{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Набор данных для анализа и прогнозирования сердечного приступа"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вывод всех столбцов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['HeartDisease', 'BMI', 'Smoking', 'AlcoholDrinking', 'Stroke',\n",
      "       'PhysicalHealth', 'MentalHealth', 'DiffWalking', 'Sex', 'AgeCategory',\n",
      "       'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'SleepTime',\n",
      "       'Asthma', 'KidneyDisease', 'SkinCancer'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "df = pd.read_csv(\"..//static//csv//heart_2020_cleaned.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Бизнес-цели: \n",
    "1. Разработка персонализированных программ профилактики сердечно-сосудистых заболеваний.\n",
    "Цель технического проекта: создание модели машинного обучения, которая будет прогнозировать риск сердечного приступа для каждого пациента на основе его индивидуальных факторов риска, и разработка онлайн-платформы или приложения для предоставления персонализированных рекомендаций по профилактике.\n",
    "2. Улучшение качества медицинской \n",
    "Цель технического проекта: использование данных для выявления групп населения с наибольшим риском сердечного приступа и разработки целевых программ профилактики и раннего выявления заболеваний."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выполним разбиение на 3 выборки: обучающую, контрольную и тестовую"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 156699\n",
      "Размер контрольной выборки: 67157\n",
      "Размер тестовой выборки: 95939\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df = pd.read_csv(\"..//static//csv//heart_2020_cleaned.csv\")\n",
    "\n",
    "# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n",
    "train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Вывод размеров выборок\n",
    "print(\"Размер обучающей выборки:\", len(train_df))\n",
    "print(\"Размер контрольной выборки:\", len(val_df))\n",
    "print(\"Размер тестовой выборки:\", len(test_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение классов в HeartDisease:\n",
      "HeartDisease\n",
      "No     292422\n",
      "Yes     27373\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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3z+F+SXnT8ubncXNzc9j/TzzxhMqVK6fp06fr/fffv+x23eiy16Kwr6m1a9fqxIkT+vrrrxUSEmJOz/uWb2Fd7+vmyy+/1NmzZxUWFnbV2gcffFAPPvigxo8fr3nz5qlHjx764osv1K9fv5u2vYGBgQoMDNSIESO0efNmNW/eXLNmzdK4ceOu+b0A1uKaKtxRnJ2dZRiGw7SFCxfm+wp6586ddfz4cYevTOe5dPlr8cknnzhch/Hll1/q6NGjat++vSQpKChI1atX13//+1+dOXMm3/KX3sl54cKFcnZ2vupdqTt16iRnZ2eNHj06X/+GYejEiRPm8wsXLuirr75S06ZNr/i/1i5duignJ0djx47NN+/ChQvmKbPrER8fryVLluitt9667Adfly5d9Oeff+rDDz/MN+/s2bPKzMy86npq1qxpXtczbdo05ebm6pVXXnGoKez+cHZ2ls1mczilfPDgwXzBsWPHjnJyctKYMWPyHVHJ+9m0bdtWZcqUUXR0dL7rzvJqGjduLB8fH82aNcvhVOPy5cu1Z88eh28bFiQ7O1sXLlwo1O0nrFz2Sgr7mso7wnLxazk7O1szZ868pvWVLl36ml+n27dv16BBg1S2bFlFRERctu7UqVP5ftfyQnTefrN6ezMyMnThwgWHaYGBgXJycjLXeS3vBbAeR6pwR3nsscc0ZswY9e7dWw899JB27typuXPn6t5773Woe+655/TJJ58oMjJSW7duVYsWLZSZmanvv/9e//rXv/SPf/zjutZfrlw5Pfzww+rdu7dSUlI0ZcoU1ahRQ88//7wkycnJSR999JHat2+vevXqqXfv3rrnnnv0559/as2aNbLb7fr222+VmZmpGTNm6N1331XNmjW1du1acx15YWzHjh2Kj49XcHCwqlevrnHjxmn48OE6ePCgOnbsqDJlyigpKUmLFi1S//799dprr+n777/Xm2++qR07dujbb7+94ra0bNlSL7zwgqKjo5WYmKi2bduqZMmS2r9/vxYuXKipU6fmu3FiYa1cuVKPPvroFY/WPfvss1qwYIFefPFFrVmzRs2bN1dOTo727t2rBQsWaMWKFVc9gncxPz8/TZgwQf369dM///lPdejQ4Zr2R3h4uCZNmqR27drpmWee0bFjxzRjxgzVqFFDO3bsMOtq1KihN954Q2PHjlWLFi3UqVMnubq66scff5S/v7+io6Nlt9s1efJk9evXT02aNNEzzzyjsmXLavv27frrr780Z84clSxZUm+//bZ69+6tli1bqnv37uYtFapWrarBgwc79JeZmelwCu/TTz/VuXPn9OSTT15139zIsteisK+phx56SGXLllXPnj318ssvy2az6dNPP73m//AEBQXpvffe07hx41SjRg35+Pg4HPXbsGGDzp07Z36pZdOmTfrmm2/k6empRYsWXfF08Jw5czRz5kw9+eSTql69uk6fPq0PP/xQdrvdvAWL1du7evVqDRgwQE8//bRq1qypCxcu6NNPP5Wzs7M6d+4sSYV+L8BNcqu/bghcj7yvM1/p68uG8fctFV599VWjYsWKhru7u9G8eXMjPj7e4ev1ef766y/jjTfeMKpVq2aULFnS8PPzM5566injwIEDhmFc3y0VPv/8c2P48OGGj4+P4e7uboSHhxv/+9//8i3/888/G506dTLKly9vuLq6GlWqVDG6dOlixMXFOaz7ao+ePXs6jPvVV18ZDz/8sFG6dGmjdOnSRu3atY2IiAhj3759hmEYxsCBA42QkBAjNjY2X08F3ULAMP7+en1QUJDh7u5ulClTxggMDDSGDh1qHDlyxKy51lsq2Gw2IyEhwWF6QT+j7Oxs4+233zbq1atnuLq6GmXLljWCgoKM0aNHG+np6fnWd7XxDMMw2rRpY1SuXNk4ffr0Ne+P//u//zPuu+8+w9XV1ahdu7Yxe/bsy+63jz/+2HjggQfMvlu2bGmsWrXKoeabb74xHnroIcPd3d2w2+1G06ZNjc8//9yhZv78+eY45cqVM3r06GHeQiRPz549HV4XHh4eRqNGjYxPP/30ivvoRpfN2/bU1NQC51/udVGY19SmTZuMBx980HB3dzf8/f2NoUOHGitWrDAkGWvWrDHrWrZsadSrV6/A9ScnJxvh4eFGmTJlDEnm6yHv9zXvUbJkSaNChQpGSEiIMX78eOPYsWP5xrr0lgo//fST0b17d6Ny5cqGq6ur4ePjYzz22GPGtm3bbtr2/v7770afPn2M6tWrG25ubka5cuWM1q1bG99//32+dV7tvQA3h80wbuBcBwBJf18T0bp1ay1cuPC6j95c7ODBg6pWrZqSkpJUtWrVAmtGjRqlgwcPFvgHYwEAtx7XVAEAAFiAa6qA25CHh4d69OhxxQun77//fvPP7gAAih6hCrgNeXt7mxcOX06nTp1uUTcAgMLgmioAAAALcE0VAACABQhVAAAAFuCaqlsoNzdXR44cUZkyZa77zycAAIBbyzAMnT59Wv7+/nJyuvzxKELVLXTkyJFC/RFYAABw+zl8+LAqVap02fmEqluoTJkykv7+odjt9iLuBgAAFEZGRoYCAgLMz/HLIVTdQnmn/Ox2O6EKAIBi5mqX7nChOgAAgAWKNFS99957uv/++80jN8HBwVq+fLk5/9y5c4qIiFD58uXl4eGhzp07KyUlxWGMQ4cOKTw8XKVKlZKPj4+GDBmiCxcuONSsXbtWjRo1kqurq2rUqFHg30qbMWOGqlatKjc3NzVr1kxbt251mF+YXgAAwN2rSENVpUqV9NZbbykhIUHbtm1TmzZt9I9//EO7d++WJA0ePFjffvutFi5cqHXr1unIkSMOd5HOyclReHi4srOztXnzZs2ZM0cxMTGKiooya5KSkhQeHq7WrVsrMTFRgwYNUr9+/bRixQqzZv78+YqMjNTIkSP1008/qUGDBgoLC9OxY8fMmqv1AgAA7nLGbaZs2bLGRx99ZKSlpRklS5Y0Fi5caM7bs2ePIcmIj483DMMwvvvuO8PJyclITk42a9577z3DbrcbWVlZhmEYxtChQ4169eo5rKNr165GWFiY+bxp06ZGRESE+TwnJ8fw9/c3oqOjDcMwCtVLYaSnpxuSjPT09EIvAwAAilZhP79vm2uqcnJy9MUXXygzM1PBwcFKSEjQ+fPnFRoaatbUrl1blStXVnx8vCQpPj5egYGB8vX1NWvCwsKUkZFhHu2Kj493GCOvJm+M7OxsJSQkONQ4OTkpNDTUrClMLwAA4O5W5N/+27lzp4KDg3Xu3Dl5eHho0aJFqlu3rhITE+Xi4iIvLy+Hel9fXyUnJ0uSkpOTHQJV3vy8eVeqycjI0NmzZ3Xq1Cnl5OQUWLN3715zjKv1UpCsrCxlZWWZzzMyMq6yNwAAQHFV5EeqatWqpcTERG3ZskUvvfSSevbsqV9++aWo27JEdHS0PD09zQc3/gQA4M5V5KHKxcVFNWrUUFBQkKKjo9WgQQNNnTpVfn5+ys7OVlpamkN9SkqK/Pz8JEl+fn75voGX9/xqNXa7Xe7u7vL29pazs3OBNRePcbVeCjJ8+HClp6ebj8OHDxdupwAAgGKnyEPVpXJzc5WVlaWgoCCVLFlScXFx5rx9+/bp0KFDCg4OliQFBwdr586dDt/SW7Vqlex2u+rWrWvWXDxGXk3eGC4uLgoKCnKoyc3NVVxcnFlTmF4K4urqat4ught+AgBwh7tFF84X6PXXXzfWrVtnJCUlGTt27DBef/11w2azGStXrjQMwzBefPFFo3Llysbq1auNbdu2GcHBwUZwcLC5/IULF4z69esbbdu2NRITE43Y2FijQoUKxvDhw82a33//3ShVqpQxZMgQY8+ePcaMGTMMZ2dnIzY21qz54osvDFdXVyMmJsb45ZdfjP79+xteXl4O3yq8Wi+Fwbf/AAAofgr7+V2koapPnz5GlSpVDBcXF6NChQrGI488YgYqwzCMs2fPGv/617+MsmXLGqVKlTKefPJJ4+jRow5jHDx40Gjfvr3h7u5ueHt7G6+++qpx/vx5h5o1a9YYDRs2NFxcXIx7773XmD17dr5epk2bZlSuXNlwcXExmjZtavzwww8O8wvTy9UQqgAAKH4K+/ltMwzDKNpjZXePjIwMeXp6Kj09nVOBAAAUE4X9/L7trqkCAAAojghVAAAAFiBUAQAAWKDI76gO6wUN+aSoWwBuOwkTnivqFgDc4ThSBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABggSINVdHR0WrSpInKlCkjHx8fdezYUfv27XOoadWqlWw2m8PjxRdfdKg5dOiQwsPDVapUKfn4+GjIkCG6cOGCQ83atWvVqFEjubq6qkaNGoqJicnXz4wZM1S1alW5ubmpWbNm2rp1q8P8c+fOKSIiQuXLl5eHh4c6d+6slJQUa3YGAAAo1oo0VK1bt04RERH64YcftGrVKp0/f15t27ZVZmamQ93zzz+vo0ePmo933nnHnJeTk6Pw8HBlZ2dr8+bNmjNnjmJiYhQVFWXWJCUlKTw8XK1bt1ZiYqIGDRqkfv36acWKFWbN/PnzFRkZqZEjR+qnn35SgwYNFBYWpmPHjpk1gwcP1rfffquFCxdq3bp1OnLkiDp16nQT9xAAACgubIZhGEXdRJ7U1FT5+Pho3bp1CgkJkfT3kaqGDRtqypQpBS6zfPlyPfbYYzpy5Ih8fX0lSbNmzdKwYcOUmpoqFxcXDRs2TMuWLdOuXbvM5bp166a0tDTFxsZKkpo1a6YmTZpo+vTpkqTc3FwFBARo4MCBev3115Wenq4KFSpo3rx5euqppyRJe/fuVZ06dRQfH68HH3zwqtuXkZEhT09Ppaeny263X/d+upqgIZ/ctLGB4iphwnNF3QKAYqqwn9+31TVV6enpkqRy5co5TJ87d668vb1Vv359DR8+XH/99Zc5Lz4+XoGBgWagkqSwsDBlZGRo9+7dZk1oaKjDmGFhYYqPj5ckZWdnKyEhwaHGyclJoaGhZk1CQoLOnz/vUFO7dm1VrlzZrAEAAHevEkXdQJ7c3FwNGjRIzZs3V/369c3pzzzzjKpUqSJ/f3/t2LFDw4YN0759+/T1119LkpKTkx0ClSTzeXJy8hVrMjIydPbsWZ06dUo5OTkF1uzdu9ccw8XFRV5eXvlq8tZzqaysLGVlZZnPMzIyCrs7AABAMXPbhKqIiAjt2rVLGzdudJjev39/89+BgYGqWLGiHnnkER04cEDVq1e/1W1ek+joaI0ePbqo2wAAALfAbXH6b8CAAVq6dKnWrFmjSpUqXbG2WbNmkqTffvtNkuTn55fvG3h5z/38/K5YY7fb5e7uLm9vbzk7OxdYc/EY2dnZSktLu2zNpYYPH6709HTzcfjw4StuGwAAKL6KNFQZhqEBAwZo0aJFWr16tapVq3bVZRITEyVJFStWlCQFBwdr586dDt/SW7Vqlex2u+rWrWvWxMXFOYyzatUqBQcHS5JcXFwUFBTkUJObm6u4uDizJigoSCVLlnSo2bdvnw4dOmTWXMrV1VV2u93hAQAA7kxFevovIiJC8+bN05IlS1SmTBnz2iRPT0+5u7vrwIEDmjdvnjp06KDy5ctrx44dGjx4sEJCQnT//fdLktq2bau6devq2Wef1TvvvKPk5GSNGDFCERERcnV1lSS9+OKLmj59uoYOHao+ffpo9erVWrBggZYtW2b2EhkZqZ49e6px48Zq2rSppkyZoszMTPXu3dvsqW/fvoqMjFS5cuVkt9s1cOBABQcHF+qbfwAA4M5WpKHqvffek/T3bRMuNnv2bPXq1UsuLi76/vvvzYATEBCgzp07a8SIEWats7Ozli5dqpdeeknBwcEqXbq0evbsqTFjxpg11apV07JlyzR48GBNnTpVlSpV0kcffaSwsDCzpmvXrkpNTVVUVJSSk5PVsGFDxcbGOly8PnnyZDk5Oalz587KyspSWFiYZs6ceZP2DgAAKE5uq/tU3em4TxVQdLhPFYDrVSzvUwUAAFBcEaoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsUKShKjo6Wk2aNFGZMmXk4+Ojjh07at++fQ41586dU0REhMqXLy8PDw917txZKSkpDjWHDh1SeHi4SpUqJR8fHw0ZMkQXLlxwqFm7dq0aNWokV1dX1ahRQzExMfn6mTFjhqpWrSo3Nzc1a9ZMW7duveZeAADA3alIQ9W6desUERGhH374QatWrdL58+fVtm1bZWZmmjWDBw/Wt99+q4ULF2rdunU6cuSIOnXqZM7PyclReHi4srOztXnzZs2ZM0cxMTGKiooya5KSkhQeHq7WrVsrMTFRgwYNUr9+/bRixQqzZv78+YqMjNTIkSP1008/qUGDBgoLC9OxY8cK3QsAALh72QzDMIq6iTypqany8fHRunXrFBISovT0dFWoUEHz5s3TU089JUnau3ev6tSpo/j4eD344INavny5HnvsMR05ckS+vr6SpFmzZmnYsGFKTU2Vi4uLhg0bpmXLlmnXrl3murp166a0tDTFxsZKkpo1a6YmTZpo+vTpkqTc3FwFBARo4MCBev311wvVy9VkZGTI09NT6enpstvtlu67iwUN+eSmjQ0UVwkTnivqFgAUU4X9/L6trqlKT0+XJJUrV06SlJCQoPPnzys0NNSsqV27tipXrqz4+HhJUnx8vAIDA81AJUlhYWHKyMjQ7t27zZqLx8iryRsjOztbCQkJDjVOTk4KDQ01awrTy6WysrKUkZHh8AAAAHem2yZU5ebmatCgQWrevLnq168vSUpOTpaLi4u8vLwcan19fZWcnGzWXByo8ubnzbtSTUZGhs6ePavjx48rJyenwJqLx7haL5eKjo6Wp6en+QgICCjk3gAAAMXNbROqIiIitGvXLn3xxRdF3Yplhg8frvT0dPNx+PDhom4JAADcJCWKugFJGjBggJYuXar169erUqVK5nQ/Pz9lZ2crLS3N4QhRSkqK/Pz8zJpLv6WX9428i2su/ZZeSkqK7Ha73N3d5ezsLGdn5wJrLh7jar1cytXVVa6urtewJwAAQHFVpEeqDMPQgAEDtGjRIq1evVrVqlVzmB8UFKSSJUsqLi7OnLZv3z4dOnRIwcHBkqTg4GDt3LnT4Vt6q1atkt1uV926dc2ai8fIq8kbw8XFRUFBQQ41ubm5iouLM2sK0wsAALh7FemRqoiICM2bN09LlixRmTJlzGuTPD095e7uLk9PT/Xt21eRkZEqV66c7Ha7Bg4cqODgYPPbdm3btlXdunX17LPP6p133lFycrJGjBihiIgI8yjRiy++qOnTp2vo0KHq06ePVq9erQULFmjZsmVmL5GRkerZs6caN26spk2basqUKcrMzFTv3r3Nnq7WCwAAuHsVaah67733JEmtWrVymD579mz16tVLkjR58mQ5OTmpc+fOysrKUlhYmGbOnGnWOjs7a+nSpXrppZcUHBys0qVLq2fPnhozZoxZU61aNS1btkyDBw/W1KlTValSJX300UcKCwsza7p27arU1FRFRUUpOTlZDRs2VGxsrMPF61frBQAA3L1uq/tU3em4TxVQdLhPFYDrVSzvUwUAAFBcEaoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwALXFaratGmjtLS0fNMzMjLUpk2bG+0JAACg2LmuULV27VplZ2fnm37u3Dlt2LDhhpsCAAAobkpcS/GOHTvMf//yyy9KTk42n+fk5Cg2Nlb33HOPdd0BAAAUE9cUqho2bCibzSabzVbgaT53d3dNmzbNsuYAAACKi2sKVUlJSTIMQ/fee6+2bt2qChUqmPNcXFzk4+MjZ2dny5sEAAC43V1TqKpSpYokKTc396Y0AwAAUFxdU6i62P79+7VmzRodO3YsX8iKioq64cYAAACKk+sKVR9++KFeeukleXt7y8/PTzabzZxns9kIVQAA4K5zXaFq3LhxGj9+vIYNG2Z1PwAAAMXSdd2n6tSpU3r66aet7gUAAKDYuq5Q9fTTT2vlypVW9wIAAFBsXdfpvxo1aujNN9/UDz/8oMDAQJUsWdJh/ssvv2xJcwAAAMXFdYWqDz74QB4eHlq3bp3WrVvnMM9msxGqAADAXee6QlVSUpLVfQAAABRr13VNFQAAABxd15GqPn36XHH+xx9/fF3NAAAAFFfXFapOnTrl8Pz8+fPatWuX0tLSCvxDywAAAHe66zr9t2jRIofH0qVL9fvvv6tr16568MEHCz3O+vXr9fjjj8vf3182m02LFy92mN+rVy/ZbDaHR7t27RxqTp48qR49eshut8vLy0t9+/bVmTNnHGp27NihFi1ayM3NTQEBAXrnnXfy9bJw4ULVrl1bbm5uCgwM1Hfffecw3zAMRUVFqWLFinJ3d1doaKj2799f6G0FAAB3NsuuqXJyclJkZKQmT55c6GUyMzPVoEEDzZgx47I17dq109GjR83H559/7jC/R48e2r17t1atWqWlS5dq/fr16t+/vzk/IyNDbdu2VZUqVZSQkKAJEyZo1KhR+uCDD8yazZs3q3v37urbt69+/vlndezYUR07dtSuXbvMmnfeeUfvvvuuZs2apS1btqh06dIKCwvTuXPnCr29AADgznXdf1C5IAcOHNCFCxcKXd++fXu1b9/+ijWurq7y8/MrcN6ePXsUGxurH3/8UY0bN5YkTZs2TR06dNB///tf+fv7a+7cucrOztbHH38sFxcX1atXT4mJiZo0aZIZvqZOnap27dppyJAhkqSxY8dq1apVmj59umbNmiXDMDRlyhSNGDFC//jHPyRJn3zyiXx9fbV48WJ169at0NsMAADuTNcVqiIjIx2eG4aho0ePatmyZerZs6cljeVZu3atfHx8VLZsWbVp00bjxo1T+fLlJUnx8fHy8vIyA5UkhYaGysnJSVu2bNGTTz6p+Ph4hYSEyMXFxawJCwvT22+/rVOnTqls2bKKj4/Pt01hYWHm6cikpCQlJycrNDTUnO/p6almzZopPj7+sqEqKytLWVlZ5vOMjIwb3h8AAOD2dF2h6ueff3Z47uTkpAoVKmjixIlX/WbgtWjXrp06deqkatWq6cCBA/r3v/+t9u3bKz4+Xs7OzkpOTpaPj4/DMiVKlFC5cuWUnJwsSUpOTla1atUcanx9fc15ZcuWVXJysjnt4pqLx7h4uYJqChIdHa3Ro0dfx5YDAIDi5rpC1Zo1a6zuo0AXHwEKDAzU/fffr+rVq2vt2rV65JFHbkkPN2L48OEOR8AyMjIUEBBQhB0BAICb5YYuVE9NTdXGjRu1ceNGpaamWtXTZd17773y9vbWb7/9Jkny8/PTsWPHHGouXLigkydPmtdh+fn5KSUlxaEm7/nVai6ef/FyBdUUxNXVVXa73eEBAADuTNcVqjIzM9WnTx9VrFhRISEhCgkJkb+/v/r27au//vrL6h5Nf/zxh06cOKGKFStKkoKDg5WWlqaEhASzZvXq1crNzVWzZs3MmvXr1+v8+fNmzapVq1SrVi2VLVvWrImLi3NY16pVqxQcHCxJqlatmvz8/BxqMjIytGXLFrMGAADc3a4rVEVGRmrdunX69ttvlZaWprS0NC1ZskTr1q3Tq6++Wuhxzpw5o8TERCUmJkr6+4LwxMREHTp0SGfOnNGQIUP0ww8/6ODBg4qLi9M//vEP1ahRQ2FhYZKkOnXqqF27dnr++ee1detWbdq0SQMGDFC3bt3k7+8vSXrmmWfk4uKivn37avfu3Zo/f76mTp3qcFrulVdeUWxsrCZOnKi9e/dq1KhR2rZtmwYMGCDp7z8SPWjQII0bN07ffPONdu7cqeeee07+/v7q2LHj9exCAABwh7EZhmFc60Le3t768ssv1apVK4fpa9asUZcuXQp9KnDt2rVq3bp1vuk9e/bUe++9p44dO+rnn39WWlqa/P391bZtW40dO9bhgvGTJ09qwIAB+vbbb+Xk5KTOnTvr3XfflYeHh1mzY8cORURE6Mcff5S3t7cGDhyoYcOGOaxz4cKFGjFihA4ePKj77rtP77zzjjp06GDONwxDI0eO1AcffKC0tDQ9/PDDmjlzpmrWrFmobZX+Prrl6emp9PT0m3oqMGjIJzdtbKC4SpjwXFG3AKCYKuzn93WFqlKlSikhIUF16tRxmL579241bdpUmZmZ197xXYBQBRQdQhWA61XYz+/rOv0XHByskSNHOtxN/OzZsxo9ejTXGAEAgLvSdd1SYcqUKWrXrp0qVaqkBg0aSJK2b98uV1dXrVy50tIGAQAAioPrClWBgYHav3+/5s6dq71790qSunfvrh49esjd3d3SBgEAAIqD6wpV0dHR8vX11fPPP+8w/eOPP1Zqamq+i8ABAADudNd1TdX777+v2rVr55ter149zZo164abAgAAKG6uK1QlJyebN+C8WIUKFXT06NEbbgoAAKC4ua5QFRAQoE2bNuWbvmnTJvOmmwAAAHeT67qm6vnnn9egQYN0/vx5tWnTRpIUFxenoUOHXtMd1QEAAO4U1xWqhgwZohMnTuhf//qXsrOzJUlubm4aNmyYhg8fbmmDAAAAxcF1hSqbzaa3335bb775pvbs2SN3d3fdd999cnV1tbo/AACAYuG6QlUeDw8PNWnSxKpeAAAAiq3rulAdAAAAjghVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFijSULV+/Xo9/vjj8vf3l81m0+LFix3mG4ahqKgoVaxYUe7u7goNDdX+/fsdak6ePKkePXrIbrfLy8tLffv21ZkzZxxqduzYoRYtWsjNzU0BAQF655138vWycOFC1a5dW25ubgoMDNR33313zb0AAIC7V5GGqszMTDVo0EAzZswocP4777yjd999V7NmzdKWLVtUunRphYWF6dy5c2ZNjx49tHv3bq1atUpLly7V+vXr1b9/f3N+RkaG2rZtqypVqighIUETJkzQqFGj9MEHH5g1mzdvVvfu3dW3b1/9/PPP6tixozp27Khdu3ZdUy8AAODuZTMMwyjqJiTJZrNp0aJF6tixo6S/jwz5+/vr1Vdf1WuvvSZJSk9Pl6+vr2JiYtStWzft2bNHdevW1Y8//qjGjRtLkmJjY9WhQwf98ccf8vf313vvvac33nhDycnJcnFxkSS9/vrrWrx4sfbu3StJ6tq1qzIzM7V06VKznwcffFANGzbUrFmzCtVLYWRkZMjT01Pp6emy2+2W7LeCBA355KaNDRRXCROeK+oWABRThf38vm2vqUpKSlJycrJCQ0PNaZ6enmrWrJni4+MlSfHx8fLy8jIDlSSFhobKyclJW7ZsMWtCQkLMQCVJYWFh2rdvn06dOmXWXLyevJq89RSml4JkZWUpIyPD4QEAAO5Mt22oSk5OliT5+vo6TPf19TXnJScny8fHx2F+iRIlVK5cOYeagsa4eB2Xq7l4/tV6KUh0dLQ8PT3NR0BAwFW2GgAAFFe3bai6EwwfPlzp6enm4/Dhw0XdEgAAuElu21Dl5+cnSUpJSXGYnpKSYs7z8/PTsWPHHOZfuHBBJ0+edKgpaIyL13G5movnX62Xgri6usputzs8AADAnem2DVXVqlWTn5+f4uLizGkZGRnasmWLgoODJUnBwcFKS0tTQkKCWbN69Wrl5uaqWbNmZs369et1/vx5s2bVqlWqVauWypYta9ZcvJ68mrz1FKYXAABwdyvSUHXmzBklJiYqMTFR0t8XhCcmJurQoUOy2WwaNGiQxo0bp2+++UY7d+7Uc889J39/f/MbgnXq1FG7du30/PPPa+vWrdq0aZMGDBigbt26yd/fX5L0zDPPyMXFRX379tXu3bs1f/58TZ06VZGRkWYfr7zyimJjYzVx4kTt3btXo0aN0rZt2zRgwABJKlQvAADg7laiKFe+bds2tW7d2nyeF3R69uypmJgYDR06VJmZmerfv7/S0tL08MMPKzY2Vm5ubuYyc+fO1YABA/TII4/IyclJnTt31rvvvmvO9/T01MqVKxUREaGgoCB5e3srKirK4V5WDz30kObNm6cRI0bo3//+t+677z4tXrxY9evXN2sK0wsAALh73Tb3qbobcJ8qoOhwnyoA16vY36cKAACgOCFUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFritQ9WoUaNks9kcHrVr1zbnnzt3ThERESpfvrw8PDzUuXNnpaSkOIxx6NAhhYeHq1SpUvLx8dGQIUN04cIFh5q1a9eqUaNGcnV1VY0aNRQTE5OvlxkzZqhq1apyc3NTs2bNtHXr1puyzQAAoHi6rUOVJNWrV09Hjx41Hxs3bjTnDR48WN9++60WLlyodevW6ciRI+rUqZM5PycnR+Hh4crOztbmzZs1Z84cxcTEKCoqyqxJSkpSeHi4WrdurcTERA0aNEj9+vXTihUrzJr58+crMjJSI0eO1E8//aQGDRooLCxMx44duzU7AQAA3PZshmEYRd3E5YwaNUqLFy9WYmJivnnp6emqUKGC5s2bp6eeekqStHfvXtWpU0fx8fF68MEHtXz5cj322GM6cuSIfH19JUmzZs3SsGHDlJqaKhcXFw0bNkzLli3Trl27zLG7deumtLQ0xcbGSpKaNWumJk2aaPr06ZKk3NxcBQQEaODAgXr99dcLvT0ZGRny9PRUenq67Hb79e6Wqwoa8slNGxsorhImPFfULQAopgr7+X3bH6nav3+//P39de+996pHjx46dOiQJCkhIUHnz59XaGioWVu7dm1VrlxZ8fHxkqT4+HgFBgaagUqSwsLClJGRod27d5s1F4+RV5M3RnZ2thISEhxqnJycFBoaatYAAACUKOoGrqRZs2aKiYlRrVq1dPToUY0ePVotWrTQrl27lJycLBcXF3l5eTks4+vrq+TkZElScnKyQ6DKm58370o1GRkZOnv2rE6dOqWcnJwCa/bu3XvF/rOyspSVlWU+z8jIKPzGAwCAYuW2DlXt27c3/33//ferWbNmqlKlihYsWCB3d/ci7KxwoqOjNXr06KJuAwAA3AK3/em/i3l5ealmzZr67bff5Ofnp+zsbKWlpTnUpKSkyM/PT5Lk5+eX79uAec+vVmO32+Xu7i5vb285OzsXWJM3xuUMHz5c6enp5uPw4cPXvM0AAKB4KFah6syZMzpw4IAqVqyooKAglSxZUnFxceb8ffv26dChQwoODpYkBQcHa+fOnQ7f0lu1apXsdrvq1q1r1lw8Rl5N3hguLi4KCgpyqMnNzVVcXJxZczmurq6y2+0ODwAAcGe6rUPVa6+9pnXr1ungwYPavHmznnzySTk7O6t79+7y9PRU3759FRkZqTVr1ighIUG9e/dWcHCwHnzwQUlS27ZtVbduXT377LPavn27VqxYoREjRigiIkKurq6SpBdffFG///67hg4dqr1792rmzJlasGCBBg8ebPYRGRmpDz/8UHPmzNGePXv00ksvKTMzU7179y6S/QIAAG4/t/U1VX/88Ye6d++uEydOqEKFCnr44Yf1ww8/qEKFCpKkyZMny8nJSZ07d1ZWVpbCwsI0c+ZMc3lnZ2ctXbpUL730koKDg1W6dGn17NlTY8aMMWuqVaumZcuWafDgwZo6daoqVaqkjz76SGFhYWZN165dlZqaqqioKCUnJ6thw4aKjY3Nd/E6AAC4e93W96m603CfKqDocJ8qANfrjrlPFQAAQHFAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALFCiqBsAABRe0JBPiroF4LaTMOG5om5BEkeqAAAALEGoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEqms0Y8YMVa1aVW5ubmrWrJm2bt1a1C0BAIDbAKHqGsyfP1+RkZEaOXKkfvrpJzVo0EBhYWE6duxYUbcGAACKGKHqGkyaNEnPP/+8evfurbp162rWrFkqVaqUPv7446JuDQAAFDFCVSFlZ2crISFBoaGh5jQnJyeFhoYqPj6+CDsDAAC3gxJF3UBxcfz4ceXk5MjX19dhuq+vr/bu3VvgMllZWcrKyjKfp6enS5IyMjJuXqOScrLO3tTxgeLoZv/e3Sr8fgP53ezf77zxDcO4Yh2h6iaKjo7W6NGj800PCAgogm6Au5vntBeLugUAN8mt+v0+ffq0PD09LzufUFVI3t7ecnZ2VkpKisP0lJQU+fn5FbjM8OHDFRkZaT7Pzc3VyZMnVb58edlstpvaL4peRkaGAgICdPjwYdnt9qJuB4CF+P2+uxiGodOnT8vf3/+KdYSqQnJxcVFQUJDi4uLUsWNHSX+HpLi4OA0YMKDAZVxdXeXq6uowzcvL6yZ3ituN3W7nTRe4Q/H7ffe40hGqPISqaxAZGamePXuqcePGatq0qaZMmaLMzEz17t27qFsDAABFjFB1Dbp27arU1FRFRUUpOTlZDRs2VGxsbL6L1wEAwN2HUHWNBgwYcNnTfcDFXF1dNXLkyHyngAEUf/x+oyA242rfDwQAAMBVcfNPAAAACxCqAAAALECoAgAAsAChCgAAwAKEKuAG9OrVSzabTW+99ZbD9MWLF3PXfKAYMgxDoaGhCgsLyzdv5syZ8vLy0h9//FEEnaE4IFQBN8jNzU1vv/22Tp06VdStALhBNptNs2fP1pYtW/T++++b05OSkjR06FBNmzZNlSpVKsIOcTsjVAE3KDQ0VH5+foqOjr5szVdffaV69erJ1dVVVatW1cSJE29hhwCuRUBAgKZOnarXXntNSUlJMgxDffv2Vdu2bfXAAw+offv28vDwkK+vr5599lkdP37cXPbLL79UYGCg3N3dVb58eYWGhiozM7MItwa3EqEKuEHOzs76z3/+o2nTphV4WiAhIUFdunRRt27dtHPnTo0aNUpvvvmmYmJibn2zAAqlZ8+eeuSRR9SnTx9Nnz5du3bt0vvvv682bdrogQce0LZt2xQbG6uUlBR16dJFknT06FF1795dffr00Z49e7R27Vp16tRJ3A7y7sHNP4Eb0KtXL6WlpWnx4sUKDg5W3bp19X//939avHixnnzySRmGoR49eig1NVUrV640lxs6dKiWLVum3bt3F2H3AK7k2LFjqlevnk6ePKmvvvpKu3bt0oYNG7RixQqz5o8//lBAQID27dunM2fOKCgoSAcPHlSVKlWKsHMUFY5UARZ5++23NWfOHO3Zs8dh+p49e9S8eXOHac2bN9f+/fuVk5NzK1sEcA18fHz0wgsvqE6dOurYsaO2b9+uNWvWyMPDw3zUrl1bknTgwAE1aNBAjzzyiAIDA/X000/rww8/5FrLuwyhCrBISEiIwsLCNHz48KJuBYBFSpQooRIl/v4zuWfOnNHjjz+uxMREh8f+/fsVEhIiZ2dnrVq1SsuXL1fdunU1bdo01apVS0lJSUW8FbhV+IPKgIXeeustNWzYULVq1TKn1alTR5s2bXKo27Rpk2rWrClnZ+db3SKA69SoUSN99dVXqlq1qhm0LmWz2dS8eXM1b95cUVFRqlKlihYtWqTIyMhb3C2KAkeqAAsFBgaqR48eevfdd81pr776quLi4jR27Fj9+uuvmjNnjqZPn67XXnutCDsFcK0iIiJ08uRJde/eXT/++KMOHDigFStWqHfv3srJydGWLVv0n//8R9u2bdOhQ4f09ddfKzU1VXXq1Cnq1nGLEKoAi40ZM0a5ubnm80aNGmnBggX64osvVL9+fUVFRWnMmDHq1atX0TUJ4Jr5+/tr06ZNysnJUdu2bRUYGKhBgwbJy8tLTk5OstvtWr9+vTp06KCaNWtqxIgRmjhxotq3b1/UreMW4dt/AAAAFuBIFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFADfB2rVrZbPZlJaWVtStALhFCFUAbku9evVSx44d802/VWFl1KhRatiwYb7pVatWlc1mk81mk7u7u6pWraouXbpo9erVDnUPPfSQjh49Kk9Pz5vaJ4DbB6EKAC5iGIYuXLhwxZoxY8bo6NGj2rdvnz755BN5eXkpNDRU48ePN2tcXFzk5+cnm812s1sGcJsgVAEo1jZu3KgWLVrI3d1dAQEBevnll5WZmWnO//TTT9W4cWOVKVNGfn5+euaZZ3Ts2DFzft6Rr+XLlysoKEiurq767LPPNHr0aG3fvt08KhUTE2MukzdW5cqVFRISog8++EBvvvmmoqKitG/fPodx846o/e9//9Pjjz+usmXLqnTp0qpXr56+++47c8xdu3apffv28vDwkK+vr5599lkdP37cnB8bG6uHH35YXl5eKl++vB577DEdOHDAnJ+dna0BAwaoYsWKcnNzU5UqVRQdHW3OT0tLU79+/VShQgXZ7Xa1adNG27dvt+znAIBQBaAYO3DggNq1a6fOnTtrx44dmj9/vjZu3KgBAwaYNefPn9fYsWO1fft2LV68WAcPHizwj1m//vrreuutt7Rnzx49+uijevXVV1WvXj0dPXpUR48eVdeuXa/YyyuvvCLDMLRkyZIC50dERCgrK0vr16/Xzp079fbbb8vDw0PS34GnTZs2euCBB7Rt2zbFxsYqJSVFXbp0MZfPzMxUZGSktm3bpri4ODk5OenJJ580/3j3u+++q2+++UYLFizQvn37NHfuXFWtWtVc/umnn9axY8e0fPlyJSQkqFGjRnrkkUd08uTJwu5uAFdjAMBtqGfPnoazs7NRunRph4ebm5shyTh16pTRt29fo3///g7LbdiwwXBycjLOnj1b4Lg//vijIck4ffq0YRiGsWbNGkOSsXjxYoe6kSNHGg0aNMi3fJUqVYzJkycXOLavr6/x0ksvOYx76tQpwzAMIzAw0Bg1alSBy40dO9Zo27atw7TDhw8bkox9+/YVuExqaqohydi5c6dhGIYxcOBAo02bNkZubm6+2g0bNhh2u904d+6cw/Tq1asb77//foHjA7h2HKkCcNtq3bq1EhMTHR4fffSROX/79u2KiYmRh4eH+QgLC1Nubq6SkpIkSQkJCXr88cdVuXJllSlTRi1btpQkHTp0yGFdjRs3vuF+DcO47DVUL7/8ssaNG6fmzZtr5MiR2rFjh8N2rFmzxmE7ateuLUnmKb79+/ere/fuuvfee2W3282jUHnb0atXLyUmJqpWrVp6+eWXtXLlSofxz5w5o/LlyzusIykpyeEUIoAbU6KoGwCAyyldurRq1KjhMO2PP/4w/33mzBm98MILevnll/MtW7lyZWVmZiosLExhYWGaO3euKlSooEOHDiksLEzZ2dn51nUjTpw4odTUVFWrVq3A+f369VNYWJiWLVumlStXKjo6WhMnTtTAgQN15swZPf7443r77bfzLVexYkVJ0uOPP64qVaroww8/lL+/v3Jzc1W/fn1zOxo1aqSkpCQtX75c33//vbp06aLQ0FB9+eWXOnPmjCpWrKi1a9fmG9/Ly+uGthvA/0eoAlBsNWrUSL/88ku+4JVn586dOnHihN566y0FBARIkrZt21aosV1cXJSTk1PoXqZOnSonJ6cCbwORJyAgQC+++KJefPFFDR8+XB9++KEGDhyoRo0a6auvvlLVqlVVokT+t+UTJ05o3759+vDDD9WiRQtJf1+gfym73a6uXbuqa9eueuqpp9SuXTudPHlSjRo1UnJyskqUKOFwnRUAa3H6D0CxNWzYMG3evFkDBgxQYmKi9u/fryVLlpgXqleuXFkuLi6aNm2afv/9d33zzTcaO3ZsocauWrWqkpKSlJiYqOPHjysrK8ucd/r0aSUnJ+vw4cNav369+vfvr3Hjxmn8+PGXDXiDBg3SihUrlJSUpJ9++klr1qxRnTp1JP19EfvJkyfVvXt3/fjjjzpw4IBWrFih3r17KycnR2XLllX58uX1wQcf6LffftPq1asVGRnpMP6kSZP0+eefa+/evfr111+1cOFC+fn5mbd7CA4OVseOHbVy5UodPHhQmzdv1htvvFHokAng6ghVAIqt+++/X+vWrdOvv/6qFi1a6IEHHlBUVJT8/f0lSRUqVFBMTIwWLlyounXr6q233tJ///vfQo3duXNntWvXTq1bt1aFChX0+eefm/OioqJUsWJF1ahRQ88++6zS09MVFxenYcOGXXa8nJwcRUREqE6dOmrXrp1q1qypmTNnSpL8/f21adMm5eTkqG3btgoMDNSgQYPk5eUlJycnOTk56YsvvlBCQoLq16+vwYMHa8KECQ7jlylTRu+8844aN26sJk2a6ODBg/ruu+/k5OQkm82m7777TiEhIerdu7dq1qypbt266X//+598fX2vdbcDuAybYRhGUTcBAABQ3HGkCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsMD/A8EDBhsbeGvMAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение классов в Обучающей выборке:\n",
      "HeartDisease\n",
      "No     143331\n",
      "Yes     13368\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение классов в Контрольной выборке:\n",
      "HeartDisease\n",
      "No     61442\n",
      "Yes     5715\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение классов в Тестовой выборке:\n",
      "HeartDisease\n",
      "No     87649\n",
      "Yes     8290\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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KlSt323EyTyvf7tSFJP3++++SpNKlSzvMK1OmjL7//nu7aSdOnLDbV4GBgVq0aNFfbtO9LmvFkSNHtG/fvr98LY8ePaqgoCDlz5//ntf5+++/q2TJkg7BNTMIZu7fTD179rT7T1SnTp30+uuv241Xo0YNh/XcPN6tr2d6errefPNNtWvXTv/973/vqv8WLVqY/3ZxcdHbb7+tli1bOtTd/PPh5+enrl27atiwYXJ1dXWotdlsKlWq1G33Sebn1O+//66goCC7/wDeXHfrvsvqc6dUqVK6fPmyEhMTFRAQYE5v1qyZDh8+LD8/P4froxITE5WUlKSZM2dq5syZDmNK1t73/2SEJtyV6tWr23177lbvvPOOBg0apM6dO2vEiBHKnz+/XFxc1Lt3b4cjOM6Q2cP48eNVqVKlLGtu/kWVlpam06dP68knn/zLcW02m1auXJnlh+Wtv/zi4+Mlye7DLKsx/fz8NHfu3Czn3/oLsEaNGho5cqTdtGnTpmnp0qVZLn/w4EHNnj1bX3zxRZbXRmVkZCg0NFTvvfdelssHBwfftvdMv/32m6pUqaKJEyeqffv2mjNnTpaB1cr+2LJli5o3b646depo+vTpCgwMVK5cuTRr1iyHi7edwd/fX1988YWkP4P3p59+qkaNGun7779XaGjo37asFRkZGXryySfVr1+/LOeXKlXqntdxr/r27auGDRsqPT1dP//8s4YPHy7DMDRr1qxsj/nJJ5/o2LFjWrVq1V0v++6776pixYq6fv26du7cqZEjR8rNzc3hKNyiRYvk4+Ojy5cva/HixRo1apR8fHyy3Nc54T5Rhw4d0sqVK9WqVSv16dPHbv9mfj6+8MILt/2P5d2Gz38aQhPuq4ULF6pevXr65JNP7KYnJSWZF4pKUvHixbV9+3Zdv379vn71NvNIUibDMPTbb7+Zb/TMC8x9fHwUERHxl+Pt3btX169fv2NQzBzXMAyFhIRY+gX0yy+/yGazZXkU4+Yx165dq1q1aln6sC1YsKDDNt3pYu2BAweqUqVKat269W3Xv3fvXjVo0CDbp0wzT436+/tr6dKl6tOnj5o0aeIQ+Kzsj0WLFsnT01OrVq2yO4136y/V4sWLKyMjQ7/88sttg3Hmz8GBAwdUokSJLGuKFi0qSTp8+LDDaZnDhw+b8zN5enra7f/mzZsrf/78mjZtmj788MPbbte9LmtF8eLFdenSpb/8mS9evLhWrVql8+fP3/PRpqJFi2rfvn3KyMiwO7Jy6NAhc/7NypUrZ/YXGRmpa9eu6c0339SoUaMUFBSkokWL6vDhww7rud14ly9f1rBhw/Tqq686zLMiLCzM/CZg48aNderUKY0dO1aDBg2y2546deqYn23NmzfX1q1bFRMTk2VoCgkJ0e7du2+7T4oVK2Zuy9q1a3Xx4kW7o02329ZbP/ck6ddff9Ujjzzi8F5btmyZateurdGjR6tHjx564YUXzNOghQoVUp48eZSenm7p8/HfiGuacF+5uro6HPJdsGCBw1e0W7ZsqbNnz2ratGkOY9y6/N347LPPdPHiRfP5woULdfr0aTVu3FjSnx+ExYsX17vvvqtLly45LJ+YmOjQu6ura5Zf57/ZM888I1dXVw0bNsyhf8MwdO7cOfP5jRs3tGjRIlWvXv2Op19atWql9PR089tDN7tx44bdn4i4W9u2bdPSpUs1ZsyY2waiVq1a6dSpU1l+HfrKlSuWruEoVaqU+W2qqVOnKiMjw+FbU1b3h6urq2w2m90p32PHjjkEwxYtWsjFxUXDhw93OLqZ+do0bNhQefLk0ejRox2u+8qsqVq1qvz8/DRjxgy7U4ErV67UwYMH7b6tl5W0tDTduHHD0u0Z7ueyWWnVqpW2bduW5RGXpKQk3bhxQ9Kf70vDMDRs2DCHurt9XzZp0kTx8fGaP3++Oe3GjRuaOnWqvL29Vbdu3Tsuf+XKFUn///VuTZo00Y4dO7Rt2zazJjU1VTNnzlSxYsUcTsdOnjxZqampeuutt+6q7zv1c+PGDXNfZcUwDBmGkeXR5sxtuHWfZF7S4OHhYQaVJk2aKD093eHzceLEibLZbObnWaZt27bZXWt04sQJLV26VA0bNnToJfPbqa+++qpq1qyp7t27m/va1dVVLVu21KJFi3TgwAGH/m/9fPw34kgT7qtmzZpp+PDh6tSpk2rWrKn9+/dr7ty5+s9//mNX9+KLL+qzzz5TdHS0duzYodq1ays1NVVr167Vq6++qv/7v//L1vrz58+vxx9/XJ06dVJCQoImTZqkEiVKqGvXrpL+vDbh448/VuPGjVW+fHl16tRJjz76qE6dOqUNGzbIx8dH3377rVJTU/X+++9rypQpKlWqlDZu3GiuIzNs7du3T9u2bVN4eLiKFy+ukSNHauDAgTp27JhatGihPHnyKC4uTosXL1a3bt30xhtvaO3atRo0aJD27dunb7/99o7bUrduXXXv3l2jR4/Wnj171LBhQ+XKlUtHjhzRggULNHnyZD377LPZ2k+rV6/Wk08+ecf/TbZv315ff/21Xn75ZW3YsEG1atVSenq6Dh06pK+//lqrVq36yyNwNwsICND48eP10ksv6YUXXlCTJk3uan80bdpU7733nho1aqTnn39eZ86c0fvvv68SJUpo3759Zl2JEiX01ltvacSIEapdu7aeeeYZeXh4aOfOnQoKCtLo0aPl4+OjiRMn6qWXXlK1atX0/PPPK1++fNq7d68uX76sOXPmKFeuXBo7dqw6deqkunXrqm3btuYtB4oVK2Z3vY305y/wm0+xff7557p69eodvwp/P5a1om/fvlq2bJmaNWumjh07KiwsTKmpqdq/f78WLlyoY8eOqWDBgqpXr57at2+vKVOm6MiRI2rUqJEyMjK0ZcsW1atXTz169LC8zm7duunDDz9Ux44dFRsbq2LFimnhwoXaunWrJk2a5HC9zrZt2+Tm5maenps6daoqV65sHn0ZMGCAvvzySzVu3Fi9evVS/vz5NWfOHMXFxWnRokUO1wmtXr1ao0aNsruW8m6sWbNGJ0+eNE/PzZ07V82bN5e7u7td3fr16+1Oz/3222+3/Vp+ly5d9MEHH6hjx47atWuXQkJCtGTJEq1bt05jxowxe33qqadUr149vfXWWzp27JgqVqyo1atXa+nSperdu7fdLVmkP6/Ni4yMtLvlgKQsw28mm82mjz/+WJUqVdKQIUM0btw4SdKYMWO0YcMG1ahRQ127dlW5cuV0/vx57d69W2vXrtX58+eztT//MR701/XwcMr8avHOnTvvWHf16lWjT58+RmBgoOHl5WXUqlXL2LZtm93XzzNdvnzZeOutt4yQkBAjV65cRkBAgPHss88aR48eNQwje7cc+PLLL42BAwcafn5+hpeXl9G0aVPj999/d1j+p59+Mp555hmjQIEChoeHh1G0aFGjVatWxrp16+zW/VePm79ObBiGsWjRIuPxxx83cufObeTOndsoU6aMERUVZRw+fNgwDMPo2bOnUadOHSMmJsahp6y+Ym8Yf379PCwszPDy8jLy5MljhIaGGv369TP++OMPs+Zubzlgs9mM2NhYu+lZvUZpaWnG2LFjjfLlyxseHh5Gvnz5jLCwMGPYsGFGcnKyw/r+ajzDMIz69esbRYoUMS5evHjX++OTTz4xSpYsaXh4eBhlypQxZs2addv99umnnxqVK1c2+65bt66xZs0au5ply5YZNWvWNLy8vAwfHx+jevXqxpdffmlXM3/+fHOc/PnzG+3atTNvsZGpQ4cOdj8X3t7eRpUqVYzPP//8jvvoXpe92Z1uOWAYf349f+DAgUaJEiUMd3d3o2DBgkbNmjWNd99910hLSzPrbty4YYwfP94oU6aM4e7ubhQqVMho3Lixw8+LYdz5lgOGYRgJCQlGp06djIIFCxru7u5GaGiow60Fbn2vubi4GIULFzY6dOjgsJ+PHj1qPPvss4avr6/h6elpVK9e3Vi+fLldTeZnQWBgoJGammo3T3dxy4HMh5ubm1G0aFGjV69exoULF8y6zJ+7zIeXl5dRrlw5Y+LEiWbNrbccMAzDOHPmjNG5c2dzn1SoUMH46KOPHPq4ePGi8frrrxtBQUFGrly5jJIlSxrjx493uPWDJCMqKsr44osvzPdG5cqV7W6ZcHO/txo2bJjh5uZm7N6925yWkJBgREVFGcHBweZnc4MGDYyZM2fecd/9G/BnVPCPsHHjRtWrV08LFizI9tGXmx07dkwhISGKi4sz/6d7q6FDh+rYsWOaPXv2Pa8PALLDZrMpKioqy0sdcP9xTRMAAIAFXNMEZMHb21vt2rW744XJ//3vf80/CwMA+OcjNAFZKFiwoHlh7u0888wzD6gbAEBOwDVNAAAAFnBNEwAAgAWEJgAAAAu4puk+ycjI0B9//KE8efJk+09OAACAB8swDF28eFFBQUEON0m9FaHpPvnjjz8s/QFTAACQ85w4cUKFCxe+Yw2h6T7J/JMAJ06ckI+Pj5O7AQAAVqSkpCg4ONjhT/tkhdB0n2SekvPx8SE0AQDwkLFyaQ0XggMAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFrg5uwHcnbC+nzm7BSDHiR3/orNbAPAvwJEmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAAC5wamtLT0zVo0CCFhITIy8tLxYsX14gRI2QYhlljGIYGDx6swMBAeXl5KSIiQkeOHLEb5/z582rXrp18fHzk6+urLl266NKlS3Y1+/btU+3ateXp6ang4GCNGzfOoZ8FCxaoTJky8vT0VGhoqL777ru/Z8MBAMBDx6mhaezYsfrggw80bdo0HTx4UGPHjtW4ceM0depUs2bcuHGaMmWKZsyYoe3btyt37tyKjIzU1atXzZp27drp559/1po1a7R8+XJt3rxZ3bp1M+enpKSoYcOGKlq0qGJjYzV+/HgNHTpUM2fONGt++OEHtW3bVl26dNFPP/2kFi1aqEWLFjpw4MCD2RkAACBHsxk3H9Z5wJo1ayZ/f3998skn5rSWLVvKy8tLX3zxhQzDUFBQkPr06aM33nhDkpScnCx/f3/Nnj1bbdq00cGDB1WuXDnt3LlTVatWlSTFxMSoSZMmOnnypIKCgvTBBx/orbfeUnx8vNzd3SVJAwYM0JIlS3To0CFJUuvWrZWamqrly5ebvTz22GOqVKmSZsyY8ZfbkpKSorx58yo5OVk+Pj73bR/dKqzvZ3/b2MDDKnb8i85uAcBD6m5+fzv1SFPNmjW1bt06/frrr5KkvXv36vvvv1fjxo0lSXFxcYqPj1dERIS5TN68eVWjRg1t27ZNkrRt2zb5+vqagUmSIiIi5OLiou3bt5s1derUMQOTJEVGRurw4cO6cOGCWXPzejJrMtdzq2vXriklJcXuAQAA/rncnLnyAQMGKCUlRWXKlJGrq6vS09M1atQotWvXTpIUHx8vSfL397dbzt/f35wXHx8vPz8/u/lubm7Knz+/XU1ISIjDGJnz8uXLp/j4+Duu51ajR4/WsGHDsrPZAADgIeTUI01ff/215s6dq3nz5mn37t2aM2eO3n33Xc2ZM8eZbVkycOBAJScnm48TJ044uyUAAPA3cuqRpr59+2rAgAFq06aNJCk0NFS///67Ro8erQ4dOiggIECSlJCQoMDAQHO5hIQEVapUSZIUEBCgM2fO2I1748YNnT9/3lw+ICBACQkJdjWZz/+qJnP+rTw8POTh4ZGdzQYAAA8hpx5punz5slxc7FtwdXVVRkaGJCkkJEQBAQFat26dOT8lJUXbt29XeHi4JCk8PFxJSUmKjY01a9avX6+MjAzVqFHDrNm8ebOuX79u1qxZs0alS5dWvnz5zJqb15NZk7keAADw7+bU0PTUU09p1KhRWrFihY4dO6bFixfrvffe09NPPy1Jstls6t27t0aOHKlly5Zp//79evHFFxUUFKQWLVpIksqWLatGjRqpa9eu2rFjh7Zu3aoePXqoTZs2CgoKkiQ9//zzcnd3V5cuXfTzzz9r/vz5mjx5sqKjo81eXnvtNcXExGjChAk6dOiQhg4dql27dqlHjx4PfL8AAICcx6mn56ZOnapBgwbp1Vdf1ZkzZxQUFKTu3btr8ODBZk2/fv2Umpqqbt26KSkpSY8//rhiYmLk6elp1sydO1c9evRQgwYN5OLiopYtW2rKlCnm/Lx582r16tWKiopSWFiYChYsqMGDB9vdy6lmzZqaN2+e3n77bb355psqWbKklixZogoVKjyYnQEAAHI0p96n6Z+E+zQBzsN9mgBk10NznyYAAICHBaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAscHpoOnXqlF544QUVKFBAXl5eCg0N1a5du8z5hmFo8ODBCgwMlJeXlyIiInTkyBG7Mc6fP6927drJx8dHvr6+6tKliy5dumRXs2/fPtWuXVuenp4KDg7WuHHjHHpZsGCBypQpI09PT4WGhuq77777ezYaAAA8dJwami5cuKBatWopV65cWrlypX755RdNmDBB+fLlM2vGjRunKVOmaMaMGdq+fbty586tyMhIXb161axp166dfv75Z61Zs0bLly/X5s2b1a1bN3N+SkqKGjZsqKJFiyo2Nlbjx4/X0KFDNXPmTLPmhx9+UNu2bdWlSxf99NNPatGihVq0aKEDBw48mJ0BAAByNJthGIazVj5gwABt3bpVW7ZsyXK+YRgKCgpSnz599MYbb0iSkpOT5e/vr9mzZ6tNmzY6ePCgypUrp507d6pq1aqSpJiYGDVp0kQnT55UUFCQPvjgA7311luKj4+Xu7u7ue4lS5bo0KFDkqTWrVsrNTVVy5cvN9f/2GOPqVKlSpoxY8ZfbktKSory5s2r5ORk+fj43NN+uZOwvp/9bWMDD6vY8S86uwUAD6m7+f3t1CNNy5YtU9WqVfXcc8/Jz89PlStX1kcffWTOj4uLU3x8vCIiIsxpefPmVY0aNbRt2zZJ0rZt2+Tr62sGJkmKiIiQi4uLtm/fbtbUqVPHDEySFBkZqcOHD+vChQtmzc3ryazJXM+trl27ppSUFLsHAAD453JqaPrf//6nDz74QCVLltSqVav0yiuvqFevXpozZ44kKT4+XpLk7+9vt5y/v785Lz4+Xn5+fnbz3dzclD9/fruarMa4eR23q8mcf6vRo0crb9685iM4OPiutx8AADw8nBqaMjIyVKVKFb3zzjuqXLmyunXrpq5du1o6HeZsAwcOVHJysvk4ceKEs1sCAAB/I6eGpsDAQJUrV85uWtmyZXX8+HFJUkBAgCQpISHBriYhIcGcFxAQoDNnztjNv3Hjhs6fP29Xk9UYN6/jdjWZ82/l4eEhHx8fuwcAAPjncmpoqlWrlg4fPmw37ddff1XRokUlSSEhIQoICNC6devM+SkpKdq+fbvCw8MlSeHh4UpKSlJsbKxZs379emVkZKhGjRpmzebNm3X9+nWzZs2aNSpdurT5Tb3w8HC79WTWZK4HAAD8uzk1NL3++uv68ccf9c477+i3337TvHnzNHPmTEVFRUmSbDabevfurZEjR2rZsmXav3+/XnzxRQUFBalFixaS/jwy1ahRI3Xt2lU7duzQ1q1b1aNHD7Vp00ZBQUGSpOeff17u7u7q0qWLfv75Z82fP1+TJ09WdHS02ctrr72mmJgYTZgwQYcOHdLQoUO1a9cu9ejR44HvFwAAkPO4OXPl1apV0+LFizVw4EANHz5cISEhmjRpktq1a2fW9OvXT6mpqerWrZuSkpL0+OOPKyYmRp6enmbN3Llz1aNHDzVo0EAuLi5q2bKlpkyZYs7PmzevVq9eraioKIWFhalgwYIaPHiw3b2catasqXnz5untt9/Wm2++qZIlS2rJkiWqUKHCg9kZAAAgR3PqfZr+SbhPE+A83KcJQHY9NPdpAgAAeFgQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALshWa6tevr6SkJIfpKSkpql+//r32BAAAkONkKzRt3LhRaWlpDtOvXr2qLVu23HNTAAAAOY3b3RTv27fP/Pcvv/yi+Ph483l6erpiYmL06KOP3r/uAAAAcoi7Ck2VKlWSzWaTzWbL8jScl5eXpk6det+aAwAAyCnuKjTFxcXJMAz95z//0Y4dO1SoUCFznru7u/z8/OTq6nrfmwQAAHC2uwpNRYsWlSRlZGT8Lc0AAADkVHcVmm525MgRbdiwQWfOnHEIUYMHD77nxgAAAHKSbIWmjz76SK+88ooKFiyogIAA2Ww2c57NZiM0AQCAf5xshaaRI0dq1KhR6t+///3uBwAAIEfK1n2aLly4oOeee+5+9wIAAJBjZSs0Pffcc1q9evX97gUAACDHytbpuRIlSmjQoEH68ccfFRoaqly5ctnN79Wr131pDgAAIKfIVmiaOXOmvL29tWnTJm3atMluns1mIzQBAIB/nGyFpri4uPvdBwAAQI6WrWuaAAAA/m2ydaSpc+fOd5z/6aefZqsZAACAnCpboenChQt2z69fv64DBw4oKSkpyz/kCwAA8LDLVmhavHixw7SMjAy98sorKl68+D03BQAAkNPct2uaXFxcFB0drYkTJ96vIQEAAHKM+3oh+NGjR3Xjxo37OSQAAECOkK3Tc9HR0XbPDcPQ6dOntWLFCnXo0OG+NAYAAJCTZCs0/fTTT3bPXVxcVKhQIU2YMOEvv1kHAADwMMpWaNqwYcP97gMAACBHy1ZoypSYmKjDhw9LkkqXLq1ChQrdl6YAAABymmxdCJ6amqrOnTsrMDBQderUUZ06dRQUFKQuXbro8uXL97tHAAAAp8tWaIqOjtamTZv07bffKikpSUlJSVq6dKk2bdqkPn363O8eAQAAnC5bp+cWLVqkhQsX6oknnjCnNWnSRF5eXmrVqpU++OCD+9UfAABAjpCtI02XL1+Wv7+/w3Q/Pz9OzwEAgH+kbIWm8PBwDRkyRFevXjWnXblyRcOGDVN4ePh9aw4AACCnyNbpuUmTJqlRo0YqXLiwKlasKEnau3evPDw8tHr16vvaIAAAQE6QrdAUGhqqI0eOaO7cuTp06JAkqW3btmrXrp28vLzua4MAAAA5QbZC0+jRo+Xv76+uXbvaTf/000+VmJio/v3735fmAAAAcopsXdP04YcfqkyZMg7Ty5cvrxkzZtxzUwAAADlNtkJTfHy8AgMDHaYXKlRIp0+fvuemAAAAcppshabg4GBt3brVYfrWrVsVFBR0z00BAADkNNm6pqlr167q3bu3rl+/rvr160uS1q1bp379+nFHcAAA8I+UrdDUt29fnTt3Tq+++qrS0tIkSZ6enurfv78GDhx4XxsEAADICbJ1es5ms2ns2LFKTEzUjz/+qL179+r8+fMaPHhwthsZM2aMbDabevfubU67evWqoqKiVKBAAXl7e6tly5ZKSEiwW+748eNq2rSpHnnkEfn5+alv3766ceOGXc3GjRtVpUoVeXh4qESJEpo9e7bD+t9//30VK1ZMnp6eqlGjhnbs2JHtbQEAAP882QpNmby9vVWtWjVVqFBBHh4e2R5n586d+vDDD/Xf//7Xbvrrr7+ub7/9VgsWLNCmTZv0xx9/6JlnnjHnp6enq2nTpkpLS9MPP/ygOXPmaPbs2XbhLS4uTk2bNlW9evW0Z88e9e7dWy+99JJWrVpl1syfP1/R0dEaMmSIdu/erYoVKyoyMlJnzpzJ9jYBAIB/FpthGIYzG7h06ZKqVKmi6dOna+TIkapUqZImTZqk5ORkFSpUSPPmzdOzzz4rSTp06JDKli2rbdu26bHHHtPKlSvVrFkz/fHHH+bfwpsxY4b69++vxMREubu7q3///lqxYoUOHDhgrrNNmzZKSkpSTEyMJKlGjRqqVq2apk2bJknKyMhQcHCwevbsqQEDBljajpSUFOXNm1fJycny8fG5n7vITljfz/62sYGHVez4F53dAoCH1N38/r6nI033Q1RUlJo2baqIiAi76bGxsbp+/brd9DJlyqhIkSLatm2bJGnbtm0KDQ21++PBkZGRSklJ0c8//2zW3Dp2ZGSkOUZaWppiY2PtalxcXBQREWHWZOXatWtKSUmxewAAgH+ubF0Ifr989dVX2r17t3bu3OkwLz4+Xu7u7vL19bWb7u/vr/j4eLPm5sCUOT9z3p1qUlJSdOXKFV24cEHp6elZ1mT+iZisjB49WsOGDbO2oQAA4KHntCNNJ06c0Guvvaa5c+fK09PTWW1k28CBA5WcnGw+Tpw44eyWAADA38hpoSk2NlZnzpxRlSpV5ObmJjc3N23atElTpkyRm5ub/P39lZaWpqSkJLvlEhISFBAQIEkKCAhw+DZd5vO/qvHx8ZGXl5cKFiwoV1fXLGsyx8iKh4eHfHx87B4AAOCfy2mhqUGDBtq/f7/27NljPqpWrap27dqZ/86VK5fWrVtnLnP48GEdP35c4eHhkqTw8HDt37/f7ltua9askY+Pj8qVK2fW3DxGZk3mGO7u7goLC7OrycjI0Lp168waAAAAp13TlCdPHlWoUMFuWu7cuVWgQAFzepcuXRQdHa38+fPLx8dHPXv2VHh4uB577DFJUsOGDVWuXDm1b99e48aNU3x8vN5++21FRUWZt0B4+eWXNW3aNPXr10+dO3fW+vXr9fXXX2vFihXmeqOjo9WhQwdVrVpV1atX16RJk5SamqpOnTo9oL0BAAByOqdeCP5XJk6cKBcXF7Vs2VLXrl1TZGSkpk+fbs53dXXV8uXL9corryg8PFy5c+dWhw4dNHz4cLMmJCREK1as0Ouvv67JkyercOHC+vjjjxUZGWnWtG7dWomJiRo8eLDi4+NVqVIlxcTEOFwcDgAA/r2cfp+mfwru0wQ4D/dpApBdD9V9mgAAAB4GhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFjg1NA0evRoVatWTXny5JGfn59atGihw4cP29VcvXpVUVFRKlCggLy9vdWyZUslJCTY1Rw/flxNmzbVI488Ij8/P/Xt21c3btywq9m4caOqVKkiDw8PlShRQrNnz3bo5/3331exYsXk6empGjVqaMeOHfd9mwEAwMPJqaFp06ZNioqK0o8//qg1a9bo+vXratiwoVJTU82a119/Xd9++60WLFigTZs26Y8//tAzzzxjzk9PT1fTpk2VlpamH374QXPmzNHs2bM1ePBgsyYuLk5NmzZVvXr1tGfPHvXu3VsvvfSSVq1aZdbMnz9f0dHRGjJkiHbv3q2KFSsqMjJSZ86ceTA7AwAA5Gg2wzAMZzeRKTExUX5+ftq0aZPq1Kmj5ORkFSpUSPPmzdOzzz4rSTp06JDKli2rbdu26bHHHtPKlSvVrFkz/fHHH/L395ckzZgxQ/3791diYqLc3d3Vv39/rVixQgcOHDDX1aZNGyUlJSkmJkaSVKNGDVWrVk3Tpk2TJGVkZCg4OFg9e/bUgAED/rL3lJQU5c2bV8nJyfLx8bnfu8YU1vezv21s4GEVO/5FZ7cA4CF1N7+/c9Q1TcnJyZKk/PnzS5JiY2N1/fp1RUREmDVlypRRkSJFtG3bNknStm3bFBoaagYmSYqMjFRKSop+/vlns+bmMTJrMsdIS0tTbGysXY2Li4siIiLMmltdu3ZNKSkpdg8AAPDPlWNCU0ZGhnr37q1atWqpQoUKkqT4+Hi5u7vL19fXrtbf31/x8fFmzc2BKXN+5rw71aSkpOjKlSs6e/as0tPTs6zJHONWo0ePVt68ec1HcHBw9jYcAAA8FHJMaIqKitKBAwf01VdfObsVSwYOHKjk5GTzceLECWe3BAAA/kZuzm5Aknr06KHly5dr8+bNKly4sDk9ICBAaWlpSkpKsjvalJCQoICAALPm1m+5ZX677uaaW79xl5CQIB8fH3l5ecnV1VWurq5Z1mSOcSsPDw95eHhkb4MBAMBDx6lHmgzDUI8ePbR48WKtX79eISEhdvPDwsKUK1curVu3zpx2+PBhHT9+XOHh4ZKk8PBw7d+/3+5bbmvWrJGPj4/KlStn1tw8RmZN5hju7u4KCwuzq8nIyNC6devMGgAA8O/m1CNNUVFRmjdvnpYuXao8efKY1w/lzZtXXl5eyps3r7p06aLo6Gjlz59fPj4+6tmzp8LDw/XYY49Jkho2bKhy5cqpffv2GjdunOLj4/X2228rKirKPBL08ssva9q0aerXr586d+6s9evX6+uvv9aKFSvMXqKjo9WhQwdVrVpV1atX16RJk5SamqpOnTo9+B0DAAByHKeGpg8++ECS9MQTT9hNnzVrljp27ChJmjhxolxcXNSyZUtdu3ZNkZGRmj59ulnr6uqq5cuX65VXXlF4eLhy586tDh06aPjw4WZNSEiIVqxYoddff12TJ09W4cKF9fHHHysyMtKsad26tRITEzV48GDFx8erUqVKiomJcbg4HAAA/DvlqPs0Pcy4TxPgPNynCUB2PbT3aQIAAMipCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAVuzm4AAPCnsL6fObsFIMeJHf+is1swcaQJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA0AQAAWEBoAgAAsIDQBAAAYAGhCQAAwAJCEwAAgAWEJgAAAAsITQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAADAAkITAACABYQmAAAACwhNAAAAFhCaAAAALCA03eL9999XsWLF5OnpqRo1amjHjh3ObgkAAOQAhKabzJ8/X9HR0RoyZIh2796tihUrKjIyUmfOnHF2awAAwMkITTd577331LVrV3Xq1EnlypXTjBkz9Mgjj+jTTz91dmsAAMDJ3JzdQE6Rlpam2NhYDRw40Jzm4uKiiIgIbdu2zaH+2rVrunbtmvk8OTlZkpSSkvK39pl+7crfOj7wMPq733cPCu9vwNHf/f7OHN8wjL+sJTT9P2fPnlV6err8/f3tpvv7++vQoUMO9aNHj9awYcMcpgcHB/9tPQLIWt6pLzu7BQB/kwf1/r548aLy5s17xxpCUzYNHDhQ0dHR5vOMjAydP39eBQoUkM1mc2JneBBSUlIUHBysEydOyMfHx9ntALiPeH//uxiGoYsXLyooKOgvawlN/0/BggXl6uqqhIQEu+kJCQkKCAhwqPfw8JCHh4fdNF9f37+zReRAPj4+fKgC/1C8v/89/uoIUyYuBP9/3N3dFRYWpnXr1pnTMjIytG7dOoWHhzuxMwAAkBNwpOkm0dHR6tChg6pWrarq1atr0qRJSk1NVadOnZzdGgAAcDJC001at26txMREDR48WPHx8apUqZJiYmIcLg4HPDw8NGTIEIdTtAAefry/cTs2w8p37AAAAP7luKYJAADAAkITAACABYQmAAAACwhNAAAAFhCagNvo2LGjbDabxowZYzd9yZIl3PUdeAgZhqGIiAhFRkY6zJs+fbp8fX118uRJJ3SGhwWhCbgDT09PjR07VhcuXHB2KwDukc1m06xZs7R9+3Z9+OGH5vS4uDj169dPU6dOVeHChZ3YIXI6QhNwBxEREQoICNDo0aNvW7No0SKVL19eHh4eKlasmCZMmPAAOwRwN4KDgzV58mS98cYbiouLk2EY6tKlixo2bKjKlSurcePG8vb2lr+/v9q3b6+zZ8+ayy5cuFChoaHy8vJSgQIFFBERodTUVCduDR40QhNwB66urnrnnXc0derULA/bx8bGqlWrVmrTpo3279+voUOHatCgQZo9e/aDbxaAJR06dFCDBg3UuXNnTZs2TQcOHNCHH36o+vXrq3Llytq1a5diYmKUkJCgVq1aSZJOnz6ttm3bqnPnzjp48KA2btyoZ555Rtzq8N+Fm1sCt9GxY0clJSVpyZIlCg8PV7ly5fTJJ59oyZIlevrpp2UYhtq1a6fExEStXr3aXK5fv35asWKFfv75Zyd2D+BOzpw5o/Lly+v8+fNatGiRDhw4oC1btmjVqlVmzcmTJxUcHKzDhw/r0qVLCgsL07Fjx1S0aFEndg5n4kgTYMHYsWM1Z84cHTx40G76wYMHVatWLbtptWrV0pEjR5Senv4gWwRwF/z8/NS9e3eVLVtWLVq00N69e7VhwwZ5e3ubjzJlykiSjh49qooVK6pBgwYKDQ3Vc889p48++ohrHf+FCE2ABXXq1FFkZKQGDhzo7FYA3Cdubm5yc/vzT7BeunRJTz31lPbs2WP3OHLkiOrUqSNXV1etWbNGK1euVLly5TR16lSVLl1acXFxTt4KPEj8wV7AojFjxqhSpUoqXbq0Oa1s2bLaunWrXd3WrVtVqlQpubq6PugWAWRTlSpVtGjRIhUrVswMUrey2WyqVauWatWqpcGDB6to0aJavHixoqOjH3C3cBaONAEWhYaGql27dpoyZYo5rU+fPlq3bp1GjBihX3/9VXPmzNG0adP0xhtvOLFTAHcrKipK58+fV9u2bbVz504dPXpUq1atUqdOnZSenq7t27frnXfe0a5du3T8+HF98803SkxMVNmyZZ3dOh4gQhNwF4YPH66MjAzzeZUqVfT111/rq6++UoUKFTR48GANHz5cHTt2dF6TAO5aUFCQtm7dqvT0dDVs2FChoaHq3bu3fH195eLiIh8fH23evFlNmjRRqVKl9Pbbb2vChAlq3Lixs1vHA8S35wAAACzgSBMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACwgNAEAABgAaEJAO7Sxo0bZbPZlJSU5OxWADxAhCYAD1zHjh3VokULh+kPKowMHTpUlSpVcpherFgx2Ww22Ww2eXl5qVixYmrVqpXWr19vV1ezZk2dPn1aefPm/Vv7BJCzEJoA/GsYhqEbN27csWb48OE6ffq0Dh8+rM8++0y+vr6KiIjQqFGjzBp3d3cFBATIZrP93S0DyEEITQByrO+//161a9eWl5eXgoOD1atXL6WmpprzP//8c1WtWlV58uRRQECAnn/+eZ05c8acn3nkauXKlQoLC5OHh4e++OILDRs2THv37jWPKs2ePdtcJnOsIkWKqE6dOpo5c6YGDRqkwYMH6/Dhw3bjZh4R+/333/XUU08pX758yp07t8qXL6/vvvvOHPPAgQNq3LixvL295e/vr/bt2+vs2bPm/JiYGD3++OPy9fVVgQIF1KxZMx09etScn5aWph49eigwMFCenp4qWrSoRo8ebc5PSkrSSy+9pEKFCsnHx0f169fX3r1779vrAOBPhCYAOdLRo0fVqFEjtWzZUvv27dP8+fP1/fffq0ePHmbN9evXNWLECO3du1dLlizRsWPHsvxjyQMGDNCYMWN08OBBPfnkk+rTp4/Kly+v06dP6/Tp02rduvUde3nttddkGIaWLl2a5fyoqChdu3ZNmzdv1v79+zV27Fh5e3tL+jPQ1K9fX5UrV9auXbsUExOjhIQEtWrVylw+NTVV0dHR2rVrl9atWycXFxc9/fTT5h+HnjJlipYtW6avv/5ahw8f1ty5c1WsWDFz+eeee05nzpzRypUrFRsbqypVqqhBgwY6f/681d0NwAoDAB6wDh06GK6urkbu3LntHp6enoYk48KFC0aXLl2Mbt262S23ZcsWw8XFxbhy5UqW4+7cudOQZFy8eNEwDMPYsGGDIclYsmSJXd2QIUOMihUrOixftGhRY+LEiVmO7e/vb7zyyit24164cMEwDMMIDQ01hg4dmuVyI0aMMBo2bGg37cSJE4Yk4/Dhw1kuk5iYaEgy9u/fbxiGYfTs2dOoX7++kZGR4VC7ZcsWw8fHx7h69ard9OLFixsffvhhluMDyB6ONAFwinr16mnPnj12j48//ticv3fvXs2ePVve3t7mIzIyUhkZGYqLi5MkxcbG6qmnnlKRIkWUJ08e1a1bV5J0/Phxu3VVrVr1nvs1DOO21zD16tVLI0eOVK1atTRkyBDt27fPbjs2bNhgtx1lypSRJPMU3JEjR9S2bVv95z//kY+Pj3kUKXM7OnbsqD179qh06dLq1auXVq9ebTf+pUuXVKBAAbt1xMXF2Z3iA3Dv3JzdAIB/p9y5c6tEiRJ2006ePGn++9KlS+revbt69erlsGyRIkWUmpqqyMhIRUZGau7cuSpUqJCOHz+uyMhIpaWlOazrXpw7d06JiYkKCQnJcv5LL72kyMhIrVixQqtXr9bo0aM1YcIE9ezZU5cuXdJTTz2lsWPHOiwXGBgoSXrqqadUtGhRffTRRwoKClJGRoYqVKhgbkeVKlUUFxenlStXau3atWrVqpUiIiK0cOFCXbp0SYGBgdq4caPD+L6+vve03QDsEZoA5EhVqlTRL7/84hCsMu3fv1/nzp3TmDFjFBwcLEnatWuXpbHd3d2Vnp5uuZfJkyfLxcUly9skZAoODtbLL7+sl19+WQMHDtRHH32knj17qkqVKlq0aJGKFSsmNzfHj9xz587p8OHD+uijj1S7dm1Jf14AfysfHx+1bt1arVu31rPPPqtGjRrp/PnzqlKliuLj4+Xm5mZ3nROA+4/TcwBypP79++uHH35Qjx49tGfPHh05ckRLly41LwQvUqSI3N3dNXXqVP3vf//TsmXLNGLECEtjFytWTHFxcdqzZ4/Onj2ra9eumfMuXryo+Ph4nThxQps3b1a3bt00cuRIjRo16rYBrnfv3lq1apXi4uK0e/dubdiwQWXLlpX050Xi58+fV9u2bbVz504dPXpUq1atUqdOnZSenq58+fKpQIECmjlzpn777TetX79e0dHRduO/9957+vLLL3Xo0CH9+uuvWrBggQICAszbIYSHh6tFixZavXq1jh07ph9++EFvvfWW5RAJwBpCE4Ac6b///a82bdqkX3/9VbVr11blypU1ePBgBQUFSZIKFSqk2bNna8GCBSpXrpzGjBmjd99919LYLVu2VKNGjVSvXj0VKlRIX375pTlv8ODBCgwMVIkSJdS+fXslJydr3bp16t+//23HS09PV1RUlMqWLatGjRqpVKlSmj59uiQpKChIW7duVXp6uho2bKjQ0FD17t1bvr6+cnFxkYuLi7766ivFxsaqQoUKev311zV+/Hi78fPkyaNx48apatWqqlatmo4dO6bvvvtOLi4ustls+u6771SnTh116tRJpUqVUps2bfT777/L39//bnc7gDuwGYZhOLsJAACAnI4jTQAAABYQmgAAACwgNAEAAFhAaAIAALCA0AQAAGABoQkAAMACQhMAAIAFhCYAAAALCE0AAAAWEJoAAAAsIDQBAABYQGgCAACw4P8D9HJIA7hu5D8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Проверка распределения классов в целевой переменной\n",
    "class_distribution = df['HeartDisease'].value_counts()\n",
    "print(\"Распределение классов в HeartDisease:\")\n",
    "print(class_distribution)\n",
    "\n",
    "# Визуализация распределения классов\n",
    "sns.countplot(x='HeartDisease', data=df)\n",
    "plt.title('Распределение классов в HeartDisease')\n",
    "plt.show()\n",
    "\n",
    "# Проверка сбалансированности для каждой выборки\n",
    "def check_balance(df, title):\n",
    "    class_distribution = df['HeartDisease'].value_counts()\n",
    "    print(f\"Распределение классов в {title}:\")\n",
    "    print(class_distribution)\n",
    "    sns.countplot(x='HeartDisease', data=df)\n",
    "    plt.title(f'Распределение классов в {title}')\n",
    "    plt.show()\n",
    "\n",
    "# Проверка сбалансированности для обучающей, контрольной и тестовой выборок\n",
    "check_balance(train_df, 'Обучающей выборке')\n",
    "check_balance(val_df, 'Контрольной выборке')\n",
    "check_balance(test_df, 'Тестовой выборке')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно заметить, что данные не сбалансированы - во всех выборках количество значений \"No\" превышает \"Yes\" в среднем в 10 раз. Для балансировки данных будет применен метод upsampling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки до upsampling: 156699\n",
      "Размер контрольной выборки: 67157\n",
      "Размер тестовой выборки: 95939\n",
      "\n",
      "Распределение классов в всем датасете:\n",
      "Класс No: 292422 (91.44%)\n",
      "Класс Yes: 27373 (8.56%)\n",
      "\n",
      "Распределение классов в Обучающей выборке до upsampling:\n",
      "Класс No: 143331 (91.47%)\n",
      "Класс Yes: 13368 (8.53%)\n",
      "Размер обучающей выборки после upsampling: 286662\n",
      "\n",
      "Распределение классов в Обучающей выборке после upsampling:\n",
      "Класс No: 143331 (50.00%)\n",
      "Класс Yes: 143331 (50.00%)\n",
      "\n",
      "Распределение классов в Контрольной выборке:\n",
      "Класс No: 61442 (91.49%)\n",
      "Класс Yes: 5715 (8.51%)\n",
      "\n",
      "Распределение классов в Тестовой выборке:\n",
      "Класс No: 87649 (91.36%)\n",
      "Класс Yes: 8290 (8.64%)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n",
    "train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Вывод размеров выборок\n",
    "print(\"Размер обучающей выборки до upsampling:\", len(train_df))\n",
    "print(\"Размер контрольной выборки:\", len(val_df))\n",
    "print(\"Размер тестовой выборки:\", len(test_df))\n",
    "\n",
    "# Функция для проверки балансировки данных\n",
    "def check_balance(df, title):\n",
    "    class_distribution = df['HeartDisease'].value_counts()\n",
    "    print(f\"\\nРаспределение классов в {title}:\")\n",
    "    for cls, count in class_distribution.items():\n",
    "        print(f\"Класс {cls}: {count} ({count / len(df) * 100:.2f}%)\")\n",
    "\n",
    "# Проверка балансировки для всего датасета\n",
    "check_balance(df, 'всем датасете')\n",
    "\n",
    "# Проверка балансировки для обучающей выборки до upsampling\n",
    "check_balance(train_df, 'Обучающей выборке до upsampling')\n",
    "\n",
    "# Применение upsampling к обучающей выборке\n",
    "X_train = train_df.drop('HeartDisease', axis=1)  # Отделяем признаки от целевой переменной\n",
    "y_train = train_df['HeartDisease']  # Целевая переменная\n",
    "\n",
    "# Инициализация RandomOverSampler\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "\n",
    "# Применение upsampling\n",
    "X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)\n",
    "\n",
    "# Создание нового DataFrame с балансированными данными\n",
    "train_df_resampled = pd.concat([X_train_resampled, y_train_resampled], axis=1)\n",
    "\n",
    "# Вывод размеров выборок после upsampling\n",
    "print(\"Размер обучающей выборки после upsampling:\", len(train_df_resampled))\n",
    "\n",
    "# Проверка балансировки для обучающей выборки после upsampling\n",
    "check_balance(train_df_resampled, 'Обучающей выборке после upsampling')\n",
    "\n",
    "# Проверка балансировки для контрольной и тестовой выборок (они не должны измениться)\n",
    "check_balance(val_df, 'Контрольной выборке')\n",
    "check_balance(test_df, 'Тестовой выборке')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данные были сбалансированы. Теперь можно перейти к конструированию признаков. Поставлены следующие задачи:\n",
    "\n",
    "1. Разработка персонализированных программ профилактики сердечно-сосудистых заболеваний\n",
    "2. Улучшение качества медицинской помощи"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Унитарное кодирование категориальных признаков (one-hot encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "# Определение категориальных признаков\n",
    "categorical_features = [\n",
    "    'Smoking', 'AlcoholDrinking', 'Stroke', 'DiffWalking', 'Sex', 'AgeCategory', 'Race',\n",
    "    'Diabetic', 'PhysicalActivity', 'GenHealth', 'Asthma', 'KidneyDisease', 'SkinCancer'\n",
    "]\n",
    "\n",
    "# Применение one-hot encoding к обучающей выборке\n",
    "train_df_resampled_encoded = pd.get_dummies(train_df_resampled, columns=categorical_features)\n",
    "\n",
    "# Применение one-hot encoding к контрольной выборке\n",
    "val_df_encoded = pd.get_dummies(val_df, columns=categorical_features)\n",
    "\n",
    "# Применение one-hot encoding к тестовой выборке\n",
    "test_df_encoded = pd.get_dummies(test_df, columns=categorical_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Дискретизация числовых признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//heart_2020_cleaned.csv\")\n",
    "\n",
    "# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n",
    "train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Применение upsampling к обучающей выборке\n",
    "X_train = train_df.drop('HeartDisease', axis=1)  # Отделяем признаки от целевой переменной\n",
    "y_train = train_df['HeartDisease']  # Целевая переменная\n",
    "\n",
    "# Инициализация RandomOverSampler\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "\n",
    "# Применение upsampling\n",
    "X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)\n",
    "\n",
    "# Создание нового DataFrame с балансированными данными\n",
    "train_df_resampled = pd.concat([X_train_resampled, y_train_resampled], axis=1)\n",
    "\n",
    "# Определение числовых признаков для дискретизации\n",
    "numerical_features = ['BMI', 'PhysicalHealth', 'MentalHealth', 'SleepTime']\n",
    "\n",
    "# Функция для дискретизации числовых признаков\n",
    "def discretize_features(df, features, bins=5, labels=False):\n",
    "    for feature in features:\n",
    "        df[f'{feature}_bin'] = pd.cut(df[feature], bins=bins, labels=labels)\n",
    "    return df\n",
    "\n",
    "# Применение дискретизации к обучающей, контрольной и тестовой выборкам\n",
    "train_df_resampled = discretize_features(train_df_resampled, numerical_features)\n",
    "val_df = discretize_features(val_df, numerical_features)\n",
    "test_df = discretize_features(test_df, numerical_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ручной синтез. \n",
    "Создание новых признаков на основе экспертных знаний и логики предметной области. Например, для данных о продаже автомобилей можно создать признак \"возраст автомобиля\" как разницу между текущим годом и годом выпуска."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//heart_2020_cleaned.csv\")\n",
    "\n",
    "# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n",
    "train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Применение upsampling к обучающей выборке\n",
    "X_train = train_df.drop('HeartDisease', axis=1)  # Отделяем признаки от целевой переменной\n",
    "y_train = train_df['HeartDisease']  # Целевая переменная\n",
    "\n",
    "# Инициализация RandomOverSampler\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "\n",
    "# Применение upsampling\n",
    "X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)\n",
    "\n",
    "# Создание нового DataFrame с балансированными данными\n",
    "train_df_resampled = pd.concat([X_train_resampled, y_train_resampled], axis=1)\n",
    "\n",
    "# Создание нового признака \"Age\" на основе категориального признака \"AgeCategory\"\n",
    "age_mapping = {\n",
    "    '18-24': 21,\n",
    "    '25-29': 27,\n",
    "    '30-34': 32,\n",
    "    '35-39': 37,\n",
    "    '40-44': 42,\n",
    "    '45-49': 47,\n",
    "    '50-54': 52,\n",
    "    '55-59': 57,\n",
    "    '60-64': 62,\n",
    "    '65-69': 67,\n",
    "    '70-74': 72,\n",
    "    '75-79': 77,\n",
    "    '80 or older': 80\n",
    "}\n",
    "\n",
    "train_df_resampled['Age'] = train_df_resampled['AgeCategory'].map(age_mapping)\n",
    "val_df['Age'] = val_df['AgeCategory'].map(age_mapping)\n",
    "test_df['Age'] = test_df['AgeCategory'].map(age_mapping)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Масштабирование признаков - это процесс преобразования числовых признаков таким образом, чтобы они имели одинаковый масштаб. Это важно для многих алгоритмов машинного обучения, которые чувствительны к масштабу признаков, таких как линейная регрессия, метод опорных векторов (SVM) и нейронные сети."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//heart_2020_cleaned.csv\")\n",
    "\n",
    "# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n",
    "train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Применение upsampling к обучающей выборке\n",
    "X_train = train_df.drop('HeartDisease', axis=1)  # Отделяем признаки от целевой переменной\n",
    "y_train = train_df['HeartDisease']  # Целевая переменная\n",
    "\n",
    "# Инициализация RandomOverSampler\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "\n",
    "# Применение upsampling\n",
    "X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)\n",
    "\n",
    "# Создание нового DataFrame с балансированными данными\n",
    "train_df_resampled = pd.concat([X_train_resampled, y_train_resampled], axis=1)\n",
    "\n",
    "# Создание нового признака \"Age\" на основе категориального признака \"AgeCategory\"\n",
    "age_mapping = {\n",
    "    '18-24': 21,\n",
    "    '25-29': 27,\n",
    "    '30-34': 32,\n",
    "    '35-39': 37,\n",
    "    '40-44': 42,\n",
    "    '45-49': 47,\n",
    "    '50-54': 52,\n",
    "    '55-59': 57,\n",
    "    '60-64': 62,\n",
    "    '65-69': 67,\n",
    "    '70-74': 72,\n",
    "    '75-79': 77,\n",
    "    '80 or older': 80\n",
    "}\n",
    "\n",
    "train_df_resampled['Age'] = train_df_resampled['AgeCategory'].map(age_mapping)\n",
    "val_df['Age'] = val_df['AgeCategory'].map(age_mapping)\n",
    "test_df['Age'] = test_df['AgeCategory'].map(age_mapping)\n",
    "\n",
    "# Определение числовых признаков для масштабирования\n",
    "numerical_features_to_scale = ['BMI', 'PhysicalHealth', 'MentalHealth', 'SleepTime', 'Age']\n",
    "\n",
    "# Инициализация StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Масштабирование числовых признаков в обучающей выборке\n",
    "train_df_resampled[numerical_features_to_scale] = scaler.fit_transform(train_df_resampled[numerical_features_to_scale])\n",
    "\n",
    "# Масштабирование числовых признаков в контрольной и тестовой выборках\n",
    "val_df[numerical_features_to_scale] = scaler.transform(val_df[numerical_features_to_scale])\n",
    "test_df[numerical_features_to_scale] = scaler.transform(test_df[numerical_features_to_scale])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Конструирование признаков с применением фреймворка Featuretools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index id not found in dataframe, creating new integer column\n",
      "  warnings.warn(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Обучающая выборка после конструирования признаков:\n",
      "    HeartDisease    BMI  Smoking  AlcoholDrinking  Stroke  PhysicalHealth  \\\n",
      "id                                                                          \n",
      "0          False  32.23    False            False   False             0.0   \n",
      "1          False  29.53    False            False   False             0.0   \n",
      "2          False  30.13    False            False   False             0.0   \n",
      "3          False  35.43    False            False   False             0.0   \n",
      "4          False  29.53    False            False   False             0.0   \n",
      "\n",
      "    MentalHealth  DiffWalking     Sex AgeCategory      Race Diabetic  \\\n",
      "id                                                                     \n",
      "0            0.0         True    Male       75-79     White      Yes   \n",
      "1            0.0         True  Female       50-54     White       No   \n",
      "2            0.0        False    Male       50-54     White       No   \n",
      "3           15.0        False  Female       18-24  Hispanic       No   \n",
      "4            0.0         True  Female       65-69     White      Yes   \n",
      "\n",
      "    PhysicalActivity  GenHealth  SleepTime  Asthma  KidneyDisease  SkinCancer  \\\n",
      "id                                                                              \n",
      "0               True       Fair        6.0   False           True        True   \n",
      "1               True  Very good        8.0   False          False       False   \n",
      "2               True  Excellent        7.0   False          False       False   \n",
      "3               True       Good        7.0   False          False       False   \n",
      "4              False       Good       10.0   False          False       False   \n",
      "\n",
      "    Age  \n",
      "id       \n",
      "0    77  \n",
      "1    52  \n",
      "2    52  \n",
      "3    21  \n",
      "4    67  \n",
      "Контрольная выборка после конструирования признаков:\n",
      "        HeartDisease    BMI  Smoking  AlcoholDrinking  Stroke  PhysicalHealth  \\\n",
      "id                                                                              \n",
      "80125          False  22.71    False            False   False             0.0   \n",
      "116296         False  25.80    False            False   False             0.0   \n",
      "18780          False  17.74     True            False   False             0.0   \n",
      "233006          <NA>    NaN     <NA>             <NA>    <NA>             NaN   \n",
      "182306          <NA>    NaN     <NA>             <NA>    <NA>             NaN   \n",
      "\n",
      "        MentalHealth  DiffWalking     Sex AgeCategory      Race Diabetic  \\\n",
      "id                                                                         \n",
      "80125            0.0        False    Male       25-29     White       No   \n",
      "116296           0.0        False    Male       50-54  Hispanic       No   \n",
      "18780            0.0        False  Female       65-69     White       No   \n",
      "233006           NaN         <NA>     NaN         NaN       NaN      NaN   \n",
      "182306           NaN         <NA>     NaN         NaN       NaN      NaN   \n",
      "\n",
      "        PhysicalActivity  GenHealth  SleepTime  Asthma  KidneyDisease  \\\n",
      "id                                                                      \n",
      "80125               True  Excellent        8.0   False          False   \n",
      "116296              True       Good        7.0   False          False   \n",
      "18780               True       Good        7.0   False          False   \n",
      "233006              <NA>        NaN        NaN    <NA>           <NA>   \n",
      "182306              <NA>        NaN        NaN    <NA>           <NA>   \n",
      "\n",
      "        SkinCancer   Age  \n",
      "id                        \n",
      "80125        False    27  \n",
      "116296       False    52  \n",
      "18780        False    67  \n",
      "233006        <NA>  <NA>  \n",
      "182306        <NA>  <NA>  \n",
      "Тестовая выборка после конструирования признаков:\n",
      "        HeartDisease   BMI  Smoking  AlcoholDrinking  Stroke  PhysicalHealth  \\\n",
      "id                                                                             \n",
      "271884          <NA>   NaN     <NA>             <NA>    <NA>             NaN   \n",
      "270361          <NA>   NaN     <NA>             <NA>    <NA>             NaN   \n",
      "219060          <NA>   NaN     <NA>             <NA>    <NA>             NaN   \n",
      "24010          False  26.5    False            False   False            14.0   \n",
      "181930          <NA>   NaN     <NA>             <NA>    <NA>             NaN   \n",
      "\n",
      "        MentalHealth  DiffWalking   Sex AgeCategory   Race Diabetic  \\\n",
      "id                                                                    \n",
      "271884           NaN         <NA>   NaN         NaN    NaN      NaN   \n",
      "270361           NaN         <NA>   NaN         NaN    NaN      NaN   \n",
      "219060           NaN         <NA>   NaN         NaN    NaN      NaN   \n",
      "24010            0.0         True  Male       75-79  White      Yes   \n",
      "181930           NaN         <NA>   NaN         NaN    NaN      NaN   \n",
      "\n",
      "        PhysicalActivity  GenHealth  SleepTime  Asthma  KidneyDisease  \\\n",
      "id                                                                      \n",
      "271884              <NA>        NaN        NaN    <NA>           <NA>   \n",
      "270361              <NA>        NaN        NaN    <NA>           <NA>   \n",
      "219060              <NA>        NaN        NaN    <NA>           <NA>   \n",
      "24010               True  Excellent        9.0   False          False   \n",
      "181930              <NA>        NaN        NaN    <NA>           <NA>   \n",
      "\n",
      "        SkinCancer   Age  \n",
      "id                        \n",
      "271884        <NA>  <NA>  \n",
      "270361        <NA>  <NA>  \n",
      "219060        <NA>  <NA>  \n",
      "24010        False    77  \n",
      "181930        <NA>  <NA>  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import featuretools as ft\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//heart_2020_cleaned.csv\")\n",
    "\n",
    "# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n",
    "train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Создание нового признака \"Age\" на основе категориального признака \"AgeCategory\"\n",
    "age_mapping = {\n",
    "    '18-24': 21,\n",
    "    '25-29': 27,\n",
    "    '30-34': 32,\n",
    "    '35-39': 37,\n",
    "    '40-44': 42,\n",
    "    '45-49': 47,\n",
    "    '50-54': 52,\n",
    "    '55-59': 57,\n",
    "    '60-64': 62,\n",
    "    '65-69': 67,\n",
    "    '70-74': 72,\n",
    "    '75-79': 77,\n",
    "    '80 or older': 80\n",
    "}\n",
    "\n",
    "train_df['Age'] = train_df['AgeCategory'].map(age_mapping)\n",
    "val_df['Age'] = val_df['AgeCategory'].map(age_mapping)\n",
    "test_df['Age'] = test_df['AgeCategory'].map(age_mapping)\n",
    "\n",
    "# Определение сущностей\n",
    "es = ft.EntitySet(id='heart_data')\n",
    "es = es.add_dataframe(dataframe_name='train', dataframe=train_df, index='id')\n",
    "\n",
    "# Генерация признаков\n",
    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='train', max_depth=2)\n",
    "\n",
    "# Преобразование признаков для контрольной и тестовой выборок\n",
    "val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=val_df.index)\n",
    "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=test_df.index)\n",
    "\n",
    "# Вывод первых нескольких строк для проверки\n",
    "print(\"Обучающая выборка после конструирования признаков:\")\n",
    "print(feature_matrix.head())\n",
    "print(\"Контрольная выборка после конструирования признаков:\")\n",
    "print(val_feature_matrix.head())\n",
    "print(\"Тестовая выборка после конструирования признаков:\")\n",
    "print(test_feature_matrix.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оценка качества каждого набора признаков\n",
    "\n",
    "Предсказательная способность\n",
    "Метрики: RMSE, MAE, R²\n",
    "\n",
    "Методы: Обучение модели на обучающей выборке и оценка на контрольной и тестовой выборках.\n",
    "\n",
    "Скорость вычисления\n",
    "Методы: Измерение времени выполнения генерации признаков и обучения модели.\n",
    "\n",
    "Надежность\n",
    "Методы: Кросс-валидация, анализ чувствительности модели к изменениям в данных.\n",
    "\n",
    "Корреляция\n",
    "Методы: Анализ корреляционной матрицы признаков, удаление мультиколлинеарных признаков.\n",
    "\n",
    "Цельность\n",
    "Методы: Проверка логической связи между признаками и целевой переменной, интерпретация результатов модели."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 156699\n",
      "Размер контрольной выборки: 67157\n",
      "Размер тестовой выборки: 95939\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index id not found in dataframe, creating new integer column\n",
      "  warnings.warn(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Importance:\n",
      "                                feature  importance\n",
      "0                          HeartDisease    0.851120\n",
      "1                                   BMI    0.014203\n",
      "9                             Stroke_No    0.012600\n",
      "2                        PhysicalHealth    0.008628\n",
      "12                      DiffWalking_Yes    0.008111\n",
      "11                       DiffWalking_No    0.007721\n",
      "36                         Diabetic_Yes    0.007583\n",
      "10                           Stroke_Yes    0.007551\n",
      "4                             SleepTime    0.007525\n",
      "43                       GenHealth_Poor    0.006605\n",
      "27              AgeCategory_80 or older    0.006269\n",
      "34                          Diabetic_No    0.005300\n",
      "3                          MentalHealth    0.005102\n",
      "41                       GenHealth_Fair    0.004277\n",
      "48                    KidneyDisease_Yes    0.003435\n",
      "47                     KidneyDisease_No    0.003086\n",
      "13                           Sex_Female    0.002607\n",
      "26                    AgeCategory_75-79    0.002567\n",
      "25                    AgeCategory_70-74    0.002462\n",
      "14                             Sex_Male    0.002457\n",
      "6                           Smoking_Yes    0.002127\n",
      "5                            Smoking_No    0.001934\n",
      "42                       GenHealth_Good    0.001787\n",
      "44                  GenHealth_Very good    0.001734\n",
      "33                           Race_White    0.001731\n",
      "50                       SkinCancer_Yes    0.001687\n",
      "38                  PhysicalActivity_No    0.001658\n",
      "39                 PhysicalActivity_Yes    0.001585\n",
      "49                        SkinCancer_No    0.001513\n",
      "40                  GenHealth_Excellent    0.001451\n",
      "24                    AgeCategory_65-69    0.001318\n",
      "46                           Asthma_Yes    0.001315\n",
      "45                            Asthma_No    0.001256\n",
      "23                    AgeCategory_60-64    0.001091\n",
      "30                           Race_Black    0.000885\n",
      "22                    AgeCategory_55-59    0.000853\n",
      "31                        Race_Hispanic    0.000825\n",
      "21                    AgeCategory_50-54    0.000715\n",
      "32                           Race_Other    0.000699\n",
      "7                    AlcoholDrinking_No    0.000560\n",
      "8                   AlcoholDrinking_Yes    0.000550\n",
      "20                    AgeCategory_45-49    0.000520\n",
      "28  Race_American Indian/Alaskan Native    0.000503\n",
      "35     Diabetic_No, borderline diabetes    0.000479\n",
      "19                    AgeCategory_40-44    0.000444\n",
      "18                    AgeCategory_35-39    0.000412\n",
      "17                    AgeCategory_30-34    0.000267\n",
      "29                           Race_Asian    0.000260\n",
      "15                    AgeCategory_18-24    0.000231\n",
      "16                    AgeCategory_25-29    0.000217\n",
      "37      Diabetic_Yes (during pregnancy)    0.000184\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "import featuretools as ft\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//heart_2020_cleaned.csv\")\n",
    "\n",
    "# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n",
    "train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Вывод размеров выборок\n",
    "print(\"Размер обучающей выборки:\", len(train_df))\n",
    "print(\"Размер контрольной выборки:\", len(val_df))\n",
    "print(\"Размер тестовой выборки:\", len(test_df))\n",
    "\n",
    "# Определение категориальных признаков\n",
    "categorical_features = [\n",
    "    'Smoking', 'AlcoholDrinking', 'Stroke', 'DiffWalking', 'Sex', 'AgeCategory', 'Race',\n",
    "    'Diabetic', 'PhysicalActivity', 'GenHealth', 'Asthma', 'KidneyDisease', 'SkinCancer'\n",
    "]\n",
    "\n",
    "# Применение one-hot encoding к обучающей выборке\n",
    "train_df_encoded = pd.get_dummies(train_df, columns=categorical_features)\n",
    "\n",
    "# Применение one-hot encoding к контрольной выборке\n",
    "val_df_encoded = pd.get_dummies(val_df, columns=categorical_features)\n",
    "\n",
    "# Применение one-hot encoding к тестовой выборке\n",
    "test_df_encoded = pd.get_dummies(test_df, columns=categorical_features)\n",
    "\n",
    "# Определение сущностей\n",
    "es = ft.EntitySet(id='heart_data')\n",
    "es = es.add_dataframe(dataframe_name='heart', dataframe=train_df_encoded, index='id')\n",
    "\n",
    "# Генерация признаков\n",
    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='heart', max_depth=2)\n",
    "\n",
    "# Преобразование признаков для контрольной и тестовой выборок\n",
    "val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=val_df_encoded.index)\n",
    "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=test_df_encoded.index)\n",
    "\n",
    "# Оценка важности признаков\n",
    "X = feature_matrix\n",
    "y = train_df_encoded['HeartDisease']\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Обучение модели\n",
    "model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Получение важности признаков\n",
    "importances = model.feature_importances_\n",
    "feature_names = feature_matrix.columns\n",
    "\n",
    "# Сортировка признаков по важности\n",
    "feature_importance = pd.DataFrame({'feature': feature_names, 'importance': importances})\n",
    "feature_importance = feature_importance.sort_values(by='importance', ascending=False)\n",
    "\n",
    "print(\"Feature Importance:\")\n",
    "print(feature_importance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 156699\n",
      "Размер контрольной выборки: 67157\n",
      "Размер тестовой выборки: 95939\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index id not found in dataframe, creating new integer column\n",
      "  warnings.warn(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9977068603095739\n",
      "Precision: 0.9982521847690387\n",
      "Recall: 0.9753598438643571\n",
      "F1 Score: 0.9866732477788747\n",
      "ROC AUC: 0.9875985227973351\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[31], line 84\u001b[0m\n\u001b[0;32m     81\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mROC AUC: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mroc_auc\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     83\u001b[0m \u001b[38;5;66;03m# Кросс-валидация\u001b[39;00m\n\u001b[1;32m---> 84\u001b[0m scores \u001b[38;5;241m=\u001b[39m \u001b[43mcross_val_score\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscoring\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43maccuracy\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     85\u001b[0m accuracy_cv \u001b[38;5;241m=\u001b[39m scores\u001b[38;5;241m.\u001b[39mmean()\n\u001b[0;32m     86\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCross-validated Accuracy: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00maccuracy_cv\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    208\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m    209\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m    210\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m    211\u001b[0m         )\n\u001b[0;32m    212\u001b[0m     ):\n\u001b[1;32m--> 213\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    215\u001b[0m     \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m    216\u001b[0m     \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m    217\u001b[0m     \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m    218\u001b[0m     \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m    219\u001b[0m     msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[0;32m    220\u001b[0m         \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    221\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    222\u001b[0m         \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[0;32m    223\u001b[0m     )\n",
      "File \u001b[1;32mc:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:712\u001b[0m, in \u001b[0;36mcross_val_score\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, params, pre_dispatch, error_score)\u001b[0m\n\u001b[0;32m    709\u001b[0m \u001b[38;5;66;03m# To ensure multimetric format is not supported\u001b[39;00m\n\u001b[0;32m    710\u001b[0m scorer \u001b[38;5;241m=\u001b[39m check_scoring(estimator, scoring\u001b[38;5;241m=\u001b[39mscoring)\n\u001b[1;32m--> 712\u001b[0m cv_results \u001b[38;5;241m=\u001b[39m \u001b[43mcross_validate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    713\u001b[0m \u001b[43m    \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    714\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    715\u001b[0m \u001b[43m    \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    716\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgroups\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    717\u001b[0m \u001b[43m    \u001b[49m\u001b[43mscoring\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mscore\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mscorer\u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    718\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcv\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    719\u001b[0m \u001b[43m    \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    720\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    721\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfit_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfit_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    722\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    723\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpre_dispatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpre_dispatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    724\u001b[0m \u001b[43m    \u001b[49m\u001b[43merror_score\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merror_score\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    725\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    726\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cv_results[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest_score\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[1;32mc:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:213\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    208\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m    209\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m    210\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m    211\u001b[0m         )\n\u001b[0;32m    212\u001b[0m     ):\n\u001b[1;32m--> 213\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    215\u001b[0m     \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m    216\u001b[0m     \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m    217\u001b[0m     \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m    218\u001b[0m     \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m    219\u001b[0m     msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[0;32m    220\u001b[0m         \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    221\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    222\u001b[0m         \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[0;32m    223\u001b[0m     )\n",
      "File \u001b[1;32mc:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:423\u001b[0m, in \u001b[0;36mcross_validate\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, params, pre_dispatch, return_train_score, return_estimator, return_indices, error_score)\u001b[0m\n\u001b[0;32m    420\u001b[0m \u001b[38;5;66;03m# We clone the estimator to make sure that all the folds are\u001b[39;00m\n\u001b[0;32m    421\u001b[0m \u001b[38;5;66;03m# independent, and that it is pickle-able.\u001b[39;00m\n\u001b[0;32m    422\u001b[0m parallel \u001b[38;5;241m=\u001b[39m Parallel(n_jobs\u001b[38;5;241m=\u001b[39mn_jobs, verbose\u001b[38;5;241m=\u001b[39mverbose, pre_dispatch\u001b[38;5;241m=\u001b[39mpre_dispatch)\n\u001b[1;32m--> 423\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mparallel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    424\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_fit_and_score\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    425\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    426\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    427\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    428\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscorer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscorers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    429\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtrain\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    430\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtest\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    431\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    432\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparameters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    433\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfit_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrouted_params\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    434\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscore_params\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrouted_params\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscorer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    435\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_train_score\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_train_score\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    436\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_times\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    437\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_estimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_estimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    438\u001b[0m \u001b[43m        \u001b[49m\u001b[43merror_score\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merror_score\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    439\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    440\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mindices\u001b[49m\n\u001b[0;32m    441\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    443\u001b[0m _warn_or_raise_about_fit_failures(results, error_score)\n\u001b[0;32m    445\u001b[0m \u001b[38;5;66;03m# For callable scoring, the return type is only know after calling. If the\u001b[39;00m\n\u001b[0;32m    446\u001b[0m \u001b[38;5;66;03m# return type is a dictionary, the error scores can now be inserted with\u001b[39;00m\n\u001b[0;32m    447\u001b[0m \u001b[38;5;66;03m# the correct key.\u001b[39;00m\n",
      "File \u001b[1;32mc:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\sklearn\\utils\\parallel.py:74\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m     69\u001b[0m config \u001b[38;5;241m=\u001b[39m get_config()\n\u001b[0;32m     70\u001b[0m iterable_with_config \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m     71\u001b[0m     (_with_config(delayed_func, config), args, kwargs)\n\u001b[0;32m     72\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m delayed_func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m iterable\n\u001b[0;32m     73\u001b[0m )\n\u001b[1;32m---> 74\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miterable_with_config\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\joblib\\parallel.py:1918\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1916\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_sequential_output(iterable)\n\u001b[0;32m   1917\u001b[0m     \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[1;32m-> 1918\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1920\u001b[0m \u001b[38;5;66;03m# Let's create an ID that uniquely identifies the current call. If the\u001b[39;00m\n\u001b[0;32m   1921\u001b[0m \u001b[38;5;66;03m# call is interrupted early and that the same instance is immediately\u001b[39;00m\n\u001b[0;32m   1922\u001b[0m \u001b[38;5;66;03m# re-used, this id will be used to prevent workers that were\u001b[39;00m\n\u001b[0;32m   1923\u001b[0m \u001b[38;5;66;03m# concurrently finalizing a task from the previous call to run the\u001b[39;00m\n\u001b[0;32m   1924\u001b[0m \u001b[38;5;66;03m# callback.\u001b[39;00m\n\u001b[0;32m   1925\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n",
      "File \u001b[1;32mc:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\joblib\\parallel.py:1847\u001b[0m, in \u001b[0;36mParallel._get_sequential_output\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1845\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_batches \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   1846\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m-> 1847\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1848\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_completed_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   1849\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_progress()\n",
      "File \u001b[1;32mc:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\sklearn\\utils\\parallel.py:136\u001b[0m, in \u001b[0;36m_FuncWrapper.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    134\u001b[0m     config \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m    135\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig):\n\u001b[1;32m--> 136\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:888\u001b[0m, in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, score_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[0;32m    886\u001b[0m         estimator\u001b[38;5;241m.\u001b[39mfit(X_train, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\n\u001b[0;32m    887\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 888\u001b[0m         \u001b[43mestimator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    890\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m    891\u001b[0m     \u001b[38;5;66;03m# Note fit time as time until error\u001b[39;00m\n\u001b[0;32m    892\u001b[0m     fit_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m start_time\n",
      "File \u001b[1;32mc:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\sklearn\\base.py:1473\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1466\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m   1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1469\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1470\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1471\u001b[0m     )\n\u001b[0;32m   1472\u001b[0m ):\n\u001b[1;32m-> 1473\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py:489\u001b[0m, in \u001b[0;36mBaseForest.fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    478\u001b[0m trees \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m    479\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_estimator(append\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, random_state\u001b[38;5;241m=\u001b[39mrandom_state)\n\u001b[0;32m    480\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n_more_estimators)\n\u001b[0;32m    481\u001b[0m ]\n\u001b[0;32m    483\u001b[0m \u001b[38;5;66;03m# Parallel loop: we prefer the threading backend as the Cython code\u001b[39;00m\n\u001b[0;32m    484\u001b[0m \u001b[38;5;66;03m# for fitting the trees is internally releasing the Python GIL\u001b[39;00m\n\u001b[0;32m    485\u001b[0m \u001b[38;5;66;03m# making threading more efficient than multiprocessing in\u001b[39;00m\n\u001b[0;32m    486\u001b[0m \u001b[38;5;66;03m# that case. However, for joblib 0.12+ we respect any\u001b[39;00m\n\u001b[0;32m    487\u001b[0m \u001b[38;5;66;03m# parallel_backend contexts set at a higher level,\u001b[39;00m\n\u001b[0;32m    488\u001b[0m \u001b[38;5;66;03m# since correctness does not rely on using threads.\u001b[39;00m\n\u001b[1;32m--> 489\u001b[0m trees \u001b[38;5;241m=\u001b[39m \u001b[43mParallel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    490\u001b[0m \u001b[43m    \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    491\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    492\u001b[0m \u001b[43m    \u001b[49m\u001b[43mprefer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mthreads\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    493\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    494\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_parallel_build_trees\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    495\u001b[0m \u001b[43m        \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    496\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbootstrap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    497\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    498\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    499\u001b[0m \u001b[43m        \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    500\u001b[0m \u001b[43m        \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    501\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrees\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    502\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    503\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclass_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclass_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    504\u001b[0m \u001b[43m        \u001b[49m\u001b[43mn_samples_bootstrap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_samples_bootstrap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    505\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmissing_values_in_feature_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmissing_values_in_feature_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    506\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    507\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrees\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    508\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    510\u001b[0m \u001b[38;5;66;03m# Collect newly grown trees\u001b[39;00m\n\u001b[0;32m    511\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimators_\u001b[38;5;241m.\u001b[39mextend(trees)\n",
      "File \u001b[1;32mc:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\sklearn\\utils\\parallel.py:74\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m     69\u001b[0m config \u001b[38;5;241m=\u001b[39m get_config()\n\u001b[0;32m     70\u001b[0m iterable_with_config \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m     71\u001b[0m     (_with_config(delayed_func, config), args, kwargs)\n\u001b[0;32m     72\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m delayed_func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m iterable\n\u001b[0;32m     73\u001b[0m )\n\u001b[1;32m---> 74\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miterable_with_config\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\joblib\\parallel.py:1918\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1916\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_sequential_output(iterable)\n\u001b[0;32m   1917\u001b[0m     \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[1;32m-> 1918\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1920\u001b[0m \u001b[38;5;66;03m# Let's create an ID that uniquely identifies the current call. If the\u001b[39;00m\n\u001b[0;32m   1921\u001b[0m \u001b[38;5;66;03m# call is interrupted early and that the same instance is immediately\u001b[39;00m\n\u001b[0;32m   1922\u001b[0m \u001b[38;5;66;03m# re-used, this id will be used to prevent workers that were\u001b[39;00m\n\u001b[0;32m   1923\u001b[0m \u001b[38;5;66;03m# concurrently finalizing a task from the previous call to run the\u001b[39;00m\n\u001b[0;32m   1924\u001b[0m \u001b[38;5;66;03m# callback.\u001b[39;00m\n\u001b[0;32m   1925\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n",
      "File \u001b[1;32mc:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\joblib\\parallel.py:1847\u001b[0m, in \u001b[0;36mParallel._get_sequential_output\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1845\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_batches \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   1846\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m-> 1847\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1848\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_completed_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   1849\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_progress()\n",
      "File \u001b[1;32mc:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\sklearn\\utils\\parallel.py:136\u001b[0m, in \u001b[0;36m_FuncWrapper.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    134\u001b[0m     config \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m    135\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig):\n\u001b[1;32m--> 136\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py:192\u001b[0m, in \u001b[0;36m_parallel_build_trees\u001b[1;34m(tree, bootstrap, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap, missing_values_in_feature_mask)\u001b[0m\n\u001b[0;32m    189\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m class_weight \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbalanced_subsample\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    190\u001b[0m         curr_sample_weight \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m compute_sample_weight(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbalanced\u001b[39m\u001b[38;5;124m\"\u001b[39m, y, indices\u001b[38;5;241m=\u001b[39mindices)\n\u001b[1;32m--> 192\u001b[0m     \u001b[43mtree\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    193\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    194\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    195\u001b[0m \u001b[43m        \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcurr_sample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    196\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcheck_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    197\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmissing_values_in_feature_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmissing_values_in_feature_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    198\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    199\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    200\u001b[0m     tree\u001b[38;5;241m.\u001b[39m_fit(\n\u001b[0;32m    201\u001b[0m         X,\n\u001b[0;32m    202\u001b[0m         y,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    205\u001b[0m         missing_values_in_feature_mask\u001b[38;5;241m=\u001b[39mmissing_values_in_feature_mask,\n\u001b[0;32m    206\u001b[0m     )\n",
      "File \u001b[1;32mc:\\storage\\university\\3 course\\AIM\\AIM-PIbd-32-Chubykina-P-P\\aimenv\\Lib\\site-packages\\sklearn\\tree\\_classes.py:472\u001b[0m, in \u001b[0;36mBaseDecisionTree._fit\u001b[1;34m(self, X, y, sample_weight, check_input, missing_values_in_feature_mask)\u001b[0m\n\u001b[0;32m    461\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    462\u001b[0m     builder \u001b[38;5;241m=\u001b[39m BestFirstTreeBuilder(\n\u001b[0;32m    463\u001b[0m         splitter,\n\u001b[0;32m    464\u001b[0m         min_samples_split,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    469\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmin_impurity_decrease,\n\u001b[0;32m    470\u001b[0m     )\n\u001b[1;32m--> 472\u001b[0m \u001b[43mbuilder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtree_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmissing_values_in_feature_mask\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    474\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_outputs_ \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m is_classifier(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    475\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_classes_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_classes_[\u001b[38;5;241m0\u001b[39m]\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//static//csv//heart_2020_cleaned.csv\")\n",
    "\n",
    "# Разделение на обучающую и тестовую выборки (например, 70% обучающая, 30% тестовая)\n",
    "train_df, test_df = train_test_split(df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную (например, 70% обучающая, 30% контрольная)\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.3, random_state=42)\n",
    "\n",
    "# Вывод размеров выборок\n",
    "print(\"Размер обучающей выборки:\", len(train_df))\n",
    "print(\"Размер контрольной выборки:\", len(val_df))\n",
    "print(\"Размер тестовой выборки:\", len(test_df))\n",
    "\n",
    "# Определение категориальных признаков\n",
    "categorical_features = [\n",
    "    'Smoking', 'AlcoholDrinking', 'Stroke', 'DiffWalking', 'Sex', 'AgeCategory', 'Race',\n",
    "    'Diabetic', 'PhysicalActivity', 'GenHealth', 'Asthma', 'KidneyDisease', 'SkinCancer'\n",
    "]\n",
    "\n",
    "# Применение one-hot encoding к обучающей выборке\n",
    "train_df_encoded = pd.get_dummies(train_df, columns=categorical_features)\n",
    "\n",
    "# Применение one-hot encoding к контрольной выборке\n",
    "val_df_encoded = pd.get_dummies(val_df, columns=categorical_features)\n",
    "\n",
    "# Применение one-hot encoding к тестовой выборке\n",
    "test_df_encoded = pd.get_dummies(test_df, columns=categorical_features)\n",
    "\n",
    "# Определение сущностей\n",
    "es = ft.EntitySet(id='heart_data')\n",
    "es = es.add_dataframe(dataframe_name='heart', dataframe=train_df_encoded, index='id')\n",
    "\n",
    "# Генерация признаков\n",
    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='heart', max_depth=2)\n",
    "\n",
    "# Преобразование признаков для контрольной и тестовой выборок\n",
    "val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=val_df_encoded.index)\n",
    "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=test_df_encoded.index)\n",
    "\n",
    "# Удаление строк с NaN\n",
    "feature_matrix = feature_matrix.dropna()\n",
    "val_feature_matrix = val_feature_matrix.dropna()\n",
    "test_feature_matrix = test_feature_matrix.dropna()\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки\n",
    "X_train = feature_matrix.drop('HeartDisease', axis=1)\n",
    "y_train = feature_matrix['HeartDisease']\n",
    "X_val = val_feature_matrix.drop('HeartDisease', axis=1)\n",
    "y_val = val_feature_matrix['HeartDisease']\n",
    "X_test = test_feature_matrix.drop('HeartDisease', axis=1)\n",
    "y_test = test_feature_matrix['HeartDisease']\n",
    "\n",
    "# Выбор модели\n",
    "model = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# Обучение модели\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Предсказание и оценка\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "roc_auc = roc_auc_score(y_test, y_pred)\n",
    "\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "print(f\"Precision: {precision}\")\n",
    "print(f\"Recall: {recall}\")\n",
    "print(f\"F1 Score: {f1}\")\n",
    "print(f\"ROC AUC: {roc_auc}\")\n",
    "\n",
    "# Кросс-валидация\n",
    "scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')\n",
    "accuracy_cv = scores.mean()\n",
    "print(f\"Cross-validated Accuracy: {accuracy_cv}\")\n",
    "\n",
    "# Анализ важности признаков\n",
    "feature_importances = model.feature_importances_\n",
    "feature_names = X_train.columns\n",
    "\n",
    "importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importances})\n",
    "importance_df = importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Importance', y='Feature', data=importance_df)\n",
    "plt.title('Feature Importance')\n",
    "plt.show()\n",
    "\n",
    "# Проверка на переобучение\n",
    "y_train_pred = model.predict(X_train)\n",
    "\n",
    "accuracy_train = accuracy_score(y_train, y_train_pred)\n",
    "precision_train = precision_score(y_train, y_train_pred)\n",
    "recall_train = recall_score(y_train, y_train_pred)\n",
    "f1_train = f1_score(y_train, y_train_pred)\n",
    "roc_auc_train = roc_auc_score(y_train, y_train_pred)\n",
    "\n",
    "print(f\"Train Accuracy: {accuracy_train}\")\n",
    "print(f\"Train Precision: {precision_train}\")\n",
    "print(f\"Train Recall: {recall_train}\")\n",
    "print(f\"Train F1 Score: {f1_train}\")\n",
    "print(f\"Train ROC AUC: {roc_auc_train}\")\n",
    "\n",
    "# Визуализация результатов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_pred, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel('Actual HeartDisease')\n",
    "plt.ylabel('Predicted HeartDisease')\n",
    "plt.title('Actual vs Predicted HeartDisease')\n",
    "plt.show()"
   ]
  }
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