08장.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 환경 설정\n",
    "from mlwpy import *\n",
    "%matplotlib inline\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "# iris 데이터셋\n",
    "tts = skms.train_test_split(iris.data, iris.target, \n",
    "                            test_size=.33, random_state=21)\n",
    "(iris_train_ftrs, iris_test_ftrs, \n",
    " iris_train_tgt,  iris_test_tgt) = tts\n",
    "\n",
    "# 이진 분류 문제\n",
    "useclass = 1\n",
    "tts_1c = skms.train_test_split(iris.data, iris.target==useclass, \n",
    "                               test_size=.33, random_state = 21)\n",
    "(iris_1c_train_ftrs, iris_1c_test_ftrs, \n",
    " iris_1c_train_tgt,  iris_1c_test_tgt) = tts_1c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_classifiers = {'DTC' : tree.DecisionTreeClassifier(max_depth=3)}\n",
    "\n",
    "fig, ax = plt.subplots(1,1,figsize=(4,3))\n",
    "for name, mod in tree_classifiers.items():\n",
    "    # [0, 1] [sepal len/width]만을 이용해서 예측을 수행하고 경계를 그립니다.\n",
    "    plot_boundary(ax, iris.data, iris.target, mod, [0,1])\n",
    "    ax.set_title(name)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9804, 0.9216, 0.9792])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc = tree.DecisionTreeClassifier()\n",
    "skms.cross_val_score(dtc, \n",
    "                     iris.data, iris.target, \n",
    "                     cv=3, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_1c_tree = (tree.DecisionTreeClassifier()\n",
    "                    .fit(iris_1c_train_ftrs, iris_1c_train_tgt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "image/png": {
       "height": 75,
       "width": 75
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "아래 코드 실행시 오류가 나면..\n",
    "pydot.InvocationException: GraphViz's executables not found\n",
    "\n",
    "맥 terminal에서 아래 명령어를 실행해서 설치하면 됩니다.\n",
    "brew install graphviz\n",
    "pip install -U pydotplus\n",
    "\n",
    "\"\"\"\n",
    "# 라이브러리를 추가로 설치합니다. 아래 명령어 중 하나를 선택해 실행합니다.\n",
    "# conda install pydotplus\n",
    "# pip install pydotplus\n",
    "import pydotplus \n",
    "dot_data = tree.export_graphviz(iris_1c_tree, out_file=None) \n",
    "graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "graph.write_png(\"outputs/iris_1c.png\")\n",
    "Image(\"outputs/iris_1c.png\", width=75, height=75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_tree = (tree.DecisionTreeClassifier()\n",
    "                 .fit(iris_train_ftrs, iris_train_tgt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'rm'은(는) 내부 또는 외부 명령, 실행할 수 있는 프로그램, 또는\n",
      "배치 파일이 아닙니다.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "image/png": {
       "height": 140,
       "width": 140
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .dot 파일을 만드는데 별도의 라이브러리는 필요없습니다.\n",
    "with open(\"outputs/iris.dot\", 'w') as f:\n",
    "    dot_data = tree.export_graphviz(iris_tree, out_file=f, \n",
    "                                    feature_names=iris.feature_names,  \n",
    "                                    class_names=iris.target_names,  \n",
    "                                    filled=True, rounded=True)\n",
    "\n",
    "# 아래 !로 시작하는 줄은 “쉘\" 커맨드입니다.\n",
    "# 이 커맨드는 ‘dot’ 프로그램을 이용해서 dot을 png로 바꿔줍니다.\n",
    "!dot -Tpng outputs/iris.dot -o outputs/iris.png\n",
    "!rm outputs/iris.dot\n",
    "\n",
    "Image(\"outputs/iris.png\", width=140, height=140)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2,2,figsize=(4,4))\n",
    "\n",
    "depths = [1, 2, 3, None]\n",
    "for depth, ax in zip(depths, axes.flat):\n",
    "    dtc_model = tree.DecisionTreeClassifier(max_depth=depth)\n",
    "    # plot_boundary 함수는 지정한 열 [0, 1]만 사용합니다.\n",
    "    # 따라서 여기서는 sepal length와 width만 사용하여 예측을 수행합니다.\n",
    "    plot_boundary(ax, iris.data, iris.target, dtc_model, [0,1])\n",
    "    ax.set_title(\"DTC (max_depth={})\".format(dtc_model.max_depth))\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(4,3))\n",
    "\n",
    "# 포인트들의 가위곱을 구하는 멋진 방법입니다.\n",
    "left  = np.mgrid[1:4.0, 1:10].reshape(2, -1).T\n",
    "right = np.mgrid[6:9.0, 1:10].reshape(2, -1).T\n",
    "\n",
    "# 데이터 포인트\n",
    "ax.scatter(left[:,0] , left[:,1] , c='b', marker='x')\n",
    "ax.scatter(right[:,0], right[:,1], c='r', marker='o')\n",
    "\n",
    "# 분리 직선\n",
    "ax.plot([3.5, 5.5], [1,9], 'y', label='A')\n",
    "ax.plot([4.5, 4.5], [1,9], 'k', label='B')\n",
    "ax.plot([3.5, 5.5], [9,1], 'g', label='C')\n",
    "ax.legend(loc='lower center');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(4,3))\n",
    "\n",
    "# 포인트들의 가위곱을 구합니다.\n",
    "left  = np.mgrid[1:4:2, 1:10].reshape(2, -1).T\n",
    "right = np.mgrid[6:9:2, 1:10].reshape(2, -1).T\n",
    "\n",
    "ax.scatter(left[:,0] , left[:,1] , c='b', marker='x')\n",
    "ax.scatter([2,2], [1,9], c='b', marker='x')\n",
    "ax.scatter(right[:,0], right[:,1], c='r', marker='o')\n",
    "ax.scatter([7,7], [1,9], c='r', marker='o')\n",
    "ax.set_xlim(0,9);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(4,3))\n",
    "\n",
    "left  = np.mgrid[3:4, 1:10].reshape(2, -1).T\n",
    "right = np.mgrid[6:7, 1:10].reshape(2, -1).T\n",
    "\n",
    "ax.scatter(left[:,0] , left[:,1] , c='b', marker='x')\n",
    "ax.scatter(right[:,0], right[:,1], c='r', marker='o')\n",
    "ax.set_xlim(0,9);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sv_classifiers = {\"SVC(Linear)\"   : svm.SVC(kernel='linear'),\n",
    "                  \"NuSVC(Linear)\" : svm.NuSVC(kernel='linear', nu=.9)} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2,figsize=(6,3))\n",
    "for (name, mod), ax in zip(sv_classifiers.items(), axes.flat):\n",
    "    plot_boundary(ax, iris.data, iris.target, mod, [0,1])\n",
    "    ax.set_title(name)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_linear_svc_separators(svc_maker, pname, params, ax):\n",
    "    'svc(params)를 만들고 분리 경계를 그립니다.'\n",
    "    xys = (np.linspace(2,8,100),\n",
    "           np.linspace(2,8,100))\n",
    "\n",
    "    for p in params:\n",
    "        kwargs = {pname:p, 'kernel':'linear'}\n",
    "        svc = svc_maker(**kwargs).fit(ftrs, tgt)\n",
    "        # plot_separator 함수는 mlwpy.py에 구현되어 있습니다.\n",
    "        plot_separator(svc, *xys, \n",
    "                       '{}={:g}'.format(pname, p), ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ftrs = np.array([[3,3],\n",
    "                 [3,6],\n",
    "                 [7,3],\n",
    "                 [7,6],\n",
    "                 [6,3]])\n",
    "tgt  = np.array([0,0,1,1,0])\n",
    "colors = np.array(['r', 'b'])\n",
    "\n",
    "Cs = [.1, 1.0, 10]\n",
    "nus = [.3, .4, .5]\n",
    "\n",
    "fig, axes = plt.subplots(1,3,figsize=(12,4),\n",
    "                         sharex=True, sharey=True)\n",
    "for ax in axes:\n",
    "    ax.scatter(ftrs[:,0], ftrs[:,1], c=colors[tgt])\n",
    "ax.set_xlim(2,8); ax.set_ylim(2,7)\n",
    "\n",
    "do_linear_svc_separators(svm.SVC,   \"C\",   Cs, axes[1])\n",
    "do_linear_svc_separators(svm.NuSVC, \"nu\", nus, axes[2])\n",
    "\n",
    "axes[0].set_title(\"No Boundary\")\n",
    "axes[1].set_title(\"C Boundaries\")\n",
    "axes[2].set_title(r\"$\\nu$ Boundaries\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ftrs, tgt = datasets.make_blobs(centers=2,\n",
    "                                n_features=3,\n",
    "                                n_samples=200,\n",
    "                                center_box = [-2.0, 2.0],\n",
    "                                random_state=1099)\n",
    "\n",
    "# 세가지 특성을 모두 사용하지만 그래프에는 두가지만 표현합니다.\n",
    "fig, ax = plt.subplots(1,1,figsize=(4,3))\n",
    "ax.scatter(ftrs[:, 0], ftrs[:, 1], \n",
    "           marker='o', c=tgt, s=25, edgecolor='k')\n",
    "ax.axis('off');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "nus = np.linspace(0.05, .85, 9)\n",
    "tt = skms.validation_curve(svm.NuSVC(kernel='linear'), \n",
    "                           ftrs, tgt, \n",
    "                           param_name='nu',\n",
    "                           param_range=nus,\n",
    "                           cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,1,figsize=(4,3))\n",
    "\n",
    "tidy_df = sk_graph_to_tidy(tt, nus, 'nu', 5)\n",
    "ax = sns.lineplot(x='nu', y='score', hue='set', data=tidy_df)\n",
    "\n",
    "ax.set_title('5-fold CV Performance for NuSVC')\n",
    "ax.set_xlabel(\"\\n\".join([r'$\\nu$ for $\\nu$-SVC']))\n",
    "ax.set_ylim(.3, 1.01)\n",
    "ax.legend(loc='lower center');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cs = [0.0001, 0.001, 0.01, .1, 1.0, 10, 100, 1000]\n",
    "tt = skms.validation_curve(svm.SVC(kernel='linear'), \n",
    "                           ftrs, tgt, \n",
    "                           param_name='C',\n",
    "                           param_range=cs,\n",
    "                           cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,1,figsize=(4,3))\n",
    "\n",
    "tidy_df = sk_graph_to_tidy(tt, cs, 'c', 5)\n",
    "ax = sns.lineplot(x='c',y='score',hue='set',data=tidy_df)\n",
    "\n",
    "ax.set_title('5-fold CV Performance for SVC')\n",
    "ax.set_xlabel(\"\\n\".join([r'C for SVC']))\n",
    "ax.set_ylim(.8, 1.01)\n",
    "ax.set_xlim(.00001, 10001)\n",
    "ax.set_xscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simple_argmax(arr):\n",
    "    ' np.argmax를 무언가 쓸만한 것으로 바꿔주는 헬퍼함수'\n",
    "    return np.array(np.unravel_index(np.argmax(arr), \n",
    "                                     arr.shape))\n",
    "\n",
    "def df_names(df, idxs):\n",
    "    ' 숫자를 인덱스와 컬럼으로 바꿔주는 헬퍼함수'\n",
    "    r,c = idxs\n",
    "    return df.index[r], df.columns[c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Betting Scenario (for Mark) for These Values:\n",
      "(Andy, Mark): (11, 1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Mark Bet</th>\n",
       "      <th>1</th>\n",
       "      <th>3</th>\n",
       "      <th>5</th>\n",
       "      <th>7</th>\n",
       "      <th>9</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andy Bet</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.8000</td>\n",
       "      <td>-2.6000</td>\n",
       "      <td>-4.4000</td>\n",
       "      <td>-6.2000</td>\n",
       "      <td>-8.0000</td>\n",
       "      <td>-9.8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.6000</td>\n",
       "      <td>-2.4000</td>\n",
       "      <td>-4.2000</td>\n",
       "      <td>-6.0000</td>\n",
       "      <td>-7.8000</td>\n",
       "      <td>-9.6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.4000</td>\n",
       "      <td>-2.2000</td>\n",
       "      <td>-4.0000</td>\n",
       "      <td>-5.8000</td>\n",
       "      <td>-7.6000</td>\n",
       "      <td>-9.4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.2000</td>\n",
       "      <td>-2.0000</td>\n",
       "      <td>-3.8000</td>\n",
       "      <td>-5.6000</td>\n",
       "      <td>-7.4000</td>\n",
       "      <td>-9.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-1.8000</td>\n",
       "      <td>-3.6000</td>\n",
       "      <td>-5.4000</td>\n",
       "      <td>-7.2000</td>\n",
       "      <td>-9.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.2000</td>\n",
       "      <td>-1.6000</td>\n",
       "      <td>-3.4000</td>\n",
       "      <td>-5.2000</td>\n",
       "      <td>-7.0000</td>\n",
       "      <td>-8.8000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "{\\centering\n",
       "\\begin{tabular}{lrrrrrr}\n",
       "\\toprule\n",
       "Mark Bet &                   1  &                   3  &                   5  &                   7  &                   9  &                   11 \\\\\n",
       "Andy Bet &                      &                      &                      &                      &                      &                      \\\\\n",
       "\\midrule\n",
       "1        &              -0.8000 &              -2.6000 &              -4.4000 &              -6.2000 &              -8.0000 &              -9.8000 \\\\\n",
       "3        &              -0.6000 &              -2.4000 &              -4.2000 &              -6.0000 &              -7.8000 &              -9.6000 \\\\\n",
       "5        &              -0.4000 &              -2.2000 &              -4.0000 &              -5.8000 &              -7.6000 &              -9.4000 \\\\\n",
       "7        &              -0.2000 &              -2.0000 &              -3.8000 &              -5.6000 &              -7.4000 &              -9.2000 \\\\\n",
       "9        &               0.0000 &              -1.8000 &              -3.6000 &              -5.4000 &              -7.2000 &              -9.0000 \\\\\n",
       "11       &               0.2000 &              -1.6000 &              -3.4000 &              -5.2000 &              -7.0000 &              -8.8000 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\n",
       "\\medskip}"
      ],
      "text/plain": [
       "Mark Bet                   1                    3                    5   \\\n",
       "Andy Bet                                                                  \n",
       "1                     -0.8000              -2.6000              -4.4000   \n",
       "3                     -0.6000              -2.4000              -4.2000   \n",
       "5                     -0.4000              -2.2000              -4.0000   \n",
       "7                     -0.2000              -2.0000              -3.8000   \n",
       "9                      0.0000              -1.8000              -3.6000   \n",
       "11                     0.2000              -1.6000              -3.4000   \n",
       "\n",
       "Mark Bet                   7                    9                    11  \n",
       "Andy Bet                                                                 \n",
       "1                     -6.2000              -8.0000              -9.8000  \n",
       "3                     -6.0000              -7.8000              -9.6000  \n",
       "5                     -5.8000              -7.6000              -9.4000  \n",
       "7                     -5.6000              -7.4000              -9.2000  \n",
       "9                     -5.4000              -7.2000              -9.0000  \n",
       "11                    -5.2000              -7.0000              -8.8000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_bets = np.arange(1,12,2)\n",
    "mark_bet, andy_bet = np.meshgrid(base_bets, base_bets)\n",
    "\n",
    "mark_winnings = .1 * andy_bet + .9 * -mark_bet\n",
    "\n",
    "df = pd.DataFrame(mark_winnings, \n",
    "                  index  =base_bets, \n",
    "                  columns=base_bets)\n",
    "df.index.name = \"Andy Bet\"\n",
    "df.columns.name = \"Mark Bet\"\n",
    "\n",
    "print(\"Best Betting Scenario (for Mark) for These Values:\")\n",
    "print(\"(Andy, Mark):\", df_names(df, simple_argmax(mark_winnings)))\n",
    "\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prob(E)</th>\n",
       "      <th>Odds(E:not E)</th>\n",
       "      <th>Log-Odds</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pct(%)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0%</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1%</th>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-6.9068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0%</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>-4.5951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0%</th>\n",
       "      <td>0.0500</td>\n",
       "      <td>0.0526</td>\n",
       "      <td>-2.9444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0%</th>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.1111</td>\n",
       "      <td>-2.1972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.0%</th>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>-1.0986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33.3%</th>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>-0.6931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.0%</th>\n",
       "      <td>0.5000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66.7%</th>\n",
       "      <td>0.6667</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.6931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75.0%</th>\n",
       "      <td>0.7500</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>1.0986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90.0%</th>\n",
       "      <td>0.9000</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>2.1972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95.0%</th>\n",
       "      <td>0.9500</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>2.9444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99.0%</th>\n",
       "      <td>0.9900</td>\n",
       "      <td>99.0000</td>\n",
       "      <td>4.5951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99.9%</th>\n",
       "      <td>0.9990</td>\n",
       "      <td>999.0000</td>\n",
       "      <td>6.9068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.0%</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "{\\centering\n",
       "\\begin{tabular}{lrrr}\n",
       "\\toprule\n",
       "{} &              Prob(E) &        Odds(E:not E) &             Log-Odds \\\\\n",
       "Pct(\\%) &                      &                      &                      \\\\\n",
       "\\midrule\n",
       " 0.0\\%  &               0.0000 &               0.0000 &                 -inf \\\\\n",
       " 0.1\\%  &               0.0010 &               0.0010 &              -6.9068 \\\\\n",
       " 1.0\\%  &               0.0100 &               0.0101 &              -4.5951 \\\\\n",
       " 5.0\\%  &               0.0500 &               0.0526 &              -2.9444 \\\\\n",
       "10.0\\%  &               0.1000 &               0.1111 &              -2.1972 \\\\\n",
       "25.0\\%  &               0.2500 &               0.3333 &              -1.0986 \\\\\n",
       "33.3\\%  &               0.3333 &               0.5000 &              -0.6931 \\\\\n",
       "50.0\\%  &               0.5000 &               1.0000 &               0.0000 \\\\\n",
       "66.7\\%  &               0.6667 &               2.0000 &               0.6931 \\\\\n",
       "75.0\\%  &               0.7500 &               3.0000 &               1.0986 \\\\\n",
       "90.0\\%  &               0.9000 &               9.0000 &               2.1972 \\\\\n",
       "95.0\\%  &               0.9500 &              19.0000 &               2.9444 \\\\\n",
       "99.0\\%  &               0.9900 &              99.0000 &               4.5951 \\\\\n",
       "99.9\\%  &               0.9990 &             999.0000 &               6.9068 \\\\\n",
       "100.0\\% &               1.0000 &                  inf &                  inf \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\n",
       "\\medskip}"
      ],
      "text/plain": [
       "                    Prob(E)        Odds(E:not E)             Log-Odds\n",
       "Pct(%)                                                               \n",
       " 0.0%                0.0000               0.0000                 -inf\n",
       " 0.1%                0.0010               0.0010              -6.9068\n",
       " 1.0%                0.0100               0.0101              -4.5951\n",
       " 5.0%                0.0500               0.0526              -2.9444\n",
       "10.0%                0.1000               0.1111              -2.1972\n",
       "25.0%                0.2500               0.3333              -1.0986\n",
       "33.3%                0.3333               0.5000              -0.6931\n",
       "50.0%                0.5000               1.0000               0.0000\n",
       "66.7%                0.6667               2.0000               0.6931\n",
       "75.0%                0.7500               3.0000               1.0986\n",
       "90.0%                0.9000               9.0000               2.1972\n",
       "95.0%                0.9500              19.0000               2.9444\n",
       "99.0%                0.9900              99.0000               4.5951\n",
       "99.9%                0.9990             999.0000               6.9068\n",
       "100.0%               1.0000                  inf                  inf"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tail_probs = [0.0, .001, .01, .05, .10, .25, 1.0/3.0]\n",
    "\n",
    "lwr_probs = np.array(tail_probs)\n",
    "upr_probs = 1-lwr_probs[::-1]\n",
    "cent_prob = np.array([.5])\n",
    "\n",
    "probs = np.concatenate([lwr_probs, cent_prob, upr_probs])\n",
    "\n",
    "# 0으로 나눌 때 발생하는 에러를 편하게 처리할 수 있습니다.\n",
    "with np.errstate(divide='ignore'):\n",
    "    odds     = probs / (1-probs)\n",
    "    log_odds = np.log(odds)\n",
    "\n",
    "index=[\"{:4.1f}%\".format(p) for p in np.round(probs,3)*100]\n",
    "\n",
    "polo_dict = co.OrderedDict([(\"Prob(E)\",       probs), \n",
    "                            (\"Odds(E:not E)\", odds), \n",
    "                            (\"Log-Odds\",      log_odds)])\n",
    "polo_df = pd.DataFrame(polo_dict, index=index)\n",
    "polo_df.index.name=\"Pct(%)\"\n",
    "polo_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def helper(ax,x,y,x_name,y_name):\n",
    "    ax.plot(x,y, 'r--o')\n",
    "    ax.set_xlabel(x_name)\n",
    "    ax.set_ylabel(y_name)\n",
    "\n",
    "# 플롯이 너무 좁아보여서 90% 이상의 값은 제거했습니다. \n",
    "# (응? 로그 스케일을 쓰면 그럴 필요가 없는것 아닌가요..?!)\n",
    "fig, (ax0, ax1) = plt.subplots(1,2, figsize=(9,3))\n",
    "helper(ax0, probs[:-5], odds[:-5], 'probability', 'odds')\n",
    "helper(ax1, odds[:-5], probs[:-5], 'odds', 'probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax0, ax1) = plt.subplots(1,2, figsize=(9,3))\n",
    "helper(ax0, probs, log_odds, 'probability', 'log-odds')\n",
    "helper(ax1, log_odds, probs, 'log-odds', 'probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Prob(E)</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Odds(E:not E)</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Log-Odds</th>\n",
       "      <td>-inf</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "{\\centering\n",
       "\\begin{tabular}{lrr}\n",
       "\\toprule\n",
       "{} &                  min &                  max \\\\\n",
       "\\midrule\n",
       "Prob(E)       &               0.0000 &               1.0000 \\\\\n",
       "Odds(E:not E) &               0.0000 &                  inf \\\\\n",
       "Log-Odds      &                 -inf &                  inf \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\n",
       "\\medskip}"
      ],
      "text/plain": [
       "                               min                  max\n",
       "Prob(E)                     0.0000               1.0000\n",
       "Odds(E:not E)               0.0000                  inf\n",
       "Log-Odds                      -inf                  inf"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([polo_df.min(axis=0),\n",
    "              polo_df.max(axis=0)], index=['min', 'max']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def logit(probs):\n",
    "    odds = probs / (1-probs)\n",
    "    log_odds = np.log(odds)\n",
    "    return log_odds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 두 모델 모두 “regularization”이 기본으로 활성화 되어있습니다.\n",
    "# 이에 대해서는 9장에서 소개합니다.\n",
    "LogReg = linear_model.LogisticRegression\n",
    "SGD    = linear_model.SGDClassifier\n",
    "logreg_classifiers = {'LogReg(saga)': LogReg(solver='saga', \n",
    "                                             multi_class='multinomial',\n",
    "                                             max_iter=1000),\n",
    "                      'LogReg(SGD)' :  SGD(loss='log', max_iter=1000)}\n",
    "\n",
    "fig, axes = plt.subplots(1,2,figsize=(12,4))\n",
    "axes = axes.flat\n",
    "for (name, mod), ax in zip(logreg_classifiers.items(), axes):\n",
    "    plot_boundary(ax, iris.data, iris.target, mod, [0,1])\n",
    "    ax.set_title(name)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(4,1))\n",
    "\n",
    "x = np.array([1,2,5,10]).reshape(-1, 1)\n",
    "y = ['red', 'blue', 'red', 'blue']\n",
    "ax.scatter(x,np.zeros_like(x), c=y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.595215\n",
      "         Iterations 5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.7183, 0.6583, 0.4537, 0.1697])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# note:  different import source\n",
    "import statsmodels.api as sm\n",
    "\n",
    "x = np.c_[x, np.ones_like(x)] # +1 trick\n",
    "tgt = (np.array(y) == 'red')\n",
    "\n",
    "# sm.Logit is statsmodels name for logistic regression\n",
    "(sm.Logit(tgt, x, method='newton')\n",
    "   .fit()\n",
    "   .predict(x))  # training predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(4,1))\n",
    "\n",
    "x = np.array([1,4,6,10]).reshape(-1, 1)\n",
    "y = ['red', 'red', 'blue', 'blue']\n",
    "ax.scatter(x, np.zeros_like(x), c=y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perfect separation detected, results not available\n"
     ]
    }
   ],
   "source": [
    "x = np.c_[x, np.ones_like(x)] # +1 트릭\n",
    "tgt = (np.array(y) == 'red')\n",
    "try:\n",
    "    (sm.Logit(tgt, x, method='newton')\n",
    "       .fit()\n",
    "       .predict(x)) # 훈련 데이터에 예측을 수행합니다.\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "long way: 46.16\n",
      "built in: 46.16\n",
      "   close: True\n"
     ]
    }
   ],
   "source": [
    "X = np.array([1,3,5,10,20])\n",
    "n = len(X)\n",
    "\n",
    "mean_X = sum(X) / n\n",
    "errors = X - mean_X\n",
    "var_X = np.dot(errors, errors) / n\n",
    "\n",
    "fmt = \"long way: {}\\nbuilt in: {}\\n   close: {}\"\n",
    "print(fmt.format(var_X, \n",
    "                 np.var(X), \n",
    "                 np.allclose(var_X, np.var(X)))) # 맞는지 확인해봅니다.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "long way: 21.28\n",
      "built in: 21.28\n"
     ]
    }
   ],
   "source": [
    "X = np.array([1,3,5,10,20])\n",
    "Y = np.array([2,4,1,-2,12])\n",
    "\n",
    "mean_X = sum(X) / n\n",
    "mean_Y = sum(Y) / n\n",
    "\n",
    "errors_X = X - mean_X\n",
    "errors_Y = Y - mean_Y\n",
    "\n",
    "cov_XY = np.dot(errors_X, errors_Y) / n\n",
    "print(\"long way: {:5.2f}\".format(cov_XY))\n",
    "print(\"built in:\", np.cov(X,Y,bias=True)[0,1])\n",
    "# 노트:\n",
    "# np.cov(X,Y,bias=True)의 결과:  [Cov(X,X), Cov(X,Y)\n",
    "#                               Cov(Y,X), Cov(Y,Y)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Var(X): 46.16\n"
     ]
    }
   ],
   "source": [
    "var_x = 0\n",
    "n = len(X)\n",
    "for i in range(n):\n",
    "    for j in range(i, n): # 나머지 X를 순회합니다.\n",
    "        var_x += (X[i] - X[j])**2\n",
    "print(\"Var(X):\", var_x / n**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cov(X,Y): 21.28\n"
     ]
    }
   ],
   "source": [
    "cov_xy = 0\n",
    "for i in range(len(X)):\n",
    "    for j in range(i, len(X)): # 나머지 X와 Y를 순회합니다.\n",
    "        cov_xy += (X[i] - X[j])*(Y[i]-Y[j])\n",
    "print(\"Cov(X,Y):\", cov_xy / n**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cov(X,Y): 21.28\n"
     ]
    }
   ],
   "source": [
    "cov_XY = 0.0\n",
    "xy_pairs = it.product(zip(X,Y), repeat=2)\n",
    "for (x_i, y_i), (x_j,y_j) in xy_pairs:\n",
    "    cov_XY += (x_i - x_j) * (y_i - y_j)\n",
    "print(\"Cov(X,Y):\", cov_XY / (2 * n**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cov(X,X) == Var(X): 46.16\n"
     ]
    }
   ],
   "source": [
    "cov_XX = 0.0\n",
    "for x_i, x_j in it.combinations(X, 2):\n",
    "    cov_XX += (x_i - x_j)**2\n",
    "print(\"Cov(X,X) == Var(X):\", cov_XX / (n**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cov(X,Y): 21.28\n"
     ]
    }
   ],
   "source": [
    "cov_XY = 0.0\n",
    "for (x_i, y_i), (x_j,y_j) in it.combinations(zip(X,Y), 2):\n",
    "    cov_XY += (x_i - x_j) * (y_i - y_j)\n",
    "print(\"Cov(X,Y):\", cov_XY / (n**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 컬러 코딩\n",
    "# -inf -> 0; 0 -> .5; inf -> 1\n",
    "# 양끝의 기울기는 천천히, 중간은 빠르게 변합니다.\n",
    "def sigmoid(x):  \n",
    "    return np.exp(-np.logaddexp(0, -x))\n",
    "\n",
    "# 우리가 원하는 색을 얻기 위해서 \n",
    "# 원래 값이 채워진 배열을 만들어야 합니다.\n",
    "# 화면에 직접 그리는 것이 아니라 numpy 배열에\n",
    "# 그림을 그리는 것입니다.\n",
    "def draw_rectangle(arr, pt1, pt2):\n",
    "    (x1,y1),(x2,y2) = pt1,pt2\n",
    "    delta_x, delta_y = x2-x1, y2-y1\n",
    "    r,c = min(y1,y2), min(x1,x2)  # x,y -> r,c\n",
    "    # 직사각형의 각 블록에 +/-1을 할당합니다.  \n",
    "    # 값의 총합은 직사각형의 영역과 같습니다.\n",
    "    arr[r:r+abs(delta_y), \n",
    "        c:c+abs(delta_x)] += np.sign(delta_x * delta_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 포인트\n",
    "pts = [(1,1), (3,6), (6,3)]\n",
    "pt_array = np.array(pts, dtype=np.float64)\n",
    "\n",
    "# 그림을 그릴 배열\n",
    "draw_arr = np.zeros((10,10))\n",
    "ct = len(pts)\n",
    "c_magic = 1 / ct**2 # 중복 카운팅을 제거하지 않습니다.\n",
    "\n",
    "# 중복되지 않는 쌍을 만듭니다.\n",
    "for pt1, pt2 in it.combinations(pts, 2):\n",
    "    draw_rectangle(draw_arr, pt1, pt2)\n",
    "draw_arr *= c_magic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 배열을 화면에 출력합니다.\n",
    "from matplotlib import cm\n",
    "fig, ax = plt.subplots(1,1,figsize=(4,3))\n",
    "ax.matshow(sigmoid(draw_arr), origin='lower', cmap=cm.bwr, vmin=0, vmax=1)\n",
    "fig.tight_layout()\n",
    "\n",
    "# 각 직사각형의 대각선을 그립니다.\n",
    "# 배열의 요소들은 각 그리드의 중앙에 위치해 있습니다.\n",
    "# 0.5만큼 왼쪽으로 이동시켜 직선을 그립니다.\n",
    "ax.plot([ .5, 2.5],[ .5, 5.5], 'r')  # from 1,1 to 3,6 \n",
    "ax.plot([ .5, 5.5],[ .5, 2.5], 'r')  # from 1,1 to 6,3\n",
    "ax.plot([2.5, 5.5],[5.5, 2.5], 'b');  # from 3,6 to 6,3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cov(x,y) -   from numpy: 1.22\n",
      "Cov(x,y) - our long way: 1.22\n"
     ]
    }
   ],
   "source": [
    "np_cov = np.cov(pt_array[:,0], pt_array[:,1], bias=True)[0,1]\n",
    "print(\"Cov(x,y) -   from numpy: {:4.2f}\".format(np_cov))\n",
    "\n",
    "# 그래프를 통해 계산한 공분산을 출력합니다.\n",
    "print(\"Cov(x,y) - our long way: {:4.2f}\".format(draw_arr.sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 324x324 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4.5,4.5))\n",
    "hm = sns.heatmap(draw_arr, center=0, \n",
    "                 square=True, annot=True, \n",
    "                 cmap='bwr', fmt=\".1f\")\n",
    "hm.invert_yaxis()\n",
    "hm.tick_params(bottom=False, left=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>examples</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "{\\centering\n",
       "\\begin{tabular}{lrrr}\n",
       "\\toprule\n",
       "{} &  X &  Y &   Z \\\\\n",
       "examples &    &    &     \\\\\n",
       "\\midrule\n",
       "0        &  1 &  1 &  10 \\\\\n",
       "1        &  3 &  6 &   5 \\\\\n",
       "2        &  6 &  3 &   1 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\n",
       "\\medskip}"
      ],
      "text/plain": [
       "          X  Y   Z\n",
       "examples          \n",
       "0         1  1  10\n",
       "1         3  6   5\n",
       "2         6  3   1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Covariance:\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>6.3333</td>\n",
       "      <td>1.8333</td>\n",
       "      <td>-11.1667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>1.8333</td>\n",
       "      <td>6.3333</td>\n",
       "      <td>-5.1667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Z</th>\n",
       "      <td>-11.1667</td>\n",
       "      <td>-5.1667</td>\n",
       "      <td>20.3333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "{\\centering\n",
       "\\begin{tabular}{lrrr}\n",
       "\\toprule\n",
       "{} &                    X &                    Y &                    Z \\\\\n",
       "\\midrule\n",
       "X &               6.3333 &               1.8333 &             -11.1667 \\\\\n",
       "Y &               1.8333 &               6.3333 &              -5.1667 \\\\\n",
       "Z &             -11.1667 &              -5.1667 &              20.3333 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\n",
       "\\medskip}"
      ],
      "text/plain": [
       "                     X                    Y                    Z\n",
       "X               6.3333               1.8333             -11.1667\n",
       "Y               1.8333               6.3333              -5.1667\n",
       "Z             -11.1667              -5.1667              20.3333"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.DataFrame({'X':[ 1, 3, 6], \n",
    "                     'Y':[ 1, 6, 3], \n",
    "                     'Z':[10, 5, 1]})\n",
    "data.index.name = 'examples'\n",
    "\n",
    "# 이 예제에서 크게 중요한 부분은 아닙니다만, \n",
    "# Pandas의 공분산은 “비편향\"입니다만,\n",
    "# 그동안 우리가 다룬 공분산은 “편향\"되어있습니다.\n",
    "# 자세한 내용은 이 장의 마무리에서 읽어보세요.\n",
    "display(data)\n",
    "print(\"Covariance:\")\n",
    "display(data.cov())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>examples</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "{\\centering\n",
       "\\begin{tabular}{lrrr}\n",
       "\\toprule\n",
       "{} &  x &  y &  z \\\\\n",
       "examples &    &    &    \\\\\n",
       "\\midrule\n",
       "0        &  3 &  9 &  1 \\\\\n",
       "1        &  6 &  6 &  4 \\\\\n",
       "2        &  3 &  3 &  7 \\\\\n",
       "3        &  4 &  0 &  0 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\n",
       "\\medskip}"
      ],
      "text/plain": [
       "          x  y  z\n",
       "examples         \n",
       "0         3  9  1\n",
       "1         6  6  4\n",
       "2         3  3  7\n",
       "3         4  0  0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Covariance:\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>15.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>10.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "{\\centering\n",
       "\\begin{tabular}{lrrr}\n",
       "\\toprule\n",
       "{} &                    x &                    y &                    z \\\\\n",
       "\\midrule\n",
       "x &               2.0000 &               0.0000 &               0.0000 \\\\\n",
       "y &               0.0000 &              15.0000 &               0.0000 \\\\\n",
       "z &               0.0000 &               0.0000 &              10.0000 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\n",
       "\\medskip}"
      ],
      "text/plain": [
       "                     x                    y                    z\n",
       "x               2.0000               0.0000               0.0000\n",
       "y               0.0000              15.0000               0.0000\n",
       "z               0.0000               0.0000              10.0000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.DataFrame({'x':[ 3, 6, 3, 4], \n",
    "                     'y':[ 9, 6, 3, 0],\n",
    "                     'z':[ 1, 4, 7, 0]})\n",
    "data.index.name = 'examples'\n",
    "display(data)\n",
    "print(\"Covariance:\")\n",
    "display(data.cov()) # 편향된 공분산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1,figsize=(4,3))\n",
    "data.plot(ax=ax)\n",
    "ax.vlines([0,1,2,3], 0, 10, colors=\".5\")\n",
    "\n",
    "ax.legend(loc='lower center', ncol=3)\n",
    "\n",
    "plt.box(False)\n",
    "ax.set_xticks([0,1,2,3])\n",
    "ax.set_ylabel(\"values\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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17nUKa9duYP36jRQVFTFz5mwGDewX1+prRa5TO7eleZP6e6HokH4XoeZKcoxEUlBmdgMwG7geWG5mg0vNviuKdZYnJ7cd7xZsLnlcsKmQnJx2ca2+QqHmKu2xJav4zoQ5jJn1Mjs++TTpOJEJ9XcRaq7S4hgjUW1BXQWc6u5DgD7AT81sZGZebFeGMTt4Ve4e1+orFGqu/Yae1pW5Nw5hxnUDaN2sMffOey3pSJEJ9XcRaq794hojURVUlrvvBHD3DaRL6jwzG8chCsrMRphZnpnl7du3q9ohNhUU0rFDTsnjDrntKSzcWu3Xra5Qc+3XqmljslIpUinjgl7HsbxgW9KRSmiMhCGuMRJVQW0xs5P3P8iU1QCgNXBSRQu5+yR37+nuPVOpI6sdYmneMrp06UynTh3Jzs5m6NDBzJk7v9qvW1dz7ffejt0l95/L30iXtkcnmOZAGiNhiGuMRPUu3uXA3tIT3H0vcLmZPRTROg9SXFzMyFGjmffUNLJSKR6ZMoP8/NVxrb5W5LptxiLy1m1l++499L1nFtec8xXy1m9lVeGHGJDToimjB5+WSLY4hPS7CDVXkmPEQtqvLa1Bw9wwgwUq1Ct2NP726MiOOWqMVE1tHCN1/oOaIlJ7qaBEJFgqKBEJlgpKRIKlghKRYKmgRCRYKigRCZYKSkSCpYISkWCpoEQkWCooEQmWCkpEgqWCEpFgqaBEJFjBnm6lJpnZCHeflHSOspQrHKH+zPU9V33ZghqRdIAKKFc4Qv2Z63Wu+lJQIlILqaBEJFj1paCC24fPUK5whPoz1+tc9eIguYjUTvVlC0pEaiEVlIgESwUVMTPbYGZbzezIUtOuNLPnM/fdzHaZ2U4z22Zm080snCtlSrVlxsAnZvaxmW03s5fN7GozS2XmP2Jmv6hg2dLj430zW2BmF1Xw3EfMbK+Z5ZQ3vzZSQcWjATDyEPO/6u5NgS8BLYDb4wglsRro7s2ALwJjgVuByZVcdv/46AY8AjxgZmNKPyHzB/BC4CPguzUVOmkqqHj8CrjpcFtG7r4DeBL4ciypJHbu/pG7PwlcBAw3sxOrsOw2d/8TcA3wX2bWqtTsC4HtwM+B4TWZOUkqqHjkAc8DNx3qSWbWAhgCLIkhkyTI3V8FCoAzP8fis0lvlfcuNW04MB14DDjezHpUO2QAVFDx+RlwvZkdU868181sO7AN+ALwUKzJJCmbgZZVXcjdi0iPlZYAZvYF4CxgmrtvBRZQR7aiVFAxcfflwFzgtnJm93D3o4FGwIPAIjNrFGc+SUQu8EFVFzKzbOCYUsteBqx092WZx38GLs08r1ZTQcVrDHAV6YF5kMxfxj8AnYFKH5uQ2sfMepEeBy9+jsUHA3uBVzOPLwe+ZGZbzGwLMA5oDZxXE1mTpIKKkbv/A5gB3FDefDPLAr4PfAKsizGaxMTMmpvZANLHiqa6+9uZWVlm1qjUrWE5y7Y0s+8CvwXucff3zez/AceSPh51cuZ2IjCNOrCb1yDpAPXQz0lvkpf2ppk5sA9YBZzv7lXe9JegzTGzvaR/x/mkt3J+V2r+bRy4+/8S8B+Z+/vHx2fAm8CP3X1aZt5wYHapogPAzMaTPlTQsjaPJX0XT0SCpV08EQmWCkpEgqWCEpFgqaBEJFgqKBEJVrAfMyjati7Itxcb53yer07VX3s/22RRvbbGSN1wqDGiLSgRCZYKSkSCpYISkWCpoEQkWCooEQmWCkpEgqWCEpFgqaBEJFgqKBEJlgpKRIKlghKRYKmgRCRYda6gRt81jq9/62KGDLu6ZNo7q9dy6VWjuHD4tQz9wQ28nb8qwYRp/fr2YcXyhbyT/yK33Hxt0nFKhJqrJmmMVE+cuepcQQ3p/01+N+4XB0y7d+JkrvnBd5k15bdcd+Uw7p04OaF0aalUignj72TAwGGc9NWzuOiiIXTvflyimULOVdM0RmpPrsgKysyON7NbzWyCmY3P3O8e1fr263nySRzVvFnZLOzctRuAnbt206Z1q/IWjU3vXqewdu0G1q/fSFFRETNnzmbQwH6JZgo5V03TGKk9uSIpKDO7lfR1v4z0xQWXZu5PN7PyrqwbqVtH/pB7J07mnPMv49cP/IFRV38v7ggHyMltx7sFm0seF2wqJCenXYKJ0kLNFQeNkcqJO1dUW1BXAL3cfay7T83cxpK+uOAVEa2zQjOeeIpbrx/Bgif+xC03jOBnd98fd4QDmB18fq4QLv8Vaq44aIxUTty5oiqofUBOOdPbZ+aVy8xGmFmemeX94dHpNRbmyaef5Rt9zgCg39lnJn4AdFNBIR07/Pufp0NuewoLtyaYKC3UXKVpjCQr7lxRFdQoYIGZPW1mkzK3Z4AFwMiKFnL3Se7e0917Xnn5JTUW5pjWrVj6RvrCq6+8towvdsytsdf+PJbmLaNLl8506tSR7Oxshg4dzJy58xPNFHKu0jRGkhV3rkjOSe7uz5hZV9K7dLmkjz8VAEvdvTiKde5385ixLH3jLbZv38E5Q4bxoysu445bb2Ds+IfYW1zMEQ0bMuaWG6KMcFjFxcWMHDWaeU9NIyuV4pEpM8jPX51oppBz1TSNkdqTK9hLn+uE+HWDLpogh6OLJohIraSCEpFgqaBEJFgqKBEJlgpKRIKlghKRYKmgRCRYKigRCZYKSkSCpYISkWCpoEQkWCooEQmWCkpEgqWCEpFgRXI+qJoQ6ikrPp52TdIRytXz2jlJR4idxkjV1MYxoi0oEQmWCkpEgqWCEpFgqaBEJFgqKBEJlgpKRIKlghKRYKmgRCRYKigRCZYKSkSCpYISkWCpoEQkWCooEQlWsGczqCn9+vZh3Lifk5VK8fAfp/PLX/02kRxjZr3MwlUFtDyyEbNGDgLgwQVv8tela2hxZCMAru97Cmd2y00kH0C7nDbc/cDttDqmJb7P+cvU/2Hq72cklicuGiOVF/cYqdMFlUqlmDD+Ts7tfwkFBYUsWTyPOXPns3LlmtizDOpxLBef3o3Rj790wPRhZ3Rn+JknxJ6nPHv3FvPLMeNZ+fYqmhzZhL/8bQqLX3iVtavXJx0tMhojVRP3GKnTu3i9e53C2rUbWL9+I0VFRcycOZtBA/slkuXUzm1p3uSIRNZdWdv+9T4r314FwO5du1m3ZgNt2h2TcKpoaYxUTdxjpE4XVE5uO94t2FzyuGBTITk57RJMdLDHlqziOxPmMGbWy+z45NOk45TI6die7id25a3XVyQdJVIaI59fHGMk9oIys+/HuK6Dprl7XKs/rKGndWXujUOYcd0AWjdrzL3zXks6EgBNmjTm/sljGfvT+9i1c1fScSKlMfL5xDVGktiCuqOiGWY2wszyzCxv377q/9CbCgrp2CGn5HGH3PYUFm6t9uvWlFZNG5OVSpFKGRf0Oo7lBduSjkSDBlnc//BYnpr1DM/Oez7pOAfRGKlfYySSgjKztyq4vQ20rWg5d5/k7j3dvWcqdWS1cyzNW0aXLp3p1Kkj2dnZDB06mDlz51f7dWvKezt2l9x/Ln8jXdoenWCatJ/fN5p1azYw5aHpSUcpl8ZI/RojUb2L1xboB3xYZroBL0e0zoMUFxczctRo5j01jaxUikemzCA/f3Vcqz/AbTMWkbduK9t376HvPbO45pyvkLd+K6sKP8SAnBZNGT34tESy7dej91cZPLQ/q/LXMGvBnwC4/64HWbQgtl9Z7DRGqibuMWJR7G+b2WTgj+7+Yjnzprn7pYd7jQYNc8M5EFCKrthRNSu2vnLwQZ4aojFSNbVxjESyBeXuVxxi3mHLSUQE6vjHDESkdlNBiUiwVFAiEiwVlIgESwUlIsFSQYlIsFRQIhIsFZSIBEsFJSLBUkGJSLBUUCISLBWUiARLBSUiwarTV3WJQrNLH0w6QrlCPcVHfTTuxpVJRyjXlOz2SUeoMm1BiUiwVFAiEiwVlIgESwUlIsFSQYlIsFRQIhIsFZSIBEsFJSLBUkGJSLBUUCISLBWUiARLBSUiwVJBiUiw6nxB9evbhxXLF/JO/ovccvO1SccpEUquMbNe5qy7ZnLh+CdLpj244E2+OfZxhv5mLkN/M5dFqzYlli8OofwuyjqieRMueHAkP1zwK3644Jfk9uiSdCQA2lwxgBOeHc8JCybQ5oqBka6rTp9uJZVKMWH8nZzb/xIKCgpZsngec+bOZ+XKNcqVMajHsVx8ejdGP/7SAdOHndGd4WeeEHueuIX0uyir75jLWPfCm/z1mvGksrPIbnxE0pFo1O0LHHPJN1k54Gb2Fe2l69QxfPRcHp+uL4xkfXV6C6p3r1NYu3YD69dvpKioiJkzZzNoYL+kYwWV69TObWneJPmBn5SQfhelNWzamC+cdjzLHnsegH1FxXy6Y3eyoYDGXTqw843V7NvzGRTv4+MlK2hx7umRrS+ygjKz483sHDNrWmb6uVGts6yc3Ha8W7C55HHBpkJyctrFtfoKhZqrtMeWrOI7E+YwZtbL7Pjk06TjRCbU30WLL7Rh9/sfM+DXP+SKeXfyrXuuDGIL6pNVG2l22pfJOroZqUYNOersHmTntI5sfZEUlJndAMwGrgeWm9ngUrPvimKdFeQ4aJq7x7X6CoWaa7+hp3Vl7o1DmHHdAFo3a8y9815LOlJkQv1dpLJStDuxE69PfZbJ/X/CZ7s/5Ws/ivZ4T2Xs+UcBWyY+Qdfpt3Pc1DHszt8Ae4sjW19UW1BXAae6+xCgD/BTMxuZmXfwiNg/w2yEmeWZWd6+fbuqHWJTQSEdO+SUPO6Q257Cwq3Vft3qCjXXfq2aNiYrlSKVMi7odRzLC7YlHalEfRkjO7Z8wI7CD9i8bC0A78x7lXYndko2VMa2x55l5Xk3surbP6F4+072RHT8CaIrqCx33wng7htIl9R5ZjaOQxSUu09y957u3jOVOrLaIZbmLaNLl8506tSR7Oxshg4dzJy586v9unU1137vlTrW8Vz+Rrq0PTrBNAeqL2Nk13sfsaPwfVp+KX0e8U5nnMB7a8J4N7VBq6MAaJjTmqPPO50PZi+Mbl0Rve4WMzvZ3ZcBuPtOMxsAPAycFNE6D1JcXMzIUaOZ99Q0slIpHpkyg/z81XGtvlbkum3GIvLWbWX77j30vWcW15zzFfLWb2VV4YcYkNOiKaMHn5ZItjiE9Lsoa/6YRxky/kekshuwfeO/mHvTQ0lHAuDYSbfSoEUzfO9eNv5kEsUfVX9LtiIWxf62mXUA9rr7lnLmneHuL5Wz2AEaNMxN/kBALRLqVV0af3t0hVvM1RXqGLmjfZ+kI5SrX3F0RVIdPQv+p8IxEskWlLsXHGLeYctJRATq+OegRKR2U0GJSLBUUCISLBWUiARLBSUiwVJBiUiwVFAiEiwVlIgESwUlIsFSQYlIsFRQIhIsFZSIBEsFJSLBUkGJSLAiOR9UaMxshLtPSjpHWcoVjlB/5vqeq75sQY1IOkAFlCscof7M9TpXfSkoEamFVFAiEqz6UlDB7cNnKFc4Qv2Z63WuenGQXERqp/qyBSUitVCdLygzO9fMVpnZP8zstqTzAJjZw2b2LzNbnnSW0syso5n93cxWmtmKUleDrtM0Riov7jFSp3fxzCwLWA18EygAlgKXuHt+wrm+DuwEHnX3E5PMUpqZtQfau/vrZtYMeA0YkvS/V5Q0Rqom7jFS17egegP/cPd17v4Z8BgwOOFMuPtC4IOkc5Tl7oXu/nrm/sfASiA32VSR0xipgrjHSF0vqFzg3VKPC6j7/+FqhJl1Ak4BXkk2SeQ0Rj6nOMZIXS+o8i6pXHf3aWuImTUFZgGj3H1H0nkipjHyOcQ1Rup6QRUAHUs97gBsTihLrWBm2aQH3p/d/a9J54mBxkgVxTlG6npBLQWOM7POZtYQuBh4MuFMwTIzAyYDK919XNJ5YqIxUgVxj5E6XVDuvhe4Dvhf0gfzZrr7imRTgZlNBxYD3cyswMyuSDpTxhnAZcDZZrYsc+ufdKgoaYxUWaxjpE5/zEBEarc6vQUlIrWbCkpEgqWCEpFgqaBEJFgqKBEJlgqqEsysOPN26nIz+4uZNanGa/Uxs7mZ+4MO9e15MzvazH70OdZxu5nd9HkzioRCBVU5n7j7yZlvlX8GXF16pqVV+d/S3fqt7BUAAAITSURBVJ9097GHeMrRQJULSqSuUEFV3SKgi5l1ypwTZyLwOtDRzPqa2WIzez2zpdUUSs439I6ZvQhcsP+FzOx7ZvZA5n5bM3vCzN7M3L4GjAWOzWy9/SrzvJvNbKmZvWVmd5R6rZ9kzmn0LNAttn8NkQipoKrAzBoA5wFvZyZ1I32+nlOAXcBo4Bvu3gPIA/7TzBoBvwcGAmcC7Sp4+QnAC+7+VaAHsAK4DVib2Xq72cz6AseRPkXIycCpZvZ1MzuV9Fc0TiFdgL1q+EcXSUSDpAPUEo3NbFnm/iLS30XKAf7p7ksy008Hvgy8lP66Eg1Jf1XheGC9u68BMLOplH9NsbOBywHcvRj4yMxalHlO38ztjczjpqQLqxnwhLvvzqxD3yWTOkEFVTmfuPvJpSdkSmhX6UnA39z9kjLPO5maO32HAXe7+0Nl1jGqBtchEgzt4tWcJcAZZtYFwMyamFlX4B2gs5kdm3neJRUsvwC4JrNslpk1Bz4mvXW03/8CPyh1bCvXzNoAC4Hzzaxx5jSsA2v4ZxNJhAqqhrj7e8D3gOlm9hbpwjre3feQ3qV7KnOQ/J8VvMRI4Cwze5v0eZ5PcPf3Se8yLjezX7n7fGAasDjzvMeBZplTsM4AlpE+T8+iyH5QkRjpbAYiEixtQYlIsFRQIhIsFZSIBEsFJSLBUkGJSLBUUCISLBWUiARLBSUiwfo/HKeD39zUZwQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 324x324 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "qda  = discriminant_analysis.QuadraticDiscriminantAnalysis()\n",
    "lda  = discriminant_analysis.LinearDiscriminantAnalysis()\n",
    "nb   = naive_bayes.GaussianNB()\n",
    "dlda = DLDA() # mlwpy.py에 구현되어 있습니다.\n",
    "\n",
    "da_methods = [qda, lda, nb, dlda]\n",
    "names = [\"QDA\", \"LDA\", \"NB\", \"DLDA\"]\n",
    "    \n",
    "fig, axes = plt.subplots(2,2, figsize=(4.5, 4.5),\n",
    "                         sharex=True, sharey = True)\n",
    "for ax, model, name in zip(axes.flat, da_methods, names):\n",
    "    preds = (model.fit(iris_train_ftrs, iris_train_tgt)\n",
    "                  .predict(iris_test_ftrs))\n",
    "    cm = metrics.confusion_matrix(iris_test_tgt, preds)\n",
    "    sns.heatmap(cm, annot=True, cbar=False, ax=ax)\n",
    "    ax.set_title(name)\n",
    "\n",
    "axes[0,0].set_ylabel('Actual')\n",
    "axes[1,0].set_xlabel('Predicted');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 324x324 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2,2,figsize=(4.5, 4.5))\n",
    "axes = axes.flat\n",
    "\n",
    "for model, ax, name in zip(da_methods, axes, names):\n",
    "    # 특성 2개로 예측하는 경계를 그립니다.\n",
    "    plot_boundary(ax, iris.data, iris.target, model, [0,1])\n",
    "    ax.set_title(name)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ftrs = np.mgrid[1:10, 1:10].T.reshape(-1,2)\n",
    "tgt  = ftrs[:,0] > ftrs[:,1]\n",
    "\n",
    "fig, axes = plt.subplots(1,3,figsize=(9,3))\n",
    "axes = axes.flat\n",
    "\n",
    "svc = svm.SVC(kernel='linear')\n",
    "dt_shallow  = tree.DecisionTreeClassifier(max_depth=3)\n",
    "dt_deep     = tree.DecisionTreeClassifier()\n",
    "models = [svc, dt_shallow, dt_deep]\n",
    "\n",
    "for model, ax in zip(models, axes):\n",
    "    # 특성 2개로 예측하는 경계를 그립니다.\n",
    "    plot_boundary(ax, ftrs, tgt, model, [0,1])\n",
    "    ax.set_title(get_model_name(model))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (8, 8)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Shape:\", digits.images[0].shape)\n",
    "plt.figure(figsize=(3,3))\n",
    "plt.imshow(digits.images[0], cmap='gray');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_parade = \\\n",
    "    {'LogReg(1)' : linear_model.LogisticRegression(max_iter=1000),\n",
    "     'LogReg(2)' : linear_model.SGDClassifier(loss='log',\n",
    "                                              max_iter=1000),\n",
    "\n",
    "     'QDA' : discriminant_analysis.QuadraticDiscriminantAnalysis(),\n",
    "     'LDA' : discriminant_analysis.LinearDiscriminantAnalysis(),\n",
    "     'GNB' : naive_bayes.GaussianNB(),\n",
    "\n",
    "     'SVC(1)' : svm.SVC(kernel=\"linear\"),\n",
    "     'SVC(2)' : svm.LinearSVC(),\n",
    "\n",
    "     'DTC' : tree.DecisionTreeClassifier(),\n",
    "     '5NN-C' : neighbors.KNeighborsClassifier(),\n",
    "     '10NN-C' : neighbors.KNeighborsClassifier(n_neighbors=10)}\n",
    "\n",
    "baseline = dummy.DummyClassifier(strategy=\"uniform\")\n",
    "\n",
    "base_score = skms.cross_val_score(baseline, digits.data, digits.target==1, \n",
    "                                  cv=10, scoring='average_precision', n_jobs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:693: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:693: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:693: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:693: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:693: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:693: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:693: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:693: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:693: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:693: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "C:\\Users\\it\\anaconda3\\envs\\book_base\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,4))\n",
    "ax.plot(base_score, label='base')\n",
    "for name, model in classifier_parade.items():    \n",
    "    cv_scores = skms.cross_val_score(model, \n",
    "                                     digits.data, digits.target, \n",
    "                                     cv=10, \n",
    "                                     scoring='f1_macro', \n",
    "                                     n_jobs=1)\n",
    "    my_lbl = \"{} {:.3f}\".format(name, cv_scores.mean())\n",
    "    ax.plot(cv_scores, label=my_lbl, marker=next(markers))\n",
    "ax.set_ylim(0.0, 1.1)\n",
    "ax.set_xlabel('Fold')\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.legend(loc='lower center', ncol=2);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "book_base",
   "language": "python",
   "name": "book_base"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}