=\"#4de88e\" stroke=\"black\" d=\"M196,-68C196,-68 99,-68 99,-68 93,-68 87,-62 87,-56 87,-56 87,-12 87,-12 87,-6 93,0 99,0 99,0 196,0 196,0 202,0 208,-6 208,-12 208,-12 208,-56 208,-56 208,-62 202,-68 196,-68\"/>\n",
       "<text text-anchor=\"middle\" x=\"147.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.168</text>\n",
       "<text text-anchor=\"middle\" x=\"147.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 54</text>\n",
       "<text text-anchor=\"middle\" x=\"147.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 49, 5]</text>\n",
       "<text text-anchor=\"middle\" x=\"147.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n",
       "</g>\n",
       "<!-- 2&#45;&gt;3 -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>2&#45;&gt;3</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M190.81,-103.73C185.52,-95.34 179.94,-86.47 174.6,-78.01\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"177.58,-76.17 169.29,-69.58 171.66,-79.91 177.58,-76.17\"/>\n",
       "</g>\n",
       "<!-- 4 -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>4</title>\n",
       "<path fill=\"#843de6\" stroke=\"black\" d=\"M331,-68C331,-68 238,-68 238,-68 232,-68 226,-62 226,-56 226,-56 226,-12 226,-12 226,-6 232,0 238,0 238,0 331,0 331,0 337,0 343,-6 343,-12 343,-12 343,-56 343,-56 343,-62 337,-68 331,-68\"/>\n",
       "<text text-anchor=\"middle\" x=\"284.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.043</text>\n",
       "<text text-anchor=\"middle\" x=\"284.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 46</text>\n",
       "<text text-anchor=\"middle\" x=\"284.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1, 45]</text>\n",
       "<text text-anchor=\"middle\" x=\"284.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
       "</g>\n",
       "<!-- 2&#45;&gt;4 -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>2&#45;&gt;4</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M241.82,-103.73C247.03,-95.34 252.53,-86.47 257.79,-78.01\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"260.72,-79.93 263.02,-69.59 254.77,-76.23 260.72,-79.93\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.sources.Source at 0x112965de0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from graphviz import Source\n",
    "\n",
    "Source.from_file(IMAGES_PATH / \"iris_tree.dot\")  # 경로가 책과 다릅니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graphviz는 `.dot` 파일을 다양한 포맷으로 변환할 수 있는 `dot` 명령줄 도구도 제공합니다. 다음 명령은 dot 파일을 png 이미지로 변환합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 추가 코드\n",
    "!dot -Tpng {IMAGES_PATH / \"iris_tree.dot\"} -o {IMAGES_PATH / \"iris_tree.png\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 예측하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 추가 코드 - 세부 서식 지정\n",
    "from matplotlib.colors import ListedColormap\n",
    "custom_cmap = ListedColormap(['#fafab0', '#9898ff', '#a0faa0'])\n",
    "plt.figure(figsize=(8, 4))\n",
    "\n",
    "lengths, widths = np.meshgrid(np.linspace(0, 7.2, 100), np.linspace(0, 3, 100))\n",
    "X_iris_all = np.c_[lengths.ravel(), widths.ravel()]\n",
    "y_pred = tree_clf.predict(X_iris_all).reshape(lengths.shape)\n",
    "plt.contourf(lengths, widths, y_pred, alpha=0.3, cmap=custom_cmap)\n",
    "for idx, (name, style) in enumerate(zip(iris.target_names, (\"yo\", \"bs\", \"g^\"))):\n",
    "    plt.plot(X_iris[:, 0][y_iris == idx], X_iris[:, 1][y_iris == idx],\n",
    "             style, label=f\"Iris {name}\")\n",
    "\n",
    "# 추가 코드 - 이 섹션에서는 그림 6-2를 아름답게 꾸미고 저장합니다.\n",
    "tree_clf_deeper = DecisionTreeClassifier(max_depth=3, random_state=42)\n",
    "tree_clf_deeper.fit(X_iris, y_iris)\n",
    "th0, th1, th2a, th2b = tree_clf_deeper.tree_.threshold[[0, 2, 3, 6]]\n",
    "plt.xlabel(\"Petal length (cm)\")\n",
    "plt.ylabel(\"Petal width (cm)\")\n",
    "plt.plot([th0, th0], [0, 3], \"k-\", linewidth=2)\n",
    "plt.plot([th0, 7.2], [th1, th1], \"k--\", linewidth=2)\n",
    "plt.plot([th2a, th2a], [0, th1], \"k:\", linewidth=2)\n",
    "plt.plot([th2b, th2b], [th1, 3], \"k:\", linewidth=2)\n",
    "plt.text(th0 - 0.05, 1.0, \"Depth=0\", horizontalalignment=\"right\", fontsize=15)\n",
    "plt.text(3.2, th1 + 0.02, \"Depth=1\", verticalalignment=\"bottom\", fontsize=13)\n",
    "plt.text(th2a + 0.05, 0.5, \"(Depth=2)\", fontsize=11)\n",
    "plt.axis([0, 7.2, 0, 3])\n",
    "plt.legend()\n",
    "save_fig(\"decision_tree_decision_boundaries_plot\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "트리 구조는 `tree_` 속성을 통해 액세스할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.tree._tree.Tree at 0x1529906c0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.tree_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "자세한 내용은 이 클래스의 문서를 확인하세요:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# help(sklearn.tree._tree.Tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "예시로 아래의 추가 자료 섹션을 참조하세요."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 클래스 확률 추정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.   , 0.907, 0.093]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.predict_proba([[5, 1.5]]).round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.predict([[5, 1.5]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 규제 매개변수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(min_samples_leaf=5, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(min_samples_leaf=5, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(min_samples_leaf=5, random_state=42)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "\n",
    "X_moons, y_moons = make_moons(n_samples=150, noise=0.2, random_state=42)\n",
    "\n",
    "tree_clf1 = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf2 = DecisionTreeClassifier(min_samples_leaf=5, random_state=42)\n",
    "tree_clf1.fit(X_moons, y_moons)\n",
    "tree_clf2.fit(X_moons, y_moons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 추가 코드 - 이 셀은 그림 6-3을 생성하고 저장합니다.\n",
    "\n",
    "def plot_decision_boundary(clf, X, y, axes, cmap):\n",
    "    x1, x2 = np.meshgrid(np.linspace(axes[0], axes[1], 100),\n",
    "                         np.linspace(axes[2], axes[3], 100))\n",
    "    X_new = np.c_[x1.ravel(), x2.ravel()]\n",
    "    y_pred = clf.predict(X_new).reshape(x1.shape)\n",
    "    \n",
    "    plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=cmap)\n",
    "    plt.contour(x1, x2, y_pred, cmap=\"Greys\", alpha=0.8)\n",
    "    colors = {\"Wistia\": [\"#78785c\", \"#c47b27\"], \"Pastel1\": [\"red\", \"blue\"]}\n",
    "    markers = (\"o\", \"^\")\n",
    "    for idx in (0, 1):\n",
    "        plt.plot(X[:, 0][y == idx], X[:, 1][y == idx],\n",
    "                 color=colors[cmap][idx], marker=markers[idx], linestyle=\"none\")\n",
    "    plt.axis(axes)\n",
    "    plt.xlabel(r\"$x_1$\")\n",
    "    plt.ylabel(r\"$x_2$\", rotation=0)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_decision_boundary(tree_clf1, X_moons, y_moons,\n",
    "                       axes=[-1.5, 2.4, -1, 1.5], cmap=\"Wistia\")\n",
    "plt.title(\"No restrictions\")\n",
    "plt.sca(axes[1])\n",
    "plot_decision_boundary(tree_clf2, X_moons, y_moons,\n",
    "                       axes=[-1.5, 2.4, -1, 1.5], cmap=\"Wistia\")\n",
    "plt.title(f\"min_samples_leaf = {tree_clf2.min_samples_leaf}\")\n",
    "plt.ylabel(\"\")\n",
    "save_fig(\"min_samples_leaf_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.898"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_moons_test, y_moons_test = make_moons(n_samples=1000, noise=0.2,\n",
    "                                        random_state=43)\n",
    "tree_clf1.score(X_moons_test, y_moons_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf2.score(X_moons_test, y_moons_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 회귀"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "간단한 2차방정식 훈련 세트를 준비해 보겠습니다:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**코드 샘플:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=2, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "np.random.seed(42)\n",
    "X_quad = np.random.rand(200, 1) - 0.5  # 간단한 랜덤한 입력 특성\n",
    "y_quad = X_quad ** 2 + 0.025 * np.random.randn(200, 1)\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
    "tree_reg.fit(X_quad, y_quad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 7.1.0 (0)\n",
       " -->\n",
       "<!-- Title: Tree Pages: 1 -->\n",
       "<svg width=\"666pt\" height=\"269pt\"\n",
       " viewBox=\"0.00 0.00 666.00 269.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 265)\">\n",
       "<title>Tree</title>\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-265 662,-265 662,4 -4,4\"/>\n",
       "<!-- 0 -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>0</title>\n",
       "<path fill=\"#f6d6be\" stroke=\"black\" d=\"M392,-261C392,-261 265,-261 265,-261 259,-261 253,-255 253,-249 253,-249 253,-205 253,-205 253,-199 259,-193 265,-193 265,-193 392,-193 392,-193 398,-193 404,-199 404,-205 404,-205 404,-249 404,-249 404,-255 398,-261 392,-261\"/>\n",
       "<text text-anchor=\"middle\" x=\"328.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x1 &lt;= &#45;0.303</text>\n",
       "<text text-anchor=\"middle\" x=\"328.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">squared_error = 0.006</text>\n",
       "<text text-anchor=\"middle\" x=\"328.5\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 200</text>\n",
       "<text text-anchor=\"middle\" x=\"328.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.088</text>\n",
       "</g>\n",
       "<!-- 1 -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>1</title>\n",
       "<path fill=\"#eb9d65\" stroke=\"black\" d=\"M308,-157C308,-157 181,-157 181,-157 175,-157 169,-151 169,-145 169,-145 169,-101 169,-101 169,-95 175,-89 181,-89 181,-89 308,-89 308,-89 314,-89 320,-95 320,-101 320,-101 320,-145 320,-145 320,-151 314,-157 308,-157\"/>\n",
       "<text text-anchor=\"middle\" x=\"244.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x1 &lt;= &#45;0.408</text>\n",
       "<text text-anchor=\"middle\" x=\"244.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">squared_error = 0.002</text>\n",
       "<text text-anchor=\"middle\" x=\"244.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 44</text>\n",
       "<text text-anchor=\"middle\" x=\"244.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.172</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;1 -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>0&#45;&gt;1</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M301,-192.6C294.03,-184.15 286.47,-174.96 279.2,-166.13\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"282.09,-164.14 273.03,-158.64 276.68,-168.59 282.09,-164.14\"/>\n",
       "<text text-anchor=\"middle\" x=\"269.66\" y=\"-178.66\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">True</text>\n",
       "</g>\n",
       "<!-- 4 -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>4</title>\n",
       "<path fill=\"#fae6d7\" stroke=\"black\" d=\"M477,-157C477,-157 350,-157 350,-157 344,-157 338,-151 338,-145 338,-145 338,-101 338,-101 338,-95 344,-89 350,-89 350,-89 477,-89 477,-89 483,-89 489,-95 489,-101 489,-101 489,-145 489,-145 489,-151 483,-157 477,-157\"/>\n",
       "<text text-anchor=\"middle\" x=\"413.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">x1 &lt;= 0.272</text>\n",
       "<text text-anchor=\"middle\" x=\"413.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">squared_error = 0.005</text>\n",
       "<text text-anchor=\"middle\" x=\"413.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 156</text>\n",
       "<text text-anchor=\"middle\" x=\"413.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.065</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;4 -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>0&#45;&gt;4</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M356.33,-192.6C363.38,-184.15 371.03,-174.96 378.39,-166.13\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"380.92,-168.56 384.64,-158.64 375.55,-164.08 380.92,-168.56\"/>\n",
       "<text text-anchor=\"middle\" x=\"387.87\" y=\"-178.67\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">False</text>\n",
       "</g>\n",
       "<!-- 2 -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>2</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M139,-53C139,-53 12,-53 12,-53 6,-53 0,-47 0,-41 0,-41 0,-12 0,-12 0,-6 6,0 12,0 12,0 139,0 139,0 145,0 151,-6 151,-12 151,-12 151,-41 151,-41 151,-47 145,-53 139,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"75.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">squared_error = 0.001</text>\n",
       "<text text-anchor=\"middle\" x=\"75.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 20</text>\n",
       "<text text-anchor=\"middle\" x=\"75.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.213</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;2 -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>1&#45;&gt;2</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M184.92,-88.68C167.6,-79 148.75,-68.46 131.65,-58.9\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"133.56,-55.96 123.13,-54.13 130.15,-62.07 133.56,-55.96\"/>\n",
       "</g>\n",
       "<!-- 3 -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>3</title>\n",
       "<path fill=\"#f0b489\" stroke=\"black\" d=\"M308,-53C308,-53 181,-53 181,-53 175,-53 169,-47 169,-41 169,-41 169,-12 169,-12 169,-6 175,0 181,0 181,0 308,0 308,0 314,0 320,-6 320,-12 320,-12 320,-41 320,-41 320,-47 314,-53 308,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"244.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">squared_error = 0.001</text>\n",
       "<text text-anchor=\"middle\" x=\"244.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 24</text>\n",
       "<text text-anchor=\"middle\" x=\"244.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.138</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;3 -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>1&#45;&gt;3</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M244.5,-88.68C244.5,-80.99 244.5,-72.76 244.5,-64.9\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"248,-64.91 244.5,-54.91 241,-64.91 248,-64.91\"/>\n",
       "</g>\n",
       "<!-- 5 -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>5</title>\n",
       "<path fill=\"#ffffff\" stroke=\"black\" d=\"M477,-53C477,-53 350,-53 350,-53 344,-53 338,-47 338,-41 338,-41 338,-12 338,-12 338,-6 344,0 350,0 350,0 477,0 477,0 483,0 489,-6 489,-12 489,-12 489,-41 489,-41 489,-47 483,-53 477,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"413.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">squared_error = 0.001</text>\n",
       "<text text-anchor=\"middle\" x=\"413.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 110</text>\n",
       "<text text-anchor=\"middle\" x=\"413.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.028</text>\n",
       "</g>\n",
       "<!-- 4&#45;&gt;5 -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>4&#45;&gt;5</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M413.5,-88.68C413.5,-80.99 413.5,-72.76 413.5,-64.9\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"417,-64.91 413.5,-54.91 410,-64.91 417,-64.91\"/>\n",
       "</g>\n",
       "<!-- 6 -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>6</title>\n",
       "<path fill=\"#edaa79\" stroke=\"black\" d=\"M646,-53C646,-53 519,-53 519,-53 513,-53 507,-47 507,-41 507,-41 507,-12 507,-12 507,-6 513,0 519,0 519,0 646,0 646,0 652,0 658,-6 658,-12 658,-12 658,-41 658,-41 658,-47 652,-53 646,-53\"/>\n",
       "<text text-anchor=\"middle\" x=\"582.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">squared_error = 0.002</text>\n",
       "<text text-anchor=\"middle\" x=\"582.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 46</text>\n",
       "<text text-anchor=\"middle\" x=\"582.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.154</text>\n",
       "</g>\n",
       "<!-- 4&#45;&gt;6 -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>4&#45;&gt;6</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M473.08,-88.68C490.4,-79 509.25,-68.46 526.35,-58.9\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"527.85,-62.07 534.87,-54.13 524.44,-55.96 527.85,-62.07\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.sources.Source at 0x152d8fa00>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 추가 코드 - export_graphviz()를 사용하는 방법을 이미 살펴봤습니다.\n",
    "export_graphviz(\n",
    "    tree_reg,\n",
    "    out_file=str(IMAGES_PATH / \"regression_tree.dot\"),\n",
    "    feature_names=[\"x1\"],\n",
    "    rounded=True,\n",
    "    filled=True\n",
    ")\n",
    "Source.from_file(IMAGES_PATH / \"regression_tree.dot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=3, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=3, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=3, random_state=42)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_reg2 = DecisionTreeRegressor(max_depth=3, random_state=42)\n",
    "tree_reg2.fit(X_quad, y_quad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.30265072, -0.40830374, -2.        , -2.        ,  0.27175756,\n",
       "       -2.        , -2.        ])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_reg.tree_.threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.30265072, -0.40830374, -0.45416115, -2.        , -2.        ,\n",
       "       -0.37022041, -2.        , -2.        ,  0.27175756, -0.21270403,\n",
       "       -2.        , -2.        ,  0.40399227, -2.        , -2.        ])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_reg2.tree_.threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 추가 코드 - 이 셀은 그림 6-5를 생성하고 저장합니다.\n",
    "\n",
    "def plot_regression_predictions(tree_reg, X, y, axes=[-0.5, 0.5, -0.05, 0.25]):\n",
    "    x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)\n",
    "    y_pred = tree_reg.predict(x1)\n",
    "    plt.axis(axes)\n",
    "    plt.xlabel(\"$x_1$\")\n",
    "    plt.plot(X, y, \"b.\")\n",
    "    plt.plot(x1, y_pred, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_regression_predictions(tree_reg, X_quad, y_quad)\n",
    "\n",
    "th0, th1a, th1b = tree_reg.tree_.threshold[[0, 1, 4]]\n",
    "for split, style in ((th0, \"k-\"), (th1a, \"k--\"), (th1b, \"k--\")):\n",
    "    plt.plot([split, split], [-0.05, 0.25], style, linewidth=2)\n",
    "plt.text(th0, 0.16, \"Depth=0\", fontsize=15)\n",
    "plt.text(th1a + 0.01, -0.01, \"Depth=1\", horizontalalignment=\"center\", fontsize=13)\n",
    "plt.text(th1b + 0.01, -0.01, \"Depth=1\", fontsize=13)\n",
    "plt.ylabel(\"$y$\", rotation=0)\n",
    "plt.legend(loc=\"upper center\", fontsize=16)\n",
    "plt.title(\"max_depth=2\")\n",
    "\n",
    "plt.sca(axes[1])\n",
    "th2s = tree_reg2.tree_.threshold[[2, 5, 9, 12]]\n",
    "plot_regression_predictions(tree_reg2, X_quad, y_quad)\n",
    "for split, style in ((th0, \"k-\"), (th1a, \"k--\"), (th1b, \"k--\")):\n",
    "    plt.plot([split, split], [-0.05, 0.25], style, linewidth=2)\n",
    "for split in th2s:\n",
    "    plt.plot([split, split], [-0.05, 0.25], \"k:\", linewidth=1)\n",
    "plt.text(th2s[2] + 0.01, 0.15, \"Depth=2\", fontsize=13)\n",
    "plt.title(\"max_depth=3\")\n",
    "\n",
    "save_fig(\"tree_regression_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 추가 코드 - 이 셀은 그림 6-6을 생성하고 저장합니다.\n",
    "\n",
    "tree_reg1 = DecisionTreeRegressor(random_state=42)\n",
    "tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\n",
    "tree_reg1.fit(X_quad, y_quad)\n",
    "tree_reg2.fit(X_quad, y_quad)\n",
    "\n",
    "x1 = np.linspace(-0.5, 0.5, 500).reshape(-1, 1)\n",
    "y_pred1 = tree_reg1.predict(x1)\n",
    "y_pred2 = tree_reg2.predict(x1)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
    "\n",
    "plt.sca(axes[0])\n",
    "plt.plot(X_quad, y_quad, \"b.\")\n",
    "plt.plot(x1, y_pred1, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "plt.axis([-0.5, 0.5, -0.05, 0.25])\n",
    "plt.xlabel(\"$x_1$\")\n",
    "plt.ylabel(\"$y$\", rotation=0)\n",
    "plt.legend(loc=\"upper center\")\n",
    "plt.title(\"No restrictions\")\n",
    "\n",
    "plt.sca(axes[1])\n",
    "plt.plot(X_quad, y_quad, \"b.\")\n",
    "plt.plot(x1, y_pred2, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "plt.axis([-0.5, 0.5, -0.05, 0.25])\n",
    "plt.xlabel(\"$x_1$\")\n",
    "plt.title(f\"min_samples_leaf={tree_reg2.min_samples_leaf}\")\n",
    "\n",
    "save_fig(\"tree_regression_regularization_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 축 방향에 대한 민감성"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터셋을 회전하면 결정 경계가 완전히 달라집니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9IAAAF6CAYAAAD1Q2B0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAABoO0lEQVR4nO3de3yU5Z3///c9CSTawijQQJCI9qBQpSJQDlprFxQQ/NJS/KlLi90qeG7F01a3gKj9llari2uBuh7K7kot/YL46C4UpVGpGgJGtLULqPVQUAgnIUFbE5i5fn9MJiSTmWQO93lez8djHsjknpnrvgdz3Z/r8PlYxhgjAAAAAACQlYjXDQAAAAAAIEgIpAEAAAAAyAGBNAAAAAAAOSCQBgAAAAAgBwTSAAAAAADkgEAaAAAAAIAcEEgDAAAAAJADAmkAAAAAAHJQ6nUD/Cgej2vnzp3q0aOHLMvyujkAAOTFGKNDhw6pf//+ikScGzun3wQAhEEu/SaBdBo7d+5UVVWV180AAMAWO3bs0IABAxx7f/pNAECYZNNvEkin0aNHD0nSXze/op49Pu1xa4DwMJIaS7tr565d+v3vf68333xTb735pvr266clS5aovLzc6yYCodLY2KiTTz65tV9zCv0mACAMGg99pIHDhmfVbxJIp5Fcltazx6fV0+GbD6CYGEnqVqbGQ4dUXl6u7t27q7S0VN26dVPPnj0JpAGHOL3cmn4TABAm2fSbJBsDAAAAACAHBNIAAAAAAOSAQBoAAAAAgBwQSAMAAAAAkAMCaQAAAAAAckAgDQAAAABADgikAQAAAADIAYE0AAAAAAA5IJAGAAAAACAHBNIAAAAAAOSAQBoAAAAAgBwQSAMAAAAAkAMCaQAAAAAAckAgDQAAAABADgikAQAAAADIQSAC6cWLF+vkk09WeXm5hg8frhdeeKHT45uamvTDH/5QAwcOVFlZmT73uc/psccec6m1AAAAAIAwK/W6AV1Zvny5Zs+ercWLF+vss8/WQw89pAsuuEBbtmzRiSeemPY1F198sXbv3q1HH31Un//857Vnzx4dOXLE5ZYDAAAAAMLI94H0/fffryuuuEIzZ86UJC1cuFBPP/20lixZogULFnQ4fu3atVq/fr3eeecd9erVS5J00kknudlkAAAAwH9iMal2o7Rnt1TRVxo9Siop8bpVQCD5eml3c3OzXnnlFY0fP77d8+PHj1dNTU3a1/z2t7/ViBEjdM899+iEE07QKaecoltuuUV///vfM35OU1OTGhsb2z0AAEB69JtAAK1eI2vESEWmXaTINdcpMu0iWSNGSqvXeN0yIJB8HUjv27dPsVhMffv2bfd83759VV9fn/Y177zzjl588UX9+c9/1qpVq7Rw4UKtWLFC1113XcbPWbBggaLRaOujqqrK1vMAACBM6DeBgFm9RtbMWdKuXe2fr69PPE8wDeTM14F0kmVZ7f5ujOnwXFI8HpdlWVq2bJlGjhypSZMm6f7779fSpUszzkrffvvtamhoaH3s2LHD9nMAACAs6DeBAInFZM2ZKxmj1Ltny5jEn3PnJZZ9A8iar/dI9+nTRyUlJR1mn/fs2dNhljqpsrJSJ5xwgqLRaOtzgwcPljFG77//vr7whS90eE1ZWZnKysrsbTwAACFFvwkESO1GWakz0W1Yxkg7d8rUbpTOPsvFhgHB5usZ6e7du2v48OFat25du+fXrVuns85K/z/62WefrZ07d+qjjz5qfe7NN99UJBLRgAEDHG0vAAAA4Ct7dtt7HABJPg+kJemmm27SI488oscee0xbt27VjTfeqO3bt+vqq6+WlFhedtlll7UeP336dPXu3Vvf/e53tWXLFv3hD3/Qrbfeqssvv1zHHHOMV6cBAAAAuK8i/SrOvI8DIMnnS7sl6ZJLLtH+/ft11113adeuXTr99NO1Zs0aDRw4UJK0a9cubd++vfX4T3/601q3bp2+973vacSIEerdu7cuvvhi/ehHP/LqFAAAAABvjB4lU1mZSCzWsie6LWNZUmVlohQWgKz5PpCWpGuvvVbXXntt2p8tXbq0w3ODBg3qsBwcAAAAKDolJTI/ulvWzFkyltUumDYtyXvN3XdRTxrIke+XdgMAAACei8Wkl2qkVasSfwYpy/XkSTKPPCz169f++crKxPOTJ3nTLiDAAjEjDQAAAHhm9RpZc+a2y35tKitlfnR3cILQyZNkJk5IZOfeszuxJ3r0KGaigTwRSAMAAACZrF4ja+YsKXV/cX19Yrl0kGZ0S0oocQXYhKXdAAAAQDqxmKw5cyVjZKX8KLnX2Jo7L1jLvN0S5KXwQBaYkQYAAADSqd3Ybjl3KssYaefOxHJpZnqPCsNSeKALzEgDAAAA6ezZbe9xxSC5FD51AKJlKbxWr/GmXYDNCKQBAACAdCr62ntc2LEUHkWEQBoAAABIZ/SoxJJkKzUsTDCWJdO/fyL7NVqXwqe/Wolg2tq5U6rd6GqzACcQSAMAAADplJQk9vVKHYLp5N/N3XdRQiqJpfAoIgTSAAAAQCaTJyVKXPXr1/75yspglb4qRLYZuFkKjyJC1m4AAACgM5MnyUyckMjOvWd3IhAcPao4ZqJzycDdshRe9fWte6LbMpYlVVayFB6hwIw0AAAA0JWSkkSJq6lTE38WSxCdSwZulsKjiBBIAwAAAGgv3wzcLIVHkSCQBopdtvueoOpqS0OGlKi6OlM+Un+9LwAAeSskA/fkSTJ1mxRfuULxJYsUX7lC5uWNBNEIFfZIA8Usl31PRc4Yac6ciLZtszRnTkRjx8aUoRqKL94XAICCFJqBO7kUHggpZqSBzoR5tjbXfU8BZseM77p1lurqEq+vq7O0bp090a5T7wsAQEHIwA10ikAayGT1GlkjRioy7SJFrrlOkWkXyRoxMhwBZr77ngIodcY3TRLRrN7jjjsiKilJvLikxOiOO/J7Lzfe1w9Yrg4AAdeSgTs1aViSsSyZ/v3JwI2iRSANpBP22dpC9j0FjB0zvsn3iMUSr43FLFtmj516X6/ZMXgBALYI88oyp5GBG+gUgTSQqhhmawvd9xQQdsz4pr5HUqGzx069rx+wXB2AL4R5ZZlbyMANZEQgDaQqhtnaItn3ZMeMb+p7JBU6e+zU+3otzMvVAQRI2FeWuYkM3EBaBNLwP7eXZRXDbG0R7HuyY8Y3+R6RSPqDI5H8gkSn3tcPwrpcHUCAFMPKMielu+9KZuCeOjXxJ8u5AQJp+JwXy7KKYba2CPY92THj29wsbd8uxePpj43HLe3YkTguF069r9fCvFwdQIAUw8oyp7AcHsgadaThX8llWal33y3Lshzbm9MyW6v6+taR67aMZUmVlYGerZXUuu/JmjO3/dK3yspEEB3gJVttZ3zTBavJGd/zz++8ZnNZmVRbG9PevZmPqahIHJcLp97Xa233RreVGLxI/Hz8eKJpoOjFYokgds/uxKD06FH2DtwWw8oyJ3h13wUEFIE0/KmLZVnGsmTNnSczcYL9s6Yts7XWzFmJz2nToYRltrbV5EkyEyfIOHlD44HsZnyNmpu7DlarqhIPuzn1vl6xa/ACQMitXiNrzlxZbQZwTWVlYpWUXUFaMawss5uX911AQBFIw59almVlYhkj7dyZCADPPsv+zw/xbG0HyX1PIRLWGV8/s3PwAkBIuTXjWSwry+zk9X0XEEAE0vAnPyzLCulsbbEI24yv3zF4AaBTbs54FtPKMrv44b4LCBgCafiTl8uynN67BdisutrS7NkRLVwY17hx3u1BZvACQEZuz3gGbWWZ1/ceLIcHckYgDX/yalmWG3u3ABsZI82ZE9G2bZbmzIlo7Fj2IAPwIS9mPIOysszre49YTIrFZI47Tjp4MG22c5bDAx1R/gr+5EV5puTerdQR85a9W5R+gB+1zZRNvWYAvhSLSXs62ffR1ptvHq1dbIdc6x+nq6HsJC/vPWIx6b77ZZ02RJGLL5HVEkSnTl+wHB5Ij0Aa/tWyLEv9+rV/vrLS/hIMXezdkiRr7jznO1QgB6l1m6nXDMB3knWJ75jf6WHJX1uRf33Au9rFbtdQ9vLeY/UaWad/SZF7fybr4MHOj3XivgsIAQJp+NvkSTJ1mxRfuULxJYsUX7lC5uWN9v8yb9m7lWkuzzJG1s6dif1LgE8kZ6NjscS/3ES9Zn/PSldXWxoypETV1f5tIwCbZJht7TDjme61bq8G82Jm2Kt7j+S5HjiQ/nOV+E7Mcccp/v9+48x9l5+4vQoBoUEgDf/LdVlWPshWiYBJnY1O8vOsdOp+bj+2EYBNOpttTXO4p6vBvJoZ9uLeo5NzbcuSEjPVkUi4l3O7vQoBoUIgDUhkq0TgpM5GJ/l5Vpr93EAR6WK2tS3PV4N5NTPsxb1HDt+LpHBPIJAbBwUKRCC9ePFinXzyySovL9fw4cP1wgsvZPW6l156SaWlpRo6dKizDUTwtWQJT01slmQsS6Z/f7JVwheSs9GRSPop3UjEf7PS7OcGioydAZjTwVy271+fuXxXXry498j1WoZ1AoHcOLCB7wPp5cuXa/bs2frhD3+oV199Veecc44uuOACbd++vdPXNTQ06LLLLtO4ceNcaikCzYss4UCempul7duleDz9zVc8bmnHjsRxfhHE/dwACmBnAOZ0MJfl+1tz77B3ltKLe48sz9VI4Z5AIDcObOD7QPr+++/XFVdcoZkzZ2rw4MFauHChqqqqtGTJkk5fd9VVV2n69OkaM2aMSy1F4LmZJRwoQFmZVFsb08aNRzI+amtjKivzuqUJQdzPDaBAWc62mn79vF8N1kVbW334of1Lft2+98jiXJO/kkM9gUBuHNig1OsGdKa5uVmvvPKKbrvttnbPjx8/XjU1NRlf98tf/lJvv/22Hn/8cf3oRz/q8nOamprU1NTU+vfGxsb8G41gmzxJZuIEmdqNiV+eFX0THXiyI4nFEqOT6X4GuKiqKvEIgrZ7o9tKzEonfj5+PNF0kNBvoksts63WzFkyltW6XFZKmW2Vuj7G6X62k7a2lcxmbc2dJzNxgn3t6urew07ZnOvxx8v87N5wTyCQGwc28PWM9L59+xSLxdS3b/t/xH379lV9fX3a17z11lu67bbbtGzZMpWWZjdOsGDBAkWj0dZHVVDuTuGMTFnCyewI5CyI+7nRNfrNIpRPiaBsZlv9shos2Y5evTo9zNYlv22vae3GRPDsZIWSpAzX3Bx/vOK33iLz5z+FO4iWyI0DW/h6RjrJSt03YkyH5yQpFotp+vTpuvPOO3XKKadk/f633367brrppta/NzY2clOA9pKZHVPv9lsyO7L0G0gvu/3cRs3N8s1SdHSNfrPIrF4ja85cWW2yG5vKysT+3q76vmxmW92cke2qrRs2yHrk0a6PLXTJbyHX1A5+ueZeyXbFRLFcD+TF14F0nz59VFJS0mH2ec+ePR1mqSXp0KFDqqur06uvvqrrr79ekhSPx2WMUWlpqZ555hmNHTu2w+vKyspUxh0cMukis6OxLPuXeQEhkdzPvXdv5mMqKgiig4Z+s4jYMZCcXOlV6DFOW70muyBaKmzJr18G5/1wzb3UMjNvzZnbvgRWZWUiiGaCBF3wdSDdvXt3DR8+XOvWrdPUqVNbn1+3bp2+/vWvdzi+Z8+eev3119s9t3jxYj377LNasWKFTj75ZMfbjBBqyeyYiWWMtHNnYlS3mDskIIMg7ecG0EYxDSQnz7ULRpIKWfJbTNc0CIp9Zh4F8XUgLUk33XSTZsyYoREjRmjMmDH693//d23fvl1XX321pMTysg8++ED/+Z//qUgkotNPP73d6ysqKlReXt7heSBrZHYEABQjOweS/Z6ss4tzbaugJb9ODs77/Rr7VbHPzCNvvg+kL7nkEu3fv1933XWXdu3apdNPP11r1qzRwIEDJUm7du3qsqY0UBAyOwIAipFdA8le7wfORpbnaq6cVVibnRqcD8I1BkLG11m7k6699lq99957ampq0iuvvKKvfvWrrT9bunSpnn/++YyvnT9/vl577TXnG4nwyiazY+/eUv2u7DOZAgDgd3YMJCf3A6fOwrbsB/ZN5Ytsz3XCBHc+J5fB+aBcYyBkAhFIA55qyewoqUMwbaTEPqf9+xW57nuUxAIAhEehJYK62A8sJWoy+2IA2q1ySHZ/TpCuMRAyBNJANjLVuUxn1y5GgAEAwdfZQHI2JYJa9gOnDxmzrMmcT/3qXCTf/7e/lfn2t6SWhF9t2VoOqdBrmsqOawwgLwTSQLYmT5Kp26T4yhWKL3pQplcvSeo4AiwlRoYZAS4K1dWWhgwpUXV1ptsYAAiwTAPJlZVdl2kqdD/w6jWyRoxUZNpFilxznf2rvlLf/96fSccfL0Wj7Y/L5lxzUcg1TUVCVMAzvk82BvhKMrPjSzWyPvww42GWREmsImCMNGdORNu2WZozJ6KxY2PKsFoPAIIr3xJBhewHdrrWcqb3P3hQkhS/9Rbpsyc7l/3arrJLJEQFPEMgDeSjPrsSGVkfh0Bat85SXV0icq6rs7RunaXx400XrwKAAMqnRFDLfmDV17fu123LWJZUWdlxP7DTtZazef9lv5J5eaOz5aPsKLuU7zV2iCkpVby8POMecMDvjsSzv48jkAbysW+/vcchcIyR7rgjopISo1jMUkmJ0R13RHT++cxKA4Ck1v3A1sxZieC0TaDX6X5gJ2stu/H+bsr3GjsgXlaug4ePaMdbf9GRI0cc/zzACR999FHWxxJIA/no09ve4xA4bWejJSkWs1RXp6Kela6utjR7dkQLF8Y1blxxXgMAKVr2A1tz5rYvz1RZmQjw0i3Pdnrfb9j2Fedzje0Uiehw+bF6f88evfbaa3r99dfV3Nzs7GcCDmlqasr6WAJp5C8WS2SBLGRvT1D1q7T3OARK6mx0UjHPSrNfHEBGue4Hdnrfbxj3Fdu15zpXpd30t9Juevudd7Rp0yb94Q9/0DvvvMOMNAIrlkOiYAJp5Gf1Gllz5rZbGmUqKxMlHZwe+fSD5J6kDCUnjCTZUW8SvpQ6G51UzLPS7BcH0Klc9gN/eUSiMsaHH6bvYwvd9+uzfcW2sWPPda7KynSo8ZD27t2r7du3a9euXTLGqEePHhlLcgF+lssgEIE0cud0Js0gaLsnSfJ0TxLclZyNjkSM4vGOtwmRSPHNSrNfHIBtkgP1GSpj2NLH+mhfceBZlowk0+YaHnvMMbrxxht19tlne9cuIE+NjY0aOHBgVscSSCM3TmfSDBKv9yTBE83N0vbtShtES4nnd+wwam6WyspcbpxH2C8OwBaZBurbsquPpQ93VI8ePfTpT3/a62YAOYvH41kfSyCN3IQp06UdvNqTBM+UlUm1tTHt3Zv5mIqK4gmi2S8OwBadDNRLLVumeveWqa2Rune35zPpwwEUgEAauQlbpks7eLEnCZ6qqko8wH5xADbpaqBekvbvl3m5zt4+160+vJgTtAIhRSCN3IQx0yWAvLBfHIBtwjxQX+wJWoGQinjdAARMS6ZLk+Gu2FiWjB+zVcdi0ks10qpViT9zSG0PIL3s9osnjgOAToV1oD657zt1tr0lQatWr3G+DdwDAY5gRhq5CWKmS0aCAUc4uV+8utrS7NkRLVwY17hxLA0HQi+MJan8kKCVeyDAMcxII3ctmS7Vr1/75ysr/Vf6youRYEZ+UUSqqqRhwzI/BgzI/T2NkebMiWjbNktz5kQ6TeALICRaBuoldVj15tuB+q607PvOtLPFMkbWzp2JvdNO8MNsOApSXW1pyJASVVezP8qPCKSRn8mTZOo2Kb5yheJLFim+coXMyxv9FUR3MRIsSdbcefYGuqvXyBoxUpFpFylyzXWKTLtI1oiR/uusCPbhY20TmNXVWVq3jhsIoCgEaaA+G17u+/biHgi2YlDZ/1jajfz5PVu126W6MtW/bBn59c1NAMu84GOp5bQoowUUmTCVpPJy3zflSgMv3aAyVTD8hRlphJebI8FBGfllmRd8LnnjkKxJnSijxaw0UFSSA/VTpyb+DGIQLXmboNWNe6BYTPrDCzrmqad0/B//KCsez/+90E7bQWVJrYPKzEr7C4E0wsvNkWCv90FlIyjBPopW6o1DEjcQAALJy33fTt8DJbeyTZqk46+7TiNuvVU3P/igvrJnT37vh3YYVA4GAmmEl5sjwUGofxmEYB9FLfXGIYkbCACB5dW+byfvgTKsbut56JDueP11gukCMagcHATSCC83R4KDUP8yCME+ilbyxiESSX+HEIlwAwEgoLxI0OrUPVBnq9skGUnXvvUWq9sKwKBycBBII9xyGQkuJJO1l/ugshWEYB9Fq7lZ2r5disfT/z8Uj1vasSNxHAAEjhf7vp2YDe9idVtEUkVTk6J/+lPu7w0GlQOGrN0Iv2wygBaaybpl5NeaOUvGslr3HEs+qn/ZEuyrvr5d+9oykYj04YcuNwyQysqk2tqY9u7NfExFReI4AECW7MqCHosltn6tXp3V4d3378+jschuUNmoubnr/rC62tLs2REtXBjXuHFE3k4gkEZx6KxUl11lq1pGfq05c9vvG6qsTATRhSzhSnZghXSCbYN9Kf1ocjwu68qr0p+zHW0AOlFVlXgAAGxUaLnSNJMNXWnu3Tv/zytidg0qp9agHjuWEpJOIJBGcesik7WxLFlz58lMnJBd0OhE/Us76z5PniTz7w/JuupqKU2ZiuT+pg7nTO3p0GCEGgCQtQyTDZkG5OOS9pWVqeFLX3KjdaFkx6AyNajdwR5pFDcnMlnbuQ/KibrPvXp1WuuxwzlTezo0Ukeo2WMFAMgoi8RibSWD68Vf+AIr1jxEDWr3EEjDPoUk6/KKnzNZO1X3OZdzpva0r1RXWxoypETV1fmtz0o3Qg0AQFpdTTak/L2hZ0/dOWSIXqyocLpl6AQ1qN1DIA17rF4ja8RIRaZdpMg11yky7SJZI0b6f7bSz5msnar7nMs5U3vaNwqdTWaEGgCQkywH3j/67ndVd++9uv/66wmiPUYNancRSKNwTiz9dWt2289lq5yaLc/lnP08Y1+AQmd2vVDobDIj1ACAnGQ58B4bOFBlH36ogdu3K0Kk5ilqULuLQBqFcWLpr5uz2y2ZrCV1CCw9L1vl1Gx5Lufs5xn7PAVxn3Chs8mMUAMActbVwLskU1Ki6Pz5GrJgga54/HE9/tJL+sqePe62E5KoQe0FAmkUxu6lv9nMbts9W91Stkr9+rV/vrIy+9JXTnBytjzbc/bzjH2egrhPuNDZ5DCPUAdxdQGALAUx90o+/HqenQ28J/8jpa19mpp0x5//rN5/+IMLDURb2dWgThwHe1jG+H9cYvHixbr33nu1a9cunXbaaVq4cKHOOeectMc++eSTWrJkiV577TU1NTXptNNO0/z58zVhwoSsP6+xsVHRaFQH3npDPXv0sOs0wmnVKkWuua7Lw+JLFiWyWHcmFkvMPGcIzI1lSccfJ3Uvk1Vff/R5u8ow+bFOcnJgQUdn+KU2M8eFBvrZnLONbTCSGruV6f0PPtDatWu1bds2vbFtm/pVVmrp0qUqLy/P/1yy+XwjnXVWiV59NRFElpQYnXmmVFPj3/qKqW1Oyrbtyddv3py+c41EjIYN8/c1yCR5bnV1lkaMMIE8B6c1Njaqd+/eamhoUM+ePR39HPrNLPixn/FSZ9fDjrKLuVxvr76bIJSXTNfGSESKxzOWwGquqFDp9u3F/e/bAzt2qMsa1AMGuNeeIMql3/R9Henly5dr9uzZWrx4sc4++2w99NBDuuCCC7RlyxadeOKJHY7/wx/+oPPPP18//vGPddxxx+mXv/yl/s//+T/auHGjzjzzTA/OIOTsXPrbMrudiWWM9OGBDuUWkrPVBQeVybJVftIyc2zNmdt+lr6yMrH8utBONptzdroNLmo7Gy0lZ2Tl6/qKqW1Oyrbt2Y1QGzU3S2VltjXbFdTJRKAEIWByU2fXQ0pbuzin/j6X6+3Vd5OhRrNt9zV2mTxJZuIEmeRAw569itwxP+PhEUnle/Yo9uKLMuee61ozYU8NamTP9zPSo0aN0rBhw7RkyZLW5wYPHqxvfOMbWrBgQVbvcdppp+mSSy7RvHnzsjqekfUcJGeR6+vbzVYmGctKBFwvb+x6VDLL2e10cvqcIPLDLIYNbfByRrrQmV0v2DWbHMYR6iCuLvACM9I+0SZgavvP07bVRUHT2fUwRjr+eOnAgcyr07rq73O53l59N9mswvPrfU2W92ux//ovmUsvdaFBcEp1taXZsyNauDCuceN8HTLaJpd+09d7pJubm/XKK69o/Pjx7Z4fP368ampqsnqPeDyuQ4cOqVevXk40EXYm6yogYVXoyzAlZ46nTk386UWn6oc2FCCI+4Tt2u9UVSUNG5b5EbQgWiILOQLEiaScQZbN9cgQRCeP6bS/z+V6e/ndBLm8ZLb3a5WVzrYDjgpicla3+TqQ3rdvn2KxmPr2bf8/bN++fVXfZo9sZ+677z59/PHHuvjiizMe09TUpMbGxnYP5MCuZF1dJLbKSn3mpeEoXkHNZFlWJtXWxrRx45GMj9ramG1LsoOSuIss5N6j38xBkAMmJ3R1PbJ9n0xlF3O53l5+N0EuL9nF/Vpc0icVFTJf+Yq77YKtgpic1W2+DqSTrNSZTmM6PJfOE088ofnz52v58uWq6KRA/IIFCxSNRlsfVWwuyN3kSTJ1mxRfuULxJYsUX7kisRwpl+VQ2WSH7II1b74zZbIQaHbN7HoRaLo1mxykkecgri4IG/rNHAQ5YHKCXeeZaVY0l+vt5XcT5PKSndyvxZUYDHn7e98L3Mo1HFVo2c1i4etAuk+fPiopKekw+7xnz54Os9Spli9friuuuEK/+c1vdN5553V67O23366GhobWx44dOwpue1GyY+lvptnt/v1ljjuu69nqDz88WibLTX4tXQFJ9szsBinQzEdQRp6DurogbOg3cxDkgMkJWZ5npv+Fuyy7mMv19vK7CXp5yeT9Wv/+7Z7eV1amO08/Xfu/+lWPGgY7sH0qO77O2t29e3cNHz5c69at09Q2pZPWrVunr3/96xlf98QTT+jyyy/XE088ocmTJ3f5OWVlZSoLWrraMEvNDplMbLX26UQWS2Ve+mUZI2NZsubOk5k4gdIVaFVoJsswZ4huO/KcTNx1xx0RnX++/xJ3hTkLeZDQb+agJWDqKimnbwMmu2VzPY47Tjp4MNHfpyu72FnulRyvt2ffTcusrjVzVuK+Jdfz9IPJk2Quukj7n1mnXZs3a9OOHVq+c6fiksZ63TbkLfWeIMnP9wZe8fWMtCTddNNNeuSRR/TYY49p69atuvHGG7V9+3ZdffXVkhKj4pdddlnr8U888YQuu+wy3XfffRo9erTq6+tVX1+vhoYGr04B+Ug3u50c/ewicZyr+82S2T5Ty3a1lK5gmbk7nF5yHfYlTkEaeXZ73zjQQa4rkOxMyhkG2VyPn92bf+6VXK6319+NXTlmvFRSouazzlL9P/yD3jvpJMWJsAKP7VPZ8/WMtCRdcskl2r9/v+666y7t2rVLp59+utasWaOBAwdKknbt2qXt27e3Hv/QQw/pyJEjuu6663TddUdT83/nO9/R0qVL3W4+7DZ5kswnf5d13fe6Ptbp/WZdZPt0fWa8SKUuuR471v6R0iDWn85WEEeeqZMJz+S7AqklYLLmzG0/8FpZmQjUghAw2SnL65F2dVo2/Wku19vr7ybTKjzuG+CBttunMpXd9Ou9gRd8H0hL0rXXXqtrr7027c9Sg+Pnn3/e+QbBW/2yLKfg9H6zlmyfmVjGSDt3JjrHs89yti1FzOkl10EMNHOROkiQ5PfBgmKsbQmPtak33E7LCqQuZxAJmNrL5nokV6c59f75HOuEQs4TsBHbp3ITiEAaaKer/U+S1Lt3ohTWSzXOdYZkYvWcG3t7gxpoZiOoI89urEIA2rFrBRIBU3tOX49c3p/vBmjdPrV3b+ZjKioIopMIpNFeLJbYW+zn0fLOEnS0/Gnt39+6/NuxxF9kYvWc00uugxpoZiuoI89hTvwGn2IFEoAiwfap7BFI46ggZZ/OtKcpnWyX3eWKTKyecmPJdVADzWwFceQ5SBnGESKsQAIApCCQRkKue7/8MHPddk9T/S5Z8+ZL+/e7l/grDKUrAsyNJddBDDRzFbSR5zAnfoOPsQIJAJDC9+Wv4IIu9n5JkjV33tESH6vXyBoxUpFpFylyzXWKTLtI1oiR3pR6Su5p6leZWM6d4TDHSmKFoXRFALVdcp1Ocsm1HeWpqqqkYcMyPwYMKPwzkJ3UMmRJYStHBh9qWYGUWiIpyViWTP/+rEACgCJCII3WvV9ZBaF+rZvs5bK7yZNk6jYpvnKF4ksWKb5yhczLGwmiHZTdkuvEcQgPalvCM17XGwYQaNXVloYMKVF1Nf1UmLC0G9kHl/W7ZP3ox/6sm+z1sjuns336YSl9vlLbfs5XC37LYlhyjfbCnvgNAeB1vWEAgUSlifAikEb2weW+/f7NWhrmxF9BSgKXKk3bo/37q/GWWwp+66Dt7UVhwp74DQHhdb1hAIFDpYnwYmk3ut77Jckcf7z04f7s3s+LrKVhXXbn16X02cjQdmvXLp148836wuuve9Qw5MoPS9KSqxA2bjyS8VFbGyOIhvOSK5CmTk38GbR+BYBrUnN7BCGnhx/6/KAgkEbnQagkS5J14IAiDzyY3ft5lbU0bIm/ck0C5ydZtH3sb38rKx53v23ISeqSNC87fxK/AQCCJDW3h99zevipzw8CAmkkZApCU5iWR9qf+SFraZgSf+WSBM5vsmh7z4YGnbKbmqt+l25JGgAULBaTXqqRVq1K/OnHQWGgAEGsNEGfnxv2SOOo5N6vmg2yZl0pHTzYcTZRR4Pptj/z1fJppxN/ucXLTOSFyrJN0b/9zeGGoBBtbwJiMau18yehFxAiXiSzDHLujzAKckJTH2sblLaVmJWW7/ZK0+fnjhlptFdSIkUistIE0UmW1PFndi2fZoT6KK8zkRciyzY1HHusww1BIYK2JA2wTbH0RavXyBoxUpFpFylyzXWKTLtI1oiRzubfCHLujzDy4t9AEWhbaSKdZKUJP81K0+fnjkAaHWU5mxifPdve5dP8Mm+vqyRwflhKn0kWbW+MRvVmXx8OAkBSMJekAbYolr7Ii4A2yLk/wohBDcdkV2kicZwf0Ofnh0AaHWU7w3nOV+zLWsov846CnIk8i7Y/O2WKTIRfQX6VOjKdxAg1Qq1Y+iKvAtog5/4IGwY1HBW0ShP0+fnhLhYduT0Tyi/zzIKciTxD203//tp+3316a8gQjxqWQHmHzIK4JA0oWDH1RV4FtEHO/RE2IRrU8Gt/HpRKE/T5+SOQRkd2zITmsr8sRL/MHRHkTOQpbY+tXKGGV19V43nnpT3crc6Q8g6dC9qSNMAWxdQXeRXQBjn3R9iEZFCD/rxw9Pn5I2s30muZTbTmzG2/xK2yMhFEdxbE5ZqNMyS/zB0V5EzkqW3PMACT2hmOHetclsh05R38lDnTa8klaXv3Zj6mokK+WZIG2KKY+iKvAtqWFW+qr2+d5W/LWJZUWenP3B9h4+GgRnW1pdmzI1q4MK5x4wrre+nPC0efnz8CaWSWLIeVS0mE5P6y1A6yZX9Z2uXIjFBDUnV1iSudIeUdslNVlXgARaOY+iKvAtqWFW/WzFkyltXus32f+yNsPPo3YOegOf25fejz88PSbnQuOZuYTVKxfPeXdbUnW5Lp3Vuq3xXuMiRFzBjp7ru7t2aLdDJLJOUdAKQV5EoJufIymWWQc3+EiUf/BtLNIBf6XvTn8AqBNOyT7/6yzn6ZJ1+7f78i130vvGVIitzu3Wdo8+YSxztDyjsAAeJ2LecgV0rIh5cBbZBzf4SJy/8GUvvgQvpe+vPi4ddkclKegfSYMWNkWZZefvnlds8fOHBAp512msrLy7V+/XpbGogAKWR/WaZf5umErQxJkTNG2rLlH13pDCnvYC8/d24IOK9qORfbbKmXAW0uK97gHBf/Ddg5g0x/Xhz8nkwurz3SP/nJT/S1r31N8+bN0+9+9ztJ0ieffKIpU6Zo27Zt+s1vfqNzzz3X1obCZbFYYuY4273RUuH7y9ruya7fJWvefGn//rTLxI1lyZo7T2biBH92vvlcvyJ18OBIHTjw+Q7PJzpD2bZXum15h3SZKZPlHdhblR03k8OhyOSTa8NO+eQHCbIgJ7N0UyQiRUL6b6C0m5TvfXuWv/hT9zMn5bOvmf68ePg9mVxegfS5556rCy64QL/73e9UU1Oj0aNH61vf+pZefPFFLV68WNOmTbO7nXBTrlm3k+xIXJHs0F+qkbV/f8bDLGOknTsTNzp+uwHI9/oVIWOk7dtnSYor3QIZOzvD7Mo7GDU3k5kyG37v3BBQXeTacG0QleASbcSPOVaHDh/RkSNHvG6K75hDh/Thhx/qgw8+0M6dO/XJJ5+oPE0n2rbPaCufQXP68+IQhGRyeWftXrBggdauXat58+Zp0KBBevLJJzVv3jxdc801drYPbitkJsDObJxBLUPi9UxKwMTjpWpq7qtMu0zs7Awp72CfIHRuCKiWXBuZ+HoQFaFjSkrU3L1cf925U2+++ab+/ve/e90k273+el8tXXqm/umfXtWQIfndU7333nvasGGD6uvrZVmWThgwQIMHD279ud0zyPTnxSF18MXulYp2yDuQPuOMMzR9+nQtW7ZM1dXVuvLKK3XnnXd2OG7BggVauXKl3njjDR177LE699xzdc899+ikk04qpN1wgh0zAYXUn24riGVI/DKTEiAlJUd0xpdmqmf087rzzjvVvXv3DsfY2Rn6rbyDnbU03RSEzg0BFdRBVISO6V6mg7G43vjzn1VbW6vNmzeHLpA2RnruuZ/o4MGoFi06Qf/wDw/mNRj64Ycf6u9/+5u6de+uSZMm6frrr9cxxxzT+nMnZpD91p/DXnZuBXBSQXWk+/TpI0mKRqN68MEH0x6zfv16fe9739OXv/xlNTU16Qc/+IEuuOACvf766yotpYy1r9g1E2DH/jKvalwWgpmUvJSV7dHxx5do6NC4ysu9bo17grrHOCidGwIqiIOoCJ/yY9R4JKY333pLNTU1euGFF1RfX69IJFy/3A4eHKWDB7/Q8t9f0NatA3TccRu7eFU6lnr37qNbbr1F55xzToefMoOMXNm5FcBJeUeyDzzwgB544AH17dtXu3fv1uOPP67LL7+8w3Fr165t9/dHH31UJ554orZs2aIvfelL+X48nGDnTECh+8vsXCbuFmZSkIOg7jEOSueGgAriICrCp6REzZ98osOHD+vjjz9WU1OTyrqXqW+/vjrzzDO9bp0tjJGWLPknWVZcxkRkWXEdOvQDffvbS3MeDI1Go5o2bVrrBFs6zCAjW0FKJpdXIP3rX/9aN954o8477zz9x3/8hwYNGqT58+dr+vTpKu9iSqmhoUGS1KtXr3w+Gk7y20yAXcvEnZKambvPZ7J7HTMpRS+oe4yD1LkhoII4iIqiUFIS0WWXXaYLLrjA66bY4plnLH3wwdH/j4yJ6IMP+uvMM38QyMHQoG6VQkdBSiaXcyD9+9//Xt/5znc0dOhQPfnkk+rRo4duuOEG/ehHP9KiRYt08803Z3xtPB7XzTffrEmTJmnAgAEFNRwO8ONMgF/LkKTLzN2vn8zxx0sHD/rn+sGXgrrHOEidGwLM74OooMRjwIVti05Qt0ohvSBtBcgpkN68ebO++c1vasCAAfrd736nHj16SJJuueUWLVq0SAsWLNCsWbPUs2fPDq81xuiqq67Su+++q5deesme1sNefp0J8FsZkkyZuXfvbn3OV9cPvhLkG5ggdW4IOL8OooISjyEQti06Qd0qhcyCshUgfc2ZNN5++21NmjRJ5eXlevrpp9W379HlqdFoVLfccov279+ve++9t8NrjTG69tpr9fvf/17V1dX6zGeyXAIL97XMBKhfv/bPV1ZSuknqMjO3LEs6/niuHzJKdvhtg2gpeQOTuAHws6oqadiwzA8WG8E2yUHUqVMTfxJEey85kJyaWLOlxKNWr/GmXcha2y066SS36KRZWOdLbQenpaOD0kFpP4It6xnpz33uc6qvr8/483/5l3/Rv/zLv3R43hij6667TqtXr9b69etVFYThhWLHTEBm2WTmPnBA8d8slykp4fqhHfYYAwgsSjyGQti26AR1qxQSgr63PesZ6Xxde+21euKJJ/SrX/1KxxxzjOrr61VfX6/m5uas32Px4sU6+eSTVV5eruHDh+uFF17o9Pj169dr+PDhKi8v12c/+1n94he/KPQ0ig8zAellm3F7/z6uHzrI7gYmcRwA+ErLQHKmMT7LGFk7dyb2TsO3klt0Nm48kvFRWxsLRBCdOhudxKx0MKTubQ/i9+V4IedkEJtaV+65557T1772tS5fv3z5cs2ePVuLFy/W2WefrYceekgXXHCBtmzZohNPPLHD8e+++64mTZqkWbNm6fHHH9dLL72ka6+9Vp/5zGc0bdo0W84JRcxvmc0RKOwxBhBYlHgMjaDsP+1K2PZ6F5sw7G13PJA2BQ4v3H///briiis0c+ZMSdLChQv19NNPa8mSJVqwYEGH43/xi1/oxBNP1MKFCyVJgwcPVl1dnX72s58RSCOzbDOQ+jGzOQKl0BuYoC+DAhBQDCTDR9gqFWxBLQOayvGl3YVobm7WK6+8ovHjx7d7fvz48aqpqUn7mg0bNnQ4fsKECaqrq9Phw4cdaysCbPUaWSNGKjLtIkWuuU6RaRfJGjEyfdKUlszm0tFM3Elk5obTwrAMCkBAtQwkp/Z9ScayZPr3ZyAZqq62NGRIiaqrnYuI2CoVbKmJV4OScDWVrwPpffv2KRaLtcsQLkl9+/bNmPisvr4+7fFHjhzRvn370r6mqalJjY2N7R4oEvlkICWzuS3eemugVq68WwcPjvC6KYGRbhkU4AX6zSLEQLIn3AhK7eTWgK8f93oH7bvySpj2tvs6kE6yUn9hG9Phua6OT/d80oIFCxSNRlsfZBYvEl2VspJkzZ2XWPadavIkmbpNiq9cofiSRYqvXCHz8kaC6CwZI/3ud+eooaG//vrXqwL1S9MrlPiAn9BvFikGkl0VxFVIbg74+qkcYxC/K68EvQxoW74OpPv06aOSkpIOs8979uzpMOuc1K9fv7THl5aWqnfv3mlfc/vtt6uhoaH1sWPHDntOAP4Ui0kv1Ug/+1lhGUjJbJ63mppP6/33KyVJH338Re3efYbHLUrw82hyWJZBIRzoN4sYA8muCdoqpGIe8A3ad+WVsNUx93Ug3b17dw0fPlzr1q1r9/y6det01llnpX3NmDFjOhz/zDPPaMSIEerWrVva15SVlalnz57tHgiptvuh//WB7F5DBlJbGSP9/Od9ZVnxlmdi2rLlHz3/penn0eQwLYNCONBvFjkGkh0XxKC0WAd8g/hdeSVse9t9HUhL0k033aRHHnlEjz32mLZu3aobb7xR27dv19VXXy0pMSp+2WWXtR5/9dVX669//atuuukmbd26VY899pgeffRR3XLLLV6dAvwi037orpCB1FbPPtdN//u/x8qY5K+fEh048HlVV3t7I+bn0eQwLYMCAHQtaEFpMQ/4Bu278pIf97YXwveB9CWXXKKFCxfqrrvu0tChQ/WHP/xBa9as0cCBAyVJu3bt0vbt21uPP/nkk7VmzRo9//zzGjp0qO6++27927/9G6Wvil0n+6EzIQOp/YyRfvzjYzos6bGsmO6+u7tnHa2fR5PDtgwKANC5IAalxTrgG8TvqitOb3Pz0972Qvk+kJaka6+9Vu+9956ampr0yiuv6Ktf/Wrrz5YuXarnn3++3fHnnnuuNm/erKamJr377ruts9coYrUbO90PnYoMpM545vnuevXV0g5Leowp0ebNJZ51tH4eTQ7bMigAQOeCFpQW84Bv0L6rrvh5m5sfBSKQBgqW6z5nMpDazhjpjp/2kGX5q6P1+2hy2JZBAQAyC2JQWqwDvnZ+V35JdurnbW5+VOp1AwBXZLnPOT57tnTOVxLLuZmJtlVzs7TjgxIZ01lHa9TcLFeDwradRluJ0eTEz8eP9/aOpaoq8QAAOCgWS1TqaGhQt+hx0vHHu96E7IJS9/vKziQHfPfuzXxMRYV/2msXu76r1FngsWNj6qTKr2PaTizEYlbrhML553vTniAgkEZxGD1KprJSqq9vrRHdlrEsqbJSuvVmAmiHlJVJtWv36b2Gcu3es0c1NTV677339Nf33lPvPn105513asCA7q52tG1Hk9N1hMnRZDoRAAi51WtkzZkrqyUhaS9Joz7zGe36h3/QBhebEdSgtBgHfO36rtLNAnsxgJ86seCnCQW/IpBGcSgpkfnR3bJmzpKxrHbBNPuh3VN1QlzRk2J6/4NPtH37Hn300Xbt3/emjj/+kIYOjau83N32BHHkHwBgs2RVj5SB9u579+ri3/xGbw4dqmejUdeaU4xBaVAV+l35ZRY4tR1JzEp3jkAaxWPyJJlHHk5k725bAquyMhFEsx+66AR15B8AYJNOqnpYkoykq7Zt0/MjR3rQOISdX2aBg7DNzY8IpFFcJk+SmThBpnZjIgFZRV/2Qxc5Rv4BoIi1VPXIxJJU8cknGtLQ4F6bUBT8MgvMNrf8EUij+JSUSGef5XUrAAC5SiaDYiAUdsmyqkfvsKWchuf8MgvMNrf8EUgDAAD/S0kGJUmmslLmR3fbuzWHYL24ZFnVY3/37o58fHW1pdmzI1q4MK5x41g6Wyz8NAvMNrf8EUgD8C1uMABIypgMSvX1iSSSjzxsTzDtVrCeqrPgncDeWV1V9ZC0t7xcr0ejGmfzR/ul7BHc57dZYLa55YdAGoAvcYPhHgYs4GudJYMyJlGJYe48mYkTCgsw3QrW031upuBd8iawLyadVfVo+fOhQYMUd6AD8kvZI7iPWeBwiHjdACDwYjHppRpp1arEn7GY1y0KhXQ3GLBf6oBFmgkZwFstyaAy/QawjJG1c2di1jZfXQTrkmTNnWf/7/dk8J6a7Kq+XtYVM2VdMTP9z2bOklavsbctxaylqof69Wv3dFNFhX5z8cV6qW92y79z0TbRlHQ0wRS/g4tHVZU0bFjmx4ABXrcQXSGQBgqxeo2sESMVmXaRItdcp8i0i2SNGMkNToG4wXCPXwYsqqstDRlSoupqBkyQIstkUFkfl44bwXqqLIJ3Se4G9sVs8iSZuk2Kr1yh+GOP6sOVK7Xp17/Wti9+0ZGPS/7uTWZrTiSYYtAYCBICaSBfnc0kMFtQEG4wCpNtUOqXAQtmxdGpLJNBZX1cOm4E66m6Ct7VMYhu/ZkTgT2OVvX4//4/HT77LMf2oqf+7j368e7/DmYQ0z1c6/AhkAby4dUywCLgpxuMIMolKPXLgIVfZsXhUy3JoEyGParGsmT6908k4cqXG8F6KjuCcjsDe7gm9Xdvktu/gxnEdA/XOpwIpIF8eLEMsEj45QYjqLINSv0yYOGXWXH4WEsyKEkdgunk383ddxU2e+hGsJ7KjqDczsAermhb9iidZNkjN34HMojpnqBfa2bT0yOQBvLhxTLAIuCnG4wgyiUo9cuAhV9mxeFzGZJBqbLSnmzabgTrqboK3nU0a3SHnzkR2HeFxJq2yK7sUeI4JzGI6Z6gX2tm0zMjkAby4cUywCLglxuMoMo2KPXLgIVfZsUREG2TQS1ZpPjKFTIvb7SvDJTTwXqqLIL3zn5me2DfGRJr2iZZ9mjjxiMZH7W1McfLHvltEDPMM55+u9a5CvpsupOoIw3ko2UmQfX17bKrJhnLkior3Z0tcFMslli2vmd3YrBg9Chbbuioq5i/tkFp21nmZFB6/vlH63BnN2Bh1Nzs7LVu2zm3lbjJEDVV0VEyGZRTJk+SmThBxoHfbxk/75GHEzk32iaurKxMBMpS5p+5VUfaq/raPvTaa330z/9cooUL4xo3Lv/fTVVViYdXcukv3GpP2xnPsWPd/Xwn+e1a5yq1/UFpt1sIpIF8tMwkWDNnyVhWu2Dak9kCN61eI2vOXFltbuxMZWViZsWGmymvbzCCKpeg1A8DFm1nxdMF9MlZcTpruM7pYD1VF8G7q4F9qi4SaxrLkjV3nszECR3b5NCAa0EKaJMx0n/+5yD95S+5BXvV1ZZmz44UHHzbyW+DmOlmPMMyiOq3a52r1PYHpd1uYWk3kC+3lwH6gQ9KfoV5+Ve+8lmqXVUlDRuW+TFggLNtZhk/0EYyeJ86NfFn2+Cus585Ld/Emn5cCl5gmw4c+LL+8pfjJWW/vNWPe0v9srUntT1B3T/cGb9d61yx/aprzEgDhXB7GaCXCpmZsEmYl38Vwi9LtXPhh1lxAF3IJ7GmH5eCF9gmY6R337tCkUhc8Xgk6+Wtfpxp9Vt/EeYZT79d61wFfTbdDQTSQKHcXgbolZaZiUwsY6SdOxODCg5dDz/elPhBUINSlvEDPpdrYk0fDLh2YEOb9u8frkOHBrV5y64DCb/uLfVTfxH0/cNd8dO1zhXbr7JDIA0gOx6X/PLrTYlfFHNQ6sc9iCgCftwDbLdcE2v6YMC1gwLbZIz01luXSYpJOvr9dtUH+Xmm1S/9RTHMePrlWucq6LPpbmGPNIDseFzyK+jlI5CbbPfC+3EPIoqAH/cAOyHX+toeD7gW9FkZjnv77c+psfFUtQ2ipc77IPaWdi3o+4fDzi9l2vyOQBpAdlpmJlJvppKMZcn07+9IyS9uSopLLsEx9S3hqFhMeqlGWrUq8Wcs5ouki67KJbGmxwOuBX1WmuOMkZ599h8kxdO+JFOwlzrwm8QA8FEknPQ/r5OSBgFLuwFkx+GSX889V5KxPmcxLP/CUdnuhWe5PxyVodSf/v53f+0BdkO2iTVzXQruhgLadPiwpYaGqDLNO6Vb3sre0uwEef8wkEQgjeJTDPvanNIyM2HNmdt+NqayMhFE55mJ1Rhp/vzuabNxc1NSXHIJjv28BxEBlynLcyeloKQs9wAHtQ/KJrGmwwOueSmgTd27G1155cOqrX1bzU1N+vaMGTrrrPbXIDXYY29p9oK6f7gtcnQUNwJpFJcMMwzmR3eHs+6zExwo+bV79xnavDnx+tQZSG5Kgi3Xm4xsg+OwZ3uFhzrL8pzte2Tal1sMfZBDA65etSkabVTPnn9R0yef6HOfa9CwYZ1/FDOtxYOSnCCQRvHwY23LoLKx5Jcx0tat/5hxBpKbkuDK9SYjl+CY5f5wTBdZnrOSbl9uMfVBDgy4BqlNYZhpRdcoyQmSjaE4dFFHUpKsufMSS+7gqoMHR+rAgc93mo2bhBfBlGsisGwT9JDtFY4qIKN0xqSLxdgHJQdcp05N/OmH5et+bBOyrtLgJ6lJUEl+WpwIpJG7dFlM/a5lhiHTr2jLGFk7dyb2rcE1xkjbt8+SZbX/N0SHFHy53mTkEhyT7RWOyjLLc+q/1E7329IHAWkFtYQhJTkhEUgjV0GtnenH2pbQBx+cpo8+/qKMyb4+J4Ih15uMXIJj6lvCUdmU+ut1fHbloJLog4C0gljCMCwlOYO4EsBv2CON7AV5f5cfa1sWOWOkzZunKlGfs+OYHtm4gyufRGC57oVnDyIck02W53vvlXLZb0sfBHQQ1BKGYcjRQaI0exBIIztd7O/yfe1Mr2pbBrXMiZ1Sr8E5X215ukQffdRLudTnRDDke5NBcAzfyDbLc7ZJF/1YXxnwWBBLGIalJCeJ0uzh66XdBw4c0IwZMxSNRhWNRjVjxgwdPHgw4/GHDx/WD37wAw0ZMkSf+tSn1L9/f1122WXauXOne40Oq6Dv72qZYZDUYbmeY7Utg7oM3k4p16Bk2kWKDh2qnr//vUpLY5oy5W6d8aXvauzYW/XCC39jeW4IkAgMoTF5kkzdJsVXrlB8ySLFV66QeXljfiuvvOiDAB8L6vLoMOToIFGafXw9Iz19+nS9//77Wrt2rSTpyiuv1IwZM/Tf//3faY//29/+ps2bN2vu3Lk644wzdODAAc2ePVtTpkxRXV2dm00PnzDs73KztmWQl8HbJcM1sHbt0ok336wvfPvb2vbpbvr0p9/U8ccf0tChcZWXe9RW2Ia63wgVG0v9+bK+MuCRoC6PDkNJziCuBPAr3wbSW7du1dq1a1VbW6tRoxJLnR5++GGNGTNGb7zxhk499dQOr4lGo1q3bl275x588EGNHDlS27dv14knnuhK20MpLPu73KgjGfRl8HbI4hqM/e1v9T9Tp3rSPDgnDDcZgGP8WF85FVuS4LCgL48O8jakfHKYIDPfBtIbNmxQNBptDaIlafTo0YpGo6qpqUkbSKfT0NAgy7J03HHHZTymqalJTU1NrX9vbGzMu92hFab9XXbOMKTTsgw+E8sYaefOxI2Uk+3wUhbXoGdDg07ZvVvbXGwW3BHkmwxkh36zALn0QW4HtavXyJozt93vb1NZmViWzow5bMLKJe8EdSWAX/l2j3R9fb0qKio6PF9RUaH6+vqs3uOTTz7RbbfdpunTp6tnz54Zj1uwYEHrPuxoNKoq7gA7Yn9X9sKwDL5QWZ5b9G9/c7gh4Uf5CniBftMFbufZSG7HSR0EbdmSVFT5PeCoIJUwDFof21l7yWFiP9cD6fnz58uyrE4fyf3MVpq1BcaYtM+nOnz4sC699FLF43EtXry402Nvv/12NTQ0tD527NiR38mFXcv+rpxqZxajsCyDL0SW59Zw7LEONyTcUstX2NH5Be2mAd6g33SY20FtF9txJMmaOy8xQw7YoKpKGjYs82PAAK9b6Ewf66Su2huGRGl+4/rS7uuvv16XXnppp8ecdNJJ+tOf/qTduzvOau3du1d9+3Z+k3748GFdfPHFevfdd/Xss892OhstSWVlZSrzw7BXEARhf5fXwrQMPl9ZXINDPXvqzb59pYYGDxoYDnaXr6CuJLJFv+kgL/JssCUJ6CBoJaK6ai85TOzneiDdp08f9enTp8vjxowZo4aGBm3atEkjR46UJG3cuFENDQ0666zMv8STQfRbb72l5557Tr1797at7Wjh9B7joGtZBm/NnJW44WkTSBbNMvgsrsGzU6bIRHy7uyQv1dWWZs+OaOHCuMaNc7azTU0YYkeikKDdNACh5EVQy5YkoB0n+lgnZdtecpjYy7d3sYMHD9bEiRM1a9Ys1dbWqra2VrNmzdKFF17YLtHYoEGDtGrVKknSkSNHdNFFF6murk7Lli1TLBZTfX296uvr1cw6BbiJZfAZr4Hp31/b77tPbw0Z4lHDnOH2ErBk0JvMuplIFJIIfvNBXUnAJ7wIarPdatS7j/RSjbRqVeJPlnojpOzuY50WtPaGhW+zdkvSsmXL9P3vf1/jx4+XJE2ZMkU///nP2x3zxhtvqKFlaej777+v3/72t5KkoUOHtjvuueee09e+9jXH2xxalMPIHcvgO1wDU9FXjed8VY319VJLffiwcHM214nyFdSVBHzCizwb2WxJOu44WTfMJqM3Qi9oJaKC1t4w8XUg3atXLz3++OOdHmPa/MI/6aST2v0dNqEcRv5YBt/xGoRwIMHtJWB2l6+gEwZ8xIs8G11txzFGOnCg4+takp8VzUorFIWglYgKWnvDxLdLu+ETlMMAuuTmkionylektj+JpWGAB7wqN5lpS1K/ftLxx0sSGb0RekErERW09oYNgTQyoxwG0KXUvcVJTu0xtrt8RVA6Ycpyoah4lWdj8iSZuk2Kr1yh+JJFiq9cIfNvD8g6cKDDfUCSZYysnTsT27+AgAtaiaigtTdsfL20Gx6jHAbQJbeXVNldviK7Ttioudm7khiU5UJR8irPRup2nJaErl0iozdCIGglooLW3rAhkEZmlMMAOtV2NjddIJqczbV7j7Gd5SuC0AlTlgtFyw95NrxIfgZ4KGglooLW3jAhkEZmdJ5Ap4Iwm5sNP3fCQavlCYSOF8nPEEjV1ZZmz45o4cK4xo1jsBPhRyCNzOg8gU4FYTY36CjLFTCl3aVu3b1uBezUTTILFsj67nfTZ/SWZH78Y6n8GK9aaDsrEpH49ZITtuCgGBFII7OuymHIocyhcA/1wQvm59ncoKMsV/DsO3xYTc2HvW4G7DZhgsoeflg95s5VSZvcKfHKSh266y41TZgghel7bz6sffv3691339X777+vjz/+WKUlpYpEyNGbCVtwUIwIpNG5lsyh1py57UtgVVYmgmjqRgYX9cHhc9TGDJ7n16/Xscce63Uz4IRjjpF1zz36zLZtOubgQf39uOO0d9AgmUhEevZZr1tnux07dqi2tla7du2SZVk68cQTNXToUNveP0zLoNmCg2JFII2ueZU5FM5J1gdPXbLfUh/c0fIqQBa8SuSGwqxatUrdunXzuhlww/vvS3/+s9etcExjY6M+/vhjde/eXePHj9cNN9xg2yBR2JZB57MFJ0wDCSheBNLIjh8yh8IeXdQHN5Yla+48mYkTGCyBZ8KSyK3Y7NmzR6Wl3FogHHr36qWbbr5ZX/va12x93zAtg85nC07YBhJQvOjtgDDJZs8z9cERACRyC6ZvfvObKi8v97oZQME+/elPa/LkyaqoqLD1fcO2DDqfLThhGkhAcSOQBsIi2z3P1AdHQJDILXiuuuoq9ezZ0+tmAL4VpkoE+WzBCdtAAoob6Qdhr1hMeqlGWrUq8Wcs5nWLikNyz3PqTHPLnmetXnP0OeqDAwDgurZBZFvJYDJNpVFfy24LTuK4pORAQnIZeGIgITErDQQNM9KwD1mgvZHrnmfqgwMA4LqwVSLIdQsOJQ0RNsxIwx65zIjCXi17njP1PZYxsnbuTOydllrrg0tH64EnUR8cAAD7tV0GnU5yGXTQZqWrqqRhwzI/Bgw4emzqbHQSs9IIKgJpFK6LGVFJsubOY5m3U/LZ89xSH1z9+rU/prKS0ldoVV1taciQElVXc3MDAIXIZxl0mIR1IAHFjaXdKBxZoL2V755n6oOjE5QnAeCFsNYXLvZKBJQ0RBgRSKNwZIH2ViF7nqkPjgwoTwLAbWEfwCvmSgTFPpCAcCKQRuHIAu2tlj3P1sxZicRibYJp9jwjH5QnAeAFBvDCrZgHEhBO7JFG4VpmRFMTVyUZy5Lp358s0E5izzNsRHkSAG5LLQ0V1JJQAIoHgTQKRxZof5g8SaZuk+IrVyi+ZJHiK1fIvLyRIBo5CVudUwDBwACevUgWCTiPQBr2YEbUH5J7nqdOTfzJ4AVylEt5Em7UANiBATx7pe415/oBziCQhn2YEQUCLZfyJNyoAbAL9YXtlW6vOQD7EUjDXsyIJuplv1QjrVqV+JP62QiIXOqccqMGwA7UF7YXe80B95C1G7DT6jWy5sxtV1fbVFYm9pAzMw+fy7Y8SffuZPUGYA/qC9ur7SCnlJzVFxnQAQcQSAN2Wb1G1sxZ6jDsW1+fKE3FXnEEQDblSZ55hhs1APagvrB9UksXJjHYCTiDQBooRCwm1W6U6nfJmjdfMkapfZRlTKK+89x5MhMnFOdyd4QGN2oA7EZ9YXukzkYnFdNgZ3W1pdmzI1q4MK5x48J9rvAee6SBfK1eI2vESEWmXaTIdd+TtX9/hyA6yTJG1s6diaAbCDCSAgGA/7DXnCSYcB+BNJCP5DLuNnuhs7JntzPtsQuJ0tAJbtQAwJ9ySRYZViTBhNtY2g3kKhaTNWdu2mXcXaro60SL7JFPorTk0vY9uxPnNnoUS9dDjKRAAOBPxb7XPHXbEduN4AYCaSBXtRvbBZvZMJYlVVYmAk0/yidRmg8zlLM3ylnFfqMGAH5WzHvNyVYOL7C0G8hVjsuzTctQqLn7Ln/O1nYyw261BNbW3Hntl3lnWtreEnhr9Rpn25wGe6PcUVUlDRuW+TFggNctBADkqrra0pAhJaquDt70bWrt7CRqaMNpBNJArnJdnl1Z6e/SVy0z7FknSssn8HYBe6MAAMhd0AeiSYIJr/g6kD5w4IBmzJihaDSqaDSqGTNm6ODBg1m//qqrrpJlWVq4cKFjbQwtkk5lNnpUYglzhk03RpLp3VvxRQ8qvnKFzMsb/RtES9nPsCePyzXwdkHqaDSj0ADgb0GeAQ0bNwei7f7eSYIJL/k6kJ4+fbpee+01rV27VmvXrtVrr72mGTNmZPXap556Shs3blT//v0dbmUItS3rdM11iky7SNaIkZ4s1/WlkpLEPmCpQzBtLEuyLJl7fipNmyadfZY/l3O3le0Me/K4XANvF6SORjMKDQD+FfQZ0DBxcyDaie+dbOXwkm8D6a1bt2rt2rV65JFHNGbMGI0ZM0YPP/yw/ud//kdvvPFGp6/94IMPdP3112vZsmXq1q2bSy0OCR/uffWlyZMSy7X79Wv/vN+XcafT1Qy7Zcn07380UVqugbfD2BsFAMHCVhz/cHMg2onvPZkEc+PGIxkftbUxkmDCEb4NpDds2KBoNKpRo45mOR49erSi0ahqamoyvi4ej2vGjBm69dZbddppp2X1WU1NTWpsbGz3KEo+3fvqW5MnydRtUnzlCsWXLArGMu50upphV0qitFwDb4exNwpwH/0m8sVWHP9wcyDaju8907JwkmDCK74NpOvr61VRUdHh+YqKCtXX12d83U9/+lOVlpbq+9//ftaftWDBgtZ92NFoVFXFWjvAh3tffa+kJLF8e+rUYCzjziSXGfZcA28HBX1vFHsEEVT0m8gXW3H8w82B6EK/d7YDwI9cD6Tnz58vy7I6fdTV1UmSrDQzXsaYtM9L0iuvvKIHHnhAS5cuzXhMOrfffrsaGhpaHzt27Mjv5ILOh3tf4aJcZth9srQ9yHujuClAkNFvIh9sxfEPNwei7fje2Q4APyp1+wOvv/56XXrppZ0ec9JJJ+lPf/qTdu/uGLDt3btXffum33v5wgsvaM+ePTrxxBNbn4vFYrr55pu1cOFCvffee2lfV1ZWpjI2T/hu7ys8kJxhz8bkSTITJ8jUbkwMrlT0TSzndnFWPrk3au/ezMdUVMiXe6PS3RSMH89dJIKBfhP5aPt7r63E7KT4Peii7AaijZqbC+9DC/3e2wbisZjVGoCff35MOcybAbZzPZDu06eP+vTp0+VxY8aMUUNDgzZt2qSRI0dKkjZu3KiGhgaddVb6G/0ZM2bovPPOa/fchAkTNGPGDH33u98tvPFh17L3VfX1rXui2zKWJVVWurb31VaxWGJJukcBX2jlEng7pKoq8QgSbgoAFJu2M6DpgrfkDCi/B93h1kC0Hd97aiDOwAv8wvVAOluDBw/WxIkTNWvWLD300EOSpCuvvFIXXnihTj311NbjBg0apAULFmjq1Knq3bu3evfu3e59unXrpn79+rV7DTJo2ftqzZwlY1ntgmm3977aavUaWXPmymqTidxUVib2+XqVGIzAvqhxU1CY6mpLs2dHtHBhXOPGcb2AIHBzBhTZcWMgutDvPXXgOYkBaPiBbwNpSVq2bJm+//3va/z48ZKkKVOm6Oc//3m7Y9544w01NDR40bxwatn7as2Z274EVmVlIogOWkbqZDmv1Bn2lnJenpSq8mNgD9dwU1CY1L3lY8dyvYAgCPJWHOSv0O89l2XhDLLCbZYxpHZI1djYqGg0qgNvvaGePXp43RxvhGHGNBaTNWKklCETeXKpunl5o3vn1iawb9um1hn/oNWgzpGR1NitTO9/8IHWrl2rbdu26Y1t29SvslJLly5VeXm510103DPPWJo8OfO/t9WrY8xKdyL1+nG9OtfY2KjevXuroaFBPXv2dPRzotGo9u/f7+jnAHYg4AoGY6SzzirR5s3pZ7QjEaNhw6SamkRZ1rPOKlFdnaURI4xqahhkRX5y6Td9W/4KHgtDWSe/lfNyqk53LCa9VCOtWpX4kzrfvhX0cl1eo/4sgEJRMSE4cqnMQVZveMHXS7uBgrhRziuXmfuWwD4Tyxhp585EFuxsE3ixTDxQ2CNYGPaWAygUFROCI9tl4d27k8AT3iCQRng5Xc4r1yDW7sDej/u/A8bt5X1+3iPo96WO7C0HUCgqJgRPNgnRnnmGQVZ4g6XdCK+Wcl4mQ+9oLEumf//8ynklg9jUGeaWIFar13R8jZ2BvVPLxIuIV8v7qqqkYcMyPwYMcKcdbQVhqWNyFqltEC0lb5hYxgega6m/R4L++6O62tKQISWqrg5m++2QuuUnia0/cAOBNMKrpZyXpA7BdEHlvPINYu0M7P22/zuA2E91lN+vBXvLARQqbAFXEAZA3cAgK7xEII1waynnpX792j9fWZn/0ud8g1g7A3s39n+HGEmrjgrCtcgl4QwApBO2gMvvA6BuYJAVXmOPNMJv8iSZiRMSSbzsKOdVnzlhWDvpgli76nQ7vf875EhadVQQroWf95YD8L+2AVemMkpB2ivNXu8EEnjCawTSKA7Jcl6FWr1G1rz52R2bKYi1I7BvWSau+vrW5eRtJWtk57X/O+RIWnVUkK5FNglnACCdsAVcQRgAdQODrPAagTT8L5cSU07KlCU7RVZBbKGBfcsycWvmLBnLahdMF7T/uwik3oAkFeONCNcCQDEIU8AVpAFQNzDICi8RSMPf/FInuZMEY20lQw5Xgli7lokXkbAt7ysE1wJAMQlLwMUAKOAfBNLwLz/VSW5JMNal3r1l7vmpe+2ye/93yIVteV8huBYAECwMgAL+QiANf+qixJSxLFlz58lMnOBO0Jhl9mtz13z3Z4Lt2v9dBMK0vK9QXAsACJagDYBWV1uaPTuihQvjGjeOWXKED4E0/KmLGWDLGGnnzsRMrBtBZLbZr/tVOtsOFCwsy/vswLUAgOBIHQDdtMnSffdFdPPNcY0cmQhU/TIAmlrneuxYZskRPtSRhj/5rU5yS5bs1PrPScayZPr3J0s2AABwTFWVNGyYdOaZ0n/8R0TvvWfpP/4jojPPTDw/YIDXLUygzjWKAYE0/MlvdZJbsmRL6hBMkyUbAAC4yc+BatvM4tLRjOJdFD0BAodAGv7kxxnglizZ6tev/fOVle4mPgMAAEXL74FqMshPludKZBT3V7AP2IFAGv7k1xngyZNk6jYpvnKF4ksWKb5yhczLGwmiAQCAK/wcqKYG+Ul+C/YBOxBIw7/8OgOczJI9dWriT5ZzAwAAF/g9UE0N8pP8FOwDdiFrN/yNOskAAACS2u+NbisRqCZ+Pn68N9E0da5RbAik4X/USQYAAEXO74Fq0OpcA4UikAYAAAB8zu+Bamqd63T8UucasAOBNAAAAOBzQQhUq6oSD6AYEEgDAAAAAUCgCvgHWbsBAACALFVXWxoypETV1WTMAooZgTQAAACQBWOkOXMi2rbN0pw53pebAuAdAmkAAAAgC23LT1EXGShuBNIAAABAF5Llp0pKEtPQJSWJclPMSgPFiUAaAAAA6EJyNjoWS8xCx2IWs9JAESOQBgAAADqROhudxKw0ULwIpAEAAIBOpM5GJzErDRQvAmkAAAAgg+RsdCSSfto5EmFWGihGBNIAAABABs3N0vbtUjyeftY5Hre0Y0fiOADFo9TrBgAAAAB+VVYm1dbGtHdv5mMqKhLHASgevp6RPnDggGbMmKFoNKpoNKoZM2bo4MGDXb5u69atmjJliqLRqHr06KHRo0dr+/btzjcYAAAAoVNVJQ0blvkxYIDXLQTgNl8H0tOnT9drr72mtWvXau3atXrttdc0Y8aMTl/z9ttv6ytf+YoGDRqk559/Xn/84x81d+5clZeXu9RqAAAAAECY+XZp99atW7V27VrV1tZq1KhRkqSHH35YY8aM0RtvvKFTTz017et++MMfatKkSbrnnntan/vsZz/rSpsBAAAAAOHn2xnpDRs2KBqNtgbRkjR69GhFo1HV1NSkfU08Htfq1at1yimnaMKECaqoqNCoUaP01FNPudRqAAAAAEDY+TaQrq+vV0VFRYfnKyoqVF9fn/Y1e/bs0UcffaSf/OQnmjhxop555hlNnTpV3/zmN7V+/fqMn9XU1KTGxsZ2DwAAkB79JgCg2LkeSM+fP1+WZXX6qKurkyRZVscyA8aYtM9LiRlpSfr617+uG2+8UUOHDtVtt92mCy+8UL/4xS8ytmnBggWtCc2i0aiqqqpsOFMAAMKJfhMAUOxc3yN9/fXX69JLL+30mJNOOkl/+tOftHv37g4/27t3r/r27Zv2dX369FFpaam++MUvtnt+8ODBevHFFzN+3u23366bbrqp9e+NjY3cFAAAkAH9JgCg2LkeSPfp00d9+vTp8rgxY8aooaFBmzZt0siRIyVJGzduVENDg84666y0r+nevbu+/OUv64033mj3/JtvvqmBAwdm/KyysjKVUfwPAICs0G8C/lBdbWn27IgWLoxr3DjjdXOAouLbPdKDBw/WxIkTNWvWLNXW1qq2tlazZs3ShRde2C5j96BBg7Rq1arWv996661avny5Hn74Yf3lL3/Rz3/+c/33f/+3rr32Wi9OAwAAALCdMdKcORFt22ZpzpyIDHE04CrfBtKStGzZMg0ZMkTjx4/X+PHj9aUvfUn/9V//1e6YN954Qw0NDa1/nzp1qn7xi1/onnvu0ZAhQ/TII49o5cqV+spXvuJ28wEAAABHrFtnqa4ukTeors7SunXpcwgBcIZv60hLUq9evfT44493eoxJM/x2+eWX6/LLL3eqWQAAAIBnjJHuuCOikhKjWMxSSYnRHXdEdP75MWXIyQvAZr6ekQYAAADQXnI2OhZLRM2xmMWsNOAyAmkAAAAgINrORreVnJVmrzTgDgJpAAAAICBSZ6OTmJUG3EUgDQAAAARAcjY6Ekk/7RyJMCsNuIVAGgAAAAiA5mZp+3YpHk8/6xyPW9qxI3EcAGf5Oms3AAAAgISyMqm2Nqa9ezMfU1GROA6AswikAQAAgICoqko8AHiLpd0AAAAAAOSAQBoAAAAAgBwQSAMAAAAAkAMCaQAAAAAAckAgDQAAAABADgikAQAAAADIAYE0AAAAAAA5IJAGAAAAACAHpV43wI+MMZKkxkMfedwSIFyMpMbSJn300Uf65JNP1NzcrCNHjujw4cNqbGxUc3Oz100EQqWxsVHS0X7NKa39ZsvnAQAQRLn0m5ZxuncNoPfff19VVVVeNwMAAFvs2LFDAwYMcOz96TcBAGGSTb9JIJ1GPB7Xzp071aNHD1mW5epnNzY2qqqqSjt27FDPnj1d/Wwvcd7Fdd5S8Z47511c5y15e+7GGB06dEj9+/dXJOLcbi76TfcV63lLxXvunHdxnbdUvOcelH6Tpd1pRCIRR0fus9GzZ8+i+h8mifMuPsV67px38fHq3KPRqOOfQb/pnWI9b6l4z53zLj7Feu5+7zdJNgYAAAAAQA4IpAEAAAAAyAGBtM+UlZXpjjvuUFlZmddNcRXnXVznLRXvuXPexXXeUnGfuxuK9foW63lLxXvunHdxnbdUvOcelPMm2RgAAAAAADlgRhoAAAAAgBwQSAMAAAAAkAMCaQAAAAAAckAg7bEDBw5oxowZikajikajmjFjhg4ePNjl67Zu3aopU6YoGo2qR48eGj16tLZv3+58g22U77knXXXVVbIsSwsXLnSsjU7I9bwPHz6sH/zgBxoyZIg+9alPqX///rrsssu0c+dO9xqdp8WLF+vkk09WeXm5hg8frhdeeKHT49evX6/hw4ervLxcn/3sZ/WLX/zCpZbaK5fzfvLJJ3X++efrM5/5jHr27KkxY8bo6aefdrG19sn1+0566aWXVFpaqqFDhzrbQAfleu5NTU364Q9/qIEDB6qsrEyf+9zn9Nhjj7nU2uAr1r6TfpN+MxX9Jv1mUIWi3zTw1MSJE83pp59uampqTE1NjTn99NPNhRde2Olr/vKXv5hevXqZW2+91WzevNm8/fbb5n/+53/M7t27XWq1PfI596RVq1aZM844w/Tv39/867/+q7MNtVmu533w4EFz3nnnmeXLl5tt27aZDRs2mFGjRpnhw4e72Orc/frXvzbdunUzDz/8sNmyZYu54YYbzKc+9Snz17/+Ne3x77zzjjn22GPNDTfcYLZs2WIefvhh061bN7NixQqXW16YXM/7hhtuMD/96U/Npk2bzJtvvmluv/12061bN7N582aXW16YXM876eDBg+azn/2sGT9+vDnjjDPcaazN8jn3KVOmmFGjRpl169aZd99912zcuNG89NJLLrY62Iq176TfpN9si36TfpN+09t+k0DaQ1u2bDGSTG1tbetzGzZsMJLMtm3bMr7ukksuMd/+9rfdaKJj8j13Y4x5//33zQknnGD+/Oc/m4EDBwbqhqCQ825r06ZNRlKXv2y9NHLkSHP11Ve3e27QoEHmtttuS3v8P//zP5tBgwa1e+6qq64yo0ePdqyNTsj1vNP54he/aO688067m+aofM/7kksuMXPmzDF33HFHYG8Icj333/3udyYajZr9+/e70bzQKda+k36TfjMV/eZR9JvBEpZ+k6XdHtqwYYOi0ahGjRrV+tzo0aMVjUZVU1OT9jXxeFyrV6/WKaecogkTJqiiokKjRo3SU0895VKr7ZHPuUuJ858xY4ZuvfVWnXbaaW401Vb5nneqhoYGWZal4447zoFWFq65uVmvvPKKxo8f3+758ePHZzzPDRs2dDh+woQJqqur0+HDhx1rq53yOe9U8Xhchw4dUq9evZxooiPyPe9f/vKXevvtt3XHHXc43UTH5HPuv/3tbzVixAjdc889OuGEE3TKKafolltu0d///nc3mhx4xdp30m/Sb6ai30yg3wyWMPWbBNIeqq+vV0VFRYfnKyoqVF9fn/Y1e/bs0UcffaSf/OQnmjhxop555hlNnTpV3/zmN7V+/Xqnm2ybfM5dkn7605+qtLRU3//+951snmPyPe+2PvnkE912222aPn26evbsaXcTbbFv3z7FYjH17du33fN9+/bNeJ719fVpjz9y5Ij27dvnWFvtlM95p7rvvvv08ccf6+KLL3aiiY7I57zfeust3XbbbVq2bJlKS0vdaKYj8jn3d955Ry+++KL+/Oc/a9WqVVq4cKFWrFih6667zo0mB16x9p30m+3Rb9JvJtFvBkuY+k0CaQfMnz9flmV1+qirq5MkWZbV4fXGmLTPS4lRN0n6+te/rhtvvFFDhw7VbbfdpgsvvNAXCSacPPdXXnlFDzzwgJYuXZrxGK84ed5tHT58WJdeeqni8bgWL15s+3nYLfWcujrPdMene97vcj3vpCeeeELz58/X8uXL0944+l225x2LxTR9+nTdeeedOuWUU9xqnqNy+c7j8bgsy9KyZcs0cuRITZo0Sffff7+WLl3q+ei6l4q176TfpN9si34zgX6zPfpNf/abwR3O8LHrr79el156aafHnHTSSfrTn/6k3bt3d/jZ3r17O4zSJPXp00elpaX64he/2O75wYMH68UXX8y/0TZx8txfeOEF7dmzRyeeeGLrc7FYTDfffLMWLlyo9957r6C2F8LJ8046fPiwLr74Yr377rt69tlnfTuqLiX+nZaUlHQYWdyzZ0/G8+zXr1/a40tLS9W7d2/H2mqnfM47afny5briiiv0//7f/9N5553nZDNtl+t5Hzp0SHV1dXr11Vd1/fXXS0p0ksYYlZaW6plnntHYsWNdaXuh8vnOKysrdcIJJygajbY+N3jwYBlj9P777+sLX/iCo232q2LtO+k3M6PfpN/sDP0m/abn/aa7W7LRVjKBxsaNG1ufq62t7TKBxpgxYzokTPnGN75h/vEf/9Gxttotn3Pft2+fef3119s9+vfvb37wgx/klHDES/l+583NzeYb3/iGOe2008yePXvcaGrBRo4caa655pp2zw0ePLjTpCmDBw9u99zVV18dyKQpuZy3Mcb86le/MuXl5WbVqlUOt845uZx3LBbr8P/yNddcY0499VTz+uuvm48++sitZtsi1+/8oYceMsccc4w5dOhQ63NPPfWUiUQi5m9/+5ujbQ2DYu076TfpN1PRb9Jv0m96228SSHts4sSJ5ktf+pLZsGGD2bBhgxkyZEiHkg6nnnqqefLJJ1v//uSTT5pu3bqZf//3fzdvvfWWefDBB01JSYl54YUX3G5+QfI591RByz5qTO7nffjwYTNlyhQzYMAA89prr5ldu3a1Ppqamrw4hawkSxs8+uijZsuWLWb27NnmU5/6lHnvvfeMMcbcdtttZsaMGa3HJ8t43HjjjWbLli3m0UcfDXQZj2zP+1e/+pUpLS01ixYtavfdHjx40KtTyEuu550qyNlHcz33Q4cOmQEDBpiLLrrI/O///q9Zv369+cIXvmBmzpzp1SkETrH2nfSb9Jv0m/SbSfSb3vebBNIe279/v/nWt75levToYXr06GG+9a1vmQMHDrQ7RpL55S9/2e65Rx991Hz+85835eXl5owzzjBPPfWUe422Sb7n3lYQbwhyPe93333XSEr7eO6551xvfy4WLVpkBg4caLp3726GDRtm1q9f3/qz73znO+bcc89td/zzzz9vzjzzTNO9e3dz0kknmSVLlrjcYnvkct7nnntu2u/2O9/5jvsNL1Cu33dbQb4hMCb3c9+6das577zzzDHHHGMGDBhgbrrpJmajc1CsfSf9Jv0m/Sb9ZhL9pvf9pmVMS1YCAAAAAADQJbJ2AwAAAACQAwJpAAAAAAByQCANAAAAAEAOCKQBAAAAAMgBgTQAAAAAADkgkAYAAAAAIAcE0gAAAAAA5IBAGgAAAACAHBBIAwAAAACQAwJpAAAAAAByQCANwDFjxoyRZVl6+eWX2z1/4MABnXbaaSovL9f69es9ah0AAP5CvwkEB4E0AMf85Cc/kSTNmzev9blPPvlEU6ZM0bZt27Rs2TKde+65XjUPAABfod8EgoNAGoBjzj33XF1wwQVau3atampqFI/H9a1vfUsvvviifv7zn2vatGleNxEAAN+g3wSCwzLGGK8bASC8/vjHP+rMM8/U2LFjNWjQIC1atEjz5s3TnXfe6XXTAADwHfpNIBgIpAE47tvf/raWLVsmSbryyiv10EMPdTjmySef1JIlS/TKK6/owIEDevfdd3XSSSe53FIAALxHvwn4H0u7ATiuT58+kqRoNKoHH3ww7TEff/yxzjnnHP3f//t/3WwaAAC+Q78J+F+p1w0AEG4PPPCAHnjgAfXt21e7d+/W448/rssvv7zDcTNmzJAkbdu2ze0mAgDgG/SbQDAwIw3AMb/+9a9144036rzzztPmzZvVo0cPzZ8/X5988onXTQMAwHfoN4HgIJAG4Ijf//73+s53vqOhQ4fqySefVP/+/XXDDTdox44dWrRokdfNAwDAV+g3gWAh2RgA223evFlf+9rX9JnPfEY1NTXq27evJKmhoUEnn3yyIpGI3nnnHfXs2bPDa7dt26bBgweTNAUAUDToN4HgYUYagK3efvttTZo0SeXl5Xr66adbbwakRNKUW265Rfv379e9997rYSsBAPAH+k0gmJiRBuArjKwDAJA9+k3AG2TtBuALH374obZv36733ntPkrRlyxYdPHhQJ554onr16uVt4wAA8Bn6TcBbzEgD8IWlS5fqu9/9bofnf/nLX+qf/umf3G8QAAA+Rr8JeItAGgAAAACAHJBsDAAAAACAHBBIAwAAAACQAwJpAAAAAAByQCANAAAAAEAOCKQBAAAAAMgBgTQAAAAAADkgkAYAAAAAIAcE0gAAAAAA5IBAGgAAAACAHBBIAwAAAACQAwJpAAAAAAByQCANAAAAAEAO/n9eHovCE+8MJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 추가 코드 - 이 셀은 그림 6-7을 생성하고 저장합니다.\n",
    "\n",
    "np.random.seed(6)\n",
    "X_square = np.random.rand(100, 2) - 0.5\n",
    "y_square = (X_square[:, 0] > 0).astype(np.int64)\n",
    "\n",
    "angle = np.pi / 4  # 45 도\n",
    "rotation_matrix = np.array([[np.cos(angle), -np.sin(angle)],\n",
    "                            [np.sin(angle), np.cos(angle)]])\n",
    "X_rotated_square = X_square.dot(rotation_matrix)\n",
    "\n",
    "tree_clf_square = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf_square.fit(X_square, y_square)\n",
    "tree_clf_rotated_square = DecisionTreeClassifier(random_state=42)\n",
    "tree_clf_rotated_square.fit(X_rotated_square, y_square)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4), sharey=True)\n",
    "plt.sca(axes[0])\n",
    "plot_decision_boundary(tree_clf_square, X_square, y_square,\n",
    "                       axes=[-0.7, 0.7, -0.7, 0.7], cmap=\"Pastel1\")\n",
    "plt.sca(axes[1])\n",
    "plot_decision_boundary(tree_clf_rotated_square, X_rotated_square, y_square,\n",
    "                       axes=[-0.7, 0.7, -0.7, 0.7], cmap=\"Pastel1\")\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "save_fig(\"sensitivity_to_rotation_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=2, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2, random_state=42)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "pca_pipeline = make_pipeline(StandardScaler(), PCA())\n",
    "X_iris_rotated = pca_pipeline.fit_transform(X_iris)\n",
    "tree_clf_pca = DecisionTreeClassifier(max_depth=2, random_state=42)\n",
    "tree_clf_pca.fit(X_iris_rotated, y_iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 추가 코드 - 이 셀은 그림 6-8을 생성하고 저장합니다.\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "\n",
    "axes = [-2.2, 2.4, -0.6, 0.7]\n",
    "z0s, z1s = np.meshgrid(np.linspace(axes[0], axes[1], 100),\n",
    "                       np.linspace(axes[2], axes[3], 100))\n",
    "X_iris_pca_all = np.c_[z0s.ravel(), z1s.ravel()]\n",
    "y_pred = tree_clf_pca.predict(X_iris_pca_all).reshape(z0s.shape)\n",
    "\n",
    "plt.contourf(z0s, z1s, y_pred, alpha=0.3, cmap=custom_cmap)\n",
    "for idx, (name, style) in enumerate(zip(iris.target_names, (\"yo\", \"bs\", \"g^\"))):\n",
    "    plt.plot(X_iris_rotated[:, 0][y_iris == idx],\n",
    "             X_iris_rotated[:, 1][y_iris == idx],\n",
    "             style, label=f\"Iris {name}\")\n",
    "\n",
    "plt.xlabel(\"$z_1$\")\n",
    "plt.ylabel(\"$z_2$\", rotation=0)\n",
    "th1, th2 = tree_clf_pca.tree_.threshold[[0, 2]]\n",
    "plt.plot([th1, th1], axes[2:], \"k-\", linewidth=2)\n",
    "plt.plot([th2, th2], axes[2:], \"k--\", linewidth=2)\n",
    "plt.text(th1 - 0.01, axes[2] + 0.05, \"Depth=0\",\n",
    "         horizontalalignment=\"right\", fontsize=15)\n",
    "plt.text(th2 - 0.01, axes[2] + 0.05, \"Depth=1\",\n",
    "         horizontalalignment=\"right\", fontsize=13)\n",
    "plt.axis(axes)\n",
    "plt.legend(loc=(0.32, 0.67))\n",
    "save_fig(\"pca_preprocessing_plot\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 결정 트리의 분산 문제"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터셋의 작은 변화(예를 들면, 회전)로 인해 매우 다른 결정 트리가 생성될 수 있음을 살펴보았습니다. 사이킷런에서 사용하는 CART 학습 알고리즘은 확률적이기 때문에 동일한 데이터에 대해 동일한 모델을 학습하더라도 매번 매우 다른 모델이 생성될 수 있습니다. 이를 보여주기 위해 `random_state`를 이전과 다른 값으로 설정하겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=2, random_state=40)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=2, random_state=40)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2, random_state=40)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf_tweaked = DecisionTreeClassifier(max_depth=2, random_state=40)\n",
    "tree_clf_tweaked.fit(X_iris, y_iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 추가 코드 - 이 셀은 그림 6-9를 생성하고 저장합니다.\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "y_pred = tree_clf_tweaked.predict(X_iris_all).reshape(lengths.shape)\n",
    "plt.contourf(lengths, widths, y_pred, alpha=0.3, cmap=custom_cmap)\n",
    "\n",
    "for idx, (name, style) in enumerate(zip(iris.target_names, (\"yo\", \"bs\", \"g^\"))):\n",
    "    plt.plot(X_iris[:, 0][y_iris == idx], X_iris[:, 1][y_iris == idx],\n",
    "             style, label=f\"Iris {name}\")\n",
    "\n",
    "th0, th1 = tree_clf_tweaked.tree_.threshold[[0, 2]]\n",
    "plt.plot([0, 7.2], [th0, th0], \"k-\", linewidth=2)\n",
    "plt.plot([0, 7.2], [th1, th1], \"k--\", linewidth=2)\n",
    "plt.text(1.8, th0 + 0.05, \"Depth=0\", verticalalignment=\"bottom\", fontsize=15)\n",
    "plt.text(2.3, th1 + 0.05, \"Depth=1\", verticalalignment=\"bottom\", fontsize=13)\n",
    "plt.xlabel(\"Petal length (cm)\")\n",
    "plt.ylabel(\"Petal width (cm)\")\n",
    "plt.axis([0, 7.2, 0, 3])\n",
    "plt.legend()\n",
    "save_fig(\"decision_tree_high_variance_plot\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 추가 자료 - 트리 구조에 액세스"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "훈련된 `DecisionTreeClassifier`에는 트리의 구조를 저장하는 `tree_` 속성이 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.tree._tree.Tree at 0x1529906c0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = tree_clf.tree_\n",
    "tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "트리에 있는 총 노드 수를 얻을 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.node_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그 외 설명이 필요 없는 다른 속성이 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.max_depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.max_n_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.n_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.n_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.n_leaves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "노드에 대한 모든 정보는 넘파이 배열에 저장됩니다. 예를 들어, 각 노드의 불순도는 다음과 같습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.66666667, 0.        , 0.5       , 0.16803841, 0.04253308])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.impurity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "루트 노드의 인덱스는 0입니다. 노드 _i_의 왼쪽 및 오른쪽 자식 노드는 `tree.children_left[i]` 및 `tree.children_right[i]`입니다. 예를 들어 루트 노드의 자식 노드는 다음과 같습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.children_left[0], tree.children_right[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "왼쪽 노드와 오른쪽 노드가 같으면 이것이 리프 노드임을 의미합니다(자식 노드 ID는 임의적임):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1, -1)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.children_left[3], tree.children_right[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "따라서 다음과 같이 리프 노드 ID를 얻을 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 4])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_leaf = (tree.children_left == tree.children_right)\n",
    "np.arange(tree.node_count)[is_leaf]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "리프 노드가 아닌 노드를 _분할 노드_라고 합니다. 분할 특성은 `feature` 배열을 통해 참조할 수 있습니다. 리프 노드에 대한 값은 무시해야 합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, -2,  1, -2, -2], dtype=int64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그리고 해당 임계값은 다음과 같습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.44999999, -2.        ,  1.75      , -2.        , -2.        ])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "또한 각 노드에 도달한 클래스당 인스턴스 수도 확인할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[50., 50., 50.]],\n",
       "\n",
       "       [[50.,  0.,  0.]],\n",
       "\n",
       "       [[ 0., 50., 50.]],\n",
       "\n",
       "       [[ 0., 49.,  5.]],\n",
       "\n",
       "       [[ 0.,  1., 45.]]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([150,  50, 100,  54,  46], dtype=int64)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.n_node_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(tree.value.sum(axis=(1, 2)) == tree.n_node_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "각 노드의 깊이를 계산하는 방법은 다음과 같습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 1., 2., 2.])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compute_depth(tree_clf):\n",
    "    tree = tree_clf.tree_\n",
    "    depth = np.zeros(tree.node_count)\n",
    "    stack = [(0, 0)]\n",
    "    while stack:\n",
    "        node, node_depth = stack.pop()\n",
    "        depth[node] = node_depth\n",
    "        if tree.children_left[node] != tree.children_right[node]:\n",
    "            stack.append((tree.children_left[node], node_depth + 1))\n",
    "            stack.append((tree.children_right[node], node_depth + 1))\n",
    "    return depth\n",
    "\n",
    "depth = compute_depth(tree_clf)\n",
    "depth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다음은 깊이 1에서 모든 분할 노드의 임계값을 구하는 방법입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.tree_.feature[(depth == 1) & (~is_leaf)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.75])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.tree_.threshold[(depth == 1) & (~is_leaf)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 연습문제 해답"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. to 6."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "부록 A 참조"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_문제: moons 데이터셋에 결정 트리를 훈련시키고 세밀하게 튜닝해보세요._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a. `make_moons(n_samples=10000, noise=0.4)`를 사용해 데이터셋을 생성합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`random_state = 42`를 추가하여 이 노트북의 출력을 일정하게 만듭니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "\n",
    "X_moons, y_moons = make_moons(n_samples=10000, noise=0.4, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b. 이를 `train_test_split()`를 사용해 훈련 세트와 테스트 세트로 나눕니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_moons, y_moons,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "c. `DecisionTreeClassifier`의 최적의 매개변수를 찾기 위해 교차 검증과 함께 그리드 탐색을 수행합니다(`GridSearchCV`를 사용하면 됩니다). 힌트: 여러 가지 `max_leaf_nodes` 값을 시도해보세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=DecisionTreeClassifier(random_state=42),\n",
       "             param_grid={&#x27;max_depth&#x27;: [1, 2, 3, 4, 5, 6],\n",
       "                         &#x27;max_leaf_nodes&#x27;: [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
       "                                            13, 14, 15, 16, 17, 18, 19, 20, 21,\n",
       "                                            22, 23, 24, 25, 26, 27, 28, 29, 30,\n",
       "                                            31, ...],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 3, 4]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=DecisionTreeClassifier(random_state=42),\n",
       "             param_grid={&#x27;max_depth&#x27;: [1, 2, 3, 4, 5, 6],\n",
       "                         &#x27;max_leaf_nodes&#x27;: [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
       "                                            13, 14, 15, 16, 17, 18, 19, 20, 21,\n",
       "                                            22, 23, 24, 25, 26, 27, 28, 29, 30,\n",
       "                                            31, ...],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 3, 4]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3, estimator=DecisionTreeClassifier(random_state=42),\n",
       "             param_grid={'max_depth': [1, 2, 3, 4, 5, 6],\n",
       "                         'max_leaf_nodes': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
       "                                            13, 14, 15, 16, 17, 18, 19, 20, 21,\n",
       "                                            22, 23, 24, 25, 26, 27, 28, 29, 30,\n",
       "                                            31, ...],\n",
       "                         'min_samples_split': [2, 3, 4]})"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "params = {\n",
    "    'max_leaf_nodes': list(range(2, 100)),\n",
    "    'max_depth': list(range(1, 7)),\n",
    "    'min_samples_split': [2, 3, 4]\n",
    "}\n",
    "grid_search_cv = GridSearchCV(DecisionTreeClassifier(random_state=42),\n",
    "                              params,\n",
    "                              cv=3)\n",
    "\n",
    "grid_search_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=6, max_leaf_nodes=17, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=6, max_leaf_nodes=17, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=6, max_leaf_nodes=17, random_state=42)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "d. 찾은 매개변수를 사용해 전체 훈련 세트에 대해 모델을 훈련시키고 테스트 세트에서 성능을 측정합니다. 대략 85~87%의 정확도가 나올 것입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "기본적으로 `GridSearchCV`는 전체 훈련 세트로 찾은 최적의 모델을 다시 훈련시킵니다(`refit=False`로 지정해서 바꿀 수 있습니다). 그래서 별도로 작업할 필요가 없습니다. 모델의 정확도를 바로 평가할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8595"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = grid_search_cv.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_문제: 랜덤 포레스트를 만들어보세요._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a. 이전 연습문제에 이어서, 훈련 세트의 서브셋을 1,000개 생성합니다. 각각은 무작위로 선택된 100개의 샘플을 담고 있습니다. 힌트: 사이킷런의 `ShuffleSplit`을 사용할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "\n",
    "n_trees = 1000\n",
    "n_instances = 100\n",
    "\n",
    "mini_sets = []\n",
    "\n",
    "rs = ShuffleSplit(n_splits=n_trees, test_size=len(X_train) - n_instances,\n",
    "                  random_state=42)\n",
    "\n",
    "for mini_train_index, mini_test_index in rs.split(X_train):\n",
    "    X_mini_train = X_train[mini_train_index]\n",
    "    y_mini_train = y_train[mini_train_index]\n",
    "    mini_sets.append((X_mini_train, y_mini_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b. 앞에서 찾은 최적의 매개변수를 사용해 각 서브셋에 결정 트리를 훈련시킵니다. 테스트 세트로 이 1,000개의 결정 트리를 평가합니다. 더 작은 데이터셋에서 훈련되었기 때문에 이 결정 트리는 앞서 만든 결정 트리보다 성능이 떨어져 약 80%의 정확도를 냅니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.805671"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.base import clone\n",
    "\n",
    "forest = [clone(grid_search_cv.best_estimator_) for _ in range(n_trees)]\n",
    "\n",
    "accuracy_scores = []\n",
    "\n",
    "for tree, (X_mini_train, y_mini_train) in zip(forest, mini_sets):\n",
    "    tree.fit(X_mini_train, y_mini_train)\n",
    "    \n",
    "    y_pred = tree.predict(X_test)\n",
    "    accuracy_scores.append(accuracy_score(y_test, y_pred))\n",
    "\n",
    "np.mean(accuracy_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "c. 이제 마술을 부릴 차례입니다. 각 테스트 세트 샘플에 대해 1,000개의 결정 트리 예측을 만들고 다수로 나온 예측만 취합니다(사이파이의 `mode()` 함수를 사용할 수 있습니다). 그러면 테스트 세트에 대한 _다수결 예측_이 만들어집니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = np.empty([n_trees, len(X_test)], dtype=np.uint8)\n",
    "\n",
    "for tree_index, tree in enumerate(forest):\n",
    "    Y_pred[tree_index] = tree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import mode\n",
    "\n",
    "# scipy 1.11에서 keepdims의 기본값이 False로 바뀝니다.\n",
    "# 이와 관련된 경고를 피하기 위해 명시적으로 False로 지정합니다.\n",
    "y_pred_majority_votes, n_votes = mode(Y_pred, axis=0, keepdims=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2000,), (2000,))"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_majority_votes.shape, n_votes.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "d. 테스트 세트에서 이 예측을 평가합니다. 앞서 만든 모델보다 조금 높은(약 0.5~1.5% 정도) 정확도를 얻게 될 것입니다. 축하합니다. 랜덤 포레스트 분류기를 훈련시켰습니다!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.873"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred_majority_votes)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "nav_menu": {
   "height": "309px",
   "width": "468px"
  },
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}